HK40017741A

HK40017741A - Methods for melanoma detection

Info

Publication number: HK40017741A
Application number: HK62020006706.9A
Authority: HK
Inventors: Irvin Modlin; Mark Kidd; Ignat Drozdov
Original assignee: Liquid Biopsy Research LLC
Priority date: 2017-05-25
Filing date: 2018-05-21
Publication date: 2020-09-25

Description

Cross-Reference to Related Applications

This application claims the benefit of and priority to U.S. Provisional Application No. 62/511,058, filed on May 25, 2017 , the contents of which are hereby incorporated by reference.

Incorporation by Reference of Sequence Listing

The contents of the text file named "LBIO-001_001WO SEQ LISTING.txt", which was created on May 12, 2018 and is 265 kB in size, are hereby incorporated by reference in their entireties.

Field of the Invention

The present invention relates to melanoma detection.

Background of the Invention

Melanoma is a common (-24-35/100,000 incidence - US), highly aggressive, skin cancer with an incidence that continues to rise. The most common, cutaneous melanomas, are associated with UV exposure and immune dysregulation. As a group, melanoma is known to carry the highest mutational burden (>10mutations/Mb). Major mutations include BRAF (~50%), N-Ras (~20%) and NF-1 (~5%), which together, comprise 75% of all mutations. Melanomas are addicted to MAPK pathway activation, regardless of whether tumors exhibit mutations in genes coding for proteins in this pathway. This provides the rationale for targeted therapy e.g., BRAF v600E agents, in this tumor. Other gain-of-function and loss-of-function mutations e.g., in RASopathy genes and amplification of cyclin D1/cdk4 and/or mutation/loss of the tumor suppressor PTEN, also characterize the tumor. This makes melanoma one of the most aggressive and therapy-resistant cancers.

Five-year survival rates range from 95-100% for stage I, 65-93% for stage II, to 41-71% and 9-28% for stage III and IV, respectively. Surgery, immunotherapy and targeted therapies provide the basis for management, with chemotherapy and radiation as adjuncts. Surgery, however, has a critical role in melanoma care (diagnosis, cure and palliation). Sentinel lymph node biopsy has become widespread, as it provides prognostic information. Melanoma, however, lacks a clinically useful non-invasive e.g., blood-based biomarker of disease activity to help guide patient management by providing predictive or prognostic information.

Blood-based factors include lactate dehydrogenase (LDH), detecting mutations in circulating tumor (ct) DNA, measurements of circulating tumor cells (CTCs) and circulating mRNA. LDH is typically used to identify aggressive tumor behavior and predict recurrence but its metrics are very low e.g., 30-50% accurate. It is also non-specific for melanoma. Mutations in target genes, like BRAF, can be detected in the blood in ctDNA but its utility as an indicator of therapeutic efficacy is limited e.g., 45-70% accurate. CTCs do not appear to be an accurate marker in melanomas and there is no consensus as to their clinical utility. Circulating microNAs (miRNA) have been detected but there is no evidence yet for clinical usefulness.

Marker sets for the diagnosis of melanoma and the prediction of treatment response are known in the prior art (see e.g. US 2010248225 A1 , BANKATTIS-DAVIS DANUTE). Disclosed herein are biomarkers that can be used to monitor the efficacy of surgery or drug therapy in melanomas

Summary of the Invention

Among other things, disclosed herein is a 28-gene expression tool for melanoma. It has high sensitivity and specificity (>95%) for the detection of melanoma and can differentiate aggressive untreated disease from stable, treated disease.

One aspect of the present disclosure relates to a method for detecting a melanoma in a subject in need thereof, comprising: (1) determining the expression level of at least 29 biomarkers from a test sample from the subject by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 biomarkers, wherein the at least 29 biomarkers comprise ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, and at least one housekeeping gene; (2) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of the at least one housekeeping gene, thereby obtaining a normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (3) inputting each normalized expression level into an algorithm to generate a score; (4) comparing the score with a first predetermined cutoff value; and (5) producing a report, wherein the report identifies the presence of a melanoma in the subject when the score is equal to or greater than the first predetermined cutoff value or identifies the absence of a melanoma in the subject when the score is below the first predetermined cutoff value, wherein the first predetermined cutoff value is 20 on a scale of 0 to 100.

Another aspect of the present disclosure relates to a method for determining whether a melanoma in a subject is stable or progressive, comprising: (1) determining the expression level of at least 29 biomarkers from a test sample from the subject by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 biomarkers, wherein the at least 29 biomarkers comprise ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, and at least one housekeeping gene; (2) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of the at least one housekeeping gene, thereby obtaining a normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (3) inputting each normalized expression level into an algorithm to generate a score; (4) comparing the score with a second predetermined cutoff value; and (5) producing a report, wherein the report identifies that the melanoma is progressive when the normalized expression level is equal to or greater than the second predetermined cutoff value or identifies that the melanoma is stable when the normalized expression level is below the second predetermined cutoff value, wherein the second predetermined cutoff value is 50 on a scale of 0 to 100.

Another aspect of the present disclosure relates to a method for evaluating the extent of surgical resection in a subject having a melanoma, comprising: (1) determining the expression level of at least 29 biomarkers from a test sample from the subject after the surgical resection by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 biomarkers, wherein the at least 29 biomarkers comprise ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, and at least one housekeeping gene; (2) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of the at least one housekeeping gene, thereby obtaining a normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (3) inputting each normalized expression level into an algorithm to generate a score; (4) comparing the score with a third predetermined cutoff value; and (5) producing a report, wherein the report identifies that the surgical resection does not remove the entire melanoma when the normalized expression level is equal to or greater than the third predetermined cutoff value or identifies that the surgical resection removes the entire melanoma when the normalized expression level is below the third predetermined cutoff value, wherein the third predetermined cutoff value is 20 on a scale of 0 to 100.

In some embodiments, the report further identifies that the risk of melanoma recurrence is high when the normalized expression level is equal to or greater than the third predetermined cutoff value or identifies that the risk of melanoma recurrence is low when the normalized expression level is below the third predetermined cutoff value.

Yet another aspect of the present disclosure relates to a method for determining a response by a subject having a melanoma to a therapy, comprising: (1) determining a first expression level of at least 28 biomarkers from a first test sample from the subject at a first time point by contacting the first test sample with a plurality of agents specific to detect the expression of the at least 28 biomarkers, wherein the 28 biomarkers comprise ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (2) determining a second expression level of the at least 28 biomarkers from a second test sample from the subject at a second time point by contacting the second test sample with a plurality of agents specific to detect the expression of the at least 28 biomarkers, wherein the second time point is after the first time point and after the administration of the therapy to the subject; (3) comparing the first expression level with the second expression level; and (4) producing a report, wherein the report identifies that the subject is responsive to the therapy when the second expression level is significantly decreased as compared to the first expression level.

In some embodiments, the first time point is prior to the administration of the therapy to the subject. In some embodiments, the first time point is after the administration of the therapy to the subject. In some embodiments, the therapy comprises an immunotherapy or a targeted therapy (e.g., a BRAF inhibitor).

In some embodiments, the at least one housekeeping gene is selected from the group consisting of ALG9, SEPN, YWHAQ, VPS37A, PRRC2B, DOPEY2, NDUFB11, ND4, MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, TFRC, MORF4L1, 18S, PPIA, PGK1, RPL13A, B2M, YWHAZ, SDHA, HPRT1, TOX4, and TPT1.

In some embodiments, the at least one housekeeping gene comprises TOX4 and TPT1.

In some embodiments, the normalized expression level is obtained by: (1) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TOX4, thereby obtaining a first normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (2) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TPT1, thereby obtaining a second normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; and (3) averaging the first normalized expression level and the second normalized expression level to obtain the normalized expression level.

In some embodiments, the method can have a specificity, sensitivity, and/or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. In some embodiments, the method has a sensitivity of greater than 90%. In some embodiments, the method has a specificity of greater than 90%.

In the embodiments, the biomarker is RNA. Protein and cDNA are also disclosed as biomarkers. When the biomarker is RNA, the RNA can be reverse transcribed to produce cDNA, and the produced cDNA expression level is detected. In some embodiments, the expression level of the biomarker is detected by forming a complex between the biomarker and a labeled probe or primer. When the biomarker is RNA or cDNA, the RNA or cDNA can be detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer. When the biomarker is protein, the protein can be detected by forming a complex between the protein and a labeled antibody. In some embodiments, the label is a fluorescent label.

In some embodiments, the test sample is blood, serum, plasma, or neoplastic tissue.

In some embodiments, the first predetermined cutoff value can be derived from a plurality of reference samples obtained from subjects free of a neoplastic disease. The second predetermined cutoff value can be derived from a plurality of reference samples obtained from subjects whose melanomas are being adequately controlled by therapies like immune therapy. The third predetermined cutoff value can be derived from a plurality of reference samples obtained from subjects whose melanomas have been completely removed by surgery and they are considered "disease free." In some embodiments, each reference sample can be blood, serum, plasma, or non-neoplastic tissue.

In some embodiments, the subject in need thereof is a subject diagnosed with a melanoma, a subject having at least one melanoma symptom, or a subject having a predisposition or familial history for developing a melanoma. In some embodiments, the subject is a human.

In some embodiments, the algorithm is XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, NNET, or mlp.

Brief Description of the Drawings

FIG. 1 is a graph showing visualization of the melanoma score as a system of three contributors to the clinical picture - Control, Response, and Progression. Samples towards each of the corner represent pure representations of each clinical group. Samples in the middle are in the area of both algorithmic and clinical uncertainty.
FIG. 2 is a graph showing the metrics for the test in the test set ranged from 87-100%.
FIGs. 3A-3C are a set of graphs showing the evaluation of the circulating melanoma gene test (Melanomx) in test set 2. Values were significantly higher in melanoma samples than in controls (FIG. 3A). Patients who were responding to therapy had values similar to controls. Receiver operator curve analysis of test set 2 identifying the AUC for differentiating melanoma from controls was >0.95 (FIG. 3B). The metrics for the test ranged from 78-92% (FIG. 3C).
FIGs. 4A-4B are a set of graphs showing the effect of surgery on the Melanomx. Levels were significantly decreased by surgery (p<0.0001) (FIG. 4A). Values in the NED (no evidence of disease after surgery) group were significantly lower than in those with residual disease after surgery (p=0.0007) (FIG. 4B).
FIG. 5 is a graph showing the effect of therapy on the Melanomx score. Levels were significantly decreased by immunotherapy (ipilimumab) or a BRAF inhibitor (Vemurafenib).
FIGs. 6A-6B are a set of graphs showing Melanomx score in 3 different melanoma cell lines. FIG. 6A identifies the cell lines demonstrate elevated expression - Melanomx score ranging from 40 (A375) to 95 (Hs294). FIG. 6B identifies that spiking these cells into blood from a subject that does not have a melanoma, resulted in detectable gene expression and scores. A minimum of 1 cell/ml of blood could be consistently identified.
FIGs. 7A-7B are a set of graphs showing expression in tumor tissue and its correlation with blood samples collected at the same time. In FIG. 7A, the Melanomx score ranged 40-97 in melanoma tumor tissue. In contrast, normal epithelium exhibited values <20. In FIG. 7B, gene expression in tumor tissue is compared to matched blood samples. This is highly concordant (correlation ~0.80).

Detailed Description of the Invention

The details of the invention are set forth in the accompanying description below. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, illustrative methods and materials are now described. Other features, objects, and advantages of the invention will be apparent from the description and from the claims. In the specification and the appended claims, the singular forms also include the plural unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

Early signs of melanoma include changes to the shape or color of existing moles or, in the case of nodular melanoma, the appearance of a new lump anywhere on the skin. At later stages, the mole may itch, ulcerate or bleed. Visual inspection is the most common diagnostic technique. Melanoma can be divided into the following types: lentigo maligna, lentigo maligna melanoma, superficial spreading melanoma, acral lentiginous melanoma, mucosal melanoma, nodular melanoma, polypoid melanoma, and desmoplastic melanoma.

Measurements of circulating melanoma transcripts - the Melanomx -identify melanomas and decreases in the Melanomx score correlate with the efficacy of therapeutic interventions such as immunotherapy and targeted therapy. Targeted gene expression profile of RNA can be isolated from the peripheral blood of patients with melanoma. This expression profile is evaluated in an algorithm and converted to an output (prediction). It can identify active disease, provide an assessment of treatment responses, or predict risk of relapse, in conjunction with standard clinical assessment and imaging.

In one aspect, the present disclosure provides a method for detecting a melanoma in a subject in need thereof, including: (1) determining the expression level of at least 29 biomarkers from a test sample from the subject by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 biomarkers, wherein the at least 29 biomarkers comprise ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, and at least one housekeeping gene; (2) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of the at least one housekeeping gene, thereby obtaining a normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (3) inputting each normalized expression level into an algorithm to generate a score; (4) comparing the score with a first predetermined cutoff value; and (5) producing a report, wherein the report identifies the presence of a melanoma in the subject when the score is equal to or greater than the first predetermined cutoff value or identifies the absence of a melanoma in the subject when the score is below the first predetermined cutoff value, wherein the first predetermined cutoff value is 20 on a scale of 0 to 100.

In some embodiments, the at least one housekeeping gene comprises TOX4 and TPT1. In some embodiments, the normalized expression level is obtained by: (1) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TOX4, thereby obtaining a first normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (2) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TPT1, thereby obtaining a second normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; and (3) averaging the first normalized expression level and the second normalized expression level to obtain the normalized expression level.

Among the provided methods are those that are able to classify or detect a melanoma. In some embodiments, the provided methods can identify or classify a melanoma in a human blood sample. In some examples, the methods can provide such information with a specificity, sensitivity, and/or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

The agents can be any agents for detection of the biomarkers, and typically are isolated polynucleotides or isolated polypeptides or proteins, such as antibodies, for example, those that specifically hybridize to or bind to the at least 29 biomarkers.

The biomarker can be RNA, cDNA, or protein. When the biomarker is RNA, the RNA can be reverse transcribed to produce cDNA (such as by RT-PCR), and the produced cDNA expression level is detected. The expression level of the biomarker can be detected by forming a complex between the biomarker and a labeled probe or primer. When the biomarker is RNA or cDNA, the RNA or cDNA detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer. The complex between the RNA or cDNA and the labeled nucleic acid probe or primer can be a hybridization complex.

When the biomarker is protein, the protein can be detected by forming a complex between the protein and a labeled antibody. The label can be any label, for example a fluorescent label, chemiluminescence label, radioactive label, etc. The protein level can be measured by methods including, but not limited to, immunoprecipitation, ELISA, Western blot analysis, or immunohistochemistry using an agent, e.g., an antibody, that specifically detects the protein encoded by the gene.

In some embodiments, the methods are performed by contacting the test sample with one of the provided agents, more typically with a plurality of the provided agents, for example, a set of polynucleotides that specifically bind to the at least 29 biomarkers. In some embodiments, the set of polynucleotides includes DNA, RNA, cDNA, PNA, genomic DNA, or synthetic oligonucleotides. In some embodiments, the methods include the step of isolating RNA from the test sample prior to detection, such as by RT-PCR, e.g., QPCR. Thus, in some embodiments, detection of the melanoma biomarkers, such as expression levels thereof, includes detecting the presence, absence, or amount of RNA. In one example, the RNA is detected by PCR or by hybridization.

In some embodiments, the polynucleotides include sense and antisense primers, such as a pair of primers that is specific to each of the at least 29 biomarkers. In one aspect of this embodiment, the detection of the at least 29 biomarkers is carried out by PCR, typically quantitative or real-time PCR. For example, in one aspect, detection is carried out by producing cDNA from the test sample by reverse transcription; then amplifying the cDNA using the pairs of sense and antisense primers that specifically hybridize to the panel of at least 28 biomarkers, and detecting products of the amplification.

The test sample can be any biological fluid obtained from the subject. Preferably, the test sample is blood, serum, plasma or neoplastic tissue.

The first predetermined cutoff value can be derived from a plurality of reference samples obtained from subjects free of a neoplastic disease. Each reference sample can be any biological fluid obtained from a subject not having, showing symptoms of or diagnosed with a neoplastic disease. In some embodiments, the reference sample is blood, serum, plasma, or non-neoplastic tissue.

The subject in need thereof can be a subject diagnosed with a melanoma, a subject having at least one melanoma symptom or a subject having a predisposition or familial history for developing a melanoma. The subject can be any mammal. Preferably, the subject is human. The terms "subject" and "patient" are used interchangeably herein.

The score is the Melanomx score, which has a scale of 0 to 100. The Melanomx score is the product of a classifier built from predictive classification algorithms, e.g., XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, NNET, or mlp. The algorithm analyzes the data (i.e., expression levels) and then assigns a score.

The method can further include treating the subject identified as having a melanoma with surgery, drug therapy, radiation therapy, or a combination thereof. The drug therapy can be an immunotherapy, a targeted therapy, a chemotherapy, or a combination thereof. In some embodiments, the drug therapy includes an immunotherapy. Examples of immunotherapies for treating a melanoma include, but are not limited to, Imlygic (T-VEC), Yervoy in combination with Opdivo, Opdivo (nivolumab), Keytruda (pembrolizumab), Yervoy (ipilimumab), Interleukin-2 (IL-2), and Interferon alpha 2-b. In some embodiments, the drug therapy includes a targeted therapy such as a BRAF inhibitor. Examples of targeted therapies for treating a melanoma include, but are not limited to, Zelboraf in combination with Cotellic (cobimetinib), Tafinlar in combination with Mekinist, Tafinlar (dabrafenib), Mekinist (trametinib), and Zelboraf (vemurafenib). In some embodiments, the drug therapy includes a chemotherapy. In some embodiments, the chemotherapy includes dacarbazine.

The present disclosure also provides a method for determining whether a melanoma in a subject is stable or progressive, including: (1) determining the expression level of at least 29 biomarkers from a test sample from the subject by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 biomarkers, wherein the at least 29 biomarkers comprise ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, and at least one housekeeping gene; (2) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of the at least one housekeeping gene, thereby obtaining a normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (3) inputting each normalized expression level into an algorithm to generate a score; (4) comparing the score with a second predetermined cutoff value; and (5) producing a report, wherein the report identifies that the melanoma is progressive when the normalized expression level is equal to or greater than the second predetermined cutoff value or identifies that the melanoma is stable when the normalized expression level is below the second predetermined cutoff value, wherein the second predetermined cutoff value is 50 on a scale of 0 to 100.

The second predetermined cutoff value can be derived from a plurality of reference samples obtained from subjects whose melanomas are being adequately controlled by therapies like immune therapy.

Surgical resection is a procedure that removes melanoma tissues from the subject in need thereof. The present disclosure also provides a method for evaluating the extent of surgical resection in a subject having a melanoma, including: (1) determining the expression level of at least 29 biomarkers from a test sample from the subject after the surgical resection by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 biomarkers, wherein the at least 29 biomarkers comprise ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, and at least one housekeeping gene; (2) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of the at least one housekeeping gene, thereby obtaining a normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (3) inputting each normalized expression level into an algorithm to generate a score; (4) comparing the score with a third predetermined cutoff value; and (5) producing a report, wherein the report identifies that the surgical resection does not remove the entire melanoma when the normalized expression level is equal to or greater than the third predetermined cutoff value or identifies that the surgical resection removes the entire melanoma when the normalized expression level is below the third predetermined cutoff value, wherein the third predetermined cutoff value is 20 on a scale of 0 to 100.

The third predetermined cutoff value can be derived from a plurality of reference samples obtained from subjects whose melanoma disease has been completely removed by surgery and they are considered "disease free."

When it is determined that the surgical resection does not remove the entire melanoma, the subject is at risk of melanoma recurrence. Accordingly, in some embodiments, the report further identifies that the risk of melanoma recurrence is high when the normalized expression level is equal to or greater than the third predetermined cutoff value or identifies that the risk of melanoma recurrence is low when the normalized expression level is below the third predetermined cutoff value.

The present disclosure also provides a method for determining a response by a subject having a melanoma to a therapy, comprising: (1) determining a first expression level of at least 28 biomarkers from a first test sample from the subject at a first time point by contacting the first test sample with a plurality of agents specific to detect the expression of the at least 28 biomarkers, wherein the 28 biomarkers comprise ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (2) determining a second expression level of the at least 28 biomarkers from a second test sample from the subject at a second time point by contacting the second test sample with a plurality of agents specific to detect the expression of the at least 28 biomarkers, wherein the second time point is after the first time point and after the administration of the therapy to the subject; (3) comparing the first expression level with the second expression level; and (4) determining that the subject is responsive to the therapy when the second expression level is significantly decreased as compared to the first expression level.

In some embodiments, the methods can predict treatment responsiveness to, or determine whether a patient has become clinically stable following, or is responsive or non-responsive to, a melanoma treatment, such as a surgical intervention or drug therapy (for example, an immunotherapy or targeted therapy). In some cases, the methods can do so with a specificity, sensitivity, and/or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. In some cases, it can differentiate between treated and untreated melanoma with a specificity, sensitivity, and/or accuracy of at least 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

In some embodiments, the first and second test samples can be of the same type. In some embodiments, the first and second test samples can be of different types.

In some embodiments, the therapy can be a drug therapy. The drug therapy can be an immunotherapy, a targeted therapy, a chemotherapy, or a combination thereof. In some embodiments, the therapy can be a radiation therapy.

In some embodiments, the first time point is prior to the administration of the therapy to the subject. In some embodiments, the first time point is after the administration of the therapy to the subject. The second time point can be a few days, a few weeks, or a few months after the first time point. For example, the second time point can be at least 1 day, at least 7 days, at least 14 days, at least 30 days, at least 60 days, or at least 90 days after the first time point.

In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 10% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 20% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 30% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 40% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 50% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 60% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 70% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 80% less than the first expression level. In some embodiments, the second expression level is significantly decreased as compared to the first expression level when the second expression level is at least 90% less than the first expression level.

In some embodiments, the method further comprises determining a third expression level of the at least 28 biomarkers from a third test sample from the subject at a third time point by contacting the third test sample with a plurality of agents specific to detect the expression of the at least 28 biomarkers, wherein the third time point is after the second time point. The method can further comprise creating a plot showing the trend of the expression level change.

The present disclosure also provides an assay comprising: (1) determining the expression level of biomarkers consisting essentially of the following 30 biomarkers from a test sample from a patient diagnosed of a melanoma or a subject suspected of having a melanoma: ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, TOX4, and TPT1, wherein the expression level is measured by contacting the test sample with a plurality of agents specific to detect the expression of the 30 biomarkers; (2) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TOX4, thereby obtaining a first normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (3) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TPT1, thereby obtaining a second normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (4) averaging the first normalized expression level and the second normalized expression level to obtain a normalized expression level for each biomarker; (5) inputting each normalized expression level into an algorithm to generate a score; and (6) comparing the score with a first predetermined cutoff value.

The present disclosure also provides an assay comprising: (1) determining the expression level of biomarkers consisting of the following 30 biomarkers from a test sample from a patient diagnosed of a melanoma or a subject suspected of having a melanoma: ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, TOX4, and TPT1, wherein the expression level is measured by contacting the test sample with a plurality of agents specific to detect the expression of the 30 biomarkers; (2) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TOX4, thereby obtaining a first normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (3) normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TPT1, thereby obtaining a second normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2; (4) averaging the first normalized expression level and the second normalized expression level to obtain a normalized expression level for each biomarker; (5) inputting each normalized expression level into an algorithm to generate a score; and (6) comparing the score with a first predetermined cutoff value.

The sequence information of the melanoma biomarkers and housekeepers is shown in Table 1.

Table 1. Melanoma Biomarker/Housekeeper Sequence Information


ATL1	NM_0 011277 13.1	1
ATP6V 0D	NM_0 04691. 4	2
C1ORF 21	NM_0 30806. 3	3
CFLAR	NM_0 011271 83.2	4
CFLAR -AS1	NR_04 0030.1	5
CHP1	NM_0 07236. 4	6
DDX55	NM_0 20936. 2	7
DMD	NM_0 00109. 3	8
DNAJC 9	NM_0 15190. 4	9
ENOSF 1	10
FANCL	NM_0 011146 36.1	11
HJURP	NM_0 18410. 4	12
HLA-DOA	NM_0 02119. 3	13
HLA-DRA	NM_0 19111. 4	14
HNRNP A3P1	NR_00 2726.2	15
IL23A	NM_0 16584. 2	16
IQGAP 1	NM_0 03870. 3	17
LOC49 4127	NR_03 6691.1	18
LOC64 6471	NR_02 4498.1	19
LOH12 CR	NR_02 4061.1	20
PBXIP1	NM_0 20524. 3	21
RNF5	NM_0 06913. 3	22
SERTA D2	NM_0 14755. 2	23
SLC35 G5	NM_0 54028. 1	24
SPATS 2L	NM_0 011004 22.1	25
TDRD7	NM_0 013028 84.1	26
TOX4 (housek eeping gene)	NM_0 013035 23.1	27
TPT1 (housek eeping gene)	NM_0 012862 72.1	28
TXK	NM_0 03328. 2	29
YY2	NM_2 06923. 3	30
NM_0 010776 90.1	31
	NM_0 31475. 2	32
	NM_0 06826. 3	33
	NM_0 011451 52.1	34
	NM_0 13318. 3	35
	NM_0 05128. 3	36
	NM_0 19056. 6	37
	NC_01 2920.1	38
	NM_0 012656 03.1	39
	NM_0 14763. 3	40
	NM_1 53001. 2	41
	NM_0 05877. 5	42
	NM_0 010206 58.1	43
	NM_0 01101. 4	44
	NM_0 02046. 6	45
	NM_0 00181. 3	46
	NM_0 01002. 3	47
	NM_0 03234. 3	48
	X0320 5.1	49
	NM_0 21130. 4	50
	NM_0 00291. 3	51
	NM_0 12423. 3	52
	NM_0 04048. 2	53
	NM_0 03406. 3	54
	NM_0 04168. 3	55
	NM_0 00194. 2	56

Definitions

The articles "a" and "an" are used in this disclosure to refer to one or more than one (i.e., to at least one) of the grammatical object of the article. By way of example, "an element" means one element or more than one element.

The term "and/or" is used in this disclosure to mean either "and" or "or" unless indicated otherwise.

As used herein, the terms "polynucleotide" and "nucleic acid molecule" are used interchangeably to mean a polymeric form of nucleotides of at least 10 bases or base pairs in length, either ribonucleotides or deoxynucleotides or a modified form of either type of nucleotide, and is meant to include single and double stranded forms of DNA. As used herein, a nucleic acid molecule or nucleic acid sequence that serves as a probe in a microarray analysis preferably comprises a chain of nucleotides, more preferably DNA and/or RNA. In other embodiments, a nucleic acid molecule or nucleic acid sequence comprises other kinds of nucleic acid structures such a for instance a DNA/RNA helix, peptide nucleic acid (PNA), locked nucleic acid (LNA) and/or a ribozyme. Hence, as used herein the term "nucleic acid molecule" also encompasses a chain comprising non-natural nucleotides, modified nucleotides and/or non-nucleotide building blocks which exhibit the same function as natural nucleotides.

As used herein, the terms "hybridize," "hybridizing", "hybridizes," and the like, used in the context of polynucleotides, are meant to refer to conventional hybridization conditions, such as hybridization in 50% formamide/6XSSC/0.1% SDS/100 µg/ml ssDNA, in which temperatures for hybridization are above 37 degrees and temperatures for washing in 0.1 XSSC/0.1% SDS are above 55 degrees C, and preferably to stringent hybridization conditions.

As used herein, the term "normalization" or "normalizer" refers to the expression of a differential value in terms of a standard value to adjust for effects which arise from technical variation due to sample handling, sample preparation, and measurement methods rather than biological variation of biomarker concentration in a sample. For example, when measuring the expression of a differentially expressed protein, the absolute value for the expression of the protein can be expressed in terms of an absolute value for the expression of a standard protein that is substantially constant in expression.

The terms "diagnosis" and "diagnostics" also encompass the terms "prognosis" and "prognostics", respectively, as well as the applications of such procedures over two or more time points to monitor the diagnosis and/or prognosis over time, and statistical modeling based thereupon. Furthermore, the term diagnosis includes: a. prediction (determining if a patient will likely develop aggressive disease (hyperproliferative/invasive)), b. prognosis (predicting whether a patient will likely have a better or worse outcome at a pre-selected time in the future), c. therapy selection, d. therapeutic drug monitoring, and e. relapse monitoring.

The term "providing" as used herein with regard to a biological sample refers to directly or indirectly obtaining the biological sample from a subject. For example, "providing" may refer to the act of directly obtaining the biological sample from a subject (e.g., by a blood draw, tissue biopsy, lavage and the like). Likewise, "providing" may refer to the act of indirectly obtaining the biological sample. For example, providing may refer to the act of a laboratory receiving the sample from the party that directly obtained the sample, or to the act of obtaining the sample from an archive.

"Accuracy" refers to the degree of conformity of a measured or calculated quantity (a test reported value) to its actual (or true) value. Clinical accuracy relates to the proportion of true outcomes (true positives (TP) or true negatives (TN) versus misclassified outcomes (false positives (FP) or false negatives (FN)), and may be stated as a sensitivity, specificity, positive predictive values (PPV) or negative predictive values (NPV), or as a likelihood, odds ratio, among other measures.

The term "biological sample" as used herein refers to any sample of biological origin potentially containing one or more biomarkers. Examples of biological samples include tissue, organs, or bodily fluids such as whole blood, plasma, serum, tissue, lavage or any other specimen used for detection of disease.

The term "subject" as used herein refers to a mammal, preferably a human.

"Treating" or "treatment" as used herein with regard to a condition may refer to preventing the condition, slowing the onset or rate of development of the condition, reducing the risk of developing the condition, preventing or delaying the development of symptoms associated with the condition, reducing or ending symptoms associated with the condition, generating a complete or partial regression of the condition, or some combination thereof.

Biomarker levels may change due to treatment of the disease. The changes in biomarker levels may be measured by the present disclosure. Changes in biomarker levels may be used to monitor the progression of disease or therapy.

"Altered", "changed" or "significantly different" refer to a detectable change or difference from a reasonably comparable state, profile, measurement, or the like. Such changes may be all or none. They may be incremental and need not be linear. They may be by orders of magnitude. A change may be an increase or decrease by 1%, 5%, 10%, 20%,30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 100%, or more, or any value in between 0% and 100%. Alternatively, the change may be 1-fold, 1.5- fold, 2-fold, 3-fold, 4-fold, 5-fold or more, or any values in between 1-fold and five-fold. The change may be statistically significant with a p value of 0.1, 0.05, 0.001, or 0.0001.

The term "stable disease" refers to a diagnosis for the presence of a melanoma, however the melanoma has been treated and remains in a stable condition, i.e. one that that is not progressive, as determined by imaging data and/or best clinical judgment.

The term "progressive disease" refers to a diagnosis for the presence of a highly active state of a melanoma, i.e. one has not been treated and is not stable or has been treated and has not responded to therapy, or has been treated and active disease remains, as determined by imaging data and/or best clinical judgment.

Examples

The disclosure is further illustrated by the following examples, which are not to be construed as limiting this disclosure in scope or spirit to the specific procedures herein described. It is to be understood that the examples are provided to illustrate certain embodiments and that no limitation to the scope of the disclosure is intended thereby.

EXAMPLE 1 Derivation of a 28-marker gene panel

Raw probe intensities (n = 6,892,960 features) from n = 49 whole blood samples were used to identify genes that best discriminated between different types of melanoma samples e.g., treated versus untreated, simultaneously. A total of 28 transcripts were identified in an unbiased manner as potential markers of melanoma behavior (Table 2).

An artificial intelligence model of melanoma disease dynamics was built using normalized gene expression of these 28 markers in whole blood from Controls (n = 90), Responders/Stable (n = 68), and Progressive (n = 66) samples. The dataset was randomly split into training (n = 169) and testing (n = 55) partitions for model creation and validation respectively. Five algorithms (XGB, RF, TreeBag, SVM, NNET) were identified that best predicted the training data. In the test set, each algorithm produced probability scores that predicted the sample. Each probability score reflects the "certainty" of an algorithm that an unknown sample belongs to Control, Responder/Stable or Progressive class. For example, an unknown sample S1 can have the following probability vector {Control = 20, Responder = 50, Progressive = 30). This sample would be considered a responder, given a score of 50, but there may be potential of progression (probability of 30) (FIG. 1).

Table 2.


ATL1	atlastin GTPase 1				Hs.5 849 05	NM_0 01127 713.1	65	1544	12-13
ATP6V0D	ATPase H+ transporting V0 subunit d1	Hs.1 068 76	NM_0 04691. 4	72	224	1-2
C1ORF21	chromosome 1 open reading frame 21	Hs.4 971 59	NM_0 30806. 3	84	805	5-6
CFLAR	CASP8 and FADD like apoptosis regulator	Hs.3 907 36	NM_0 01127 183.2	59	874	3-4
CFLAR-AS1	CFLAR antisense RNA 1	Hs.6 646 13	NR_04 0030.1	103	883	6-7
CHP1	calcineurin like EF-hand protein 1	Hs.4 062 34	NM_0 07236. 4	120	212	1-2
DDX55	DEAD-box helicase 55	Hs.2 861 73	NM_0 20936. 2	74	183	1-2
DMD	dystrophin	Hs.4 959 12	NM_0 00109. 3	76	9607	63-64
DNAJC9	DnaJ heat shock protein family (Hsp40) member C9	Hs.4 085 77	NM_0 15190. 4	102	198	1-1
ENOSF1	enolase superfamily member 1	Hs.6 585 50	NM_0 01126 123.3	110	X	13-14
FANCL	Fanconi anemia complementation group L	Hs.6 318 90	NM_0 01114 636.1	138	540	6-7
HJURP	Holliday junction recognition protein	Hs.5 329 68	NM_0 18410. 4	52	399	4-5
HLA-DOA	major histocompatibility complex, class II, DO alpha	Hs.6 319 91	NM_0 02119. 3	124	160	1-2
HLA-DRA	major histocompatibility complex, class II, DR alpha	Hs.5 200 48	NM_0 19111. 4	129	884	4-5
HNRNPA3 P1	heterogeneous nuclear ribonucleoprotein A3 pseudogene 1	Hs.6 329 56	NR_00 2726.2	99	2455	1-1
IL23A	interleukin 23 subunit alpha	Hs.3 822 12	NM_0 16584. 2	107	323	1-2
IQGAP1	IQ motif containing GTPase activating protein 1	Hs.4 305 51	NM_0 03870. 3	69	4562	34-35
LOC49412 7	NFYC pseudogene	Hs.6 263 16	NR_03 6691.1	86	150	1-1
LOC64647 1	uncharacterized LOC646471	Hs.7 272 71	NR_02 4498.1	80	2858	1-1
LOH12CR	loss of heterozygosity, 12, chromosomal region 2 (non-protein coding)	Hs.6 755 3	NR_02 4061.1	69	140	1-2
MTRNR2L 2_MTO1	CUSTOM PRIMER
PBXIP1	PBX homeobox interacting protein 1	Hs.5 058 06	NM_0 20524. 3	60	189	2-3
RNF5	ring finger protein 5	Hs.7 317 74	NM_0 06913. 3	67	273	1-2
SERTAD2	SERTA domain containing 2	Hs.5 915 69	NM_0 14755. 2	91	288	1-2
SLC35G5	solute carrier family 35 member G5	Hs.4 583 97	NM_0 54028. 1	144	541	1-1
SPATS2L	spermatogenesis associated serine rich 2 like	Hs.1 203 23	NM_0 01100 422.1	89	1364	10-11
TDRD7	tudor domain containing 7	Hs.1 938 42	NM_0 01302 884.1	59	3084	15-16
TOX high mobility group box family member 4	Hs.5 559 10	NM_0 01303 523.1	145	447	3-3
tumor protein, translationally-controlled 1	Hs.3 745 96	131	377	3-3
TXK	TXK tyrosine kinase	Hs.4 796 69	NM_0 03328. 2	113	1265	11-12
YY2	YY2 transcription factor	Hs.6 736 01	NM_2 06923. 3	110	552	1-1

Diagnosis: Identification of samples as melanoma

In the test set 1, the data for the utility of the test to differentiate melanoma from controls are included in Table 3. The metrics are included in FIG. 2 . These are: sensitivity >90%, specificity 100%, PPV 100%, NPV 87%. The overall accuracy is 94%. The tool can therefore differentiate between controls and aggressive and stable melanoma disease.

Table 3. Confusion matrix showing classification accuracy of the 5-model algorithm that determines whether a sample is a melanoma or a control in blood samples

Control	3	20
0
Sensitivity	91%
Specificity	100%
Positive Predictive Value	100%
Negative Predictive Value	87%
Accuracy	94%

The test was evaluated in a second test set (test set 2) that included 74 controls and 92 melanoma patients. The mean Melanomx score in the melanoma group was 73±31 versus 10±8 in the control group ( FIG. 3A ). The receiver operator curve analysis demonstrated the score exhibited an area under the curve (AUC) of 0.96 ( FIG. 3B ) and the metrics were 88-100% ( FIG. 3C ).

Correlation with surgical removal of melanoma.

In the surgical series (n = 46), removal of melanoma decreased the score from 74±16 to 20±12 ( FIG. 4A ). Evaluation of the post-surgical group identified the score was significantly different between patients with no evidence of disease 13±6 (not different to the control group) and those with residual disease (33±9) ( FIG. 4B ). The Melanomx score can therefore define the extent of surgery and identify those who are surgical cures.

Evaluation of immune-therapy and targeted (BRAF inhibitor) therapy.

In the therapy series (n = 30), treatment with ipilimumab (immune therapy targeting CTLA-4), nivolumab (immune therapy targeting PD-1) or a BRAF inibitor (vemurafenib) for 5 months significantly decreased the Melanomx scores from 88±12 to 15±5 (p<0.0001), 35±14 (p<0.001) and 38±21 (p<0.0001), respectively ( FIG. 5 ). All patients in the treated groups were stable. This indicates that the Melanomx score can therefore be used to measure the efficacy of both immune and targeted-therapy in melanoma and that a decrease in score correlated with response to therapeutic intervention.

Confirmation that gene expression is melanoma derived.

We confirmed that melanoma was the source for the blood-based gene expression assay by evaluating expression in different melanoma cell lines, in tumor tissue and by comparing expression in blood with tumor tissue collected at the same time-point during surgery.

All 28 genes were highly expressed in all 3 melanoma cell lines. Scores ranged from 94±6 (Hs294T) to 82±5 (CHL) to 42±9 (A375) ( FIG. 6A ).

Spike-in experiments using these 3 cell lines and normal whole blood demonstrated that gene expression scores were detected when as few as 1 cell was spiked into 1 ml of blood. One single melanoma cell was detectable. Scores ranged from 21±4 (A375) to 30±5 (CHL) to 34±2 (H2294) ( FIG. 6B ). Scores were significantly elevated compared to control blood (no spike-in; p<0.0001).

We then evaluated gene expression in tumor tissue. All 28 genes were highly expressed in melanoma compared to matched normal tissue and scores were significantly elevated 70±20 versus 4±3 (p<0.0001) ( FIG. 7A ).

We also compared gene expression in tumor tissue and blood collected at the same time point. Gene expression was highly concordant (Pearson r: 0.74 - 0.83, median: 0.819) identifying gene expression in tumor tissue and blood was concordant ( FIG. 7B ).

References:

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SEQUENCE LISTING

<110> Liquid Biopsy Research LLC
<120> METHODS FOR MELANOMA DETECTION
<130> LBIO-001/001WO 331068-2007
<150> US 62/511,058 <151> 2017-05-25
<160> 56
<170> PatentIn version 3.5
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Claims

A method for detecting a melanoma in a subject in need thereof, comprising:
determining the expression level of at least 29 RNA biomarkers from a test sample from the subject by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 RNA biomarkers, wherein the at least 29 RNA biomarkers comprise ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, and at least one housekeeping gene;

normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of the at least one housekeeping gene, thereby obtaining a normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2;

inputting each normalized expression level into an algorithm to generate a score;

comparing the score with a predetermined cutoff value; and

identifying the presence of a melanoma in the subject when the score is equal to or greater than the predetermined cutoff value or identifying the absence of a melanoma in the subject when the score is below the predetermined cutoff value, wherein the predetermined cutoff value is 20 on a scale of 0 to 100.
A method for evaluating the extent of surgical resection in a subject having a melanoma, comprising:
determining the expression level of at least 29 RNA biomarkers from a test sample from the subject after the surgical resection by contacting the test sample with a plurality of agents specific to detect the expression of the at least 29 RNA biomarkers, wherein the at least 29 RNA biomarkers comprise ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, YY2, and at least one housekeeping gene;

normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L,TDRD7, TXK, and YY2 to the expression level of the at least one housekeeping gene, thereby obtaining a normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2;

inputting each normalized expression level into an algorithm to generate a score;

comparing the score with a predetermined cutoff value; and

identifying that the surgical resection does not remove the entire melanoma when the normalized expression level is equal to or greater than the predetermined cutoff value or identifying that the surgical resection removes the entire melanoma when the normalized expression level is below the predetermined cutoff value, wherein the predetermined cutoff value is 20 on a scale of 0 to 100.
The method of claim 2, wherein the method further comprises identifying that the risk of melanoma recurrence is high when the normalized expression level is equal to or greater than the predetermined cutoff value or identifies that the risk of melanoma recurrence is low when the normalized expression level is below the predetermined cutoff value.
The method of any one of the preceding claims, wherein the at least one housekeeping gene is selected from the group consisting of ALG9, SEPN, YWHAQ, VPS37A, PRRC2B, DOPEY2, NDUFB11, ND4, MRPL19, PSMC4, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, TFRC, MORF4L1, 18S, PPIA, PGK1, RPL13A, B2M, YWHAZ, SDHA, HPRT1, TOX4, and TPT1, preferably wherein the at least one housekeeping gene comprises TOX4 and TPT1.
The method of claim 4, wherein the normalized expression level is obtained by:
normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TOX4, thereby obtaining a first normalized expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2;

normalizing the expression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2 to the expression level of TPT1, thereby obtaining a second normalizedexpression level of each of ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2;

averaging the first normalized expression level and the second normalized expression level to obtain the normalized expression level.
A method for determining a response by a subject having a melanoma to a therapy, comprising:
determining a first expression level of at least 28 RNA biomarkers from a first test sample from the subject at a first time point by contacting the first test sample with a plurality of agents specific to detect the expression of the at least 28 RNA biomarkers, wherein the 28 RNA biomarkers comprise ATL1, ATP6V0D, C1ORF21, CFLAR, CFLAR-AS1, CHP1, DDX55, DMD, DNAJC9, ENOSF1, FANCL, HJURP, HLA-DOA, HLA-DRA, HNRNPA3P1, IL23A, IQGAP1, LOC494127, LOC646471, LOH12CR, PBXIP1, RNF5, SERTAD2, SLC35G5, SPATS2L, TDRD7, TXK, and YY2;

determining a second expression level of the at least 28 RNA biomarkers from a second test sample from the subject at a second time point by contacting the second test sample with a plurality of agents specific to detect the expression of the at least 28 RNA biomarkers, wherein the second time point is after the first time point and after the administration of the therapy to the subject;

comparing the first expression level with the second expression level; and

identifying that the subject is responsive to the therapy when the second expression level is significantly decreased as compared to the first expression level.
The method of claim 6, wherein the first time point is
(a) prior to the administration of the therapy to the subject; or

(b) after the administration of the therapy to the subject.
The method of claim 6 or claim 7, wherein the therapy comprises
(a) an immunotherapy; or

(b) a targeted therapy, preferably wherein the targeted therapy comprises a BRAF inhibitor.
The method of any one of the preceding claims, having
(a) a sensitivity of greater than 90%; or

(b) a specificity of greater than 90%.
The method of any one of the preceding claims, wherein the melanoma is progressive.
The method of any one of the preceding claims, wherein the expression level of the RNA biomarkers are detected by forming a complex between the biomarkers and labeled probes or primers, preferably wherein the label is a fluorescent label.
The method of any one of the preceding claims, wherein RNA biomarkers are reverse transcribed to produce cDNA, and the produced cDNA expression level is detected, preferably wherein the cDNA is detected by forming a complex between the cDNA and labeled nucleic acids or primers, preferably wherein the label is a fluorescent label.
The method of any one of the preceding claims, wherein the test sample is blood, serum, plasma, or neoplastic tissue.
The method of any one of claims 1-5, wherein the predetermined cutoff value is derived from a plurality of reference samples obtained from subjects free of a neoplastic disease, preferably wherein each reference sample is blood, serum, plasma, or non-neoplastic tissue.
The method of any one of the preceding claims, wherein the subject in need thereof is a subject diagnosed with a melanoma, a subject having at least one melanoma symptom, or a subject having a predisposition or familial history for developing a melanoma, preferably wherein the subject is human.
The method of any one of claims 1-5, wherein the algorithm is XGB, RF, glmnet, cforest, CART, treebag, knn, nnet, SVM-radial, SVM-linear, NB, NNET, or mlp.