WO2025019711A2

WO2025019711A2 - Predicting therapeutic response based on biomarker signatures

Info

Publication number: WO2025019711A2
Application number: PCT/US2024/038611
Authority: WO
Inventors: Michael Howell; Emad SAMANI
Original assignee: Dermtech Inc
Current assignee: Dermtech Inc
Priority date: 2023-07-19
Filing date: 2024-07-18
Publication date: 2025-01-23
Anticipated expiration: 2026-01-19
Also published as: WO2025019711A3

Abstract

Disclosed herein are systems and methods of generating skin disease treatment classes. Also described herein are methods for using the methods described herein for assessing potential response to therapies for treating skin diseases or skin conditions.

Description

PREDICTING THERAPEUTIC RESPONSE BASED ON BIOMARKER SIGNATURES

CROSS-REFERENCE

[001] This application claims the benefit of U.S. Provisional Application No. 63/527,670, filed July 19, 2023, which is incorporated herein by reference in its entirety.

BACKGROUND

[002] Diseases having cutaneous manifestations are some of the most common human illnesses and represent an important global burden in healthcare. Existing methods for assessing optimum treatment of common diseases (such as skin cancer) suffer from invasiveness, high cost, and lengthy clinical trials.

INCORPORATION BY REFERENCE

[003] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

BRIEF SUMMARY

[004] Provided herein are methods for patient stratification comprising: obtaining at least one sample from a patient using a non-invasive sampling method; obtaining at least one baseline signature corresponding to a biological process from the at least one sample; comparing the at least one baseline signature to a database to determine one or more treatment classes; and optionally administering a treatment to the patient based on the one or more treatment classes. Further provided herein are methods wherein the baseline signature comprises one or more of a gene expression signature, genomic signature, or protein signature. Further provided herein are methods wherein the genomic signature comprises single -nucleotide polymorphism genotyping, copy number proofing, and post- transcriptional modifications. Further provided herein are methods wherein the baseline signature comprises two or more gene expression levels. Further provided herein are methods wherein the baseline signature comprises ten or more gene expression levels. Further provided herein are methods wherein the baseline signature comprises family medical history. Further provided herein are methods wherein non-invasive sampling comprises use of one or more adhesive patches applied to a skin sample of the patient. Further provided herein are methods wherein non-invasive sampling comprises obtaining a blood sample and/or use of microneedles. Further provided herein are methods wherein the treatment classes comprise one or more of Th22, Th2, Thl7, Thl, inflammation, kinase inhibitors, B cell, microbial infection, and macrophage. Further provided herein are methods wherein the method further comprises identifying a primary treatment class. Further provided herein are methods wherein the primary treatment class is selected from TH2, TH17, TH11, or B cell. Further provided herein are methods wherein the treatment is administered by intramuscular, intraperitoneal, intravenous, subcutaneous, oral, sublingual, or topical routes. Further provided herein are methods wherein the treatment class is Th2 and the treatment comprises dupilumab, tralokinumab, lebrikizumab, or nemolizumab. Further provided herein are methods wherein the treatment class is Th 17 and the treatment comprises Ixekinumab, secukinumab, guselkumab, ustekinumab, or rizankizumab. Further provided herein are methods wherein the treatment class is Thl and the treatment comprises anifrolumab. Further provided herein are methods wherein the treatment class is inflammation and the treatment comprises adalimumab or remicade. Further provided herein are methods wherein the treatment class is kinase inhibitors and the treatment comprises JAK, BTK, or TYK2 inhibitors. Further provided herein are methods wherein the treatment class is B cell and the treatment comprises belimumab or rituximab. Further provided herein are methods wherein the treatment class is macrophage and the treatment comprises mavrilimumab. Further provided herein are methods wherein the patient has previously received treatment for a skin disease or condition. Further provided herein are methods wherein the skin disease or condition comprises atopic dermatitis, psoriasis, or lupus. Further provided herein are methods wherein the at least one baseline signature comprises a differential signature. Further provided herein are methods wherein the database comprises a previous baseline signature of the patient.

[005] Provided herein are methods for generating a baseline signature database comprising: obtaining a plurality of signatures from a patient population, wherein the population comprises treated and untreated patients; determining one or more patient groups of the patient population, wherein each of the one or more patient groups of the patient population shares at least one of the plurality of signatures; identifying one or more baseline signatures for each of the one or more patient groups based on the at least one of the plurality of signatures shared by the respective patient group; and storing the one or more baseline signatures in an electronically accessible database. Further provided herein are methods wherein the method further comprises: obtaining a second plurality of signatures from a second patient population, wherein the second population comprises treated and untreated patients; determining whether each patient shares at least one of the plurality of signatures with patients in the one or more patient groups; adding each patient that shares at least one of the plurality of signatures with at least one of the one or more patient groups to each patient group that shares at least one of the plurality of signatures; determining at least one new patient group, wherein each of the at least one new patient group of the second patient population shares at least one of the second plurality of signatures; identifying at least one new baseline signature for each of the at least one new patient group based on the at least one of the second plurality of signatures shared by the respective new patient group; and storing the at least one new baseline signature in the electronically accessible database.

[006] Provided herein are methods for visualization of a treatment class comprising: obtaining at least one sample from a patient using a non-invasive sampling method; determining at least one signature for the patient based on the at least one sample; displaying, by a user interface, a plurality of treatment regimens corresponding to a plurality of baseline signatures, wherein each baseline signature of the plurality of baseline signatures corresponds to a patient group of a plurality of patient groups; receiving input to the user interface identifying a treatment regimen of the plurality of treatment regimens; matching the at least one signature for the patient to the first baseline signature of the plurality of baseline signatures; and optionally, treating the patient based on the first baseline signature and a corresponding first method of treatment. Further provided herein are methods wherein the treatment comprises administering one of: adalimumab, cankinumab, rilonacept, or spesolimab; tezepelumab; fezankizumab; dupilumab, tralokinumab, lebrikizumab, or nemolizumab; ixekizumab, secukinumab, brodalumab, guzelkumab, rizankizumab, or ustekinumab; an antibiotic; anifrolumab; benralizumab, mepolizumab, or reslizumab; mavrilimumab; or rituximab, ocrelizumab, ofatumumab, inebilizumab, or belimumab. Further provided herein are methods wherein the treatment is administered based on a higher or lower signature, as compared to the baseline signature, of: TNFa, ILlb, ILla/b, or IL-36R; TSLP; IL-22; a microbial infection; IL4Ra, IL13, or IL-31; IL-17, IL-17R, IL-23, or IL-12/23; IFNAR; IL-5R, IL5, or IL-5; GMCSFRa; or CD20, CD19, or BAFF.

[007] Provided herein are systems for patient stratification, comprising: at least one sample from a patient using a non-invasive sampling method; at least one baseline signature corresponding to a biological process associated with the at least one sample; one or more treatment classes, wherein at least one of the one or more treatment classes is determined based on comparing the baseline signature to a database; and treatment of a patient based on the one or more treatment classes. Further provided herein are systems wherein the baseline signature comprises one or more of a gene expression signature, genomic signature, or protein signature. Further provided herein are systems wherein the genomic signature comprises single-nucleotide polymorphism genotyping, copy number proofing, and post-transcriptional modifications. Further provided herein are systems wherein the baseline signature comprises two or more gene expression levels. Further provided herein are systems wherein the baseline signature comprises ten or more gene expression levels. Further provided herein are systems wherein the baseline signature comprises family medical history. Further provided herein are systems wherein non-invasive sampling comprises use of one or more adhesive patches applied to a skin sample of the patient. Further provided herein are systems wherein non-invasive sampling comprises obtaining a blood sample and/or use of microneedles. Further provided herein are systems wherein the treatment classes comprise one or more of Th22, Th2, Thl7, Thl, inflammation, kinase inhibitors, B cell, microbial infection, and macrophage. Further provided herein are systems wherein the method further comprises identifying a primary treatment class. Further provided herein are systems wherein the primary treatment class is selected from TH2, TH17, TH11, or B cell. Further provided herein are systems wherein the treatment is administered by intramuscular, intraperitoneal, intravenous, subcutaneous, oral, sublingual, or topical routes. Further provided herein are systems wherein the treatment class is Th2 and the treatment comprises dupilumab, tralokinumab, lebrikizumab, or nemolizumab. Further provided herein are systems wherein the treatment class is Th 17 and the treatment comprises Ixekinumab, secukinumab, guselkumab, ustekinumab, or rizankizumab. Further provided herein are systems wherein the treatment class is Thl and the treatment comprises anifrolumab. Further provided herein are systems wherein the treatment class is inflammation and the treatment comprises adalimumab or remicade. Further provided herein are systems wherein the treatment class is kinase inhibitors and the treatment comprises JAK, BTK, or TYK2 inhibitors. Further provided herein are systems wherein the treatment class is B cell and the treatment comprises belimumab or rituximab. Further provided herein are systems wherein the treatment class is macrophage and the treatment comprises mavrilimumab. Further provided herein are systems wherein the patient has previously received treatment for a skin disease or condition. Further provided herein are systems wherein the skin disease or condition comprises atopic dermatitis, psoriasis, or lupus. Further provided herein are systems wherein the at least one baseline signature comprises a differential signature. Further provided herein are systems wherein the database comprises a previous baseline signature of the patient.

[008] Provided herein are methods for predicting a drug response of a patient comprising: (a) obtaining at least one sample from the patient using an invasive, semi -invasive, and/or non-invasive sampling method; and (b) obtaining at least one baseline signature for a panel of genes from the at least one sample; evaluating the at least one baseline signature to predict the drug response of the patient. Further provided herein are methods wherein the method further comprises administering a treatment to the patient based on the predicted drug response. Further provided herein are methods wherein the panel of genes comprises two or more genes. Further provided herein are methods wherein the two or more genes comprises any two or more genes selected from those genes listed in FIGs. 7, 8, 10 and 11. Further provided herein are methods wherein the drug response is for treatment of psoriasis. Further provided herein are methods wherein the treatment comprises administering one or more of anti-TNF-alpha, anti-IL17A, anti-IL23pl9, anti-IL-4Ralpha, anti-lL- 13, and anti-IL17 to the patient. Further provided herein are methods wherein the method further comprises a treatment of the at least one sample and obtaining at least one treatment signature for the panel of genes following treatment of the at least one sample. Further provided herein are methods wherein the treatment of the at least one sample takes place in vitro. Further provided herein are methods wherein the treatment is for a period of days. Further provided herein are methods wherein the in vitro treatment is for a period of at least 8 days. Further provided herein are methods wherein the at least one treatment signature is used to predict a treatment response and/or efficacy of administering the treatment to the patient in vivo.

BRIEF DESCRIPTION OF THE DRAWINGS

[009] FIG. 1A illustrates an exemplary method for determining a baseline signature for a subject based on one or more biomarker signatures associated with the subject.

[0010] FIG. IB illustrates an exemplary method for assessing an optimum treatment for a disease or condition based on a differential signature and a baseline signature of a patient.

[0011] FIG. 2 illustrates a non-limiting example of a computing device; in this case, a device with one or more processors, memory, storage, and a network interface.

[0012] FIG. 3 illustrates anon-limiting example of aweb/mobile application provision system; in this case, a system providing browser-based and/or native mobile user interfaces.

[0013] FIG. 4 illustrates anon-limiting example of a cloud-based web/mobile application provision system; in this case, a system comprising an elastically load balanced, auto-scaling web server and application server resources as well synchronously replicated databases.

[0014] FIG. 5 illustrates a user interface with possible treatment regimens based on a differential signature and a baseline signature of a patient.

[0015] FIG. 6 shows the PASI scores for 14 patients taken according to the cited study of Bertelsen, T., et al. The x-axis is labeled days at intervals 0, 4, 14, 42, and 84. The y-axis is labeled PASI from 0 to 50 at 10 unit intervals.

[0016] FIG. 7 shows genes whose expressions are highly positively correlated with response to in vivo treatment (anti -IL- 17).

[0017] FIG. 8 shows genes whose expressions are highly negatively correlated with response to in vivo treatment (anti -IL- 17).

[0018] FIG. 9 is a schematic for calculating a drug response in an in vitro model of one embodiment.

[0019] FIG. 10 shows genes whose expressions are highly positively correlated with response to in vitro treatment (anti-IL-17A).

[0020] FIG. 11 shows genes whose expressions are highly negatively correlated with response to in vitro treatment (anti -IL- 17 A).

[0021] The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure may be obtained by reference to the following detailed description that sets forth illustrative embodiments.

DETAILED DESCRIPTION

[0022] Provided herein are methods and systems for predicting therapeutic response to a disease or condition. In some instances, methods and systems comprise one or more steps of: obtaining at least one sample from a patient using a non-invasive sampling method; obtaining at least one baseline signature corresponding to a biological process from the at least one sample; comparing the at least one baseline signature to a database to determine one or more treatment classes; and administering a treatment to the patient based on the one or more treatment classes. In some instances, the disease or condition comprises cutaneous manifestations. In some instances, methods and systems comprise obtaining biological samples from a subject, such as a first biological sample and a second biological sample. In some instances, methods and systems comprise obtaining biological samples from a subject, wherein at least one sample is non-invasively or minimally invasively sampled. In some instances, at least one biological sample is then analyzed to obtain a baseline biomarker signature. Baseline (biomarker) signatures in some instances are diagnostic of a disease or condition, independent of previous treatments. Signatures described herein in some instances comprise one or more biomarkers (e.g., biomarker signatures). In some instances signatures comprise additional quantitative information related to one or more biomarkers. In some instances at least one biological sample is exposed to a treatment in-vitro, and subsequently analyzed to obtain a treatment signature. By comparing baseline and treatment signatures, and outcome signature in some instances is generated which is predictive of how the subject having a specific baseline signature will respond to the treatment in-vivo. Further described herein are methods of identifying a patient as a responder or non-responder to a specific treatment by comparing the subject’s later signature (e.g., a differential signature obtained from a test sample) to a previously determined outcome signature (e.g., the patient’s baseline signature) corresponding to a treatment.

[0023] Further described herein are methods of sample preparation for in-vitro biomarker analysis using non-invasive or minimally invasive techniques. Further described herein are computer- assisted methods and systems for identifying and comparing biomarker signatures.

[0024] Provided herein are methods for generating one or more signatures for predicting therapeutic response for the disease on condition having cutaneous manifestations. In some embodiments, the method analyzes at least one biological sample described herein for generating the one or more signatures. In some embodiments, the biological sample is a skin sample. In some embodiments, the biological sample is a liquid biopsy sample. In some embodiments, the biological sample is a blood sample. In some embodiments, the method analyzes a first biological sample obtained from a subject. The first biological sample may be a first skin sample or a first liquid biopsy sample (e.g., a first blood sample). In some embodiments, the first biological sample is obtained from the subject via non- invasive or minimally invasive method. For example, the first skin sample may be obtained by an adhesive tape, a microneedle, or any skin collection method or kit described herein. In some embodiments, the first skin sample is obtained from healthy or normal looking skin. In some embodiments, the first skin sample is obtained from abnormal or lesioned skin. The first skin sample may be used in vitro for measurements and analysis of at least one biomarker isolated from the skin sample used to generate a baseline biomarker signature. In some embodiments, the first biological sample is obtained for an untreated subject. In some embodiments, a second biological sample is obtained from the same subject, where the second biological sample comprising a second skin sample is treated in vitro in the presence of one or more treatments

[0025] described herein. In some embodiments, at least one biomarker is isolated from the treated second skin sample culture to generate atreatment biomarker signature. In some embodiments, an outcome signature is generated from the comparison of the baseline biomarker signature and the treatment biomarker signature. In some embodiments, the outcome signature predicts therapeutic efficacy or outcome of the one or more treatments for treating the subject’s disease or condition. In some embodiments, the outcome signature is used to design atreatment regimen for treating the subject’s disease or condition. In some embodiments, the biological sample (e.g., the first or the second skin biological) may be analyzed for a presence or an expression level of at least one biomarker (e.g., a “gene expression level”, “expression level of a gene”, or “gene level”, also referred to as a “level of gene expression signature”, a genomic signature, or a protein signature) described herein. The biomarker may be either a genetic marker (e.g., genetic mutation or epigenetic marker), a non-genetic marker (e.g., environmental factor), metabolite, lipid, protein, or other biomarker described herein. In some embodiments, the at least one biomarker is protein, lipid, or carbohydrate. [0026] FIG. 1A illustrates an example of determining one or more baseline signatures for a plurality of patients that may later be used for determining treatment regimens for the plurality of patients. In some embodiments, the biological samples may be obtained using an adhesive (e.g., tape, patch, strip, etc.). In some embodiments, the biological samples may be obtained with microneedles. In some embodiments, the biological samples may be skin samples. In some embodiments, the biological sample is a liquid biopsy such as blood drawn from the subject.

[0027] The biological sample (e.g., skin sample) may be used to determine the one or more baseline signatures for the plurality of patients based on one or more gene expression signatures from each sample. In this depicted example, the biological sample from each patient indicates one or more gene expression signatures for the respective patient. In some embodiments, biological samples may be taken from each patient of the plurality of patients over a period of time, and one or more gene expression signatures may then be determined forthat period of time for each patient based on those biological samples taken over the period of time. In this depicted example, the biological samples are analyzed for each patient in order to determine the levels of gene expression signatures.

[0028] Based on those levels of gene expression signatures, a baseline signature for each patient may be determined. A baseline signature may be one or more “standard” gene expression levels for a patient, which indicates that, when a patient is unaffected by a disease, disorder, or malady, the patient exhibits the standard gene expression levels. For example, a biological sample may be retrieved from a patient when the patient is not affected by a disease, disorder, or malady, and analyzed to show a first gene level of a first gene, a second gene level of a second gene, and third gene level of a third gene. The baseline signature may then be determined to include the first gene level of the first gene, the second gene level of the second gene, and the third gene level of the third gene. In some embodiments, the baseline signature may be an average of gene levels overtime for specific genes. For example, a first biological sample may be retrieved from a patient and may indicate a first gene level of the first gene, the second gene level of the second gene, and the third gene level of the third gene. In the same example, a second biological sample may be retrieved from a patient and may indicate a fourth gene level of the first gene, a fifth gene level of the second gene, and a sixth gene level of the third gene. A baseline level of the first gene may be determined based on the first gene level and the fourth gene level, a baseline level of the second gene may be determined based on the second gene level and the fifth gene level, and a baseline level of the third gene may be determined based on the third gene level and the sixth gene level. The baseline signature may then be determined to be the baseline level of the first gene, the baseline level of the second gene, and the baseline level of the third gene.

[0029] As described above, the baseline signature of a patient indicates the standard gene expression for a patient. Thus, patients with the same or similar baseline levels may be grouped according to those baseline signatures. In this depicted example, a plurality of biological samples are retrieved from a plurality of patients, and the baseline signature for each patient is determined. The baseline signatures are then categorized into one or more groups. In this depicted example, three groups are determined (e.g., group 1, group 2, and group 3). Baseline signatures indicating higher gene expression of the first gene and lower gene expression of the second and third genes are placed into Group 1. Baseline signatures indicating higher gene expression of the second gene and lower gene expression of the first and third genes are placed into Group 2. Baseline signatures indicating higher gene expression of the third gene and lower gene expression of the first and second genes are placed into Group 3.

[0030] In some embodiments, the baseline signature may be determined based on a signature score of the gene expression levels generated from the biological sample of the patient. The signature score may be determined based on the signal of the gene expression levels generated. In some embodiments, the signature score that may indicate a categorization for a baseline signature based on the gene expression levels. In some embodiments, signature scores may be determined using weighting algorithms, likelihood methods, probabilistic approaches, or similar methods of determination, and may be determined using components as described with respect to FIGs. 2-4. For example, a biological sample may lead to a first gene level of gene X, a second gene level of a gene Y, and third gene level of a gene Z. Certain probabilities, such as probabilities of how a patient will respond to a specific treatment, may be determined based on the gene levels of gene X, gene Y, and gene Z. Thus a signature score, may be determined based on the probabilities of how a patient will respond to those specific treatments. Genes X, Y, and Z in some instances are exemplary genes used for predictive treatment.

[0031] While gene expressions and levels are described above as being used to determine baseline signatures, gene expressions and levels are exemplary and other expressions or levels may be used. For example, protein signatures, genomic signatures, and the like may be used to determine baseline signatures. In some instances, abaseline signature comprises agene expression signature. A baseline gene signature may comprise any number of genes, such as at least 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 75, 100, 200, or at least 500 genes. A baseline gene signature in some instances comprises no more than 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 75, 100, 200, or no more than 500 genes.

[0032] A baseline gene signature in some instances 1-500, 2-500, 5-500, 10-500, 50-500, 100- 500, 10-20, 10-50, 5-50, 1-10, 25-50, or 25-100 genes. In some instances, a baseline signature comprises a protein signature. A baseline gene signature may comprise any number of proteins, such as at least 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 75, 100, 200, or at least 500 genes. A baseline gene signature in some instances comprises no more than 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 75, 100, 200, or no more than 500 proteins. A baseline gene signature in some instances 1-500, 2-500, 5-500, 10-500, 50-500, 100-500, 10-20, 10-50, 5-50, 1-10, 25-50, or 25-100 proteins. In some instances, abaseline signature comprises a genomic signature. A baseline gene signature may comprise any number of genomic signatures, such as at least 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 75, 100, 200, or at least 500 genomic signatures. A baseline gene signature in some instances comprises no more than 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 75, 100, 200, or no more than 500 genomic signatures. A baseline gene signature in some instances 1-500, 2-500, 5-500, 10-500, SO- SOO, 100-500, 10-20, 10-50, 5-50, 1-10, 25-50, or 25-100 genomic signatures. In some instances, a genomic signature comprises single -nucleotide polymorphism genotyping, copy number proofing, and post -transcriptional modifications.

[0033] Baseline gene expressions profiles may be used to determine suitable treatment options for a patient. In some instances, patients are assigned treatment classes. In some instances, treatment classes represent classes based on one or more biomarkers. In some instances treatment classes comprise Th22, Th2, Th 17, Thl, inflammation, kinase inhibitors, B cell, microbial infection, and macrophage. In some instances, a patient is assigned to one or more treatment classes. In some instances, a primary treatment class is selected from two or more treatment classes. Treatment classes in some instance are visualized using the systems and methods described herein. In some instances, a system or method comprises one or more of: obtaining at least one sample from a patient using a non-invasive sampling method; determining at least one signature for the patient based on the at least one sample; displaying, by a user interface, a plurality of treatment regimens corresponding to a plurality of baseline signatures, wherein each baseline signature of the plurality of baseline signatures corresponds to a patient group of a plurality of patient groups; receiving input to the user interface identifying a treatment regimen of the plurality of treatment regimens; matching the at least one signature for the patient to the first baseline signature of the plurality of baseline signatures; and treating the patient based on the first baseline signature and a corresponding first method of treatment.

[0034] Provided herein are methods for generating abaseline signature database. In some instances, a method comprises one or more of the steps: obtaining a plurality of signatures from a patient population, wherein the population comprises treated and untreated patients; determining one or more patient groups of the patient population, wherein each of the one or more patient groups of the patient population shares at least one of the plurality of signatures; identifying one or more baseline signatures for each of the one or more patient groups based on the at least one of the plurality of signatures shared by the respective patient group; and storing the one or more baseline signatures in an electronically accessible database. In some instances, a method comprises one or more of the steps: obtaining a second plurality of signatures from a second patient population, wherein the second population comprises treated and untreated patients; determining whether each patient shares at least one of the plurality of signatures with patients in the one or more patient groups; adding each patient that shares at least one of the plurality of signatures with at least one of the one or more patient groups to each patient group that shares at least one of the plurality of signatures; determining at least one new patient group, wherein each of the at least one new patient group of the second patient population shares at least one of the second plurality of signatures; identifying at least one new baseline signature for each of the at least one new patient group based on the at least one of the second plurality of signatures shared by the respective new patient group; and storing the at least one new baseline signature in the electronically accessible database.

[0035] As described below with respect to FIG. IB and FIG. 5, a respective set of treatments may be administered to a patient based on what group the patient’s baseline signature is placed in and the differential signature taken when the patient is affected by a disease, disorder, or malady.

[0036] FIG. IB depicts determining a differential signature for a patient based on a new biological sample and the baseline signature of the patient. As described with respect to FIG. 1A, a patient may have one or more biological samples retrieved and used to determine the patient’s baseline signature based on one or more gene expressions derived from the biological sample. The patient may later be affected by a disease or condition, such as a disorder or malady, and may have the new biological sample retrieved while being affected. In this depicted example, new biological samples are obtained from each patient of the plurality of patients using adhesive patches. In some embodiments, the new biological samples may be obtained with microneedles and a collection device. In some embodiments, the new biological samples may be obtained In some embodiments, the new biological samples may be skin samples. In some embodiments, the new biological sample is a liquid biopsy such as blood drawn from the subject. The new biological sample (e.g., skin sample taken while the patient is affected) may be used to determine the differential signature for the patient based on one or more gene expression signatures from the patient. In some embodiments, the differential signature may be determined similarly to the original baseline signature. Thus, in this depicted example, the new biological sample from the patient indicates one or more gene expression signatures for the respective patient.

[0037] In some embodiments, the new biological sample from the patient indicates the same amount of gene expression signatures as previously retrieved biological samples. In some embodiments, the new biological sample form the patient indicates a different amount of gene expression signatures than the previously retrieved biological samples. In this depicted example, the new biological sample is analyzed for each patient in order to determine the levels of gene expression signatures in the differential signature. [0038] Based on those levels of gene expression signatures, a differential signature for each patient may be determined. A differential signature may be one or more “differential” gene expression levels for a patient, which indicates that, when a patient is affected by a disease, disorder, or malady, the patient exhibits the standard gene expression levels. For example, a biological sample may be retrieved from a patient when the patient is affected by a disease, disorder, or malady, and analyzed to show a first gene level of a first gene, a second gene level of a second gene, and third gene level of a third gene. The differential signature may then be determined to include the first gene level of the first gene, the second gene level of the second gene, and the third gene level of the third gene. In some embodiments, the differential signature may be an average of gene levels over time for specific genes. For example, a first new biological sample may be retrieved from a patient and may indicate a first gene level of the first gene, the second gene level of the second gene, and the third gene level of the third gene. In the same example, a second new biological sample may be retrieved from a patient and may indicate a fourth gene level of the first gene, a fifth gene level of the second gene, and a sixth gene level of the third gene. A differential level of the first gene may be determined based on the first gene level and the fourth gene level, a differential level of the second gene may be determined based on the second gene level and the fifth gene level, and a differential level of the third gene may be determined based on the third gene level and the sixth gene level. The differential signature may then be determined to be the differential level of the first gene, the baseline level of the second gene, and the baseline level of the third gene.

[0039] The differential signature of the patient may then be compared to the baseline signature of the patient in order to determine the treatment necessary to treat the patient, as further described with respect to FIG. 5. In some embodiments, the differential signature and the baseline signature are compared by analyzing the difference between gene levels of the differential signature and the baseline signature. For example, if a first gene had a gene expression level of 1 in the baseline signature, and the first gene had a gene expression level of 3 in the differential signature, the comparison may indicate that the differential signature has a higher gene expression level by 2 than the baseline signature for the first gene, which may indicate what treatment regimen should be administered.

[0040] As described above, the differential signature and baseline signature may indicate which treatments are optimal for treating a patient based on which gene expression levels are raised or lowered, and further, what gene levels need to be lowered or raised in order to cure the disease, disorder, or malady. For example, a patient suffering from information may give a biological sample, and the baseline signature and differential signature of the associated biological sample may show that the gene associated with TNFa is higher than normal. Thus, a treatment regimen including the administration of Adalimumab may be implemented in order to lower the level of the gene associated with TNFa. In some embodiments, the treatment regimen may be chosen from a plurality of options in a user interface, as described with respect to FIG. 5. In some embodiments, the treatment regimen may be recommended or displayed on a user interface, as described with respect to FIG. 5.

[0041] Further, as a non-limiting example, gene expressions that may be higher or lower than normal based on the baseline signature and the differential signature may include TNFa, ILlb, ILla/b, IL-36R, TSLP, IL-22, IL4Ra, IL13, IL-31, IL-17, IL-17R, IL-23, IL-12/23, IFNAR, IL-5R, IL5, IL-5, GMCSFRa, CD20, CD19, and BAFF. As a non-limiting example, gene expressions that may be higher or lower than normal and may be contributing inflammation are TNFa, ILlb, ILla/b, and IL-36R. As a non-limiting example, treatment regimens for those higher or lower gene expressions may include administering Adalimumab, Canakinumab, Rilonacept, and/or Spesolimab. As a non-limiting example, gene expressions that may be higher or lower than normal and may be contributing to epithelial conditions may be TSLP or IL-33. As a non-limiting example, the treatment regimens for those higher or lower gene expressions may include administering Tezepelumab or IL-33 targeting. As anon- limiting example, gene expressions that may be higher or lower than normal and may be contributing to Th22-induced inflammation is IL-22. As a non-limiting example, the treatment regimens for those higher or lower gene expressions may include administering Fezankizumab. As a non-limiting example, gene expressions that may be higher or lower than normal and may be contributing Th2 -induced inflammation may be IL4Ra, IL13, and/or IL-31. As anon-limiting example, the treatment regimens for those higher or lower gene expressions may include administering Dupilumab, Tralokinumab, Lebrikizumab, and/or Nemolizumab. As a non-limiting example, gene expressions that may be higher or lower than normal and may be contributing Thl7-induced inflammation is IL-17, IL-17R, IL-23, and/or IL-12/23. As a non-limiting example, the treatment regimens for those higher or lower gene expressions may include administering Ixekizumab, Secukinumab, Brodalumab, Guzelkumab, Rizankizumab, and/or Ustekinumab. As anon-limiting example, gene expressions that may be higher or lower than normal and may be contributing Th 1 -induced inflammation is IFNAR. As a non-limiting example, the treatment regimens for those higher or lower gene expressions may include administering Anifrolumab. As a non-limiting example, gene expressions that may be higher or lower than normal and may be contributing a microbial infection. As a non-limiting example, the treatment regimens forthose higher or lower gene expressions may include administering antibiotics. As anon-limiting example, gene expressions that may be higher or lower than normal and may be contributing to eosinophil-related conditions may be IL-5R, IL5, and/or IL-5. As anon-limiting example, the treatment regimens for those higher or lower gene expressions may include administering Benralizumab, Mepolizumab, and/or Reslizumab. As a non-limiting example, gene expressions that may be higher or lower than normal and may be contributing a macrophage -related conditions is GMCSFRa. As a non-limiting example, the treatment regimens for those higher or lower gene expressions may include administering Mavrilimumab. As a non-limiting example, gene expressions that may be higher or lower than normal and may be contributing B cells-related conditions is CD20, CD 19, and/or BAFF. As a non-limiting example, the treatment regimens for those higher or lower gene expressions may include administering Rituxumab, Ocrelizumab, Ofatumumab, Inebilizumab, and/or Belimumab. [0042] While gene expressions and levels are described above as being used to determine baseline signatures and differential signatures, gene expressions and levels are exemplary and other expressions or levels may be used. For example, protein signatures, genomic signatures, and the like may be used to determine baseline signatures.

[0043] Biological samples

[0044] Described herein are methods for obtaining a biological sample. In some instances, biological samples are obtained to identify baseline and treatment biomarker signatures. In some embodiments, the method comprises extracting nucleic acid, protein, carbohydrate or lipid sample from a biological sample from a subject. In some instances, the biological sample comprises a skin sample. In some instances, the biological sample is obtained using a non-invasive (or minimally invasive) sampling technique. In some instances, a non-invasive (or minimally invasive) sampling technique does not comprise a biopsy. In some instances, the biological sample is obtained from skin or blood. In some embodiments, the non-invasive sampling technique comprises contacting the skin of the subject with an adhesive tape or patch for extracting skin cells. In some instances, the biological sample is obtained from the stratum corneum. In some embodiments, the non-invasive sampling technique comprises contacting the skin of the subject with microneedle for extracting skin cells. In some embodiments, a skin sample is obtained using an invasive or minimally invasive sampling technique. The invasive or minimally invasive sample technique may include using adhesive tape or patch, where the adhesive tape or patch comprises increased adhesiveness compared to the adhesive tape or patch used for non- invasive sampling. In some embodiments, the invasive or minimally invasive sample technique may include using microneedle, where the microneedle comprises increased abrasiveness compared to the abrasiveness of the microneedle used for non-invasive sampling. In some embodiments, the biological sample is obtained by swabbing. In some embodiments, the biological sample is obtained by skin biopsy. The skin biopsy may be punch biopsy or shave biopsy. In some embodiments, the skin sample is obtained by hair root sampling (which samples skin that is deeper than the epidermis), buccal smear, or suction blistering. In some instances, the biological sample comprises cells obtained from blood. In some instances, the biological sample comprises skin progenitor cells. In some instances, the biological sample comprises PBMCs. In some instances, the biological sample is further differentiated into skin cells in-vitro. In some instances, a biological sample is contacted with one or more treatments in-vitro. In some instances, a biological sample is cultured in-vitro. Any number of biological samples in some instances obtained from a subject, such as 1, 2, 3, 4, 5, 6, 7, 8, or more than 9 biological samples. Biological samples may be prepared for in-vitro use. In some instances, biological samples comprise cells. In some instances, cells are cultured in a media or buffer. In some instances, the medium is keratinocyte basal medium. Cells cultured in some instances do not appreciably grow or divide. In some instances cultured cells are manipulated in such a way to grow or divide. In some instances, cells are utilized in-vitro for at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or at least 20 days. In some instances, cells are utilized in-vitro for about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or about 20 days. In some instances, cells are utilized in-vitro for 5- 20, 5-15, 8-12, 2-5, 5-10, 10-20, or 15-20 days. In some instances, cells used in-vitro are contacted with one or more treatments. In some instances, biological samples used in-vitro comprise cells obtained from a biopsy. In some instances, biological samples used in-vitro comprise cells obtained from blood.

[0045] Biological samples may be obtained from any part of a subject. In some embodiments, a biological sample is obtained from a bodily fluid such as blood. In some embodiments, a biological sample is obtained from the surface of a subject including, but not limited to, the face, head, neck, arm, chest, abdomen, back, leg, hand or foot. In some instances, the skin surface is not located on a mucous membrane. In some instances, the skin surface is not ulcerated or bleeding. In certain instances, the skin surface has not been previously biopsied. In certain instances, the skin surface is not located on the soles of the feet or palms. In some instances, a biological samples are obtained from the same or substantially the same area of a subject. In some instances, a biological samples are obtained from a subject at two separate time points. In some instances, the time points are separated by no more than 1 hour, 12 hours, 1 day, 2 days, 15 days, 30 days, 2 months, 6 months, 1 year, 2 years, 5 years or no more than 10 years.

[0046] Biological samples may comprise RNA. In some instances, the nucleic acid comprises RNA (e.g., mRNA). An effective amount of a biological sample contains an amount of cellular material sufficient for performing a diagnostic assay. In some instances, the diagnostic assay is performed using the cellular material isolated from the biological sample. In some embodiments, an effective amount of a biological sample comprises an amount of RNA sufficient to perform a genomic analysis. Sufficient amounts of RNA includes, but not limited to, picogram, nanogram, and microgram quantities. In some embodiments, the RNA includes mRNA. In some embodiments, the RNA includes microRNAs. In some embodiments, the RNA includes mRNA and microRNAs.

[0047] In some instances, the nucleic acid is a RNA molecule or a fragmented RNA molecule (RNA fragments). In some instances, the RNA is amicroRNA (miRNA), a pre-miRNA, a pri-miRNA, a mRNA, a pre-mRNA, a viral RNA, aviroid RNA, avirusoid RNA, circular RNA (circRNA), a ribosomal RNA (rRNA), a transfer RNA (tRNA), apre-tRNA, along non-coding RNA (IncRNA), a small nuclear RNA (snRNA), a circulating RNA, a cell-free RNA, an exosomal RNA, a vector- expressed RNA, a RNA transcript, a synthetic RNA, or combinations thereof. In some instances, the RNA is mRNA. In some instances, the RNA is cell-free circulating RNA.

[0048] In some instances, the nucleic acid comprises DNA. DNA includes, but not limited to, genomic DNA, viral DNA, mitochondrial DNA, plasmid DNA, amplified DNA, circular DNA, circulating DNA, cell-free DNA, or exosomal DNA. In some instances, the DNA is single-stranded DNA (ssDNA), double -stranded DNA, denaturing double -stranded DNA, synthetic DNA, and combinations thereof. In some instances, the DNA is genomic DNA. In some instances, the DNA is cell-free circulating DNA. [0049] A biological sample may be obtained using an adhesive tape or patch from the sample collection kit described herein. In some embodiments, the adhesive tape or patch from the sample collection kit described herein comprises a first collection area comprising an adhesive matrix and a second area extending from the periphery of the first collection area. The adhesive matrix is located on a skin facing surface of the first collection area. The second area functions as a tab, suitable for applying and removing the adhesive patch. The tab is sufficient in size so that while applying the adhesive patch to a skin surface, the applicant does not come in contact with the matrix material of the first collection area. In some embodiments, the adhesive patch does not contain a second area tab. In some instances, the adhesive patch is handled with gloves to reduce contamination of the adhesive matrix prior to use.

[0050] In some embodiments, the first collection area is a polyurethane carrier film. In some embodiments, the adhesive matrix is comprised of a synthetic rubber compound. In some embodiments, the adhesive matrix is a styrene -isoprene -styrene (SIS) linear block copolymer compound. In some instances, the adhesive patch does not comprise latex, silicone, or both. In some instances, the adhesive patch is manufactured by applying an adhesive material as a liquid-solvent mixture to the first collection area and subsequently removing the solvent. In some embodiments, the adhesive matrix is configured to adhere cells from the stratum comeum of a skin sample.

[0051] The matrix material is sufficiently sticky to adhere to a skin sample. The matrix material is not so sticky that is causes scarring or bleeding or is difficult to remove. In some embodiments, the matrix material is comprised of a transparent material. In some instances, the matrix material is biocompatible. In some instances, the matrix material does not leave residue on the surface of the skin after removal. In certain instances, the matrix material is not a skin irritant.

[0052] In some embodiments, the adhesive patch comprises a flexible material, enabling the patch to conform to the shape of the skin surface upon application. In some instances, at least the first collection area is flexible. In some instances, the tab is plastic. In an illustrative example, the adhesive patch does not contain latex, silicone, or both. In some embodiments, the adhesive patch is made of a transparent material, so that the skin sampling area of the subject is visible after application of the adhesive patch to the skin surface. The transparency ensures that the adhesive patch is applied on the desired area of skin comprising the skin area to be sampled. In some embodiments, the adhesive patch is between about 5 and about 100 mm in length. In some embodiments, the first collection area is between about 5 and about 40 mm in length. In some embodiments, the first collection area is between about 10 and about 20 mm in length. In some embodiments the length of the first collection area is configured to accommodate the area of the skin surface to be sampled, including, but not limited to, about 19 mm, about 20 mm, about 21 mm, about 22mm, about 23 mm, about 24 mm, about 25 mm, about 30 mm, about 35 mm, about 40 mm, about 45 mm, about 50 mm, about 55 mm, about 60 mm, about 65 mm, about 70 mm, about 75 mm, about 80 mm, about 85 mm, about 90 mm, and about 100 mm. In some embodiments, the first collection area is elliptical. [0053] In further embodiments, the adhesive patch of this invention is provided on a peelable release sheet in the adhesive skin sample collection kit. In some embodiments, the adhesive patch provided on the peelable release sheet is configured to be stable at temperatures between -80 °C and 30 °C for at least 6 months, at least 1 year, at least 2 years, at least 3 years, and at least 4 years. In some instances, the peelable release sheet is a panel of atri-fold skin sample collector.

[0054] In some instances, nucleic acids are stable on adhesive patch or patches when stored for a period of time or at a particular temperature. In some instances, the period of time is at least or about 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 2 weeks, 3 weeks, 4 weeks, or more than 4 weeks. In some instances, the period of time is about 7 days. In some instances, the period of time is about 10 days. In some instances, the temperature is at least or about -80 °C, -70 °C, -60 °C, -50 °C, - 40 °C, -20 °C, -10 °C, -4 °C, 0 °C, 5 °C, 15 °C, 18 °C, 20 °C, 25 °C, 30 °C, 35 °C, 40 °C, 45 °C, 50 °C, or more than 50 °C. The nucleic acids on the adhesive patch or patches, in some embodiments, are stored for any period of time described herein and any particular temperature described herein. For example, the nucleic acids on the adhesive patch or patches are stored for at least or about 7 days at about 25 °C, 7 days at about 30 °C, 7 days at about 40 °C, 7 days at about 50 °C, 7 days at about 60 °C, or 7 days at about 70 °C. In some instances, the nucleic acids on the adhesive patch or patches are stored for at least or about 10 days at about -80 °C.

[0055] The peelable release sheet, in certain embodiments, is configured to hold a single adhesive patch or a plurality of adhesive patches, e.g., including, but not limited to, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 adhesive patches. In some embodiments, the peelable release sheet is configured to hold from about 2 to about 8, from about 2 to about 7, from about 2 to about 6, from about 2 to about 4, from about 3 to about 6, from about 3 to about 8, from about 4 to about 10, from about 4 to about 8, from about 4 to about 6, from about 4 to about 5, from about 6 to about 10, from about 6 to about 8, or from about 4 to about 8 adhesive patches. In some instances, the peelable release sheet is configured to hold about 12 adhesive patches. In some instances, the peelable release sheet is configured to hold about 11 adhesive patches. In some instances, the peelable release sheet is configured to hold about 10 adhesive patches. In some instances, the peelable release sheet is configured to hold about 9 adhesive patches. In some instances, the peelable release sheet is configured to hold about 8 adhesive patches. In some instances, the peelable release sheet is configured to hold about 7 adhesive patches. In some instances, the peelable release sheet is configured to hold about 6 adhesive patches. In some instances, the peelable release sheet is configured to hold about 5 adhesive patches. In some instances, the peelable release sheet is configured to hold about 4 adhesive patches. In some instances, the peelable release sheet is configured to hold about 3 adhesive patches. In some instances, the peelable release sheet is configured to hold about 2 adhesive patches. In some instances, the peelable release sheet is configured to hold about 1 adhesive patch.

[0056] Provided herein, in certain embodiments, are methods and compositions for obtaining a sample using at least one adhesive patch, wherein the at least one adhesive patch is applied to the skin and removed from the skin. After removing the used adhesive patch from the skin surface, the patch stripping method, in some instances, further comprise storing the used patch on a placement area sheet, where the patch remains until the skin sample is isolated or otherwise utilized. In some instances, the used patch is configured to be stored on the placement area sheet for at least 1 week at temperatures between -80 °C and 30 °C. In some embodiments, the used patch is configured to be stored on the placement area sheet for at least 2 weeks, at least 3 weeks, at least 1 month, at least 2 months, at least 3 months, at least 4 months, at least 5 months, and at least 6 months at temperatures between -80 °C to 30 °c.

[0057] In some instances, the placement area sheet comprises a removable liner, provided that prior to storing the used patch on the placement area sheet, the removable liner is removed. In some instances, the placement area sheet is configured to hold a plurality of adhesive patches, including, but not limited to, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, from about 2 to about 8, from about 2to about 7, from about 2 to about 6, from about 2 to about 4, from about 3 to about 6, from about 3 to about 8, from about 4 to about 10, from about 4 to about 8, from about 4 to about 6, from about 4 to about 5, from about 6 to about 10, from about 6 to about 8, or from about 4 to about 8. In some instances, the placement area sheet is configured to hold about 12 adhesive patches. In some instances, the placement area sheet is configured to hold about 11 adhesive patches. In some instances, the placement area sheet is configured to hold about 10 adhesive patches. In some instances, the placement area sheet is configured to hold about 9 adhesive patches. In some instances, the placement area sheet is configured to hold about 8 adhesive patches. In some instances, the placement area sheet is configured to hold about 7 adhesive patches. In some instances, the placement area sheet is configured to hold about 6 adhesive patches. In some instances, the placement area sheet is configured to hold about 5 adhesive patches. In some instances, the placement area sheet is configured to hold about 4 adhesive patches. In some instances, the placement area sheet is configured to hold about 3 adhesive patches. In some instances, the placement area sheet is configured to hold about 2 adhesive patches. In some instances, the placement area sheet is configured to hold about 1 adhesive patch.

[0058] The used patch, in some instances, is stored so that the matrix containing, skin facing surface of the used patch is in contact with the placement area sheet. In some instances, the placement area sheet is a panel of the tri-fold skin sample collector. In some instances, the tri-fold skin sample collector further comprises a panel. In some instances, the tri-fold skin sample collector further comprises a clear panel. In some instances, the tri-fold skin sample collector is labeled with a unique barcode that is assigned to a subject. In some instances, the tri-fold skin sample collector comprises an area for labeling subject information.

[0059] In an exemplary embodiment, the patch stripping method further comprises preparing the skin sample prior to application of the adhesive patch. Preparation of the skin sample includes, but is not limited to, removing hairs on the skin surface, cleansing the skin surface and/or drying the skin surface. In some instances, the skin surface is cleansed with an antiseptic including, but not limited to, alcohols, quaternary ammonium compounds, peroxides, chlorhexidine, halogenated phenol derivatives and quinolone derivatives. In some instances, the alcohol is about Oto about 20%, about 20 to about 40%, about 40 to about 60%, about 60 to about 80%, or about 80 to about 100% isopropyl alcohol. In some instances, the antiseptic is 70% isopropyl alcohol.

[0060] In some embodiments, the patch stripping method is used to collect a skin sample from the surfaces including, but not limited to, the face, head, neck, arm, chest, abdomen, back, leg, hand or foot. In some instances, the skin surface is not located on a mucous membrane. In some instances, the skin surface is not ulcerated or bleeding. In certain instances, the skin surface has not been previously biopsied. In certain instances, the skin surface is not located on the soles of the feet or palms.

[0061] The patch stripping method, devices, and systems described herein are useful for the collection of a skin sample from a skin lesion. A skin lesion is a part of the skin that has an appearance or growth different from the surrounding skin. In some instances, the skin lesion is pigmented. A pigmented lesion includes, but is not limited to, a mole, dark colored skin spot and a melanin containing skin area. In some embodiments, the skin lesion is from about 5 mm to about 16 mm in diameter. In some instances, the skin lesion is from about 5 mm to about 15 mm, from about 5 mm to about 14 mm, from about 5 mm to about 13 mm, from about 5 mm to about 12 mm, from about 5 mm to about 11 mm, from about 5 mm to about 10 mm, from about 5 mm to about 9 mm, from about 5 mm to about 8 mm, from about 5 mm to about 7 mm, from about 5 mm to about 6 mm, from about 6 mm to about 15 mm, from about 7 mm to about 15 mm, from about 8 mm to about 15 mm, from about 9 mm to about 15 mm, from about 10 mm to about 15 mm, from about 11 mm to about 15 mm, from about 12 mm to about 15 mm, from about 13 mm to about 15 mm, from about 14 mm to about 15 mm, from about 6 to about 14 mm, from about 7 to about 13 mm, from about 8 to about 12 mm and from about 9 to about 11 mm in diameter. In some embodiments, the skin lesion is from about 10 mm to about 20 mm, from about 20 mm to about 30 mm, from about 30 mm to about 40 mm, from about 40 mm to about 50 mm, from about 50 mm to about 60 mm, from about 60 mm to about 70 mm, from about 70 mm to about 80 mm, from about 80 mm to about 90 mm, and from about 90 mm to about 100 mm in diameter. In some instances, the diameter is the longest diameter of the skin lesion. In some instances, the diameter is the smallest diameter of the skin lesion. Examples of subjects include but are not limited to vertebrates, animals, mammals, dogs, cats, cattle, rodents, mice, rats, primates, monkeys, and humans. In some embodiments, the subject is a vertebrate. In some embodiments, the subject is an animal. In some embodiments, the subject is a mammal. In some embodiments, the subject is an animal, a mammal, a dog, a cat, cattle, a rodent, a mouse, a rat, a primate, or a monkey. In some embodiments, the subject is a human. In some embodiments, the subject is male. In some embodiments, the subject is female. In some embodiments, the subject has skin previously exposed to UV light. [0062] Such non-invasive methods in some instances provide advantages over traditional biopsy methods, including but not limited to self-application by a patient/ subject, increased signal to noise ratio of sample exposed to the skin surface (leading to higher sensitivity and/or specificity), lack of temporary or permanent scarring at the analysis site, lower change of infection, or other advantage. [0063] A skin sample may be obtained from a subject using a collection device. In some embodiments, the collection device is a non-invasive or semi-invasive collection device. In some embodiments, the collection device comprises one or more adhesive patches. In some embodiments, the adhesive patch comprises tape or a tape portion. In some embodiments, a skin sample is obtained from the subject by applying one or more adhesive patches to a skin region of the subject in a manner sufficient to adhere skin sample cells to the one or more adhesive patches, and removing each of the one or more adhesive patches from the skin region in a manner sufficient to retain the adhered skin sample cells to each of the one or more adhesive patches. In some embodiments, a skin sample is obtained by applying a plurality of (e.g., 2 or more) adhesive patches to a skin region of a subject in a manner sufficient to adhere skin sample cells to each of the adhesive patches, and removing each of the plurality of adhesive patches from the skin region in a manner sufficient to retain the adhered skin sample cells to each of the adhesive patches. In some embodiments, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more patches are used to collect a skin sample. In some embodiments, each adhesive patch of the one or more adhesive patches is applied one or more times to the skin to collect a sample. In some embodiments, each adhesive patch of the one or more adhesive patches is applied only once to the skin to collect a sample. In some embodiments, each adhesive patch of the one or more adhesive patches is applied to the skin two or more times to collect a sample. In some embodiments, a single patch is applied to the skin only once to collect a skin sample. In some embodiments, a single patch is applied to the skin multiple times to collect a skin sample. In some embodiments, a plurality of adhesive patches are each applied to the skin only once to collect a skin sample. In some embodiments, a plurality of adhesive patches are each applied to the skin multiple times to collect a skin sample. In some embodiments, a plurality of adhesive patches are used to collect multiple samples from the same area or region of the skin. In some embodiments, the multiple samples of the same area or region of the skin are collected sequentially or serially. In some embodiments, a plurality of adhesive patches are used to collect multiple samples from multiple areas or regions of the skin. In some embodiments, a plurality of adhesive patches are used to sequentially collect skin from multiple skin areas or regions. In some embodiments, a plurality of adhesive patches are used to sequentially collect a skin sample from the same lesion or location on the skin. In some embodiments, the plurality of adhesive patches comprising a skin sample are pooled together prior to subsequent manipulation (e.g., nucleic acid extraction) or analysis.

[0064] In some embodiments, the skin sample is not obtained with an adhesive patch. In some instances, the skin sample is obtained using a brush. In some instances, the skin sample is obtained using a swab, for example a cotton swab. In some cases, the skin sample is obtained using a probe. In some cases, the skin sample is obtained using a hook. In some instances, the skin sample is obtained using a medical applicator. In some instances, the skin sample is obtained by scraping a skin surface of the subject. In some cases, the skin sample is obtained through excision. In some instances, the skin sample is biopsied. In some embodiments, the skin sample is a biopsy. In some instances, the skin sample is obtained using one or more needles. For example, the needles may be microneedles. In some instances, the biopsy is a needle biopsy, or a microneedle biopsy. In some instances, the skin sample is obtained invasively. In some instances, the skin sample is obtained semi-invasively. In some instances, the skin sample is obtained noninvasively. A skin sample in some instances is obtained iteratively from the same skin area of a subject. In some instances, multiple samples are obtained from a single skin area and are pooled prior to analysis. In some embodiments, the skin area or region comprises a skin lesion. In some embodiments, the skin area or region is a non-lesional (i.e., comprises no visible lesions). [0065] In some instances, methods generate samples from the top or superficial layers of skin, which have been exposed to higher levels of one or more environmental factors. In some embodiments, the skin sample comprises cells of the stratum corneum. In some embodiments, the skin sample consists of cells of the stratum corneum. In some instances, non-invasive sampling described herein does not fully disrupt the epidermal and dermal junction. Without being bound by theory, non-invasive sampling described herein does not trigger significant wound healing which normally results from significant damage to the epithelial barrier. In some embodiments, the skin sample comprises at least 80%, 90%, 95%, 97%, 98%, 99%, 99.5%, or at least 99.9% of cells derived from the basal keratinocyte layer. In some embodiments, the skin sample comprises less than 10%, 5%, 3%, 2%, 1%, 0.1%, 0.05%, or less than 0.01% cells derived from the basal keratinocyte layer. In some embodiments, the skin sample does not include the basal layer of the skin. In some embodiments, the skin sample comprises or consists of a skin depth of 10 pm, 50 pm, 100 pm, 150 pm, 200 pm, 250 pm, 300 pm, 350 pm, 400 pm, 450 pm, 500 pm, or a range of skin depths defined by any two of the aforementioned skin depths. In some embodiments, the skin sample comprises or consists of a skin depth of about 10 pm, 50 pm, 100 pm, 150 pm, 200 pm, 250 pm, 300 pm, 350 pm, 400 pm, 450 pm, or about 500 pm. In some embodiments, the skin sample comprises or consists of a skin depth of 50- 100 pm. In some embodiments, the skin sample comprises or consists of a skin depth of 100-200 pm. In some embodiments, the skin sample comprises or consists of a skin depth of 200-300 pm. In some embodiments, the skin sample comprises or consists of a skin depth of 300-400 pm. In some embodiments, the skin sample comprises or consists of a skin depth of 400-500 pm.

[0066] Non-invasive sampling methods described herein may comprise obtaining multiple skin samples from the same area of skin on an individual using multiple collection devices (e.g., tapes). In some instances, each sample obtained from the same area or substantially the same area results in progressively deeper layers of skin cells. In some instances, multiple samples are pooled prior to analysis.

[0067] The skin sample may be defined by thickness, or how deep into the skin cells are obtained. In some embodiments, the skin sample is no more than 10 pm thick. In some embodiments, the skin sample is no more than 50 pm thick. In some embodiments, the skin sample is no more than 100 pm thick. In some embodiments, the skin sample is no more than 150 pm thick. In some embodiments, the skin sample is no more than 200 pm thick. In some embodiments, the skin sample is no more than 250 pm thick. In some embodiments, the skin sample is no more than 300 pm thick. In some embodiments, the skin sample is no more than 350 pm thick. In some embodiments, the skin sample is no more than 400 pm thick. In some embodiments, the skin sample is no more than 450 pm thick. In some embodiments, the skin sample is no more than 500 pm thick.

[0068] In some embodiments, the skin sample is at least 10 pm thick. In some embodiments, the skin sample is at least 50 pm thick. In some embodiments, the skin sample is at least 100 pm thick. In some embodiments, the skin sample is at least 150 pm thick. In some embodiments, the skin sample is at least 200 pm thick. In some embodiments, the skin sample is at least 250 pm thick. In some embodiments, the skin sample is at least 300 pm thick. In some embodiments, the skin sample is at least 350 pm thick. In some embodiments, the skin sample is at least 400 pm thick. In some embodiments, the skin sample is at least 450 pm thick. In some embodiments, the skin sample is at least 500 pm thick.

[0069] In some embodiments, the adhesive patch removes a skin sample from the subject at a depth no greater than 10 pm. In some embodiments, the adhesive patch removes a skin sample from the subject at a depth no greater than 50 pm. In some embodiments, the adhesive patch removes a skin sample from the subject at a depth no greater than 100 pm. In some embodiments, the adhesive patch removes a skin sample from the subject at a depth no greater than 150 pm. In some embodiments, the adhesive patch removes a skin sample from the subject at a depth no greater than 200 pm. In some embodiments, the adhesive patch removes a skin sample from the subject at a depth no greater than 250 pm. In some embodiments, the adhesive patch removes a skin sample from the subject at a depth no greater than 300 pm. In some embodiments, the adhesive patch removes a skin sample from the subject at a depth no greater than 350 pm. In some embodiments, the adhesive patch removes a skin sample from the subject at a depth no greater than 400 pm. In some embodiments, the adhesive patch removes a skin sample from the subject at a depth no greater than 450 pm. In some embodiments, the adhesive patch removes a skin sample from the subject at a depth no greater than 500 pm.

[0070] In some embodiments, the adhesive patch removes 1, 2, 3, 4, or 5 layers of stratum comeum from a skin surface of the subject. In some embodiments, the adhesive patch removes a range of layers of stratum comeum from a skin surface of the subject, for example a range defined by any two of the following integers: 1, 2, 3, 4, or 5. In some embodiments, the adhesive patch removes 1-5 layers of stratum comeum from a skin surface of the subject. In some embodiments, the adhesive patch removes 2-3 layers of stratum comeum from a skin surface of the subject. In some embodiments, the adhesive patch removes 2-4 layers of stratum comeum from a skin surface of the subject. In some embodiments, the adhesive patch removes no more than the basal layer of a skin surface from the subject. [0071] Some embodiments include collecting cells from the stratum comeum of a subject, for instance, by using an adhesive tape with an adhesive matrix to adhere the cells from the stratum comeum to the adhesive matrix. In some embodiments, the cells from the stratum comeum comprise T cells or components of T cells. In some embodiments, the cells from the stratum comeum comprise keratinocytes. In some instances, the stratum comeum comprises keratinocytes, melanocytes, fibroblasts, antigen presenting cells (Langerhans cells, dendritic cells), or inflammatory cells (T cells, B cells, eosinophils, basophils). In some embodiments, the skin sample does not comprise melanocytes. In some embodiments, a skin sample is obtained by applying a plurality of adhesive patches to a skin region of a subject in a manner sufficient to adhere skin sample cells to each of the adhesive patches, and removing each of the plurality of adhesive patches from the skin region in a manner sufficient to retain the adhered skin sample cells to each of the adhesive patches. In some embodiments, the skin region comprises a skin lesion.

[0072] Non-invasive sampling described herein may obtain amounts of nucleic acids. Such nucleic acids in some instances are obtained from obtaining skin using a single collection device. In some instances, nucleic acids are obtained from samples pooled from multiple collection devices. In some instances, nucleic acids are obtained from samples from a single collection device applied to the skin multiple times (1, 2, 3, or 4 times). In additional embodiments, the adhered skin sample comprises cellular material including nucleic acids such as RNA or DNA, in an amount that is at least about 1 picogram. Cellular material in some instances is obtained from skin using a single collection device. In some instances, cellular material is obtained from samples pooled from multiple collection devices. In some instances, cellular material is obtained from samples from a single collection device applied to the skin multiple times (1, 2, 3, or 4 times). In some instances, an amount of cellular material described herein refers to the amount of material pooled from multiple collection devices (e.g., 1-6 devices). In some embodiments, the amount of cellular material is no more than about 1 nanogram. In further or additional embodiments, the amount of cellular material is no more than about 1 microgram. In still further or additional embodiments, the amount of cellular material is no more than about 1 milligram. In still further or additional embodiments, the amount of cellular material is no more than about 1 gram.

[0073] Following extraction of nucleic acids from a biological sample, the nucleic acids, in some instances, are further purified. In some instances, the nucleic acids are RNA. In some instances, the nucleic acids are DNA. In some instances, the RNA is human RNA. In some instances, the DNA is human DNA. In some instances, the RNA is microbial RNA. In some instances, the DNA is microbial DNA. In some instances, cDNA is generated by reverse transcription of RNA. In some instances, human nucleic acids and microbial nucleic acids are purified from the same biological sample. In some instances, nucleic acids are purified using a column or resin based nucleic acid purification scheme. In some instances, this technique utilizes a support comprising a surface area for binding the nucleic acids. In some instances, the support is made of glass, silica, latex or a polymeric material. In some instances, the support comprises spherical beads.

[0074] Methods for isolating nucleic acids, in certain embodiments, comprise using spherical beads. In some instances, the beads comprise material for isolation of nucleic acids. Exemplary material for isolation of nucleic acids using beads include, but not limited to, glass, silica, latex, and a polymeric material. In some instances, the beads are magnetic. In some instances, the beads are silica coated. In some instances, the beads are silica-coated magnetic beads. In some instances, a diameter of the spherical bead is at least or about 0.5 um, 1 um, 1.5 um, 2 um, 2.5 um, 3 um, 3.5 um, 4 um, 4.5 um, 5 um, 5.5 um, 6 um, 6.5 um, 7 um, 7.5 um, 8 um, 8.5 um, 9 um, 9.5 um, 10 um, or more than 10 um. [0075] In some cases, a yield of the nucleic acids products obtained using methods described herein is about 500 picograms or higher, about 600 picograms or higher, about 1000 picograms or higher, about 2000 picograms or higher, about 3000 picograms or higher, about 4000 picograms or higher, about 5000 picograms or higher, about 6000 picograms or higher, about 7000 picograms or higher, about 8000 picograms or higher, about 9000 picograms or higher, about 10000 picograms or higher, about 20000 picograms or higher, about 30000 picograms or higher, about 40000 picograms or higher, about 50000 picograms or higher, about 60000 picograms or higher, about 70000 picograms or higher, about 80000 picograms or higher, about 90000 picograms or higher, or about 100000 picograms or higher.

[0076] In some cases, a yield of the nucleic acids products obtained using methods described herein is about 100 picograms, 500 picograms, 600 picograms, 700 picograms, 800 picograms, 900 picograms, 1 nanogram, 5 nanograms, 10 nanograms, 15 nanograms, 20 nanograms, 21 nanograms, 22 nanograms, 23 nanograms, 24 nanograms, 25 nanograms, 26 nanograms, 27 nanograms, 28 nanograms, 29 nanograms, 30 nanograms, 35 nanograms, 40 nanograms, 50 nanograms, 60 nanograms, 70 nanograms, 80 nanograms, 90 nanograms, 100 nanograms, 150 nanograms, 200 nanograms, 250 nanograms, 300 nanograms, 400 nanograms, 500 nanograms, or higher. In some cases, methods described herein provide less than less than 10%, less than 8%, less than 5%, less than 2%, less than 1%, or less than 0.5% product yield variations between samples.

[0077] In some embodiments, a number of cells is obtained for use in a method described herein. Some embodiments include use of an adhesive patch comprising an adhesive comprising a tackiness that is based on the number of cells to be obtained. Some embodiments include use of a number of adhesive patches based on the number of cells to be obtained. Some embodiments include use of an adhesive patch sized based on the number of cells to be obtained. The number, size and/or tackiness may be based on the type of skin to be obtained. For example, normal looking skin generally provides less cells and RNA yield than flaky skin. In some embodiments, a skin sample is used comprising skin from a subject’s temple, forehead, cheek, or nose. In some embodiments, only one patch is used. In some embodiments, only one patch is used per skin area (e.g., skin area on a subject’s temple, forehead, cheek, or nose). In some embodiments, only one patch is applied a single time (e.g., once) to a single skin area or region. In some other embodiments, only one patch is applied multiple times (e.g., serially) to a single skin area or region. In other embodiments, a plurality of patches is applied a single time each (e.g., once) to a single skin area or region. In other embodiments, a plurality of patches is applied a single time each (e.g., once) to a plurality of skin areas or regions. In yet other embodiments, a plurality of patches is applied multiple times each (e.g., serially) to a single skin area or region. In yet other embodiments, a plurality of patches is applied multiple times each (e.g., serially) to a plurality of skin areas or regions. In some embodiments, the skin area or region comprises a lesion. In some embodiments, the skin area or region does not comprise a lesion.

[0078] In some cases, methods described herein provide a substantially homogenous population of a nucleic acid product. In some cases, methods described herein provide less than 30%, less than 25%, less than 20%, less than 15%, less than 10%, less than 8%, less than 5%, less than 2%, less than 1%, or less than 0.5% contaminants.

[0079] In some instances, following extraction, nucleic acids may be stored. In some instances, the nucleic acids may be stored in water, Tris buffer, or Tris-EDTA buffer before subsequent analysis. In some instances, this storage is less than 8° C. In some instances, this storage is less than 4° C. In certain embodiments, this storage is less than 0° C. In some instances, this storage is less than -20° C. In certain embodiments, this storage is less than -70° C. In some instances, the nucleic acids are stored for about 1, 2, 3, 4, 5, 6, or 7 days. In some instances, the nucleic acids are stored for about 1, 2, 3, or 4 weeks. In some instances, the nucleic acids are stored for about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months.

[0080] In some instances, nucleic acids isolated using methods described herein may be subjected to an amplification reaction following isolation and purification. In some instances, the nucleic acids to be amplified are RNA including, but not limited to, human RNA and human microbial RNA. In some instances, the nucleic acids to be amplified are DNA including, but not limited to, human DNA and human microbial DNA. Non-limiting amplification reactions include, but are not limited to, quantitative PCR (qPCR), self-sustained sequence replication, transcriptional amplification system, Q- Beta Replicase, rolling circle replication, or any other nucleic acid amplification known in the art. In some instances, the amplification reaction is PCR. In some instances, the amplification reaction is quantitative such as qPCR.

[0081] Biological samples may comprise lipids. Non-limiting examples of lipids include vesicles, phospholipids, glycolipids, fatty acids, triglycerides, waxes, steroids, or other lipid. In some cases, a yield of the lipids obtained using methods described herein is 1 picogram to 100 picograms, 100 picograms to 500 picograms, 100 picograms to 1 nanogram, 1 nanogram to 1 microgram, 1 nanogram to 500 nanograms, or 500 nanograms to 5 micrograms.

[0082] Biological samples may comprise carbohydrates. Non-limiting examples of carbohydrates include sugars (e.g., monosaccharides), polysaccharides (e.g., starches), nucleotides, or fibers. In some cases, a yield of the carbohydrates obtained using methods described herein is 1 picogram to 100 picograms, 100 picograms to 500 picograms, 100 picograms to 1 nanogram, 1 nanogram to 1 microgram, 1 nanogram to 500 nanograms, or 500 nanograms to 5 micrograms.

[0083] In some instances, the layers of skin include epidermis, dermis, or hypodermis. The outer layer of epidermis is the stratum comeum layer, followed by stratum lucidum, stratum granulosum, stratum spinosum, and stratum basale. In some instances, the skin sample is obtained from the epidermis layer. In some cases, the skin sample is obtained from the stratum comeum layer. In some instances, the skin sample is obtained from the dermis. In some cases, the skin sample is obtained from the stratum germinativum layer. In some cases, the skin sample is obtained from no deeper than the stratum germinativum layer.

[0084] In some instances, cells from the stratum comeum layer are obtained, which comprises keratinocytes. In some instances, cells from the stratum corneum layer comprise T cells or components of T cells. In some cases, melanocytes are not obtained from the skin sample.

[0085] The sample may comprise skin cells from a superficial depth of skin using the non- invasive sampling techniques described herein. In some instances, the sample comprises skin cells from about the superficial about 0.01, 0.02, 0.05, 0.08, 0.1, 0.2, 0.3, 0.4 mm of skin. In some instances, the sample comprises skin cells from no more than the superficial about 0.01, 0.02, 0.05, 0.08, 0.1, 0.2, 0.3, 0.4 mm of skin. In some instances, the sample comprises skin cells from at least the superficial about 0.01, 0.02, 0.05, 0.08, 0.1, 0.2, 0.3, or at least 0.4 mm of skin. In some instances, the sample comprises skin cells from the superficial about 0.01-0.1, 0.01-0.2, 0.02-0.1, 0.02-0.2 0.04-0.08, 0.02- 0.08, 0.01-0.08, 0.05-0.2, or 0.05-0.1 mm of skin. In some instances, the sample comprises skin cells from about the superficial about 0.01, 0.02, 0.05, 0.08, 0.1, 0.2, 0.3, or about 0.4 pm of skin. In some instances, the sample comprises skin cells from no more than the superficial about 0.01, 0.02, 0.05, 0.08, 0.1, 0.2, 0.3, or no more than 0.4 pm of skin. In some instances, the sample comprises skin cells from at leastthe superficial about 0.01, 0.02, 0.05, 0.08, 0.1, 0.2, 0.3, 0.4 pm of skin. In some instances, the sample comprises skin cells from the superficial about 0.01-0.1, 0.01-0.2, 0.02-0.1, 0.02- 0.2 0.04-0.08, 0.02-0.08, 0.01-0.08, 0.05-0.2, or 0.05-0.1 pm of skin.

[0086] The sample may comprise skin cells a number of skin cell layers, for example the superficial cell layers. In some instances, the sample comprises skin cells from 1-5, 1-10, 1-20, 1-25, 1-50, 1-75, or 1-100 cell layers. In some instances, the sample comprises skin cells from about 1, 2, 3, 4, 5, 8, 10, 12, 15, 20, 22, 25, 30, 35, or about 50 cell layers. In some instances, the sample comprises skin cells from no more than 1, 2, 3, 4, 5, 8, 10, 12, 15, 20, 22, 25, 30, 35, or no more than 50 cell layers. The sample may comprise skin cells collected from a defined skin area of the subject having a surface area. In some instances the sample comprises skin cells obtained from a skin surface area of 10-300 mm², 10-500 mm², 5-500 mm², 1-300 mm², 5-100 mm², 5-200 mm², or 10-100 mm². In some instances the sample comprises skin cells obtained from a skin surface area of at least 5, 10, 20, 25, 30, 50, 75, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, or at least 350 mm².

[0087] In some instances the sample comprises skin cells obtained from a skin surface area of no more than 5, 10, 20, 25, 30, 50, 75, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, or no more than 350 mm .

[0088] Provided herein are methods of sample preparation. In some instances such methods transform a biological sample from a patient into a biological sample useful for predicting a response to a disease or condition having cutaneous manifestations. In some instances, a method for preparing a nucleic acid sample from a subject useful for predicting a response to a disease or condition having cutaneous manifestations comprising one or more steps of: extracting nucleic acids and/or proteins from a first biological sample of a subject, wherein the nucleic acids are obtained from the first biological sample using anon-invasive or minimally invasive sampling technique; excising a second biological sample from the subject; applying one or more treatments to the second biological sample foratime period, wherein the treatments are applied in-vitro; extracting nucleic acids and/or proteins from the second biological sample; measuring a signature for the first biological sample to generate a baseline signature; measuring a signature for the second biological sample to generate a treatment signature; comparing the baseline signature and the treatment signature to generate an outcome signature corresponding to the one or more treatments. In some instances, the sample preparation method comprises 1, 2, 3, 4, 5, 6, or more than 6 steps. In some instances, the skin biopsy sample is contacted with keratinocyte basal medium. In some instances, the method comprises capture of nucleic acids corresponding to genes measured in the treatment signature. In some instances, the method comprises capture of proteins measured in the treatment signature. In some instances, the method comprises capture of lipids measured in the treatment signature. In some instances, the method comprises capture of metabolites measured in the treatment signature. In some instances, the first biological sample comprises cellular material from the stratum comeum which has been separated from the remainder of epidermis. In some instances, the second biological sample comprises cellular material from the epidermis. In some instances, the second biological sample is obtained from a skin biopsy. In some instances, comparing comprises correlating the presence or absence of one or more biomarkers from the first biological sample and the second biological sample. In some instances, comparing comprises correlating the abundance of one or more biomarkers from the first biological sample and the second biological sample.

[0089] Biomarker Signatures

[0090] Disclosed herein are methods for identifying and measuring biomarkers associated with diseases or conditions having cutaneous manifestations described herein. In some instances, measuring biomarkers results is used to generate biomarker signatures. In some embodiments, the method comprises identifying and measuring at least one biomarker for predicting therapeutic response or outcome. In some embodiments, a baseline biomarker signature may be determined at least based on the identifying and measuring the at least one biomarker. In some embodiments, a treatment biomarker signature may be determined at least based on the identifying and measuring the at least one biomarker. In some embodiments, an outcome signature may be determined at least based on the identifying and measuring the at least one biomarker. In some embodiments, the biomarker signature comprises a nucleic acid (e.g., genotypic biomarker, a single nucleotide polymorphism biomarker, a gene mutation biomarker, a gene copy number biomarker, a DNA methylation biomarker, a DNA acetylation biomarker, a chromosome dosage biomarker, a gene expression biomarker), a protein (e.g., protein expression, protein activation), a lipid, a carbohydrate, a metabolite, or a combination thereof. In some embodiments, biomarkers comprise nucleic acid mutations present in genetic material of a sample obtained from a subject. In some instances, methods described herein quantify the mutations of a sample obtained from a subject. In some embodiments, such biomarkers comprise nucleic acid expression levels. The nucleic acid may be gene -coding nucleic acid such as mRNA. In some embodiments, the nucleic acid is non-coding nucleic acid such as miRNA. The methods and devices provided herein, in certain embodiments, involve measuring or identifying biomarkers obtained from biological samples. In some instances, biological samples comprise one or more of nucleic acids, lipids, carbohydrates, or proteins. In some instances, one or more biomarkers are used to generate a biomarker signature. In some instances, the biomarker signature is a baseline signature obtained prior to treatment of the biological sample. In some instances, the biomarker signature is atreatment signature obtained subsequent to treatment of the biological sample. In some instances, the nucleic acid comprises RNA or DNA.

[0091] Described herein, in some embodiments, are methods for assaying biological samples. In [0092] some embodiments, the assaying of the biological samples may at least partially determine the signatures described herein. In some cases, the biological samples may be obtained directly from the subject. For example, the biological sample may comprise liquid biopsy such as serum and plasma or skin biopsy obtained from the subject. In some cases, the biological sample may comprise biomolecules such as nucleic acid, protein (e.g., cytokines secreted by the cultured skin biopsy sample described herein), or lipid such as ceramides (CERs), cholesterol, or free fatty acids (FFAs). Biological samples may be stored in a variety of storage conditions before processing, such as different temperatures (e.g., at room temperature, under refrigeration or freezer conditions, at25°C, at 4°C, at - 18°C, -20°C, or at -80°C) or different suspensions (e.g., EDTA collection tubes, RNA collection tubes, or DNA collection tubes).

[0093] After obtaining biological sample from the subject, the biological sample may be processed to generate biomarker signatures. For example, a presence, absence, or quantitative assessment of nucleic acid molecules of the biological sample at a panel of disease or condition associated genomic loci (e.g., quantitative measures of RNA transcripts such as mRNA and microRNA or DNA at the disease or condition associated genomic loci), proteomic data comprising quantitative measures of proteins of the dataset at a panel of disease or condition associated proteins, and/or metabolome data comprising quantitative measures of a panel of disease or condition associated metabolites may be indicative of the presence or severity of the disease or condition. Processing the biological sample obtained from the subject may comprise (i) subjecting the biological sample to conditions that are sufficient to isolate, enrich, or extract a plurality of nucleic acid molecules, proteins, and/or metabolites, and (ii) assaying the plurality of nucleic acid molecules, proteins, and/or metabolites to generate the dataset. In some instances, the disease comprises cutaneous manifestations. In some instances, the disease comprises a dermatological disease.

[0094] In some embodiments, a plurality of nucleic acid molecules is extracted from the biological sample and subjected to sequencing to generate a plurality of sequencing reads. The nucleic acid molecules may comprise ribonucleic acid (RNA) or deoxyribonucleic acid (DNA). The nucleic acid molecules (e.g., RNA or DNA) may be extracted from the biological sample by a variety of methods, such as a FastDNA Kit protocol from MP Biomedicals, a QIAamp DNA biological mini kit from Qiagen, or a biological DNA isolation kit protocol from Norgen Biotek. The extraction method may extract all RNA or DNA molecules from a sample. Alternatively, the extract method may selectively extract a portion of RNA or DNA molecules from a sample. Extracted RNA molecules from a sample may be converted to DNA molecules by reverse transcription (RT).

[0095] The sequencing may be performed by any suitable sequencing methods, such as massively parallel sequencing (MPS), paired-end sequencing, high-throughput sequencing, next-generation sequencing (NGS), shotgun sequencing, single -molecule sequencing, nanopore sequencing, semiconductor sequencing, pyrosequencing, sequencing-by-synthesis (SBS), sequencing-by-ligation, sequencing -by -hybridization, and RNA-Seq (Illumina).

[0096] The sequencing may comprise nucleic acid amplification (e.g., of RNA or DNA molecules). In some embodiments, the nucleic acid amplification is polymerase chain reaction (PCR). A suitable number of rounds of PCR (e.g., PCR, qPCR, reverse -transcriptase PCR, digital PCR, etc.) may be performed to sufficiently amplify an initial amount of nucleic acid (e.g., RNA or DNA) to a desired input quantity for subsequent sequencing. In some cases, the PCR may be used for global amplification of target nucleic acids. This may comprise using adapter sequences that may be first ligated to different molecules followed by PCR amplification using universal primers. PCR may be performed using any of a number of commercial kits, e.g., provided by Life Technologies, Affymetrix, Promega, Qiagen, etc. In other cases, only certain target nucleic acids within a population of nucleic acids may be amplified. Specific primers, possibly in conjunction with adapter ligation, may be used to selectively amplify certain targets for downstream sequencing. The PCR may comprise targeted amplification of one or more genomic loci, such as genomic loci associated with disease related states. The sequencing may comprise use of simultaneous reverse transcription (RT) and polymerase chain reaction (PCR), such as a OneStep RT-PCR kit protocol by Qiagen, NEB, Thermo Fisher Scientific, or Bio-Rad.

[0097] RNA or DNA molecules isolated or extracted from abiological sample may be tagged, e.g., with identifiable tags, to allow for multiplexing of a plurality of samples. Any number of RNA or

DNA samples may be multiplexed. For example, a multiplexed reaction may contain RNA or DNA from at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, or more than 100 initial biological samples. For example, a plurality of biological samples may be tagged with sample barcodes such that each DNA molecule may be traced back to the sample (and the subject) from which the DNA molecule originated. Such tags may be attached to RNA or DNA molecules by ligation or by PCR amplification with primers. [0098] After subjecting the nucleic acid molecules to sequencing, suitable bioinformatics processes may be performed on the sequence reads to generate the data indicative of the presence, absence, or relative assessment of the disease related state. For example, the sequence reads may be aligned to one or more reference genomes (e.g., a genome of one or more species such as a human genome). The aligned sequence reads may be quantified at one or more genomic loci to generate the datasets indicative of the disease related state. For example, quantification of sequences corresponding to a plurality of genomic loci associated with disease related states may generate the datasets indicative of the disease related state.

[0099] The biological sample may be processed without any nucleic acid extraction. For example, the disease related state may be identified or monitored in the subject by using probes configured to selectively enrich nucleic acid (e.g., RNA or DNA) molecules corresponding to the plurality of disease or condition associated genomic loci. The probes may be nucleic acid primers. The probes may have sequence complementarity with nucleic acid sequences from one or more of the plurality of disease or condition associated genomic loci or genomic regions. The plurality of disease or condition associated genomic loci or genomic regions may comprise at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 55, at least about 60, at least about 65, at least about 70, at least about 75, at least about 80, at least about 85, at least about 90, at least about 95, at least about 100, or more distinct disease or condition associated genomic loci or genomic regions. The plurality of disease or condition associated genomic loci or genomic regions may comprise one or more members (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, about 25, about 30, about 35, about 40, about 45, about 50, about 55, about 60, about 65, about 70, about 75, about 80, or more) selected from the any one of the genes described herein.

[00100] The probes may be nucleic acid molecules (e.g., RNA or DNA) having sequence complementarity with nucleic acid sequences (e.g., RNA or DNA) of the one or more genomic loci (e.g., disease or condition associated genomic loci). These nucleic acid molecules may be primers or enrichment sequences. The assaying of the biological sample using probes that are selective for the one or more genomic loci (e.g., disease or condition associated genomic loci) may comprise use of array hybridization (e.g., microarray-based), polymerase chain reaction (PCR), or nucleic acid sequencing (e.g., RNA sequencing or DNA sequencing). In some embodiments, DNA or RNA may be assayed by one or more of: isothermal DNA/RNA amplification methods (e.g., loop-mediated isothermal amplification (LAMP), helicase dependent amplification (HD A), rolling circle amplification (RCA), recombinase polymerase amplification (RPA)), immunoassays, electrochemical assays, surface -enhanced Raman spectroscopy (SERS), quantum dot (QD)-based assays, molecular inversion probes, droplet digital PCR (ddPCR), CRISPR/Cas-based detection (e.g., CRISPR-typing PCR (ctPCR), specific high-sensitivity enzymatic reporter un-locking (SHERLOCK), DNA endonuclease targeted CRISPR trans reporter (DETECTR), and CRISPR-mediated analog multi-event recording apparatus (CAMERA)), and laser transmission spectroscopy (LTS).

[00101] The assay readouts may be quantified at one or more genomic loci (e.g., disease or condition associated genomic loci) to generate the data indicative of the disease related state. For example, quantification of array hybridization or polymerase chain reaction (PCR) corresponding to a plurality of genomic loci (e.g., disease or condition associated genomic loci) may generate data indicative of the disease related state. Assay readouts may comprise quantitative PCR (qPCR) values, digital PCR (dPCR) values, digital droplet PCR (ddPCR) values, fluorescence values, etc., or normalized values thereof.

[00102] A biomarker signature may be quantitative (e.g., numeric or alphanumeric), with higher or lower resolution (e.g., 1-10 or high/medium/low), or qualitative (e.g., significant increase/decrease relative to a cohort), or the like. In some embodiments, the biomarker signature is quantitative. In some embodiments, the biomarker signature is numeric. In some embodiments, the biomarker signature is alphanumeric. In some embodiments, the biomarker signature is alphabetic. In some embodiments, the biomarker signature is a value or a range of values such as 1-10 or A-Z. In some embodiments, the biomarker signature is relative or general, for example: “low,” “medium,” or “high.” In some embodiments, the biomarker signature is relative to a control biomarker signature, or relative to a baseline (e.g., pre-exposure) biomarker signature.

[00103] In some embodiments, biomarker signatures are weighted (e.g., based on type of biomarker, frequency, amount of expression/concentration, ability to predict atreatment outcome, or other factor). In some embodiments, the weight of the biomarker signatures is compared to a threshold. In some embodiments, the weight of a biomarker signatures is assigned by a computer algorithm. In some embodiments, the biomarker signatures of a biomarker affects how much a particular biomarker contributes to calculating am biomarker signature, such as an outcome signature. In some embodiments, the weight of a first biomarker is less than the weight of a second biomarker. In such cases, the first biomarker may be less informative of the outcome signature than the second mutation. In some embodiments, the weight of a first biomarker is greater than the weight of a second biomarker level. In some embodiments, each biomarker is given a separate weight in the mathematical algorithm. For example, one biomarker may have a greater impact on the biomarker signature than another mutation.

[00104] In some embodiments, the weight is 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10, 50, or 100, in relation to another of the mutations. In some embodiments, the weight is 0.01-0.1 in relation to another of the mutations. In some embodiments, the weight is 0.1-0.5 in relation to another of the mutations. In some embodiments, the weight is 0.5-1 in relation to another of the mutations. In some embodiments, the weight is 1-1.5 in relation to another of the mutations. In some embodiments, the weight is 1.5-2 in relation to another of the mutations. In some embodiments, the weight is 2-10 in relation to another of the mutations. In some embodiments, the weight is 10-100 in relation to another of the mutations. In some embodiments, the mutations is weighted such that it contributes 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 3, 4, 5, 6, 7, 8, 9, 10, 50, or 100% ofthe biomarker signature.

[00105] Genetic Mutations

[00106] Disclosed herein are methods for determining signatures based on quantifying mutations from biological samples. In some instances, a baseline, treatment, or outcome signature comprises one or more mutations. In some embodiments, a baseline biomarker signature comprises one or more mutations. In some embodiments, a treatment biomarker signature may be determined based on one or more mutations. In some cases, the skins samples may be obtained by the kits and methods described herein. In some embodiments, the skin samples may be obtained by the non-invasive (e.g., the adhesive tape) methods described herein. In some instances, a identifying biomarkers comprises determining the presence of one or more mutations. In some instances, mutations are present in genomic DNA. In some instances, mutations comprise substitutions, deletions, or additions. In some instances, mutations are present in coding regions. In some instances, mutations are present in noncoding regions. In some instances, mutations are present in genes. In some instances, mutations are present in transcription factors binding sites, promoters, terminators or other regulatory element. In some instances mutations are present in the same gene. In some instances, mutations are present in multiple genes. In some instances, genetic mutations are obtained using non-invasive sampling techniques. In some cases, the genetic mutations may be multiple mutations in a single skin sample. For example, multiple mutations may be measured, detected, or used in the methods described herein. Some embodiments include quantifying biomarkers based on multiple mutations. Some embodiments include quantifying biomarkers based on a first mutation and based on a second mutation.

[00107] Mutations may be present at any abundance in a given cell population. In some instances, the cell population is comprised of different cell types. In some instances, mutations are analyzed as a function of specific cell types. In some instances, the cell population is comprised of keratinocytes, melanocytes, fibroblasts, antigen presenting cells (e.g., Langerhans cells, or dendritic cells), and/or inflammatory cells (e.g., T cells or B cells). In some instances, the cell population is comprised of at least one of keratinocytes, melanocytes, fibroblasts, antigen presenting cells (e.g., Langerhans cells or dendritic cells), or inflammatory cells (e.g., T cells or B cells). In some instances, the cell population comprises a comparator sample. In some instances, a comparator sample is a bulk sample from a population of individuals, a sample which has been exposed to none or low amounts of an environmental factor in the same or different individual, or a sample obtained from a different area of skin on the same or different individual. The abundance of a mutation in a sample in some instances is expressed as a percentage of cells comprising the mutation or a ratio of cells comprising the mutation to cells without the mutation from the same cell type, skin location, individual, or sample. In some instances, a mutation is present at a rate in the cells of the sample. In some instances, a mutation is present at a rate of about 10%, 8%, 6%, 5%, 4% 3%, 2%, 1%, 0.5%, 0.2%, 0.1%, 0.08%, 0.05%, or about 0.01%. In some instances, a mutation is present at a rate of at least 10%, 8%, 6%, 5%, 4% 3%, 2%, 1%, 0.5%, 0.2%, 0.1%, 0.08%, 0.05%, or at least 0.01%. In some instances, a mutation is present at a rate ofno more than 10%, 8%, 6%, 5%, 4% 3%, 2%, 1%, 0.5%, 0.2%, 0.1%, 0.08%, 0.05%, or no more than 0.01%. In some instances, amutation is present at arate of l%-5%, l%-4%, 1 %-3%, 0.5%- 5%, 0.5%-l%, 0.5%-2%, 2%-10%, 5%-10%, or 4%-10%. In some instances, a mutation is present in a sample at a ratio of the number of cells comprising a mutation relative to the number of total cells in the sample (e.g., mutations/cell). In some instances, a mutation is present in a sample at a ratio of at least 1:5, 1: 10, 1: 15, 1:20, 1:50, 1:70, 1: 100, or 1:200. In some instances, a mutation is present in a sample at a ratio of no more than 1:5, 1:5, 1: 15, 1:20, 1:50, 1:70, 1: 100 or 1:200. In some instances, a mutation is present in a sample at a ratio of 1:3-1: 100, 1:5-1: 100, 1: 10-1: 100, 1:20-1:500, 1:20: -1:200, 1:20-1: 100, 1:20-1:200, or 1:30-1:200. In some instances, the abundance of a mutation determines the sensitivity needed to detect the mutation. In some instances, the methods described herein detect mutations with a sensitivity of about 0.1%, 0.2%, 0.5%, 1%, 1.5%, 2%, 3%, 4%, 5%, 7%, 10%, or about 15%. In some instances, the methods described herein detect mutations with a sensitivity of at least 0.1%, 0.2%, 0.5%, 1%, 1.5%, 2%, 3%, 4%, 5%, 7%, 10%, at least 15%. In some instances, the methods described herein detect mutations with a sensitivity of no more than 0.1%, 0.2%, 0.5%, 1%, 1.5%, 2%, 3%, 4%, 5%, 7%, 10%, or no more than 15%. In some instances, the methods described herein detect mutations with a sensitivity of about 0.1 %- 10%, 0.1-1%, 0.5-5%, 0.5-3%, l%-10%, 1%- 5%, 0.5-20%, or 1%-15%.

[00108] Mutations may be present in a gene at any copy number in a cell. In some instances, a mutation is present in a gene at one, two, three, four, five, six, seven, ten, or even more than 10 copies in a cell. In some instances, a mutation is present in a gene in at least two copies in a cell. Mutations may be present in a gene at any allele frequency in a cell. In some instances, amutation is present at an allele frequency of at one, two, three, four, five, six, seven, ten, or even more than 10 copies in a cell. In some instances, amutation is present at an allele frequency of at least two copies in a cell.

[00109] In some embodiments, the genetic mutations may include more than one mutation. For example, the method may include measuring, detecting, receiving, or using mutations. In some embodiments, detecting comprises determining the presence or absence of one or more mutations. Some embodiments include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, or more mutations. Some embodiments include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, or more mutations, or a range of mutations defined by any two of the aforementioned integers. For example, some embodiments include measuring the frequency of about 10 mutations. Some embodiments include measuring the frequency of about 20 mutations. Some embodiments include measuring the frequency of about 30 mutations. Some embodiments include measuring the frequency of about 40 mutations. Some embodiments include measuring the frequency of 50 mutations. Some embodiments include measuring the frequency of 1-4 mutations. [00110] Some embodiments include measuring the frequency of 1-7 mutations. Some embodiments include measuring the frequency of 1-10 mutations. Some embodiments include measuring the frequency of 1-100 mutations. Some embodiments include at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, at least 60, at least 65, at least 70, at least 75, at least 80, at least 85, at least 90, at least 95, or at least 100 mutations. Some embodiments include no more than 1, no more than 2, no more than 3, no more than 4, no more than 5, no more than 6, no more than 7, no more than 8, no more than 9, no more than 10, no more than 11, no more than 12, no more than 13, no more than 14, no more than 15, no more than 16, no more than 17, no more than 18, no more than 19, no more than 20, no more than 25, no more than 30, no more than 35, no more than 40, no more than 45, no more than 50, no more than 55, no more than 60, no more than 65, no more than 70, no more than 75, no more than 80, no more than 85, no more than 90, no more than 95, or no more than 100 mutations.

[00111] Mutations described herein may be measured using any method known in the art. In some instances, mutations are identified using PCR. In some instances, mutations are identified using Sanger sequencing. In some instances, mutations are identified using Next Generation Sequencing or sequencing by synthesis. In some instances, mutations are identified using nanopore sequencing. In some instances, mutations are identified using real time PCR (qPCR). In some instances, mutations are identified using digital PCR (ddPCR). In some instances, mutations are identified using mass analysis. In some instances, 10, 100, 1000, 10,000, or more than 10,000 samples are assayed in parallel.

[00112] Mutations described herein may be present in a gene. In some instances, the gene is a gene which drives increased cell proliferation. In some instances, the gene is TP53, NOTCH1, NOTCH2, NOTCH3, RBM10, PPP2R1A, GNAS, CTNNB1, PIK3CA, PPP6C, HRAS, KRAS, MTOR, SMAD3, LMNA, FGFR3, ZNF750, EPAS1, RPL22, ALDH2, CBFA2T3, CCND1, FAT1, FH, KLF4, CIC, RAC1, PTCHI, or TPM4. In some instances, the mutation is a C to T or G to A substitution. [00113] In some embodiments, the one or more mutations are present in aMAPK pathway gene. In some embodiments, the MAPK pathway gene includes but is not limited to BRAF, CBL, MAP2K1, NF1, or RAS.

[00114] The at least one mutation may be present in an MTOR pathway gene. In some embodiments, the MTOR pathway gene includes but is not limited to MTOR, AKT, AKT1 (v-akt murine thymoma viral oncogene homolog 1), AKT1S1 (AKT1 substrate 1 (proline-rich)), ATG13 (autophagy related 13), BNIP3 (BCL2/adenovirus E1B 19kDa interacting protein 3), BRAF (B-Raf proto-oncogene, serine/threonine kinase), CCNE1 (cyclin El), CDK2 (cyclin-dependent kinase 2), CLIP1 (CAP-GLY domain containing linker protein 1), CYCS (cytochrome c, somatic), DDIT4 (DNA-damage -inducible transcript 4), DEPTOR (DEP domain containing MTOR-interacting protein), EEF2 (eukaryotic translation elongation factor 2), EIF4A1 (eukaryotic translation initiation factor 4A1), EIF4B (eukaryotic translation initiation factor 4B), EIF4E (eukaryotic translation initiation factor 4E), EIF4EBP1 (eukaryotic translation initiation factor 4E binding protein 1), FBXW11 (F-box and WD repeat domain containing 11), HRAS (Harvey rat sarcoma viral oncogene homolog), IKBKB (inhibitor of kappa light polypeptide gene enhancer in B cells, kinase beta), IRS1 (insulin receptor substrate 1), MAP2K1 (mitogen-activated protein kinase 1), MAP2K2 (mitogen- activated protein kinase 2), MAPK1 (mitogen-activated protein kinase 1), MAPK3 (mitogen- activated protein kinase 3), MAPKAP1 (mitogen-activated protein kinase associated protein 1), MLST8 (MTOR associated protein, LST8 homolog), MTOR (mechanistic target of rapamycin (serine/threonine kinase)), NRAS (neuroblastoma RAS viral (v-ras) oncogene homolog), PDCD4 (programmed cell death 4 (neoplastic transformation inhibitor)), PDPK1 (3-phosphoinositide dependent protein kinase 1), PLD1 (phospholipase DI, phosphatidylcholine-specific), PLD2 (phospholipase D2), PML (promyelocytic leukemia), POLDIP3 (polymerase (DNA-directed), delta interacting protein 3), PPARGC1A (peroxisome proliferator-activated receptor gamma, coactivator 1 alpha), PRKCA (protein kinase C, alpha), PRR5 (proline rich 5 (renal)), PXN (paxillin), RAC1 (ras- related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rael)), RAFI (Raf-1 proto-oncogene, serine/threonine kinase), RB1CC1 (RBI -inducible coiled-coil 1), RHEB (Ras homolog enriched in brain), RHOA (ras homolog family member A), RICTOR (RPTOR independent companion of MTOR, complex 2), RPS6KA1 (ribosomal protein S6 kinase, 90kDa, polypeptide 1), RPS6KB1 (ribosomal protein S6 kinase, 70kDa, polypeptide 1), RPTOR (regulatory associated protein of MTOR, complex 1), RRAGA (Ras-related GTP binding A), RRAGB (Ras- related GTP binding B), RRAGC (Ras-related GTP binding C), RRAGD (Ras-related GTP binding D), RRN3 (RRN3 RNA polymerase I transcription factor homolog), SFN (stratifin), SGK1 (serum/glucocorticoid regulated kinase 1), SREBF1 (sterol regulatory element binding transcription factor 1), SSPO (SCO-spondin), TSC1 (tuberous sclerosis 1), TSC2 (tuberous sclerosis 2), ULK1 (unc-51 like autophagy activating kinase 1), ULK2 (unc-51 like autophagy activating kinase 2), YWHAB (tyrosine 3-monooxygenase/tryptophan 5 -monooxygenase activation protein, beta), YWHAE (tyrosine 3-monooxygenase/tryptophan 5 -monooxygenase activation protein, epsilon), YWHAG (tyrosine 3-monooxygenase/tryptophan 5 -monooxygenase activation protein, gamma), YWHAH (tyrosine 3-monooxygenase/tryptophan 5 -monooxygenase activation protein, eta), YWHAQ (tyrosine 3-monooxygenase/tryptophan 5 -monooxygenase activation protein, theta), YWHAZ (tyrosine 3-monooxygenase/tryptophan 5 -monooxygenase activation protein, zeta), or YY1 (YY1 transcription factor).

[00115] In some embodiments, the at least one mutation is present in MTOR. In some embodiments, the at least one mutation in MTOR comprises S2215F. In some embodiments, the at least one mutation in MTOR comprises C.6644OT.

[00116] The at least one mutation may be present in an HRAS pathway gene. In some embodiments, the HRAS pathway gene includes but is not limited to HRAS. In some embodiments, the at least one mutation is present in HRAS. In some embodiments, the at least one mutation in HRAS comprises G12D, Q61L, or G13D. In some embodiments, the at least one mutation in HRAS comprises c.35G>A, c,182A>T, or c.38G>A.

[00117] In some embodiments, the one or more mutations are present in an RNA processing gene. In some embodiments, the RNA processing gene includes but is not limited to DDX3X.

[00118] In some embodiments, the one or more mutations are present in aPI3K pathway gene. In some embodiments, the one or more mutations are present in a PI3KCA family gene. In some instances, the PI3KCA family gene includes but is not limited to XIAP (BIRC4) (X-linked inhibitor of apoptosis), AKT1 (v-akt murine thymoma viral oncogene homolog 1), TWIST1 (Twist homolog 1 (Drosophila)), BAD (BCL2 -associated agonist of cell death), CDKN1A (p21) (Cyclin-dependent kinase inhibitor 1A (p21, Cipl)), ABL1 (v-abl Abelson murine leukemia viral oncogene homolog 1), CDH1 (Cadherin 1, type 1, E-cadherin), TP53 (Tumor protein p53), CASP3 (Caspase 3, apoptosis- related cysteine peptidase), PAK1 (p21/Cdc42/Racl -activated kinase 1), GAPDH (Glyceraldehyde -3- phosphate dehydrogenase), PIK3CA (Phosphoinositide-3-kinase, catalytic, a-polypeptide), FAS (TNF receptor superfamily, member 6), AKT2 (v-akt murine thymoma viral oncogene homolog 2), FRAP1 (mTOR) (FK506 binding protein 12-rapamycin associated protein 1), FOXO1A (Forkhead box 01), PTK2 (FAK) (PTK2 protein tyrosine kinase 2), CASP9 (Caspase 9, apoptosis -related cysteine peptidase), PTEN (Phosphatase and tensin homolog), CCND1 (Cyclin DI), NFKB1 (Nuclear factor K- light polypeptide gene enhancer B cells 1), GSK3B (Glycogen synthase kinase 3-P), MDM2 (Mdm2 p53 binding protein homolog (mouse)), or CDKN1B (p27) (Cyclin-dependent kinase inhibitor IB (p27, Kip I)).

[00119] In some embodiments, the one or more mutations are present in a chromatin remodeling gene. In some embodiments, the chromatin remodeling gene includes but is not limited to ARID2.In some embodiments, the one or more mutations are present in a transcription regulation region of a gene. In some embodiments, the region comprises a promoter. In some embodiments, the region comprises a terminator. In some embodiments, the region comprises a Kozak consensus sequence, stem loop structures or internal ribosome entry site. In some instances, the region comprises an enhancer, a silencer, an insulator, an operator, aa promoter, a 5’ untranslated region (5’ UTR), or a 3’ untranslated region (3’UTR).

[00120] Mutations described herein may be identified phenotypically. In some instances, mutations are identified using staining techniques. In some instances, the staining technique is an immunogenic staining technique. In some instances, samples comprise cells having p53 immunopositive patches (PIPs). In some instances, the one or more mutations are present in PIPs.

[00121] In some cases, the mutations described herein may include a cytokine or inflammatory protein or a receptor of the cytokine of the inflammatory protein. Exemplary cytokine or inflammatory protein may include 4-1BBL, acylation stimulating protein, adipokine, albinterferon, APRIL, Arh, BAFF, Bcl-6, CCL1, CCL1/TCA3, CCL11, CCL12/MCP-5, CCL13/MCP-4, CCL14, CCL15, CCL16, CCL17/TARC, CCL18, CCL19, CCL2, CCL2/MCP-1, CCL20, CCL21, CCL22/MDC, CCL23, CCL24, CCL25, CCL26, CCL27, CCL28, CCL3, CCL3L3, CCL4, CCL4L1/LAG-1, CCL5, CCL6, CCL7, CCL8, CCL9, CCR10, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, CD153, CD154, CD178, CD40LG, CD70, CD95L/CD178, Cerberus (protein), chemokines, CLCF1, CNTF, colony-stimulating factor, common b chain (CD131), common g chain (CD132), CX3CL1, CX3CR1, CXCL1, CXCL10, CXCL11, CXCL12, CXCL13, CXCL14, CXCL15, CXCL16, CXCL17, CXCL2, CXCL2/MIP-2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7, CXCL9, CXCR3, CXCR4, CXCR5, EDA-A1, Epo, erythropoietin, FAM19A1, FAM19A2, FAM19A3, FAM19A4, FAM19A5, Flt-3L, FMS-like tyrosine kinase 3 ligand, Foxp3, GATA-3, GcMAF, G-CSF, GITRL, GM-CSF, granulocyte colony-stimulating factor, granulocyte -macrophage colony- stimulating factor, hepatocyte growth factor, IFNA1, IFNA10, IFNA13, IFNA14, IFNA2, IFNA4, IFNA5/IFNaG, IFNA7, IFNA8, IFNB1, IFNE, IFNG, IFNZ, IFN-a, IFN- p, IFN-y, IFNco/IFNWl, IL-1, IL- 10, IL- 10 family, IL-10-like, IL-11, IL-12, IL-13, IL-14, IL-15, IL-16, IL-17, IL-17 family, IL-17A-F, IL-17A, IL-18, IL-18BP, IL-19, IL-1A, IL-1B, IL4, IL4R alpha, IL-1F10, IL-13, IL- 1F3/IL-1RA, IL-1F5, IL- 1F6, IL-1F7, IL-1F8, IL-1F9, IL-l-like, IL-IRA, IL-1RL2, IL-la, IL-ip, IL-2, IL-20, IL-21, IL-22, IL-23, IL, 23pl9, IL-24, IL-28A, IL-28B, IL-29, IL-3, IL-31, IL-33, IL-35, IL-4, IL-5, IL-6, IL-6-like, IL-7, IL-8/CXCL8, IL-9, inflammasome, interferome, interferon, interferon beta-la, interferon betalb, interferon gamma, interferon type I, interferon type II, interferon type III, interferons, interleukin, interleukin 1 receptor antagonist, Interleukin 8, IRF4, Leptin, leukemia inhibitory factor (LIF), leukocyte -promoting factor, LIGHT, LTA/TNFB, LT- , lymphokine, lymphotoxin, lymphotoxin alpha, lymphotoxin beta, macrophage colony-stimulating factor, macrophage inflammatory protein, macrophage -activating factor, M-CSF, MHC class III, miscellaneous hematopoietins, monokine, MSP, myokine, myonectin, nicotinamide phosphoribosyltransferase, oncostatin M (OSM), oprelvekin, OX40L, platelet factor 4, promegapoietin, RANKL, SCF, STAT3, STAT4, STAT6, stromal cell- derived factor 1, TALL-1, TBX21, TGF-a, TGF- p, TGF-pi, TGF-P2, TGF-P3, TNF, TNFa (TNF alpha), TNFSF10, TNFSF11, TNFSF12, TNFSF13, TNFSF14, TNFSF15, TNFSF4, TNFSF8, TNF-a, TNF- P, Tpo, TRAIL, TRANCE, TWEAK, vascular endothelial growth inhibitor, XCL1, or XCL2. [00122] Epigenetics

[00123] Epigenetic markers may be evaluated alone, or in combination with mutations for determining the signatures described herein. In some instances, a quantified burden is generated from at least one epigenetic marker. In some instances, the epigenetic markers an genomic modification. In some instances, the at least one genomic modification comprises methylation in a CpG island of a gene or a transcription regulation region of the gene. In some instances, the at least one epigenetic marker comprises 5 -methylcytosine (“methylation”). In some instances, the at least one genomic modification comprises N6 -methyladenine. In some instances, an epigenetic marker comprises chromatin remodeling. In some instances, chromatic remodeling comprises modification of histones. In some instances, modification of histones comprises methylation, acetylation, phosphorylation, ubiquitination, sumoylation, citrullination, or ADP-ribosylation. In some instances, the at least one genomic modification is correlated with increased exposure to environmental factors. In some instances, the at least one genomic modification is correlated with at least one additional genetic mutation.

[00124] Epigenetic markers may be found within specific genes, near genes (e.g., promoter, terminator), or outside of genes. In some instance, at least one epigenetic markers is present in a keratin family gene. In some instances, the epigenetic marker is a proliferative marker in inflammatory diseases. In some instance, at least one epigenetic marker is present in KRT1, KRT5, KRT6, KRT14, KRT15, KRT16, KRT17, or KRT80.

[00125] Numerous methods are known in the art for resolving epigenetic markers. In some embodiments, the epigenetic markers is methylation of cytosine. In some instances, methylation sensitive endonucleases are used to identify such modifications. In some instances chemical or enzymatic differentiation of methylated vs. unmethylated bases is used (e.g., methyl C conversion to U using bisulfite). After conversion and comparison to untreated samples, methylation patterns are in some instances obtained using various sequencing and analysis techniques described herein.

[00126] Mutations in samples may be processed or analyzed in parallel using high-throughput multiplex methods described herein to identify biomarker signatures (e.g., mass-array, hybridization array, specific probe hybridization, whole genome sequencing, or other method). In some embodiments, methods described herein comprise genotyping. The nucleic acids analyzed from the sample in some instances represent the entire genome or a sub-population thereof (e.g., genomic regions, genes, introns, exons, promoters, intergenic regions). In some instances, these nucleic acids are analyzed from one or more panels which target mutations or groups of mutations. In some instances, methods describe herein comprise detecting one or more mutations in these nucleic acids. In some instances, 25-50,000, 50-50,000, 100-100,000, 25-10,000, 25-5,000 or 300-700 mutations are analyzed. In some instances, at least 300, 400, 500, 750, 1000, 2000, 5000, 10,000, or more than 10,000 mutations are analyzed. In some instances, two or more mutations are used to generate a pattern or profile representative of the biomarker signature. In some examples, a subset of genomic regions will be sequenced to perform a panel analysis of mutations in the subset of genomic regions (or of the whole genome) to output a set of mutations for the sample. For instance, a variety of mutational panels could be utilized, for instance the MSK-IMPACT panel. Accordingly, the result of this process in some instances is an output of a set of mutations based on the subset of sequenced genomic regions or the whole genome. In some instances, the sequence data is transmitted over a network to be stored in a database by a server or further processed on local memory. In some examples, the server may then perform further processing on the sequence data or sequence data files.

[00127] RNA Expression levels

[00128] Biomarkers may comprise genes (or gene classifiers) and expression levels thereof. In some instances, a baseline, treatment, or outcome signature comprises a gene signature. In some instances, expression levels of genes are obtained through analysis of nucleic acids, such as RNA. In some instances, the expression level of a gene associated with a disease or condition having cutaneous manifestations is a biomarker.

[00129] A biomarker may comprise a gene associated with skin cancer. In some instances, methods herein comprise measuring the expression level of a gene associated with skin cancer. In one embodiment, the gene is any one or more of interferon regulatory factor 6, claudin 23, melan-A, osteopetrosis associated transmembrane protein 1, RAS-like family 11 member B, actinin alpha 4, transmembrane protein 68, Glycine -rich protein (GRP3 S), Transcription factor 4, hypothetical protein FLJ20489, cytochrome c somatic, transcription factor 4, Forkhead box Pl, transducer of ERBB2-2, glutaminyl -peptide cyclotransferase (glutaminyl cyclase), hypothetical protein FLJ 10770, selenophosphate synthetase 2, embryonal Fyn-associated substrate, Kruppel-like factor 8, Discs large homolog 5 (Drosophila), regulator of G-protein signaling 10, ADP-ribosylation factor related protein 2, TIMP metallopeptidase inhibitor 2, 5 -aminoimidazole -4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase, similar to RIKEN cDNA 5730421E18 gene, Regulator of G- protein signaling 10, Nuclear RNA-binding protein putative, tyrosinase -related protein 1, TIMP metallopeptidase inhibitor 2, Claudin 1 , transcription factor 4, solute carrier family 16 (monocarboxylic acid transporters) member 6 (similar to solute carrier family 16 member 6; monocarboxylate transporter 6), PTCHI, PTCH2, CDKN2A, CDK4, MITF, BAP1, BRCA2, or any combination thereof. In some cases, the expression levels are measured by contacting the isolated nucleic acids with an additional set of probes that recognizes interferon regulatory factor 6, claudin 23, melan-A, osteopetrosis associated transmembrane protein 1, RAS-like family 11 member B, actinin alpha 4, transmembrane protein 68, Glycine -rich protein (GRP3 S), Transcription factor 4, hypothetical protein FLJ20489, cytochrome c somatic, transcription factor 4, Forkhead box Pl, transducer of ERBB2-2, glutaminyl -peptide cyclotransferase (glutaminyl cyclase), hypothetical protein FLJ10770, selenophosphate synthetase 2, embryonal Fyn-associated substrate, Kruppel-like factor 8, Discs large homolog 5 (Drosophila), regulator of G-protein signaling 10, ADP-ribosylation factor related protein 2, TIMP metallopeptidase inhibitor 2, 5 -aminoimidazole -4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase, similar to RIKEN cDNA 5730421E18 gene, Regulator of G-protein signaling 10, Nuclear RNA-binding protein putative, tyrosinase -related protein 1, TIMP metallopeptidase inhibitor 2, Claudin 1, transcription factor 4, solute carrier family 16 (monocarboxylic acid transporters) member 6 (similar to solute carrier family 16 member 6; monocarboxylate transporter 6), PTCHI, PTCH2, CDKN2A, CDK4, MITF, BAP1, BRCA2, or any combination thereof.

[00130] A biomarker may comprise a gene associated with atopic dermatitis. In some instances, methods herein comprise measuring the expression level of a gene associated with atopic dermatitis. In some instances, the gene comprises Interleukin 13 (IL-13), Interleukin 31 (IL-31), Thymic Stromal Lymphopoietin (TSLP), IL4Ralpha, or a combination thereof. In some embodiments, the gene comprises Interleukin 13 Receptor (IL-13R), Interleukin 4 Receptor (IL-4R), Interleukin 17 (IL- 17), Interleukin 22 (IL-22), C-X-C Motif Chemokine Ligand 9 (CXCL9), C-X-C Motif Chemokine Ligand 10 (CXCL10), C-X-C Motif Chemokine Ligand 10 (CXCL11), S100 Calcium Binding Protein A7 (S100A7), SI 00 Calcium Binding Protein A8 (S100A8), SI 00 Calcium Binding Protein A9 (S100A9), C-C Motif Chemokine Ligand 17 (CCL17), C-C Motif Chemokine Ligand 18 (CCL18), C- C Motif Chemokine Ligand 19 (CCL19), C-C Motif Chemokine Ligand 26 (CCL26), [00131] C-C Motif Chemokine Ligand 27 (CCL27), Nitric Oxide Synthetase 2 (NOS2) or a combination thereof. In some cases, the expression levels are measured by contacting the isolated nucleic acids with an additional set of probes that recognizes IL-13R, IL-4R, IL- 17, IL-22, CXCL9, CXCL10, CXCL11, S100A7, S100A8, S100A9, CCL17, CCL18, CCL19, CCL26, CCL27, NOS2, or a combination thereof, and detects binding between IL-13R, IL-4R, IL- 17, IL-22, CXCL9, CXCL10, CXCL11, S100A7, S100A8, S100A9, CCL17, CCL18, CCL19, CCL26, CCL27, NOS2, or a combination thereof and the additional set of probes. In some cases, the expression levels are measured for one or more of genes comprising IL-4Ra, IL-13Ral, CCL17/TARC, IL- 13 (Th2), IL- 31, IL-22 (Th 17), and IL-23 (Th 17).

[00132] A biomarker may comprise a gene or gene classifier associated with psoriasis. In some instances, methods herein comprise measuring the expression level of a gene associated with psoriasis. In some instances, the gene comprises Interleukin 17A (IL-17A), Interleukin 17F (IL- 17F), Interleukin 8 (IL-8), C-X-C Motif Chemokine Ligand 5 (CXCL5), S100 Calcium Binding Protein A9 (S100A9), Defensin Beta 4A (DEFB4A), TNFalpha, or a combination thereof. In some embodiments, the method further comprises detecting the expression levels of Interleukin 17C (IL- 17C), S100 Calcium Binding Protein A7 (S100A7), Interleukin 17 Receptor A (IL-17RA), Interleukin 17 Receptor C (IL-17RC), Interleukin 23 Subunit Alpha (IL-23 A), Interleukin 22 (IL- 22), Interleukin 26 (IL-26), Interleukin 24 (IL-24), Interleukin 6 (IL-6), C-X-C Motif Chemokine Ligand 1 (CXCL1), Interferon Gamma (IFN -gamma), Interleukin 31, (IL-31), Interleukin 33 (IL- 33), Tumor Necrosis Factor (TNFa), Lipocalin 2 (LCN2), C-C Motif Chemokine Ligand 20 (CCL20), TNF Receptor Superfamily Member 1A (TNFRSF1A) or a combination thereof. In some cases, measuring gene expression levels comprises contacting the isolated nucleic acids with an [00133] additional set of probes that recognizes IL-17C, S100A7, IL-17RA, IL-17RC, IL-23A, IL-22, IL-26, IL-24, IL-6, CXCL1, IFN-gamma, IL-31, IL-33, TNFa, LCN2, CCL20, TNFRSF1A, or a combination thereof, and detects binding between IL-17C, S100A7, IL-17RA, IL-17RC, IL-23A, IL- 22, IL-26, IL-24, IL-6, CXCL1, IFN-gamma, IL-31, IL-33, TNFa, LCN2, CCL20, TNFRSF1A, or a combination thereof and the additional set of probes. In some instances, the gene comprises one or more of CCL17, IL-4Ralpha, IL-13, IL-13Ralphal, IL-22, IL-23A, IL-31, and TSLP.

[00134] A biomarker may comprise a gene associated with lupus. In some instances, methods herein comprise measuring the expression level of a gene associated with lupus erythematosus. In some instances, the gene comprises Interferon Alpha 1 (IFNA1), Interferon Alpha 2 (IFNA2), Interferon Alpha 4 (IFNA4), Interferon Alpha And Beta Receptor Subunit 1 (IFNR1), Interferon Alpha And Beta Receptor Subunit 2 (IFNR2), C-C Motif Chemokine Ligand 5 (CCL5), or a combination thereof. In some embodiments, measuring expression levels of Interferon Beta 1 (IFNB1), Interferon Epsilon (IFNE), Interferon Omega 1 (IFNW1), Adenosine Deaminase, RNA Specific (ADAR), Interferon Induced proteins with Tetratricopeptide repeat (IFIT), interferon-inducible p200 family of proteins (IFI), Interferon Regulatory Factors (IRF), 2'-5 '-Oligoadenylate Synthetase 1 (OAS1), Interleukin 1 Receptor Associated Kinase 1 (IRAKI), TNF Alpha Induced Protein 3 (TNFAIP3), Autophagy Related 5 (ATG5), Tyrosine Kinase 2 (TYK2), Signal Transducer and Activator Of Transcription 4 (STAT4), Osteopontin (OPN), Keratins (KRT), or a combination thereof. In some cases, the detecting comprises contacting the isolated nucleic acids with an additional set of probes that recognizes IFNB1, IFNE, IFNW1, ADAR, IFIT, IFI, IRF, OAS1, IRAKI, TNFAIP3, ATG5, TYK2, STAT4, OPN, KRT, or a combination thereof and the additional set of probes.

[00135] Assays

[00136] Multiple assays may be used to process biological samples of a subject and identify/measure biomarkers or biomarker signatures. For example, a first assay may be used to process a first biological sample obtained or derived from the subject to generate a first dataset; and based at least in part on the first dataset, a second assay different from said first assay may be used to process a second biological sample obtained or derived from the subject to generate a second dataset indicative of said disease related state. The first assay may be used to screen or process biological samples of a set of subjects, while the second or subsequent assays may be used to screen or process biological samples of a smaller subset of the set of subjects. The first assay may have a low cost and/or a high sensitivity of detecting one or more disease related states (e.g., disease related complication), that is amenable to screening or processing biological samples of a relatively large set of subjects. The second assay may have a higher cost and/or a higher specificity of detecting one or more disease related states (e.g., disease related complication), that is amenable to screening or processing biological samples of a relatively small set of subjects (e.g., a subset of the subjects screened using the first assay). The second assay may generate a second dataset having a specificity (e.g., for one or more disease related states such as disease related complications) greater than the first dataset generated using the first assay. As an example, one or more biological samples may be processed using a cfRNA assay on a large set of subjects and subsequently ametabolomics assay on a smaller subset of subjects, or vice versa. The smaller subset of subjects may be selected based at least in part on the results of the first assay.

[00137] Alternatively, multiple assays may be used to simultaneously process biological samples of a subject. For example, a first assay may be used to process a first biological sample obtained or derived from the subject to generate a first dataset indicative of the disease related state; and a second assay different from the first assay may be used to process a second biological sample obtained or derived from the subject to generate a second dataset indicative of the disease related state. Any or all of the first dataset and the second dataset may then be analyzed to assess the disease related state of the subject. For example, a single diagnostic index or diagnosis score may be generated based on a combination of the first dataset and the second dataset. As another example, separate diagnostic indexes or diagnosis scores may be generated based on the first dataset and the second dataset.

[00138] The biological samples may be processed using a metabolomics assay. For example, a metabolomics assay may be used to identify a quantitative measure (e.g., indicative of a presence, absence, or relative amount) of each of a plurality of disease or condition associated metabolites in a biological sample of the subject. The metabolomics assay may be configured to process biological samples such as a blood sample or a urine sample (or derivatives thereof) of the subject. A quantitative measure (e.g., indicative of a presence, absence, or relative amount) of disease or condition associated metabolites in the biological sample may be indicative of one or more disease related states. The metabolites in the biological sample may be produced (e.g., as an end product or a byproduct) as a result of one or more metabolic pathways corresponding to disease or condition associated genes. Assaying one or more metabolites of the biological sample may comprise isolating or extracting the metabolites from the biological sample. The metabolomics assay may be used to generate datasets indicative of the quantitative measure (e.g., indicative of a presence, absence, or relative amount) of each of a plurality of disease or condition associated metabolites in the biological sample of the subject.

[00139] The metabolomics assay may analyze a variety of metabolites in the biological sample, such as small molecules, lipids, amino acids, peptides, nucleotides, hormones and other signaling molecules, cytokines, minerals and elements, polyphenols, fatty acids, dicarboxylic acids, alcohols and polyols, alkanes and alkenes, keto acids, glycolipids, carbohydrates, hydroxy acids, purines, prostanoids, catecholamines, acyl phosphates, phospholipids, cyclic amines, amino ketones, nucleosides, glycerolipids, aromatic acids, retinoids, amino alcohols, pterins, steroids, carnitines, leukotrienes, indoles, porphyrins, sugar phosphates, coenzyme A derivatives, glucuronides, ketones, sugar phosphates, inorganic ions and gases, sphingolipids, bile acids, alcohol phosphates, amino acid phosphates, aldehydes, quinones, pyrimidines, pyridoxals, tricarboxylic acids, acyl glycines, cobalamin derivatives, lipoamides, biotin, and polyamines. [00140] The metabolomics assay may comprise, for example, one or more of: mass spectroscopy (MS), targeted MS, gas chromatography (GC), high performance liquid chromatography (HPLC), capillary electrophoresis (CE), nuclear magnetic resonance (NMR) spectroscopy, ion-mobility spectrometry, Raman spectroscopy, electrochemical assay, or immune assay.

[00141] The biological samples may be processed using a methylation -specific assay. For example, a methylation-specific assay may be used to identify a quantitative measure (e.g., indicative of a presence, absence, or relative amount) of methylation each of a plurality of disease or condition associated genomic loci in a biological sample of the subject. The methylation-specific assay may be configured to process biological samples such as a blood sample or a urine sample (or derivatives thereof) ofthe subject. A quantitative measure (e.g., indicative of a presence, absence, or relative amount) of methylation of disease or condition associated genomic loci in the biological sample may be indicative of one or more related states. The methylation-specific assay may be used to generate datasets indicative ofthe quantitative measure (e.g., indicative of a presence, absence, or relative amount) of methylation of each of a plurality of disease or condition associated genomic loci in the biological sample ofthe subject.

[00142] The methylation-specific assay may comprise, for example, one or more of: a methylation- aware sequencing (e.g., using bisulfite treatment), pyrosequencing, methylation -sensitive singlestrand conformation analysis (MS-SSCA), high-resolution melting analysis (HRM), methylationsensitive single -nucleotide primer extension (MS-SnuPE), base-specific cleavage/MALDI-TOF, microarray-based methylation assay, methylation-specific PCR, targeted bisulfite sequencing, oxidative bisulfite sequencing, mass spectroscopy-based bisulfite sequencing, or reduced representation bisulfite sequence (RRBS).

[00143] The biological samples may be processed using a proteomics assay. For example, a proteomics assay may be used to identify a quantitative measure (e.g., indicative of a presence, absence, or relative amount) of each of a plurality of disease or condition associated proteins or polypeptides in a biological sample of the subject. The proteomics assay may be configured to process biological samples such as a blood sample or a urine sample (or derivatives thereof) of the subject. A quantitative measure (e.g., indicative of a presence, absence, or relative amount) of disease or condition associated proteins or polypeptides in the biological sample may be indicative of one or more related states. The proteins or polypeptides in the biological sample may be produced (e.g., as an end product or a byproduct) as a result of one or more biochemical pathways corresponding to disease or condition associated genes. Assaying one or more proteins or polypeptides of the biological sample may comprise isolating or extracting the proteins or polypeptides from the biological sample. The proteomics assay may be used to generate datasets indicative of the quantitative measure (e.g., indicative of a presence, absence, or relative amount) of each of a plurality of disease or condition associated proteins or polypeptides in the biological sample of the subject.

[00144] The proteomics assay may analyze a variety of proteins or polypeptides in the biological sample, such as proteins made under different cellular conditions (e.g., development, cellular differentiation, or cell cycle). The proteomics assay may comprise, for example, one or more of: an antibody-based immunoassay, an Edman degradation assay, amass spectrometry -based assay (e.g., matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI)), a top-down proteomics assay, a bottom -up proteomics assay, amass spectrometric immunoassay (MSIA), a stable isotope standard capture with anti -peptide antibodies (SISCAPA) assay, a fluorescence two-dimensional differential gel electrophoresis (2-D DIGE) assay, a quantitative proteomics assay, a protein microarray assay, or a reverse -phased protein microarray assay. The proteomics assay may detect post -translational modifications of proteins or polypeptides (e.g., phosphorylation, ubiquitination, methylation, acetylation, glycosylation, oxidation, and nitrosylation). The proteomics assay may identify or quantify one or more proteins or polypeptides from a database (e.g., Human Protein Atlas, PeptideAtlas, and UniProt).

[00145] Treatment of biological samples

[00146] Methods described herein may comprise treatment of biological samples. In some instances, biological samples are excised or removed from a subject and treated in-vitro. In some instances, samples are removed via biopsy or non-invasive/minimally invasive sampling technique. Biological samples obtained using the methods described herein may be exposed to one or more treatments. In some instances, biological samples comprise cells, such as those obtained from biopsy. In some instances, biological samples are obtained from non-invasive or minimally-invasive techniques. In some instances, such techniques are configured to isolate specific regions or portions of a biological sample, such as a skin sample. In some instances, after exposure of biological samples to treatments, biomarker signatures are identified and compared to baseline signatures take from a subject. In some instances, such comparisons result in outcome signatures which are predictive of a subject’s response to one or more treatments. Treatments in some instances are expected to reduce, minimize, treat, or cure a subject’s disease or condition having a cutaneous manifestation. Biological samples are in some instances exposed to treatments for about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 17, or about 20 days. Biological samples are in some instances exposed to treatments for at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 17, or at least 20 days. Biological samples are in some instances exposed to treatments for 1-20, 3-20, 5-20, 5-15, 5-10, 7-20, 7-15, 10- 20, or 10-15 days. In some instances, biological samples are exposed to treatments using a titration. In some instances, a titration comprises IX, 10X, 20X, 50X, 100X, 1000X, or 10000X increases in exposure to the one or more treatments. In some instances, biological samples are aliquoted prior to contact with one or more treatments. In some instances, each aliquot is exposed to a different treatment type or treatment level (e.g., exposure time, energy, dose, or other measurable level). In some instances, a portion of the biological sample is not exposed to a treatment (control). In some instances, prior or during contact with one or more treatments, a biological sample is analyzed for biomarker signature (e.g., treatment signature). More than one signature is in some instances obtained during exposure to treatments.

[00147] Treatments may comprise any number of methods described herein. In some instances, treatment comprises exposure to radiation. In some instances, treatment comprises phototherapy. In some instances, treatment radiation comprises ultraviolet, visible, or infrared light. In some instances, treatment comprises a therapeutic agent. In some instances, treatment the therapeutic agent is atopical or systemic agent. In some instances, treatment the therapeutic agent is a small molecule or peptide. In some instances, treatment the therapeutic agent comprises an antibody (such as a monoclonal antibody), diabody, scFv, or fragment thereof. In some instances, treatment the antibody comprises anti-TNF-a, anti-IL17A, anti-IL23pl9, anti-IL-4Ralpha, or anti-IL-13. In some instances, treatment the therapeutic agent comprises a steroid. In some instances, treatment the therapeutic agent comprises an anti -proliferative. In some instances, a treatment is configured to reduce the expression of one or more genes overexpressed in a disease or condition. In some instances, a treatment is configured to increase the expression of one or more genes overexpressed in a disease or condition.

[00148] After a treatment signature is obtained, in some instances it is compared with a baseline signature. In some instances, this comparison results in an outcome signature predictive of the treatment when used on the subject, for the disease or condition.

[00149] Machine Learning

[00150] Machine learning may be used to identify or analyze biomarker signatures, or to compare biomarker signatures (e.g., outcome signatures). The systems, methods, software, and platforms as described herein may comprise computer-implemented methods of supervised or unsupervised learning methods, including SVM, random forests, clustering algorithm (or software module), gradient boosting, logistic regression, and/or decision trees. The machine learning methods as described herein may improve generation of suggestions based on recording and analyzing any of the baseline biomarker signature, treatment biomarker signature, and outcome signature described herein. In some cases, the machine learning methods may intentionally group or separate treatment options. In some embodiments, some treatment options may be intentionally clustered or removed from any one phase of the plurality of phases of the medical care encounter.

[00151] Supervised learning algorithms may be algorithms that rely on the use of a set of labeled, paired training data examples to infer the relationship between an input data and output data. Unsupervised learning algorithms may be algorithms used to draw inferences from training data sets to output data. Unsupervised learning algorithms may comprise cluster analysis, which may be used for exploratory data analysis to find hidden patterns or groupings in process data. One example of an unsupervised learning method may comprise principal component analysis.

Principal component analysis may comprise reducing the dimensionality of one or more variables. The dimensionality of a given variables may be at least 1, 5, 10, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200 1300, 1400, 1500, 1600, 1700, 1800, or greater. The dimensionality of a given variables may be at most 1800, 1600, 1500, 1400, 1300, 1200, 1100, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 50, 10 or less. Computer-implemented methods may comprise statistical techniques. In some embodiments, statistical techniques may comprise linear regression, classification, resampling methods, subset selection, shrinkage, dimension reduction, nonlinear models, tree-based methods, support vector machines, unsupervised learning, or any combination thereof.

[00152] A linear regression may be a method to predict a target variable by fitting the best linear relationship between a dependent and independent variable. The best fit may mean that the sum of all distances between a shape and actual observations at each point is the least. Linear regression may comprise simple linear regression and multiple linear regression. A simple linear regression may use a single independent variable to predict a dependent variable. A multiple linear regression may use more than one independent variable to predict a dependent variable by fitting a best linear relationship.

[00153] A classification may be a data mining technique that assigns categories to a collection of data in order to achieve accurate predictions and analysis. Classification techniques may comprise logistic regression and discriminant analysis. Logistic regression may be used when a dependent variable is dichotomous (binary). Logistic regression may be used to discover and describe a relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. A resampling may be a method comprising drawing repeated samples from original data samples. A resampling may not involve a utilization of a generic distribution tables in order to compute approximate probability values. A resampling may generate a unique sampling distribution on a basis of an actual data. In some embodiments, a resampling may use experimental methods, rather than analytical methods, to generate a unique sampling distribution. Resampling techniques may comprise bootstrapping and cross-validation.

[00154] Bootstrapping may be performed by sampling with replacement from original data and take "not chosen" data points as test cases. Cross validation may be performed by split training data into a plurality of parts.

[00155] A subset selection may identify a subset of predictors related to a response. A subset selection may comprise best-subset selection, forward stepwise selection, backward stepwise selection, hybrid method, or any combination thereof. In some instances, shrinkage fits a model involving all predictors, but estimated coefficients are shrunken towards zero relative to the least squares estimates. This shrinkage may reduce variance. A shrinkage may comprise ridge regression and a lasso. A dimension reduction may reduce a problem of estimating n + 1 coefficients to a simpler problem of m + 1 coefficients, where m < n. It may be attained by computing n different linear combinations, or projections, of variables. Then these n projections are used as predictors to fit a linear regression model by least squares. Dimension reduction may comprise principal component regression and partial least squares. A principal component regression may be used to derive a low dimensional set of features from a large set of variables. A principal component used in a principal component regression may capture the most variance in data using linear combinations of data in subsequently orthogonal directions. The partial least squares may be a supervised alternative to principal component regression because partial least squares may make use of a response variable in order to identify new features. [00156] A nonlinear regression may be a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of model parameters and depends on one or more independent variables. A nonlinear regression may comprise a step function, piecewise function, spline, generalized additive model, or any combination thereof.

[00157] Tree-based methods may be used for both regression and classification problems. Regression and classification problems may involve stratifying or segmenting the predictor space into a number of simple regions. Tree-based methods may comprise bagging, boosting, random forest, or any combination thereof. Bagging may decrease a variance of prediction by generating additional data for training from the original dataset using combinations with repetitions to produce multistep of the same carnality/ size as original data. Boosting may calculate an output using several different models and then average a result using a weighted average approach. A random forest algorithm may draw random bootstrap samples of a training set. Support vector machines may be classification techniques. Support vector machines may comprise finding a hyperplane that best separates two classes of points with the maximum margin. Support vector machines may constrain an optimization problem such that a margin is maximized subject to a constraint that it perfectly classifies data.

[00158] Unsupervised methods may be methods to draw inferences from datasets comprising input data without labeled responses. Unsupervised methods may comprise clustering, principal component analysis, k-Mean clustering, hierarchical clustering, or any combination thereof.

[00159] Trained algorithms

[00160] After using one or more assays to process one or more cell-free biological samples derived from the subject to generate one or more datasets indicative of the disease or condition, a trained algorithm may be used to process one or more of the datasets (e.g., at each of a plurality of disease or condition associated genomic loci) to determine the signatures for the disease or condition, such as baseline or treatment signatures. For example, the trained algorithm may be used to determine quantitative measures of sequences at each of the plurality of disease or condition associated genomic loci in the cell-free biological samples. The trained algorithm may be configured to identify the disease related state with an accuracy of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more than 99% for at least about 25, at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, at least about 500, or more than about 500 independent samples. [00161] The trained algorithm may comprise a supervised machine learning algorithm. The trained algorithm may comprise a classification and regression tree (CART) algorithm. The supervised machine learning algorithm may comprise, for example, a Random Forest, a support vector machine (SVM), a neural network, or a deep learning algorithm. The trained algorithm may comprise an unsupervised machine learning algorithm.

[00162] The trained algorithm may be configured to accept a plurality of input variables and to produce one or more output values based on the plurality of input variables. The plurality of input variables may comprise one or more datasets indicative of a disease related state. For example, an input variable may comprise a number of sequences corresponding to or aligning to each of the plurality of disease or condition associated genomic loci. The plurality of input variables may also include clinical health data of a subject.

[00163] The trained algorithm may comprise a classifier, such that each of the one or more output values comprises one of a fixed number of possible values (e.g., a linear classifier, a logistic regression classifier, etc.) indicating a classification of the cell-free biological sample by the classifier. The trained algorithm may comprise a binary classifier, such that each of the one or more output values comprises one of two values (e.g., {0, 1}, {positive, negative}, or {high-risk, low-risk}) indicating a classification of the cell -free biological sample by the classifier. The trained algorithm may be another type of classifier, such that each of the one or more output values comprises one of more than two values (e.g., {0, 1, 2}, {positive, negative, or indeterminate}, or {high-risk, intermediate-risk, or low- risk}) indicating a classification of the cell-free biological sample by the classifier. The output values may comprise descriptive labels, numerical values, or a combination thereof. Some of the output values may comprise descriptive labels. Such descriptive labels may provide an identification or indication of the disease or disorder state of the subject, and may comprise, for example, positive, negative, high- risk, intermediate-risk, low-risk, or indeterminate. Such descriptive labels may provide an identification of a treatment for the subject’s disease related state, and may comprise, for example, a therapeutic intervention, a duration of the therapeutic intervention, and/or a dosage of the therapeutic intervention suitable to treat a disease related condition. Such descriptive labels may provide an identification of secondary clinical tests that may be appropriate to perform on the subject, and may comprise, for example, an imaging test, a blood test, a computed tomography (CT) scan, a magnetic resonance imaging (MRI) scan, an ultrasound scan, a chest X-ray, a positron emission tomography (PET) scan, aPET-CT scan, a cell-free biological cytology, an amniocentesis, anon-invasive prenatal test (NIPT), or any combination thereof. For example, such descriptive labels may provide a prognosis of the disease related state of the subject. As another example, such descriptive labels may provide a relative assessment of the disease related state (e.g., an estimated gestational age in number of days, weeks, or months) of the subject. Some descriptive labels may be mapped to numerical values, for example, by mapping “positive” to 1 and “negative” to 0.

[00164] Some of the output values may comprise numerical values, such as binary, integer, or continuous values. Such binary output values may comprise, for example, {0, 1}, {positive, negative}, or {high-risk, low-risk}. Such integer output values may comprise, for example, {0, 1, 2}. Such continuous output values may comprise, for example, a probability value of at least 0 and no more than 1. Such continuous output values may comprise, for example, an un-normalized probability value of at least 0. Such continuous output values may indicate a prognosis of the disease related state of the subject. Some numerical values may be mapped to descriptive labels, for example, by mapping 1 to “positive” and 0 to “negative.”

[00165] Some of the output values may be assigned based on one or more cutoff values. For example, a binary classification of samples may assign an output value of “positive” or 1 if the sample indicates that the subject has at least a 50% probability of having a disease related state (e.g., disease related complication). For example, a binary classification of samples may assign an output value of “negative” or 0 if the sample indicates that the subject has less than a 50% probability of having a disease related state (e.g., disease related complication). In this case, a single cutoff value of 50% is used to classify samples into one of the two possible binary output values. Examples of single cutoff values may include about 1%, about 2%, about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, and about 99%.

[00166] As another example, a classification of samples may assign an output value of “positive” or 1 if the sample indicates that the subject has a probability of having a disease related state (e.g., disease related complication) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more. The classification of samples may assign an output value of “positive” or 1 if the sample indicates that the subject has a probability of having a disease related state (e.g., disease related complication) of more than about 50%, more than about 55%, more than about 60%, more than about 65%, more than about 70%, more than about 75%, more than about 80%, more than about 85%, more than about 90%, more than about 91%, more than about 92%, more than about 93%, more than about 94%, more than about 95%, more than about 96%, more than about 97%, more than about 98%, or more than about 99%.

[00167] The classification of samples may assign an output value of “negative” or 0 if the sample indicates that the subject has a probability of having a disease related state (e.g., disease related complication) of less than about 50%, less than about 45%, less than about 40%, less than about 35%, less than about 30%, less than about 25%, less than about 20%, less than about 15%, less than about 10%, less than about 9%, less than about 8%, less than about 7%, less than about 6%, less than about 5%, less than about 4%, less than about 3%, less than about 2%, or less than about 1%. [00168] The classification of samples may assign an output value of “negative” or 0 if the sample indicates that the subject has aprobability of having a disease related state (e.g., disease related complication) of no more than about 50%, no more than about 45%, no more than about 40%, no more than about 35%, no more than about 30%, no more than about 25%, no more than about 20%, no more than about 15%, no more than about 10%, no more than about 9%, no more than about 8%, no more than about 7%, no more than about 6%, no more than about 5%, no more than about 4%, no more than about 3%, no more than about 2%, or no more than about 1%.

[00169] The classification of samples may assign an output value of “"indeterminate" or 2 if the sample is not classified as "positive", "negative", 1, or 0. In this case, a set of two cutoff values is used to classify samples into one of the three possible output values. Examples of sets of cutoff values may include { 1%, 99%}, {2%, 98%}, {5%, 95%}, { 10%, 90%}, { 15%, 85%}, {20%, 80%}, {25%, 75%}, {30%, 70%}, {35%, 65%}, {40%, 60%}, and {45%, 55%}. Similarly, sets of n cutoff values may be used to classify samples into one of n+I possible output values, where n is any positive integer.

[00170] The trained algorithm may be trained with a plurality of independent training samples. Each of the independent training samples may comprise a cell-free biological sample from a subject, associated datasets obtained by assaying the cell-free biological sample (as described elsewhere herein), and one or more known output values corresponding to the cell-free biological sample (e.g., aclinical diagnosis, prognosis, absence, ortreatment efficacy of adisease related state of the subject). Independent training samples may comprise cell -free biological samples and associated datasets and outputs obtained or derived from a plurality of different subjects.

[00171] Independent training samples may comprise cell-free biological samples and associated datasets and outputs obtained at a plurality of different time points from the same subject (e.g., on a regular basis such as weekly, biweekly, or monthly). Independent training samples may be associated with presence of the disease related state (e.g., training samples comprising cell-free biological samples and associated datasets and outputs obtained or derived from a plurality of subjects known to have the disease related state). Independent training samples may be associated with absence of the disease related state (e.g., training samples comprising cell-free biological samples and associated datasets and outputs obtained or derived from a plurality of subjects who are known to not have a previous diagnosis of the disease related state or who have received a negative test result for the disease related state).

[00172] The trained algorithm may be trained with at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, or at least about 500 independent training samples. The independent training samples may comprise cell -free biological samples associated with presence of the disease related state and/or cell-free biological samples associated with absence of the disease related state. The trained algorithm may be trained with no more than about 500, no more than about 450, no more than about 400, no more than about 350, no more than about 300, no more than about 250, no more than about 200, no more than about 150, no more than about 100, or no more than about 50 independent training samples associated with presence of the disease related state. In some embodiments, the cell-free biological sample is independent of samples used to train the trained algorithm.

[00173] The trained algorithm may be trained with a first number of independent training samples associated with presence of the disease related state and a second number of independent training samples associated with absence of the disease related state. The first number of independent training samples associated with presence of the disease related state may be no more than the second number of independent training samples associated with absence of the disease related state. The first number of independent training samples associated with presence of the disease related state may be equal to the second number of independent training samples associated with absence of the disease related state. The first number of independent training samples associated with presence of the disease related state may be greater than the second number of independent training samples associated with absence of the disease related state.

[00174] The trained algorithm may be configured to identify the disease related state at an accuracy of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more; for at least about 5, at least about 10, at least about 15, at least about 20, at least about 25, at least about 30, at least about 35, at least about 40, at least about 45, at least about 50, at least about 100, at least about 150, at least about 200, at least about 250, at least about 300, at least about 350, at least about 400, at least about 450, or at least about 500 independent training samples. The accuracy of identifying the disease related state by the trained algorithm may be calculated as the percentage of independent test samples (e.g., subjects known to have the disease related state or subjects with negative clinical test results for the disease related state) that are correctly identified or classified as having or not having the disease related state.

[00175] The trained algorithm may be configured to identify the disease related state with a positive predictive value (PPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more.

The PPV of identifying the disease related state using the trained algorithm may be calculated as the percentage of cell-free biological samples identified or classified as having the disease related state that correspond to subjects that truly have the disease related state.

[00176] The trained algorithm may be configured to identify the disease related state with a negative predictive value (NPV) of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, or more.

The NPV of identifying the disease related state using the trained algorithm may be calculated as the percentage of cell-free biological samples identified or classified as not having the disease related state that correspond to subjects that truly do not have the disease related state.

[00177] The trained algorithm may be configured to identify the disease related state with a clinical sensitivity at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99. 1%, at least about 99.2%, at least about 99.3%, at least about 99.4%, at least about 99.5%, at least about 99.6%, at least about 99.7%, at least about 99.8%, at least about 99.9%, at least about 99.99%, at least about 99.999%, or more. The clinical sensitivity of identifying the disease related state using the trained algorithm may be calculated as the percentage of independent test samples associated with presence of the disease related state (e.g., subjects known to have the disease related state) that are correctly identified or classified as having the disease related state.

[00178] The trained algorithm may be configured to identify the disease related state with a clinical specificity of at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about 84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about 89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 99. 1%, at least about 99.2%, at least about 99.3%, at least about 99.4%, at least about 99.5%, at least about 99.6%, at least about 99.7%, at least about 99.8%, at least about 99.9%, at least about 99.99%, at least about 99.999%, or more. The clinical specificity of identifying the disease related state using the trained algorithm may be calculated as the percentage of independent test samples associated with absence of the disease related state (e.g., subjects with negative clinical test results for the disease related state) that are correctly identified or classified as not having the disease related state.

[00179] The trained algorithm may be configured to identify the disease related state with an Area- Under-Curve (AUC) of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.81, at least about 0.82, at least about 0.83, at least about 0.84, at least about 0.85, at least about 0.86, at least about 0.87, at least about 0.88, at least about 0.89, at least about 0.90, at least about 0.91, at least about 0.92, at least about 0.93, at least about 0.94, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, at least about 0.99, or more. The AUC may be calculated as an integral of the Receiver Operator Characteristic (ROC) curve (e.g., the area under the ROC curve) associated with the trained algorithm in classifying cell-free biological samples as having or not having the disease related state.

[00180] The trained algorithm may be adjusted or tuned to improve one or more of the performance, accuracy, PPV, NPV, clinical sensitivity, clinical specificity, or AUC of identifying the disease related state. The trained algorithm may be adjusted or tuned by adjusting parameters of the trained algorithm (e.g., a set of cutoff values used to classify a cell-free biological sample as described elsewhere herein, or weights of a neural network). The trained algorithm may be adjusted or tuned continuously during the training process or after the training process has completed.

[00181] After the trained algorithm is initially trained, a subset of the inputs may be identified as most influential or most important to be included for making high-quality classifications. For example, a subset of the plurality of disease or condition associated genomic loci may be identified as most influential or most important to be included for making high-quality classifications or identifications of disease related states (or sub-types of disease related states). The plurality of disease or condition associated genomic loci or a subset thereof may be ranked based on classification metrics indicative of each genomic locus’s influence or importance toward making high-quality classifications or identifications of disease related states (or sub-types of disease related states). Such metrics may be used to reduce, in some cases significantly, the number of input variables (e.g., predictor variables) that may be used to train the trained algorithm to a desired performance level (e.g., based on a desired minimum accuracy, PPV, NPV, clinical sensitivity, clinical specificity, AUC, or a combination thereof). For example, if training the trained algorithm with a plurality comprising several dozen or hundreds of input variables in the trained algorithm results in an accuracy of classification of more than 99%, then training the trained algorithm instead with only a selected subset of no more than about 5, no more than about 10, no more than about 15, no more than about 20, no more than about 25, no more than about 30, no more than about 35, no more than about 40, no more than about 45, no more than about 50, or no more than about 100 such most influential or most important input variables among the plurality may yield decreased but still acceptable accuracy of classification (e.g., at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 81%, at least about 82%, at least about 83%, at least about

84%, at least about 85%, at least about 86%, at least about 87%, at least about 88%, at least about

89%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about

94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about

99%). The subset may be selected by rank -ordering the entire plurality of input variables and selecting a predetermined number (e.g., no more than about 5, no more than about 10, no more than about 15, no more than about 20, no more than about 25, no more than about 30, no more than about 35, no more than about 40, no more than about 45, no more than about 50, or no more than about 100) of input variables with the best classification metrics.

[00182] Computing

[00183] Referring to FIG. 2, a block diagram is shown depicting an exemplary machine that includes a computer system 200 (e.g., a processing or computing system) within which a set of instructions may execute for causing a device to perform or execute any one or more of the aspects and/or methodologies for static code scheduling of the methods for determining and analyzing the baseline biomarker signature, treatment biomarker signature, and outcome signature described in the present disclosure. In some embodiments, the computing system described herein generates the baseline biomarker signature, the treatment biomarker signature, or the outcome signature for predicting the therapeutic response for treating a disease or condition. The components in FIG. 2 are examples only and do not limit the scope of use or functionality of any hardware, software, embedded logic component, or a combination of two or more such components implementing particular embodiments. [00184] Computer system 200 may include one or more processors 201, amemory 203, and a storage 208 that communicate with each other, and with other components, via a bus 240. The bus 240 may also link a display 232, one or more input devices 233 (which may, for example, include a keypad, a keyboard, a mouse, a stylus, etc.), one or more output devices 234, one or more storage devices 235, and various tangible storage media 236. All of these elements may interface directly or via one or more interfaces or adaptors to the bus 240. For instance, the various tangible storage media 236 may interface with the bus 240 via storage medium interface 226. Computer system 200 may have any suitable physical form, including but not limited to one or more integrated circuits (ICs), printed circuit boards (PCBs), mobile handheld devices (such as mobile telephones or PDAs), laptop or notebook computers, distributed computer systems, computing grids, or servers.

[00185] Computer system 200 includes one or more processor(s) 201 (e.g., central processing units (CPUs) or general purpose graphics processing units (GPGPUs)) that carry out functions.

[00186] Processor(s) 201 optionally contains a cache memory unit 202 for temporary local storage of instructions, data, or computer addresses. Processor(s) 201 are configured to assist in execution of computer readable instructions. Computer system 200 may provide functionality for the components depicted in FIG. 2 as a result of the processor(s) 201 executing non-transitory, processor-executable instructions embodied in one or more tangible computer-readable storage media, such as memory 203, storage 208, storage devices 235, and/or storage medium 236. The computer-readable media may store software that implements particular embodiments, and processor(s) 201 may execute the software. Memory 203 may read the software from one or more other computer-readable media (such as mass storage device(s) 235, 236) or from one or more other sources through a suitable interface, such as network interface 220. The software may cause processor(s) 201 to carry out one or more processes or one or more steps of one or more processes described or illustrated herein. Carrying out such processes or steps may include defining data structures stored in memory 203 and modifying the data structures as directed by the software.

[00187] The memory 203 may include various components (e.g., machine readable media) including, but not limited to, a random access memory component (e.g., RAM 204) (e.g., static RAM (SRAM), dynamic RAM (DRAM), ferroelectric random access memory (FRAM), phase -change random access memory (PRAM), etc.), a read-only memory component (e.g., ROM 205), and any combinations thereof. ROM 205 may act to communicate data and instructions unidirectionally to processor(s) 201, and RAM 204 may act to communicate data and instructions bidirectionally with processor(s) 201. ROM 205 and RAM 204 may include any suitable tangible computer-readable media described below. In one example, abasic input/output system 206 (BIOS), including basic routines that help to transfer information between elements within computer system 100, such as during start-up, may be stored in the memory 203.

[00188] Fixed storage 208 is connected bidirectionally to processor(s) 201, optionally through storage control unit 207. Fixed storage 208 provides additional data storage capacity and may also include any suitable tangible computer-readable media described herein. Storage 208 may be used to store operating system 209, executable(s) 210, data 211, applications 212 (application programs), and the like. Storage 208 may also include an optical disk drive, a solid-state memory device (e.g., flash-based systems), or a combination of any of the above. Information in storage 208 may, in appropriate cases, be incorporated as virtual memory in memory 203.

[00189] In one example, storage device(s) 235 may be removably interfaced with computer system 100 (e.g., via an external port connector (not shown)) via a storage device interface 225.

[00190] Particularly, storage device(s) 235 and an associated machine-readable medium may provide non-volatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for the computer system 200. In one example, software may reside, completely or partially, within a machine -readable medium on storage device(s) 235. In another example, software may reside, completely or partially, within processor(s) 201.

[00191] Bus 240 connects a wide variety of subsystems. Herein, reference to a bus may encompass one or more digital signal lines serving a common function, where appropriate. Bus 240 may be any of several types of bus structures including, but not limited to, amemory bus, amemory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. As an example and not by way of limitation, such architectures include an Industry Standard Architecture (ISA) bus, an Enhanced ISA (EISA) bus, a Micro Channel Architecture (MCA) bus, a Video Electronics Standards Association local bus (VLB), a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, an Accelerated Graphics Port (AGP) bus, HyperTransport (HTX) bus, serial advanced technology attachment (SATA) bus, and any combinations thereof.

[00192] Computer system 200 may also include an input device 233. In one example, a user of computer system 200 may enter commands and/or other information into computer system 200 via input device(s) 233. Examples of an input device(s) 233 include, but are not limited to, an alphanumeric input device (e.g., a keyboard), a pointing device (e.g., a mouse or touchpad), atouchpad, a touch screen, a multi-touch screen, a joystick, a stylus, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), an optical scanner, a video or still image capture device (e.g., a camera), and any combinations thereof. In some embodiments, the input device is aKinect, Leap Motion, or the like. Input device(s) 233 may be interfaced to bus 240 via any of a variety of input interfaces 223 (e.g., input interface 223) including, but not limited to, serial, parallel, game port, USB, FIREWIRE, THUNDERBOLT, or any combination of the above.

[00193] In particular embodiments, when computer system 200 is connected to network 230, computer system 200 may communicate with other devices, specifically mobile devices and enterprise systems, distributed computing systems, cloud storage systems, cloud computing systems, and the like, connected to network 230. Communications to and from computer system 200 may be sent through network interface 220. For example, network interface 220 may receive incoming communications (such as requests or responses from other devices) in the form of one or more packets (such as Internet Protocol (IP) packets) from network 230, and computer system 200 may store the incoming communications in memory 203 for processing. Computer system 200 may similarly store outgoing communications (such as requests or responses to other devices) in the form of one or more packets in memory 203 and communicated to network 230 from network interface 220. Processor(s) 201 may access these communication packets stored in memory 203 for processing.

[00194] Examples of the network interface 220 include, but are not limited to, a network interface card, a modem, and any combination thereof. Examples of a network 230 or network segment 230 include, but are not limited to, a distributed computing system, a cloud computing system, a wide area network (WAN) (e.g., the Internet, an enterprise network), a local area network (LAN) (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a direct connection between two computing devices, a peer-to-peer network, and any combinations thereof. A network, such as network 230, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used.

[00195] Information and data may be displayed through a display 232. Examples of a display 232 include, but are not limited to, a cathode ray tube (CRT), a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD), an organic liquid crystal display (OLED) such as a passive -matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display, a plasma display, and any combinations thereof. The display 232 may interface to the processor(s) 201, memory 203, and fixed storage 208, as well as other devices, such as input device(s) 233, via the bus 240. The display 232 is linked to the bus 240 via a video interface 222, and transport of data between the display 232 and the bus 240 may be controlled via the graphics control 221. In some embodiments, the display is a video projector. In some embodiments, the display is ahead-mounted display (HMD) such as a VR headset. In further embodiments, suitable VR headsets include, by way of non-limiting examples, HTC Vive, Oculus Rift, Samsung Gear VR, Microsoft HoloLens, Razer OSVR, FOVE VR, Zeiss VR One, Avegant Glyph, Freefly VR headset, and the like. In still further embodiments, the display is a combination of devices such as those disclosed herein.

[00196] In addition to a display 232, computer system 200 may include one or more other peripheral output devices 234 including, but not limited to, an audio speaker, a printer, a storage device, and any combinations thereof. Such peripheral output devices may be connected to the bus 240 via an output interface 224. Examples of an output interface 224 include, but are not limited to, a serial port, a parallel connection, a USB port, a FIREWIRE port, a THUNDERBOLT port, and any combinations thereof. In addition or as an alternative, computer system 200 may provide functionality as a result of logic hardwired or otherwise embodied in a circuit, which may operate in place of or together with software to execute one or more processes or one or more steps of one or more processes described or illustrated herein. Reference to software in this disclosure may encompass logic, and reference to logic may encompass software. Moreover, reference to a computer-readable medium may encompass a circuit (such as an IC) storing software for execution, a circuit embodying logic for execution, or both, where appropriate. The present disclosure encompasses any suitable combination of hardware, software, or both.

[00197] Those of skill in the art may appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality.

[00198] The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[00199] The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by one or more processor(s), or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor may read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[00200] In accordance with the description herein, suitable computing devices include, by way of non-limiting examples, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set -top computers, media streaming devices, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles. Those of skill in the art may also recognize that select televisions, video players, and digital music players with optional computer network connectivity are suitable for use in the system described herein. Suitable tablet computers, in various embodiments, include those with booklet, slate, and convertible configurations, known to those of skill in the art.

[00201] In some embodiments, the computing device includes an operating system configured to perform executable instructions. The operating system is, for example, software, including programs and data, which manages the device’s hardware and provides services for execution of applications. Those of skill in the art may recognize that suitable server operating systems include, by way of nonlimiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Those of skill in the art may recognize that suitable personal computer operating systems include, by way of non-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU/Linux®. In some embodiments, the operating system is provided by cloud computing. Those of skill in the art may also recognize that suitable mobile smartphone operating systems include, by way of non-limiting examples, Nokia® Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®. Those of skill in the art may also recognize that suitable media streaming device operating systems include, by way of non-limiting examples, Apple TV®, Roku®, Boxee®, Google TV®, Google Chromecast®, Amazon Fire®, and Samsung® HomeSync®. Those of skill in the art may also recognize that suitable video game console operating systems include, by way of non- limiting examples, Sony® PS3®, Sony® PS4®, Microsoft® Xbox 360®, Microsoft Xbox One, Nintendo® Wii®, Nintendo® Wii U®, and Ouya®.

[00202] Non-transitory computer readable storage medium

[00203] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system of an optionally networked computing device. In further embodiments, a computer readable storage medium is a tangible component of a computing device. In still further embodiments, a computer readable storage medium is optionally removable from a computing device. In some embodiments, a computer readable storage medium includes, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, distributed computing systems including cloud computing systems and services, and the like. In some cases, the program and instructions are permanently, substantially permanently, semi-permanently, or non-transitorily encoded on the media.

[00204] Computer program

[00205] In some embodiments, the platforms, systems, media, and methods disclosed herein include at least one computer program, or use of the same. A computer program includes a sequence of instructions, executable by one or more processor(s) of the computing device’s CPU, written to perform a specified task. Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APis), computing data structures, and the like, that perform particular tasks or implement particular abstract data types. In light of the disclosure provided herein, those of skill in the art may recognize that a computer program may be written in various versions of various languages.

[00206] The functionality of the computer readable instructions may be combined or distributed as desired in various environments. In some embodiments, a computer program comprises one sequence of instructions. In some embodiments, a computer program comprises a plurality of sequences of instructions. In some embodiments, a computer program is provided from one location. In other embodiments, a computer program is provided from a plurality of locations. In various embodiments, a computer program includes one or more software modules. In various embodiments, a computer program includes, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or addons, or combinations thereof.

[00207] Web application

[00208] In some embodiments, a computer program includes a web application. In light of the disclosure provided herein, those of skill in the art may recognize that a web application, in various embodiments, utilizes one or more software frameworks and one or more database systems. In some embodiments, a web application is created upon a software framework such as Microsoft® .NET or Ruby on Rails (RoR). In some embodiments, a web application utilizes one or more database systems including, by way of non-limiting examples, relational, non-relational, object oriented, associative, and XML database systems. In further embodiments, suitable relational database systems include, by way of non-limiting examples, Microsoft® SQL Server, mySQL™, and Oracle®. Those of skill in the art may also recognize that a web application, in various embodiments, is written in one or more versions of one or more languages. A web application may be written in one or more markup languages, presentation definition languages, client-side scripting languages, server-side coding languages, database query languages, or combinations thereof. In some embodiments, a web application is written to some extent in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or extensible Markup Language (XML). In some embodiments, a web application is written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). In some embodiments, a web application is written to some extent in a client-side scripting language such as Asynchronous Javascript and XML (AJAX), Flash® Actionscript, Javascript, or Silverlight®. In some embodiments, a web application is written to some extent in a server-side coding language such as Active Server Pages (ASP), ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python™, Ruby, Tel, Smalltalk, WebDNA®, or Groovy. In some embodiments, a web application is written to some extent in a database query language such as Structured Query Language (SQL). In some embodiments, a web application integrates enterprise server products such as IBM® Lotus Domino®. In some embodiments, a web application includes a media player element. In various further embodiments, a media player element utilizes one or more of many suitable multimedia technologies including, by way of non-limiting examples, Adobe® Flash®, HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.

[00209] Referring to FIG. 3, in a particular embodiment, an application provision system comprises one or more databases 300 accessed by a relational database management system (RDBMS) 310. Suitable RDBMSs include Firebird, MySQL, PostgreSQL, SQLite, Oracle Database, Microsoft SQL Server, IBM DB2, IBM Informix, SAP Sybase, SAP Sybase, Teradata, and the like. In this embodiment, the application provision system further comprises one or more application severs 320 (such as Java servers, .NET servers, PHP servers, and the like) and one or more web servers 330 (such as Apache, IIS, GWS and the like). The web server(s) optionally expose one or more web services via app application programming interfaces (APis) 340. Via a network, such as the Internet, the system provides browser-based and/or mobile native user interfaces.

[00210] Referring to FIG. 4, in a particular embodiment, an application provision system alternatively has a distributed, cloud-based architecture 400 and comprises elastically load balanced, auto-scaling web server resources 410 and application server resources 420 as well synchronously replicated databases 430.

[00211] Mobile Application [00212] In some embodiments, a computer program includes a mobile application provided to a mobile computing device. In some embodiments, the mobile application is provided to a mobile computing device at the time it is manufactured. In other embodiments, the mobile application is provided to a mobile computing device via the computer network described herein.

[00213] In view of the disclosure provided herein, a mobile application is created by techniques known to those of skill in the art using hardware, languages, and development environments known to the art. Those of skill in the art may recognize that mobile applications are written in several languages. Suitable programming languages include, by way of non-limiting examples, C, C++, C#, Objective-C, Java™, Javascript, Pascal, Object Pascal, Python™, Ruby, VB.NET, WML, and XHTML/HTML with or without CSS, or combinations thereof.

[00214] Suitable mobile application development environments are available from several sources. Commercially available development environments include, by way of non-limiting examples, AirplaySDK, alcheMo, Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other development environments are available without cost including, by way of non-limiting examples, Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile device manufacturers distribute software developer kits including, by way of non-limiting examples, iPhone and iPad (iOS) SDK, Android™ SDK, BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, and Windows® Mobile SDK.

[00215] Those of skill in the art may recognize that several commercial forums are available for distribution of mobile applications including, by way of non-limiting examples, Apple® App Store, Google® Play, Chrome WebStore, BlackBerry® App World, App Store for Palm devices, App Catalog for webOS, Windows® Marketplace for Mobile, Ovi Store for Nokia® devices, Samsung® Apps, and Nintendo® DSi Shop.

[00216] Standalone Application

[00217] In some embodiments, a computer program includes a standalone application, which is a program that is run as an independent computer process, not an add-on to an existing process, e.g., not a plug-in. Those of skill in the art may recognize that standalone applications are often compiled. A compiler is a computer program(s) that transforms source code written in a programming language into binary object code such as assembly language or machine code. Suitable compiled programming languages include, by way of non-limiting examples, C, C++, Objective-C, COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET, or combinations thereof. Compilation is often performed, at least in part, to create an executable program. In some embodiments, a computer program includes one or more executable complied applications.

[00218] Web Browser Plug-in

[00219] In some embodiments, the computer program includes a web browser plug-in (e.g., [00220] extension, etc.). In computing, a plug-in is one or more software components that add specific functionality to a larger software application. Makers of software applications support plug- ins to enable third-party developers to create abilities which extend an application, to support easily adding new features, and to reduce the size of an application. When supported, plug-ins enable customizing the functionality of a software application. For example, plug-ins are commonly used in web browsers to play video, generate interactivity, scan for viruses, and display particular file types. [00221] Those of skill in the art may be familiar with several web browser plug-ins including, Adobe® Flash® Player, Microsoft® Silverlight®, and Apple® QuickTime®. In some embodiments, the toolbar comprises one or more web browser extensions, add-ins, or add-ons. In some embodiments, the toolbar comprises one or more explorer bars, tool bands, or desk bands.

[00222] In view of the disclosure provided herein, those of skill in the art may recognize that several plug-in frameworks are available that enable development of plug-ins in various programming languages, including, by way of non-limiting examples, C++, Delphi, Java™, PHP, Python™, and VB .NET, or combinations thereof.

[00223] Web browsers (also called Internet browsers) are software applications, designed for use with network-connected computing devices, for retrieving, presenting, and traversing information resources on the World Wide Web. Suitable web browsers include, by way of non-limiting examples, Microsoft® Internet Explorer®, Mozilla® Firefox®, Google® Chrome, Apple® Safari®, Opera Software® Opera®, and KDE Konqueror. In some embodiments, the web browser is a mobile web browser. Mobile web browsers (also called microbrowsers, mini-browsers, and wireless browsers) are designed for use on mobile computing devices including, by way of non-limiting examples, handheld computers, tablet computers, netbook computers, subnotebook computers, smartphones, music players, personal digital assistants (PDAs), and handheld video game systems.

[00224] Suitable mobile web browsers include, by way of non-limiting examples, Google® Android® browser, RIM BlackBerry® Browser, Apple® Safari®, Palm® Blazer, Palm® WebOS® Browser, Mozilla® Firefox® for mobile, Microsoft® Internet Explorer® Mobile, Amazon® Kindle® Basic Web, Nokia® Browser, Opera Software® Opera® Mobile, and Sony® PSP™ browser.

[00225] Software Modules

[00226] In some embodiments, the platforms, systems, media, and methods disclosed herein include software, server, and/or database modules, or use of the same. In view of the disclosure provided herein, software modules are created by techniques known to those of skill in the art using machines, software, and languages known to the art. The software modules disclosed herein are implemented in a multitude of ways. In various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, or combinations thereof. In further various embodiments, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, or combinations thereof. In various embodiments, the one or more software modules comprise, by way of non-limiting examples, a web application, a mobile application, and a standalone application. In some embodiments, software modules are in one computer program or application. In other embodiments, software modules are in more than one computer program or application. In some embodiments, software modules are hosted on one machine. In other embodiments, software modules are hosted on more than one machine. In further embodiments, software modules are hosted on a distributed computing platform such as a cloud computing platform. In some embodiments, software modules are hosted on one or more machines in one location. In other embodiments, software modules are hosted on one or more machines in more than one location.

[00227] In some embodiments, the software modules may be used to determine signature scores. For example, gene expression levels determined from biological samples may be received by a software module. The software module may then use the gene expression levels to determine one or more signature scores. The software module may then provide those one or more signature scores. Further, the software modules may determine one or more baseline signatures from the one or more signature scores. The software modules may also provide those one or more baseline signatures.

[00228] Even further, the software modules may determine one or more differential signatures based on gene expression levels received that are associated with a biological sample and baseline signature. In some embodiments, the software modules may be used to provide available treatments based on at least one baseline signature and at least one differential signature. The available treatments may be associated with lowering or raising gene expression of a gene indicated in a biological sample based on at least one baseline signature and at least one differential signature. In some embodiments, the software modules may be used to provide recommendations of the treatment regimens.

[00229] Databases

[00230] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more databases, or use of the same. In view of the disclosure provided herein, those of skill in the art may recognize that many databases are suitable for determination, storage, and retrieval of the signature information described herein. In various embodiments, suitable databases include, by way of non-limiting examples, relational databases, non-relational databases, object oriented databases, object databases, entity-relationship model databases, associative databases, and XML databases. Further non-limiting examples include SQL, PostgreSQL, MySQL, Oracle, DB2, and Sybase. In some embodiments, a database is internet -based. In further embodiments, a database is web-based. In still further embodiments, a database is cloud computing -based. In a particular embodiment, a database is a distributed database. In other embodiments, a database is based on one or more local computer storage devices.

[00231] In some embodiments, the databases may include baseline signatures for patients. In some embodiments, the patients may be grouped based on their baseline signatures. In those embodiments, the groups in the database may be referenced when a differential signature is received and compared to a baseline signature in the database.

[00232] Methods Utilizing a Computer

[00233] The methods and software described herein may utilize one or more computers. The computer may be used for determining and analyzing the baseline biomarker signature, treatment biomarker signature, and outcome signature described herein. The computer may include a monitor or other graphical interface for displaying data, results, information, or analysis of the baseline biomarker signature, treatment biomarker signature, and outcome signature described herein. The computer may also include means for data or information input. The computer may include a processing unit and fixed or removable media or a combination thereof. The computer may be accessed by a user in physical proximity to the computer, for example via a keyboard and/or mouse, or by a user that does not necessarily have access to the physical computer through a communication medium such as a modem, an internet connection, a telephone connection, or a wired or wireless communication signal carrier wave. In some cases, the computer may be connected to a server or other communication device for relaying information from a user to the computer or from the computer to a user. In some cases, the user may store data or information obtained from the computer through a communication medium on media, such as removable media. It is envisioned that data relating to the methods may be transmitted over such networks or connections for reception and/or review by a party. The receiving party may be but is not limited to an individual, a health care provider or a health care manager. In one instance, a computer-readable medium includes a medium suitable for transmission of a result of an analysis of abiological sample. The medium may include a result of a subject, wherein such a result is derived using the methods described herein.

[00234] The entity obtaining the sample information may enter it into a database for the purpose of one or more of the following: inventory tracking, assay result tracking, order tracking, customer management, customer service, billing, and sales. Sample information may include, but is not limited to: customer name, unique customer identification, customer associated medical professional, indicated assay or assays, assay results, adequacy status, indicated adequacy tests, medical history of the individual, preliminary diagnosis, suspected diagnosis, sample history, insurance provider, medical provider, third party testing center or any information suitable for storage in a database. Sample history may include but is not limited to: age of the sample, type of sample, method of acquisition, method of storage, or method of transport.

[00235] The database may be accessible by a customer, medical professional, insurance provider, or other third party. Database access may take the form of digital processing communication such as a computer or telephone. The database may be accessed through an intermediary such as a customer service representative, business representative, consultant, independent testing center, or medical professional. The availability or degree of database access or sample information, such as assay results, may change upon payment of a fee for products and services rendered or to be rendered. The degree of database access or sample information may be restricted to comply with generally accepted or legal requirements for patient or customer confidentiality.

[00236] Sample analysis kits

[00237] Provided herein are sample analysis kits. In some instances sample analysis kits comprise components configured for obtaining a non-invasive or minimally invasive biological sample. In some instances, the analysis kit comprises an adhesive skin sample collection kit. The adhesive skin sample collection kit, in some embodiments, comprises at least one adhesive patch, a sample collector, and an instruction for use sheet. In an exemplary embodiment, the sample collector is a tri -fold skin sample collector comprising a peelable release panel comprising at least one adhesive patch, a placement area panel comprising a removable liner, and a clear panel. The tri-fold skin sample collector, in some instances, further comprises a barcode and/or an area fortranscribing patient information. In some instances, the adhesive skin sample collection kit is configured to include a plurality of adhesive patches, including but not limited to 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, from about 2 to about 8, from about 2 to about 7, from about 2 to about 6, from about 2 to about 4, from about 3 to about 6, from about 3 to about 8, from about 4 to about 10, from about 4 to about 8, from about 4 to about 6, from about 4 to about 5, from about 6 to about 10, from about 6 to about 8, or from about 4 to about 8. The instructions for use sheet provide the kit operator all of the necessary information for carrying out the patch stripping method. The instructions for use sheet preferably include diagrams to illustrate the patch stripping method.

[00238] In some instances, the adhesive skin sample collection kit provides all the necessary components for performing the patch stripping method. In some embodiments, the adhesive skin sample collection kit includes a lab requisition form for providing patient information. In some instances, the kit further comprises accessory components. Accessory components include, but are not limited to, a marker, a resealable plastic bag, gloves and a cleansing reagent. The cleansing reagent includes, but is not limited to, an antiseptic such as isopropyl alcohol. In some instances, the components of the skin sample collection kit are provided in a cardboard box.

[00239] In some embodiments, the kit includes a skin collection device. In some embodiments, the skin collection device includes anon-invasive skin collection device. In some embodiments, the skin collection device includes an adhesive patch as described herein. In some embodiments, the skin collection device includes a brush. In some embodiments, the skin collection device includes a swab. In some embodiments, the skin collection device includes a probe. In some embodiments, the skin collection device includes a medical applicator. In some embodiments, the skin collection device includes a scraper. In some embodiments, the skin collection device includes an invasive skin collection device such as a needle or scalpel. In some embodiments, the skin collection device includes a needle. In some embodiments, microneedles are used to collect biological samples (e.g., blood, cells, etc.) as described in US20210317513. In some embodiments, the skin collection device includes a microneedle. In some embodiments, the skin collection device includes a hook.

[00240] Disclosed herein, in some embodiments, are kits for evaluating biomarkers in a biological sample. In some embodiments, the kit includes an adhesive patch. In some embodiments, the adhesive patch comprises an adhesive matrix configured to adhere skin sample cells from the stratum comeum of a subject. Some embodiments include a nucleic acid isolation reagent. Some embodiments include a plurality of probes that recognize at least one mutation. Disclosed herein, in some embodiments, are kits for determining a biomarkers in a skin sample, comprising: an adhesive patch comprising an adhesive matrix configured to adhere skin sample cells; a nucleic acid isolation reagent; and at least one probe that recognize at least one mutation. Disclosed herein, in some embodiments, are kits for determining a biomarker in a skin sample, comprising: an adhesive patch comprising an adhesive matrix configured to adhere skin sample cells; a sample collector, and instructions for collecting the sample and storing in the collector. In some embodiments, the kit is labeled for where the skin sample comes from on the subject (e.g., high UV exposure areas vs low UV exposure areas; or specific sampling locations such as the head (bald), temple, forehead, cheek, or nose). In some embodiments, the adhesive patch is at least 1 cm², at least 2 cm², at least 3 cm², or at least 4 cm², based on the skin sampling location.

[00241] The adhesive skin sample collection kit in some instances comprises the tri-fold skin sample collector comprising adhesive patches stored on a peelable release panel. In some instances, the tri- fold skin sample collector further comprises a placement area panel with a removable liner. In some instances, the patch stripping method involves removing an adhesive patch from the tri-fold skin sample collector peelable release panel, applying the adhesive patch to a skin sample, removing the used adhesive patch containing a skin sample and placing the used patch on the placement area sheet. In some instances, the placement area panel is a single placement area panel sheet. In some instances, the identity of the skin sample collected is indexed to the tri-fold skin sample collector or placement area panel sheet by using a barcode or printing patient information on the collector or panel sheet. In some instances, the indexed tri-fold skin sample collector or placement sheet is sent to a diagnostic lab for processing. In some instances, the used patch is configured to be stored on the placement panel for at least 1 week at temperatures between -80 °C and 25 °C. In some embodiments, the used patch is configured to be stored on the placement area panel for at least 2 weeks, at least 3 weeks, at least 1 month, at least 2 months, at least 3 months, at least 4 months, at least 5 months, and at least 6 months at temperatures between -80 °C and 25 °C. In some embodiments, the indexed tri-fold skin sample collector or placement sheet is sent to a diagnostic lab using UPS or FedEx.

[00242] Methods of Treatment

[00243] Disclosed herein, in some embodiments, are methods of treating or determining a treatment regimen for a disease or condition having cutaneous manifestations based on the analysis of the baseline biomarker signature, treatment biomarker signature, outcome signature, or a combination thereof. In some embodiments, the treatments are recommended based on analysis of the baseline biomarker signature, treatment biomarker signature, outcome signature, or a combination thereof. In some embodiments, the treatments are recommended based on categorization of the subject’s the baseline biomarker signature, treatment biomarker signature, outcome signature, or a combination thereof into one or more bins, classes, categories, qualitative actionable output, numeric actionable output, pathology score, or success rate output. In some embodiments, the baseline biomarker signature, treatment biomarker signature, outcome signature, or a combination thereof is correlated with a particular treatment which results in lowering the risk of the disease or condition in a subject. In some instances, treatment comprises administration to a treatment described herein. In some instances, a previously determined outcome signature associated with one or more treatments guides an optimum treatment for a subject. In some instances, determining optimum treatment comprises obtaining a baseline signature from a biological sample obtained from the subject, and comparing to a database of outcome signatures for a set of potential treatments. In some instances, the subject is further administered the optimum treatment.

[00244] A disease or condition described herein may have cutaneous manifestations. In some instances, disease or condition comprises a condition wherein the skin is a target or surrogate target of the cutaneous manifestation. In some instances, the disease or condition comprises an autoimmune disease, proliferative disease, or other disease having cutaneous manifestations. In some instances, the disease or condition comprises atopic dermatitis, psoriasis, allergy, Crohn’s disease, lupus, asthma, or vitiligo. In some instances, the disease or condition comprises cancer or pre-cancerous conditions. In some instances, the cancer comprises melanoma or non-melanoma skin cancers. In some instances, the melanoma comprises basal cell carcinoma or squamous cell carcinoma. In some instances, the nonmelanoma comprises merkel cell carcinoma or keratinosis. In some instances, the disease or condition comprises a pre -malignant condition. In some instances, the pre-malignant condition comprises actinic keratosis.

[00245] In some embodiments, the baseline biomarker signature, treatment biomarker signature, outcome signature, or a combination thereof may be used to predict therapeutic response or outcome of a treatment regimen. In some embodiments, the treatment regimen exhibits an improved therapeutic efficacy as compared with a treatment regimen not based on the analysis of the signatures described herein. The therapeutic efficacy may be determine based on the disease or condition being treated. For example, the therapeutic efficacy may be anti-proliferative effect when the disease or condition is skin cancer. In another example, the therapeutic efficacy may be modulated cytokine levels when the disease or condition is an autoimmune or an inflammatory disease. In some embodiments, the treatment regimen based on the analysis of the signatures described herein exhibits at least an increase of 0.1 fold, 0.2 fold, 0.3 fold, 0.4 fold, 0.5 fold, 0.6 fold, 0.7 fold, 0.8 fold, 0.9 fold, 1 fold, 2 fold, 5 fold, 10 fold, 20 fold, 50 fold, 100 fold, 200 fold, 500 fold, or 1000 fold in biomarkers of a disease or condition. In some embodiments, the treatment regimen based on the analysis of genes described herein exhibits at least an increase of 0.1 fold, 0.2 fold, 0.3 fold, 0.4 fold, 0.5 fold, 0.6 fold, 0.7 fold, 0.8 fold, 0.9 fold, 1 fold, 2 fold, 5 fold, 10 fold, 20 fold, 50 fold, 100 fold, 200 fold, 500 fold, or 1000 fold in biomarkers of a disease or condition. In some embodiments, the treatment regimen based on the analysis of one or more of IL-4Ralpha, IL- 13, IL-22, and IL-23A exhibits at least an increase of 0.1 fold, 0.2 fold, 0.3 fold, 0.4 fold, 0.5 fold, 0.6 fold, 0.7 fold, 0.8 fold, 0.9 fold, 1 fold, 2 fold, 5 fold, 10 fold, 20 fold, 50 fold, 100 fold, 200 fold, 500 fold, or 1000 fold in biomarkers of a disease or condition. In some embodiments, the treatment regimen based on the analysis of one or more of IL- 31, and TSLP exhibits at least a decrease of 0.1 fold, 0.2 fold, 0.3 fold, 0.4 fold, 0.5 fold, 0.6 fold, 0.7 fold, 0.8 fold, 0.9 fold, 1 fold, 2 fold, 5 fold, 10 fold, 20 fold, 50 fold, 100 fold, 200 fold, 500 fold, or 1000 fold in biomarkers of a disease or condition.

[00246] Some embodiments of the methods described herein comprise analyzing the baseline biomarker signature, treatment biomarker signature, outcome signature, or a combination thereof to generate an actionable output. In some embodiments, the actionable output determines the presence or severity of the disease or condition described herein. In some embodiments, the actionable output determines if a treatment regimen.

[00247] A method for determining and implementing a treatment plan based on the baseline signature of a patient and the differential signature of the patient is described herein. A patient (e.g., the patient as described with respect to FIGs. 1A-1B) may have one or more biological samples retrieved and used to determine a baseline signature when the patient is not affected by a disease, disorder, or malady, and may have one or more biological samples retrieved and used to determine a differential signature when the patient is affected by a disease, disorder, or malady. The baseline signature of the patient and the differential signature of the patient may have one or more differing biomarker levels for one or more biomarkers (e.g., gene expression levels for one or more genes). A treatment regimen may be determined based on the one or more differing gene expression levels for the one or more genes, as described below.

[00248] The one or more differing gene expression levels for the one or more genes may indicate that gene expression for the one or more genes is higher or lower in the differential signature than the baseline signature. The gene expression for the one or more genes being higher or lower in the differential signature may indicate that the gene expression for the one or more genes needs to be lowered or raised, and may indicate that the disease, disorder, or malady is at least partially caused by the higher or lower level. The one or more genes needing to be lowered or raised may be in order to return the gene expression to the baseline signature, which may at least partially alleviate the disease, disorder, or malady. Based on the one or more genes needing to be lowered or raised, one or more treatment regimens may be available.

[00249] One of the one or more treatment regimens that may be available may be chosen in order to raise or lower gene expression of the one or more genes with differing expression levels. In some cases, a human may choose the treatment regimen based on the one or more differing expression levels. In other cases, a computer program as described above may be used to determine which treatment regimen to use.

[00250] In some embodiments, the computer program may be associated with one or more processing devices, such as a server (e.g., server 330 of FIG. 3 or servers of system 400 of FIG. 4) and a computing device (e.g., the WAP or native mobile device of FIG. 3 and FIG. 4). The server may further include or be associated with one or more databases of baseline signatures, gene expression levels, and treatment regimens associated with the baseline gene expression levels. The server may then determine an appropriate treatment regimen for the patient based on the gene expression levels of both the baseline signature of the patient and the differential signature of the patient (e.g., a gene expression level of the differential signature is higher than the corresponding expression level of the baseline signature, indicating the condition is induced by the higher expression level of the differential signature). For example, a baseline signature and differential signature for a patient suffering from inflammation may indicate that the patient is suffering from information due to higher than normal expressions of IL-36R. Thus, a treatment regimen of Spesolimab may be recommended in order to lower the expressions of IL-36R.

[00251] The server may further provide the appropriate treatment regimen to the computing device. In some embodiments, the computing device may be associated with the patient or a person caring for the patient.

[00252] FIG. 5 depicts a user interface 500 of a computing device (e.g., the WAP or native mobile device of FIGs. 3 and 4) displaying one or more treatment regimens. In this depicted embodiment, the user interface 500 displays one or more disease, disorder, or malady fields 502a- 502j. Further, in this depicted embodiment, the user interface 500 displays one or more treatment possibility fields 504a- 504j. As described above, the treatment possibility fields may be based on the gene expression levels that are higher or lower than normal as indicated by the baseline signature and the differential signature. The treatment possibilities displayed in treatment possibility fields 504a-504j may indicate treatments that may lower or raise the gene expression levels that are higher or lower than normal, respectively. For example, if a patient suffering from inflammation has a baseline signature and differential signature that indicates that the inflammation, as indicated by disease, disorder, or malady field 502a, is due to raised levels of IL lb, a Rilomacept treatment regimen may appear in the treatment possibility field 504a as a possible treatment. The user interface 500 may then receive user input to one or more of the treatment possibility fields 504a-504j.

[00253] Based on the user input to the one or more of the treatment possibility fields 504a-504j, the user interface 500 may display one or more treatment regimens. In some embodiments, input may be received indicating a particular treatment regimen. In other embodiments, a recommendation of a treatment regimen may be provided (e.g., by bold or highlight). In those embodiments, the recommendation may be accepted or declined based on additional user input. Once a treatment regimen is accepted, the patient may begin treatment according to the treatment regimen.

[00254] Definitions

[00255] Use of absolute or sequential terms, for example, “will,” “will not,” “shall,” “shall not,” “must,” “must not,” “first,” “initially,” “next,” “subsequently,” “before,” “after,” “lastly,” and “finally,” are not meant to limit scope of the present embodiments disclosed herein but as exemplary.

[00256] As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, to the extent that the terms “including”, “includes”, “having”, “has”, “with”, or variants thereof are used in either the detailed description and/or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”

[00257] As used herein, the phrases “at least one”, “one or more”, and “and/or” are open- ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C”, “at least one of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and/or C” means A alone, B alone, C alone, A and B together, A and C together, B and C together, or A, B and C together.

[00258] As used herein, “or” may refer to “and”, “or,” or “and/or” and may be used both exclusively and inclusively. For example, the term “A or B” may refer to “A or B”, “A but not B”, “B but not A”, and “A and B”. In some cases, context may dictate a particular meaning.

[00259] Any systems, methods, software, and platforms described herein are modular. Accordingly, terms such as “first” and “second” do not necessarily imply priority, order of importance, or order of acts.

[00260] The term “about” when referring to a number or a numerical range means that the number or numerical range referred to is an approximation within experimental variability (or within statistical experimental error), and the number or numerical range may vary from, for example, from 1% to 15% of the stated number or numerical range. In examples, the term “about” refers to ±10% of a stated number or value.

[00261] The terms “increased”, “increasing”, or “increase” are used herein to generally mean an increase by a statically significant amount. In some aspects, the terms “increased,” or “increase,” mean an increase of at least 10% as compared to a reference level, for example an increase of at least about 10%, at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or up to and including a 100% increase or any increase between 10-100% as compared to a reference level, standard, or control. Other examples of “increase” include an increase of at least 2-fold, at least 5- fold, at least 10- fold, at least 20-fold, at least 50-fold, at least 100-fold, at least 1000-fold or more as compared to a reference level.

[00262] The terms “decreased”, “decreasing”, or “decrease” are used herein generally to mean a decrease by a statistically significant amount. In some aspects, “decreased” or “decrease” means a reduction by at least 10% as compared to a reference level, for example a decrease by at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or up to and including a 100% decrease (e.g., absent level or non-detectable level as compared to a reference level), or any decrease between 10-100% as compared to a reference level. In the context of a marker or symptom, by these terms is meant a statistically significant decrease in such level. The decrease may be, for example, at least 10%, at least 20%, at least 30%, at least 40% or more, and is preferably down to a level accepted as within the range of normal for an individual without a given disease.

EXAMPLES

[00263] The following illustrative examples are representative of embodiments of the stimulation, systems, and methods described herein and are not meant to be limiting in any way.

[00264] Example 1. Method for Determining Baseline Signature For One or More Patients

[00265] One or more patients has various skin samples from lesions on their body collected through the use of non-invasive sample methods (e.g., adhesive patches or microneedles ). The skin samples are used to evaluate the genes and/or protein patterns of the patient. Based on those genes and/or protein patterns, one or more gene expression levels are determined for each patient. Based on the strength of the one or more levels, a signature score indicating a baseline signature is determined. The signature score indicates the baselines signature by indicating one gene expression level or a combination of gene expression levels present in the patient that have not been affected by any particular treatment. Thus, after the baseline signature is determined, the baseline signature can be used to determine treatment based on the difference between the baseline signature and other gene expression levels in each patient.

[00266] Example 2. Method for Determining Appearance of Baseline Signature in Patient

[00267] A patient from Example 1 develops an inflamed skin rash. The patient then has multiple tests run based on the inflamed skin rash in order to determine the gene expression levels present in the patient’s body. Once determined, the gene expression levels are analyzed to determine a “differential inflammatory signature”, which is compared to the baseline signature. The “differential inflammatory signature” indicates what current gene expression levels and/or protein patterns present in the patient are stronger or weaker than compared to when the baseline signature was determined. Based on the differential inflammatory signal, a treatment plan is determined. In some instances, the patient previously received atreatment for a skin disease. Although the patient’s overall biomarker signatures may have changed due to previous treatment, the baseline signature may be used to provide an accurate diagnosis and treatment plan.

[00268] Example 3. Method for Determining Treatment Based on Appearance of Baseline Signature in Patient

[00269] The differential inflammatory signal and the baseline signature of the patient from Example 2 are used to determine atreatment plan. The differential inflammatory signal indicates both atreatment class and mechanism of the inflammation with respect to the baseline signature. Using the treatment class and the mechanism, the treatment plan may be determined and implemented.

[00270] Example 4. Method of Treatment Based on Associated Baseline Signature in Patient

[00271] The differential inflammatory signal and the baseline signature of the patient from Example 2 are used to determine a treatment plan of Example 3. The patient from Example 2 is then treated with the treatment plan of Example 3.

[00272] Example 5. Example Tests and Compositions for Determining Baseline Signature For One or More Patients

[00273] The patient from Example 2 may have a skin sample retrieved using a sample analysis kit. The skin sample is retrieved using adhesive patches. The skin sample is analyzed using a nucleic acid isolation reagent and a plurality of probes that recognize at least one mutation. The isolated nucleic acid and the one mutation are then analyzed to determine a baseline signature of the patient.

[00274] Example 6. Method of Determining a Signature Score

[00275] The patients of Example 1 have their one or more gene signatures and/or protein patterns determined. In order to determine the baseline signature, the one or more gene signatures and/or protein patterns are analyzed. The analysis includes determining a baseline level for each gene signature and/or protein pattern. Based on the baseline level for each gene signature and/or protein pattern, a method using weighting algorithms, likelihood methods, and probabilistic approaches are used to predict how the patients will respond to each treatment. The signature score for each patient is then determined based on the gene signatures and/or protein patterns that show the highest likelihood associated with a positive response to treatment.

[00276] Example 7. Skin patch sampling and treatment

[00277] A patient has an unidentified lesion suspected of containing melanoma. Four adhesive patch devices are used sequentially in the same location on the subject to extract superficial skin cells from the lesion. After sampling, the patch devices are placed on a collection device and stored prior to analysis. The cells are lysed, RNA isolated, and a set of biomarker signatures comprising gene expression levels is obtained from the sample. The biomarkers are aggregated into an expression profile for the patient, and the profile is compared to a responder database obtained from any one of Examples 1-3. The sample is then labeled as testing positive for melanoma based on the biomarker signatures. Based on the patient's biomarker signatures and overall profile, a specific drug or combination of drugs is selected (e.g., fluorouracil) which are determined to be associated with positive outcomes using in-vitro analysis of a larger patient population. The patient is then administered the drug to treat the melanoma.

[00278] Example 8. Skin patch sampling and treatment

[00279] A patient has an unidentified lesion of unknown pathology. Four adhesive patch devices are used sequentially in the same location on the subject to extract superficial skin cells from the lesion. After sampling, the patch devices are placed on a collection device and stored prior to analysis. The cells are lysed, RNA isolated, and a set of biomarker signatures comprising gene expression levels is obtained from the sample. The biomarkers are aggregated into an expression profile for the patient, and the profile is compared to a responder database obtained from any one of Examples 1-3. The sample is then labeled as testing positive for an inflammatory disease such as psoriasis, lupus, or atopic dermatitis. Based on the patient's biomarker signatures and overall profile, a specific drug or combination of drugs is selected (e.g., corticosteroid) which are determined to be associated with positive outcomes using in-vitro analysis of a larger patient population. The patient is then administered the drug to treat the inflammatory disease.

[00280] Example 9. Database curation based on treatment

[00281] Following the general methods of Examples 8 and 9, patients are sampled with an adhesive patch after treatment. New biomarker signatures are compared to previous signatures to evaluate the efficacy of the treatment, and this information is added to the responder database generated using in-vitro testing.

[00282] Example 10. Differential gene expression following in vivo psoriasis treatment

[00283] To show the ability to predict drug response from the baseline expression of a set of genes, a public dataset was evaluated, wherein the study included skin biopsy specimens from 14 patients having plaque psoriasis. The study was an open-label, single-arm, single-center anti-IL17 treatment study. See Bertelsen, T., et al. (2020), "IKB is a key player in the antipsoriatic effects of secukinumab," Journal of Allergy and Clinical Immunology, 145(1), 379-390. According to this study, samples were collected on days 0, 4, 14, 42, and 84 and processed for microarray gene expression analysis. Each patient treatment was monitored during the study and at each visit, Psoriasis Area and Severity Index (PASI) was evaluated, and photos were taken.

[00284] Expression data from the study was deposited in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE137221. The platform for generating this data was microarray Affymetrix Human Clariom D Assay [Clariom D Human] .

[00285] FIG. 6 shows the PASI scores for the 14 patients taken during the course of the study. The mean baseline PASI score was 26 (range, 14-52). Clinically, only traces of improvement were observed on day 4, with a small decrease in erythema and scaling in most patients (mean PASI score at day 4 was 23). A more noticeable PASI score reduction was seen at day 14 (mean PASI score, 14; range, 7-27). The major shift in the skin was observed at day 42 (mean PASI score, 5; range, 0-18). Mean PASI scores were reduced to 1 (range, 0-8) after 12 weeks of treatment.

[00286] Since the major noticeable PASI score reduction was seen at day 14 and the major shift in the skin was observed at day 42, drug response was defined as the percent reduction in PASI score from Day 0 to Average of PASI score at Day 14 and Day 42. The study data was compiled in Table 1 which summarizes the response level for all 14 patients.

Table 1

[00287] Patient response to drug treatment. Patient 7 showed the lowest response at 26.2% reduction in PASI score from Day to average of Day 14 and 42. Patients 3, 5, 6, 8, 9, 10, 11, 13 showed an average response between 47-55%. Patients 1, 2, 4, 12, and 14 showed high drug response higher than 65%. [00288] Next, for all the genes analyzed in the study the correlation was correlated between gene expression at baseline with the drug response. The Spearman correlation coefficient was calculated. The genes were filtered based on p-value < 0.01 and absolute correlation coefficient >0.75. The results showed that 88 genes that have high correlation between baseline expression (Day 0) and drug response (65 positively correlated and 23 negatively correlated.) FIG. 7 and FIG. 8 show the gene expression of different patients for these 88 genes, stratified by patients' response to the drug.

[00289] Example 11. Differential gene expression following in vitro psoriasis treatment

[00290] This study aimed to investigate the response to cytokine neutralizing antibodies in cutaneous biopsies by evaluating the effect of 5 neutralizing antibodies (anti-TNF-alpha, anti-IL17A, anti- IL23pl9, anti-IL-4Ralpha, anti-IL-13) that block cytokine activity, their isotypes, and dexamethasone using in vitro cultured full-thickness lesional biopsies collected from 10 moderate- severe psoriasis patients.

[00291] Punches from lesional biopsies were obtained for each patient. Patients were without any systemic treatment for 4 weeks or topical treatment for 2 weeks before biopsies were extracted. A tape stripping skin sample was also collected in the periphery of the same lesions before being biopsied. Biopsies were cultured for 8 days in KBM + CaC12 (-(-/-treatment) and media was changed every 2-3 days.

[00292] After culturing biopsies for 8 days, RNA was extracted, and gene expression was analyzed or nucleic acid samples stored at -80° until subsequent gene expression analysis could take place. The expression of 60,660 genes were analyzed by whole -transcriptome RNA-seq and of the tested genes, a subset of genes were found to be significant.

[00293] The key advantages of using the in vitro system described herein is that it reduces the time and cost for studying the effects of drugs, as it is not necessary to monitor patients following the administration of drugs, and new unapproved drugs can be tested without a risk to patients.

[00294] However, a key limitation of such a system is that it lacks clinical information that can help understand the efficacy of the drugs that are given. As a result of this limitation, a surrogate system for the lacking clinical information was developed and described herein.

[00295] In this surrogate system, the drug response for each individual was defined based on the level of agreement between an individual's gene expression changes compared to the average change in expression over all patients tested using a pre-defined gene signature. The gene signature was defined as the set of genes that are significantly differentially expressed in treatment (+treatment) vs. control (-treatment) samples.

[00296] For all the genes tested, the correlation was calculated between gene expression at baseline (-treatment) and with the drug response to anti-IL-17A (+treatment). The Spearman correlation coefficient was calculated. The genes were filtered base on p-value < 0.01 and absolute correlation coefficient 0.75. The results showed that 469 genes that have high correlation between baseline expression (IsoType control) and drug response (218 positively correlated and 251 negatively correlated.)

[00297] The drug response was calculated as follows:

1. Significantly differentially expressed genes between anti-IL 17A treatment and control samples were identified. 244 genes were significantly differentially expressed using a paired t-test (p < 0.05, fold change> 2).

2. For each gene, average fold change over all tested individuals was calculated.

3. For each gene, the fold change for each tested individual was calculated.

4. The distance between the average fold change for all individuals and the individual fold change for a particular gene was calculated. The distance represents the degree of agreement between that individual and the average change in the treatment forthat particular gene. According to this calculation: a. if the gene fold change for an individual is in the opposite direction of the average fold change, the distance is the negative sum of the absolute average fold change and the absolute individual fold change; b. if the gene fold change for an individual is in the same direction as the average fold change, but smaller than the average fold change, then the distance is a negative absolute value of difference between individual fold change and average fold change; or c. if the gene fold change for an individual is in the same direction as the average fold change and larger than the average fold change, then the distance is the absolute value of the difference between the individual fold change and the average fold change.

[00298] The sum of the distance measured in step 4 above, over all 244 genes, represents the overall agreement for that individual response and the gene signature.

[00299] FIG. 9 illustrates the calculation used to evaluate drug response (sum difference fold change score) in the in vitro model described herein.

[00300] The study data was compiled in Table 2 which summarizes the response level for all 10 patients. FIG. 10 and FIG. 11 show the gene expression of the different patients for the top 100 genes with significant differential expression, based on the absolute correlation coefficient (41 positively correlated and 59 negatively correlated), stratified by patients' response to the drug. Table 2

[00301] Patient response to drug treatment. Patients 4, 5, 6, and 8 showed the lowest response at between -33.4 to 3.07 sum difference fold change score. Patients 1, 3, 7, and 9 showed an average response between 10-40 sum difference fold change score. Patients 2 and 10 showed high drug response higher than 59 sum difference fold change score.

[00302] While the foregoing disclosure has been described in some detail for purposes of clarity and understanding, it may be clear to one skilled in the art from a reading of this disclosure that various changes in form and detail may be made without departing from the true scope of the disclosure. For example, all the techniques and apparatus described above may be used in various combinations. All publications, patents, patent applications, and/or other documents cited in this application are incorporated by reference in their entirety for all purposes to the same extent as if each individual publication, patent, patent application, and/or other document were individually and separately indicated to be incorporated by reference for all purposes.

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A method for patient stratification comprising:

(a) obtaining at least one sample from a patient using a non-invasive sampling method;

(b) obtaining at least one baseline signature corresponding to a biological process from the at least one sample;

(c) comparing the at least one baseline signature to a database to determine one or more treatment classes; and

(d) optionally administering a treatment to the patient based on the one or more treatment classes.

2. The method of claim 1, wherein the baseline signature comprises one or more of a gene expression signature, genomic signature, or protein signature.

3. The method of claim 2, wherein the genomic signature comprises single -nucleotide polymorphism genotyping, copy number proofing, and post-transcriptional modifications.

4. The method of claim 1, wherein the baseline signature comprises two or more gene expression levels.

5. The method of claim 1, wherein the baseline signature comprises ten or more gene expression levels.

6. The method of claim 1, wherein the baseline signature comprises family medical history.

7. The method of claim 1, wherein non-invasive sampling comprises use of one or more adhesive patches applied to a skin sample of the patient.

8. The method of claim 7, wherein non-invasive sampling comprises obtaining a blood sample and/or use of microneedles.

9. The method of claim 1, wherein the treatment classes comprise one or more of Th22, Th2, Th 17, Thl, inflammation, kinase inhibitors, B cell, microbial infection, and macrophage.

10. The method of claim 1, wherein the method further comprises identifying a primary treatment class.

11. The method of claim 10, wherein the primary treatment class is selected from TH2, TH 17, TH11, or B cell.

12. The method of claim 1, wherein the treatment is administered by intramuscular, intraperitoneal, intravenous, subcutaneous, oral, sublingual, or topical routes.

13. The method of claim 1, wherein the treatment class is Th2 and the treatment comprises dupilumab, tralokinumab, lebrikizumab, or nemolizumab.

14. The method of claim 1, wherein the treatment class is Th 17 and the treatment comprises Ixekinumab, secukinumab, guselkumab, ustekinumab, or rizankizumab.

15. The method of claim 1, wherein the treatment class is Thl and the treatment comprises anifrolumab.

16. The method of claim 1, wherein the treatment class is inflammation and the treatment comprises adalimumab or remicade.

17. The method of claim 1, wherein the treatment class is kinase inhibitors and the treatment comprises JAK, BTK, or TYK2 inhibitors.

18. The method of claim 1, wherein the treatment class is B cell and the treatment comprises belimumab or rituximab.

19. The method of claim 1, wherein the treatment class is macrophage and the treatment comprises mavrilimumab.

20. The method of claim 1, wherein the patient has previously received treatment for a skin disease or condition.

21. The method of claim 20, wherein the skin disease or condition comprises atopic dermatitis, psoriasis, or lupus.

22. The method of claim 1, wherein the at least one baseline signature comprises a differential signature.

23. The method of claim 22, wherein the database comprises a previous baseline signature of the patient.

24. A method for generating a baseline signature database comprising:

(a) obtaining a plurality of signatures from a patient population, wherein the population comprises treated and untreated patients;

(b) determining one or more patient groups of the patient population, wherein each of the one or more patient groups of the patient population shares at least one of the plurality of signatures;

(c) identifying one or more baseline signatures for each of the one or more patient groups based on the at least one of the plurality of signatures shared by the respective patient group; and

(d) storing the one or more baseline signatures in an electronically accessible database.

25. The method of claim 24, further comprising:

(a) obtaining a second plurality of signatures from a second patient population, wherein the second population comprises treated and untreated patients;

(b) determining whether each patient shares at least one of the plurality of signatures with patients in the one or more patient groups;

(c) adding each patient that shares at least one of the plurality of signatures with at least one of the one or more patient groups to each patient group that shares at least one of the plurality of signatures;

(d) determining at least one new patient group, wherein each of the at least one new patient group of the second patient population shares at least one of the second plurality of signatures;

(e) identifying at least one new baseline signature for each of the at least one new patient group based on the at least one of the second plurality of signatures shared by the respective new patient group; and

(f) storing the at least one new baseline signature in the electronically accessible database.

26. A method for visualization of a treatment class comprising:

(a) obtaining at least one sample from a patient using a non-invasive sampling method; (b) determining at least one signature for the patient based on the at least one sample;

(c) displaying, by a user interface, a plurality of treatment regimens corresponding to a plurality of baseline signatures, wherein each baseline signature of the plurality of baseline signatures corresponds to a patient group of a plurality of patient groups;

(d) receiving input to the user interface identifying a treatment regimen of the plurality of treatment regimens;

(e) matching the at least one signature for the patient to the first baseline signature of the plurality of baseline signatures; and

(f) optionally, treating the patient based on the first baseline signature and a corresponding first method of treatment.

27. The method of claim 26, wherein the treatment comprises administering one of:

(a) adalimumab, cankinumab, rilonacept, or spesolimab;

(b) tezepelumab;

(c) fezankizumab;

(d) dupilumab, tralokinumab, lebrikizumab, or nemolizumab;

(e) ixekizumab, secukinumab, brodalumab, guzelkumab, rizankizumab, or ustekinumab;

(f) an antibiotic;

(g) anifrolumab;

(h) benralizumab, mepolizumab, or reslizumab;

(i) mavrilimumab; or

(j) rituximab, ocrelizumab, ofatumumab, inebilizumab, or belimumab.

28. The method of claim 26, wherein the treatment is administered based on a higher or lower signature, as compared to the baseline signature, of:

(a) TNFa, ILlb, ILla/b, or IL-36R;

(b) TSLP;

(c) IL-22;

(d) a microbial infection;

(e) IL4Ra, IL 13, or IL-31;

(f) IL- 17, IL- 17R, IL-23 , or IL- 12/23 ;

(g) IFNAR;

(h) IL-5R, IL5, or IL-5;

(i) GMCSFRa; or (j) CD20, CD 19, or BAFF.

29. A system for patient stratification, comprising:

(a) at least one sample from a patient using a non-invasive sampling method;

(b) at least one baseline signature corresponding to a biological process associated with the at least one sample;

(c) one or more treatment classes, wherein at least one of the one or more treatment classes is determined based on comparing the baseline signature to a database; and

(d) treatment of a patient based on the one or more treatment classes.

30. The system of claim 29, wherein the baseline signature comprises one or more of a gene expression signature, genomic signature, or protein signature.

31. The system of claim 30, wherein the genomic signature comprises single -nucleotide polymorphism genotyping, copy number proofing, and post-transcriptional modifications.

32. The system of claim 29, wherein the baseline signature comprises two or more gene expression levels.

33. The system of claim 29, wherein the baseline signature comprises ten or more gene expression levels.

34. The system of claim 29, wherein the baseline signature comprises family medical history.

35. The system of claim 29, wherein non-invasive sampling comprises use of one or more adhesive patches applied to a skin sample of the patient.

36. The system of claim 35, wherein non-invasive sampling comprises obtaining a blood sample and/or use of microneedles.

37. The system of claim 29, wherein the treatment classes comprise one or more of Th22, Th2, Thl7, Thl, inflammation, kinase inhibitors, B cell, microbial infection, and macrophage.

38. The system of claim 29, wherein the method further comprises identifying a primary treatment class.

39. The system of claim 38, wherein the primary treatment class is selected from TH2, TH17, TH11, or B cell.

40. The system of claim 29, wherein the treatment is administered by intramuscular, intraperitoneal, intravenous, subcutaneous, oral, sublingual, or topical routes.

41. The system of claim 29, wherein the treatment class is Th2 and the treatment comprises dupilumab, tralokinumab, lebrikizumab, or nemolizumab.

42. The system of claim 29, wherein the treatment class is Th 17 and the treatment comprises Ixekinumab, secukinumab, guselkumab, ustekinumab, or rizankizumab.

43. The system of claim 29, wherein the treatment class is Thl and the treatment comprises anifrolumab.

44. The system of claim 29, wherein the treatment class is inflammation and the treatment comprises adalimumab or remicade.

45. The system of claim 29, wherein the treatment class is kinase inhibitors and the treatment comprises JAK, BTK, or TYK2 inhibitors.

46. The system of claim 29, wherein the treatment class is B cell and the treatment comprises belimumab or rituximab.

47. The system of claim 29, wherein the treatment class is macrophage and the treatment comprises mavrilimumab.

48. The system of claim 29, wherein the patient has previously received treatment for a skin disease or condition.

49. The system of claim 48, wherein the skin disease or condition comprises atopic dermatitis, psoriasis, or lupus.

50. The system of claim 29, wherein the at least one baseline signature comprises a differential signature.

51. The system of claim 50, wherein the database comprises a previous baseline signature of the patient.

52. A method for predicting a drug response of a patient comprising:

(a) obtaining at least one sample from the patient using an invasive, semi-invasive, and/or non-invasive sampling method; and

(b) obtaining at least one baseline signature for a panel of genes from the at least one sample; evaluating the at least one baseline signature to predict the drug response of the patient.

53. The method of claim 52, wherein the method further comprises administering atreatment to the patient based on the predicted drug response.

54. The method of claim 52, wherein the panel of genes comprises two or more genes.

55. The method of claim 54, wherein the two or more genes comprises any two or more genes selected from those genes listed in FIGs. 7, 8, 10 and 11.

56. The method of claim 52, wherein the drug response is fortreatment of psoriasis.

57. The method of claim 53, wherein the treatment comprises administering one or more of anti-TNF- alpha, anti-IL17A, anti-IL23pl9, anti-IL-4Ralpha, anti-IL-13, and anti-IL17 to the patient.

58. The method of claim 52, wherein the method further comprises atreatment of the at least one sample and obtaining at least one treatment signature for the panel of genes following treatment of the at least one sample.

59. The method of claim 58, wherein the treatment of the at least one sample takes place in vitro.

60. The method of claim 59, wherein the treatment is for a period of days.

61. The method of claim 60, wherein the in vitro treatment is for a period of at least 8 days.

62. The method of claim 58, wherein the at least one treatment signature is used to predict a treatment response and/or efficacy of administering the treatment to the patient in vivo.