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WO2011146928A2 - Procédés de calibration de niveaux protéiques dans cellules et échantillons tissulaires - Google Patents

Procédés de calibration de niveaux protéiques dans cellules et échantillons tissulaires Download PDF

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WO2011146928A2
WO2011146928A2 PCT/US2011/037574 US2011037574W WO2011146928A2 WO 2011146928 A2 WO2011146928 A2 WO 2011146928A2 US 2011037574 W US2011037574 W US 2011037574W WO 2011146928 A2 WO2011146928 A2 WO 2011146928A2
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expression
biomarker
sample
calibrator
marker
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WO2011146928A3 (fr
Inventor
Brian Ward
William E. Pierceall
Stella Quan
Yan Chen
Xiaozhe Wang
Lakshmi P. Alaparthi
Kam Marie Sprott
Hua Chang
David T. Weaver
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On-Q-ity
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G01N33/57492—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites involving compounds localized on the membrane of tumor or cancer cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins

Definitions

  • the invention relates to quantifying biomarker expression from tissue samples where there is a need to monitor the non-biomarker changes of the specimen that may influence the ability to accurately determine the amount of the biomarker.
  • Biomarker expression is already known to provide meaningful data relative to diagnosis, prognosis, as well as offer valuable insights towards predicting therapeutic response for the individual patient. While diagnosis and prognostic information should not be discounted, information leading to correct, actionable decisions in patient management should prove most beneficial.
  • Protein biomarkers are measurable in terms of abundance so that quantitation will be expressed as a continuous score. For large numbers of biomarker measurements, the overall protein level may be used for normalization. For small numbers of biomarker assessment, additional strategies are required to determine a means to calibrate the general effect on a tissue sample.
  • FIG. 1 Figure 1 - SnRNP70 Expression Optimization and Characterization.
  • Panels A and B depicts optimization of staining for a rabbit polyclonal Ab for SnRNP70.
  • Four separate breast tumor -derived patient samples were stained with different Ab concentration and then scored for staining intensity. Staining is fairly consistent with some sample -to- sample variability; 2 out of the 4 samples are depicted in Panel B with 2 different Ab concentration. Small differences in QIM scores between samples in Panel A are possibly indicative of technical differences between the samples employed.
  • Panel C indicates that the Ab is specific for identifying a single protein of appropriate size upon Western analysis.
  • FIG. 1 Figure 2 - PABPN1 Expression Optimization and Characterization.
  • Panels A, B, and C depict optimization of staining for a rabbit monoclonal Ab EP3000Y and Atlas Polyclonal Ab for PABPN1.
  • Four separate breast tumor -derived patient samples were stained (Panels B and C) with different Ab concentration and then scored for staining intensity (Panel A). Staining very consistent among the 4 samples tested for both of these Abs and the same sample is slightly lower in QIM scoring for both Abs, which offers some level of cross-validation. Staining is nuclear specific and very clean for both Abs.
  • Panel D indicates that rabbit MAb EP3000Y is specific for identifying a single protein of appropriate size upon Western analysis.
  • FIG. 3 Figure 3 - SNRPA Expression Optimization and Characterization.
  • Panels A, B, and C depict optimization of staining for a mouse monoclonal 3F9-1F7 and Genway Polyclonal Ab for SNRPA.
  • Four separate breast tumor -derived patient samples were stained (Panels B and C) with different Ab concentration and then scored for staining intensity (Panel A). Similar to staining patterns among the same tissues employed in PABPN1 optimization, staining is very consistent among the 4 samples tested for both of these Abs and the same sample is slightly lower in QIM scoring for both Abs, which offers some level of cross-validation. Staining is nuclear specific and very clean for both Abs.
  • Panels D and E indicate that both Abs are specific for identifying a single protein of appropriate size upon Western analysis.
  • FIG. 4 Figure 4 - ZNF207 Expression Optimization and Characterization.
  • Panels A, B, and C depict optimization of staining for a mouse monoclonal Ab 6D7 and Atlas Polyclonal Ab for ZNF207.
  • Four separate breast tumor -derived patient samples were stained (Panels B and C) with different Ab cone and then scored for staining intensity (Panel A). Staining is somewhat consistent for the Atlas polyclonal Ab but was weak and variable between samples with the 6D7 mouse clone.
  • Panels D and E both indicate that each Ab is specific for identifying a single protein of appropriate size upon Western analysis.
  • FIG. 5 HNRPM Expression Optimization and Characterization.
  • Panels A, B, and C depict optimization of staining for a mouse monoclonal Abs for HNPRM, 2B6 and 3F7.
  • Four separate breast tumor -derived patient samples were stained with different Ab
  • Xenografts PAR Biomarker expression from three independent xenografts with nil, moderate, and low expressions is depicted on the right-hand column. QIM scores can be generated for PAR and normalized to QIM scores generated from serial sections of these same xenografts stained for candidate calibrators SNRPA (center column) and/or PABPN1 (left column).
  • FIG. 7 Candidate Calibrator to Normalize Technical Variation in Biomarker Staining in Human Tumor-derived Xenografts: PAR staining normalization to calibrator SNRPA.
  • Panel A represents a graph of a continuum of QIM scores generated for a panel of 30 xenograft samples for the Calibrator marker SNRPA and test biomarker PAR ordered by increasing QIM score (lowest to highest).
  • Panel B shows broken out for each individual xenograft the raw QIM score for SNRPA and PAR, as well as a normalized score derived from a ratio gained by QIMtest/QIMcalibrator.
  • the ordering for individual samples within this population changes for many of the samples when calibrator normalization strategies are applied (panel C).
  • Figure 8 Establishing Levels of Expression Variation in Across a Patient Population for Candidate Calibrator Markers. Serial sections from a commercial TMA comprising breast cancer patients was stained and scored for canidate calibrator biomarkers of interest.
  • FIG. 9 A series of plots to depict relative patient order rank change.
  • the rank ordering of patients from non-calibrated patient Q- score data is plotted against patient rank order ratiometrics generated from a Q- score of the test biomarker of interest normalized to a candidate calibrator of interest, here SnRNP70.
  • R values indicate an estimate on the amount of rank ordering rearrangement of patients occurs by normalized patient data relative to raw Q-scores.
  • biomarker expression In profiling and quantifying biomarker expression, establishment of a scoring strategy to incorporate the greatest diagnostic sensitivity while maintaining high specificity is paramount. It is therefore important to establish that detectable variation in biomarker expression patterns is solely due to biological criteria of the cell or tissue sample relative to a continuum of scores for this same marker or set of markers from a patient population.
  • the invention described herein provides composition and methods to calibrate the overall biomarker(s) score to correct for technical variations including but not limited to age of tissue, section thickness, and tissue fixation, so that samples can be more properly placed in a continuum of responders and non- responders in the sole context of biological differences of the patient cells/tissue under interrogation.
  • tissue cancer specimens when measured with biomarkers will exhibit a wide dynamic range of expression values.
  • the present invention provides a means to attenuate the placement of patient samples for a given biomarker(s) along a reference curve, and to refine cut-points in the process of forming a patient ranking strategy.
  • the present invention also provides a method to have every patient specimen individually analyzable along this reference curve.
  • the invention provides calibrator markers useful for normalizing expression levels of clinically relevant biomarkers in cell and tissue samples obtained from a subject.
  • the normalization method may be broadly applied to any setting where biomarker expression is evaluated.
  • the methods of the invention described herein can be used in any method that requires evaluation of biomarker expression levels of one or more proteins.
  • Suitable calibrator marker would have consistent level of expression relative to biomarkers under consideration as well as across a population of specimens within an evaluation group.
  • Chief among macromolecules that are consistent in their levels of expression are the protein products of uniformly expressed genes, such as housekeeping genes.
  • RNA expression transcriptome data may be evaluated to identify genes where there are uniform or consistent mRNA levels amongst specimens (Popovici et al., 2009; Kwon et al., 2009). Further, it may also be evaluated for this subset of genes with uniform expression whether the protein levels are also uniform. In principle, these features of consistent RNA and protein expression levels may be independent of the overall expression level.
  • the invention is based, at least in part, on the discovery of calibrators used in conjunction with DNA Response and Repair biomarkers.
  • DNARMARKERS DNA Repair and DNA Damage Response Markers
  • other DNA repair and DNA damage markers display a more consistent level of expression. Markers that display a more consistent level of expression would not be considered as informative based on the ability to predict effectiveness of treatment regimens because they do not vary. However these markers would be suited as calibrators for normalizing dynamic and informative biomarkers expressions.
  • Non-homologous end joining repair pathway NHEJ
  • these enzymes include but are not limited to Ku70, Ku80, XRCC4, and DNA PK.
  • Other suitable candidate calibrator markers include those listed on Table 2.
  • other characteristics possessed by DNA repair enzymes that are stably expressed favorable to their use as calibrators include nucleus localization (identical to the enzyme for which it serves as calibrator), similar levels of protein stability, and orthologous point of action at the level of DNA binding and base modification.
  • TP true positives
  • TN true negatives
  • FP false negatives
  • FN false negatives
  • Bio state of a subject is the condition of the subject, as with respect to circumstances or attributes of the biological condition.
  • Bio condition of a subject is the condition of the subject in a pertinent realm that is under observation, and such realm may include any aspect of the subject capable of being monitored for change in condition, such as health; disease including cancer; trauma; aging; infection; tissue degeneration; developmental steps; physical fitness; obesity; and mental state.
  • a condition in this context may be chronic or acute or simply transient.
  • a targeted biological condition may be manifest throughout the organism or population of cells or may be restricted to a specific organ (such as skin, heart, eye or blood) but in either case, the condition may be monitored directly by a sample of the affected population of cells or indirectly by a sample derived elsewhere from the subject.
  • the term "biological condition" includes a "physiological condition".
  • the biological condition is cancer such as prostate cancer, ovarian cancer, lung cancer, breast cancer, skin cancer, colon cancer, or cervical cancer.
  • Biomarker in the context of the present invention encompasses, without limitation, proteins, nucleic acids, and metabolites, together with their polymorphisms, mutations, variants, modifications, subunits, fragments, protein-ligand complexes, and degradation products, protein- ligand complexes, elements, related metabolites, and other analytes or sample-derived measures. Biomarkers can also include mutated proteins or mutated nucleic acids. Biomarkers also encompass non-blood borne factors or non-analyte physiological markers of health status, such as "clinical parameters" defined herein, as well as “traditional laboratory risk factors”, also defined herein.
  • Biomarkers also include any calculated indices created mathematically or combinations of any one or more of the foregoing measurements, including temporal trends and differences. Where available, and unless otherwise described herein, determinants which are gene products are identified based on the official letter abbreviation or gene symbol assigned by the international Human Genome Organization Naming Committee (HGNC) and listed at the date of this filing at the US National Center for Biotechnology Information (NCBI) web site.
  • HGNC Human Genome Organization Naming Committee
  • NCBI National Center for Biotechnology Information
  • “Calibrator Marker” is an analyte that permits the determination of a quantity of a biomarker present in a sample.
  • the expression level of the calibrator marker being constant across a plurality of samples and (ii) from the expression being specific for said one or more chosen cell type(s). It is sufficient that the calibrator markers are constantly expressed across the set of samples under consideration. Nevertheless, it is envisaged that the calibrator markers are constantly expressed one or more chosen cell type(s) under most or all conceivable conditions.
  • the term “constant per cell” means that each cell of one or more chosen cell type(s) expresses the same or substantially the same amount of transcript and/or protein of the calibrator marker.
  • the term "specific for one or more chosen cell type(s)” in relation to expression designates calibrator markers whose detectable expression is confined or substantially confined to one or more chosen cell type(s).
  • the term "chosen cell type(s)” may refer to a subset of the cell types present in the sample. Alternatively, the chosen cell types may embrace all cell types present in the sample. In both cases, the chosen cell type(s) is/are (a) cell types for which calibrator markers are known. In case of a plurality of chosen cell types, these calibrator markers are constantly expressed in all chosen cell types, preferably at identical or substantially identical levels across the different cell types comprised in the set of chosen cell types.
  • “Clinical parameters” encompasses all non-sample or non-analyte biomarkers of subject health status or other characteristics, such as, without limitation, age (Age), ethnicity (RACE), gender (Sex), or family history (FamHX).
  • Expression data and “gene expression data” refer to quantitative data characterizing the RNA expression level and/or the protein expression level of one or more genes.
  • the term “gene expression data” as used herein comprises both "RNA expression data” and “protein expression data”.
  • the methods of the invention can be performed irrespective of the specific type of expression data.
  • the skilled person is aware of methods for the quantitation of RNA and proteins.
  • Expression levels of purified protein in solution can be determined by physical methods, e.g. photometry. Methods of determining the expression level of a particular protein in a mixture rely on specific binding, e.g of antibodies. Specific detection and quantitation methods exploiting the specificity of antibodies comprise immunohistochemistry (in situ) and surface plasmon resonance.
  • Western blotting combines separation of a mixture of proteins by electrophoresis and specific detection with antibodies.
  • Other means of determining protein expression data include two-dimensional gel-electrophoresis, preferably in combination with mass spectrometry.
  • Protein arrays for determining protein expression data exploit interactions such as protein-antibody, protein-protein, protein- ligand, protein-drug and protein- small molecule interactions or any combination thereof.
  • Protein expression data reflect, in addition to regulation at the transcriptional level, regulation at the translational level as well as the average lifetime of a protein prior to degradation.
  • FN is false negative, which for a disease state test means classifying a disease subject incorrectly as non-disease or normal.
  • FP is false positive, which for a disease state test means classifying a normal subject incorrectly as having disease.
  • a “formula,” “algorithm,” or “model” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous or categorical inputs (herein called “parameters”) and calculates an output value, sometimes referred to as an "index” or “index value.”
  • Parameters continuous or categorical inputs
  • Non-limiting examples of “formulas” include sums, ratios, and regression operators, such as coefficients or exponents, biomarker value
  • transformations and normalizations including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity
  • rules and guidelines including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity
  • statistical classification models including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity
  • neural networks trained on historical populations.
  • biomarkers are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of biomarkers detected in a subject sample and the subject's responsiveness to chemotherapy.
  • pattern recognition features including established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELD A), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shrunken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, among others.
  • PCA Principal Components Analysis
  • LogReg Logistic Regression
  • LDA Linear Discriminant Analysis
  • ELD A Eigengene Linear Discriminant Analysis
  • SVM Support Vector Machines
  • RF Random Forest
  • RPART Recursive Partitioning Tree
  • SC Shrunken Centroids
  • StepAIC Kth-Nearest Neighbor
  • Boosting Decision Trees, Neural Networks, Bayesian Network
  • biomarker selection techniques such as forward selection, backwards selection, or stepwise selection, complete enumeration of all potential panels of a given size, genetic algorithms, or they may themselves include biomarker selection methodologies in their own technique.
  • biomarker selection methodologies such as Akaike's Information Criterion (AIC) or Bayes Information Criterion (BIC), in order to quantify the tradeoff between additional biomarkers and model improvement, and to aid in minimizing overfit.
  • AIC Akaike's Information Criterion
  • BIC Bayes Information Criterion
  • the resulting predictive models may be validated in other studies, or cross-validated in the study they were originally trained in, using such techniques as Bootstrap, Leave-One-Out (LOO) and 10-Fold cross-validation (10-Fold CV).
  • LEO Leave-One-Out
  • 10-Fold cross-validation 10-Fold CV.
  • false discovery rates may be estimated by value permutation according to techniques known in the art.
  • a "health economic utility function" is a formula that is derived from a combination of the expected probability of a range of clinical outcomes in an idealized applicable patient population, both before and after the introduction of a diagnostic or therapeutic intervention into the standard of care.
  • a cost and/or value measurement associated with each outcome, which may be derived from actual health system costs of care (services, supplies, devices and drugs, etc.) and/or as an estimated acceptable value per quality adjusted life year (QALY) resulting in each outcome.
  • the sum, across all predicted outcomes, of the product of the predicted population size for an outcome multiplied by the respective outcomes expected utility is the total health economic utility of a given standard of care.
  • the difference between (i) the total health economic utility calculated for the standard of care with the intervention versus (ii) the total health economic utility for the standard of care without the intervention results in an overall measure of the health economic cost or value of the intervention.
  • This may itself be divided amongst the entire patient group being analyzed (or solely amongst the intervention group) to arrive at a cost per unit intervention, and to guide such decisions as market positioning, pricing, and assumptions of health system acceptance.
  • Such health economic utility functions are commonly used to compare the cost-effectiveness of the intervention, but may also be transformed to estimate the acceptable value per QALY the health care system is willing to pay, or the acceptable cost- effective clinical performance characteristics required of a new intervention.
  • a health economic utility function may preferentially favor sensitivity over specificity, or PPV over NPV based on the clinical situation and individual outcome costs and value, and thus provides another measure of health economic performance and value which may be different from more direct clinical or analytical performance measures.
  • Measurement or “measurement,” or alternatively “detecting” or “detection,” means assessing the presence, absence, quantity or amount (which can be an effective amount) of either a given substance within a clinical or subject-derived sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject's non-analyte clinical parameters.
  • NDV Neuronal predictive value
  • hazard ratios and absolute and relative risk ratios within subject cohorts defined by a test are a further measurement of clinical accuracy and utility. Multiple methods are frequently used to defining abnormal or disease values, including reference limits, discrimination limits, and risk thresholds.
  • Normalizing in relation to expression data is common in the art and relates to a processing step of the raw expression data which renders the signal intensities of each gene comparable across multiple measurements.
  • Expression levels of a particular gene/protein may differ between samples for a variety of reasons. Reasons of particular relevance are different amounts of cells n the samples analyzed on the one side and different transcriptional activity of the gene(s) under consideration on the other side. While the former is generally not indicative of a distinct biological state of the samples being compared, the latter generally is.
  • protein expression levels are monitored instead of or in addition to RNA expression levels, different transcriptional and/or translational activity may contribute to different protein expression levels.
  • Normalization is a method for disentangling said contributions. Practically speaking, normalization is a transformation of the raw expression data such that the effect of different amounts of cells and/or of RNA is removed or substantially removed.
  • Global normalization a procedure well known in the art, for example involves (i) the determination of the average signal intensity across all genes whose expression is being measured and (ii) subsequent division of raw signal intensities by the average signal intensity obtained in step (i).
  • Performance is a term that relates to the overall usefulness and quality of a diagnostic or prognostic test, including, among others, clinical and analytical accuracy, other analytical and process characteristics, such as use characteristics (e.g., stability, ease of use), health economic value, and relative costs of components of the test. Any of these factors may be the source of superior performance and thus usefulness of the test, and may be measured by appropriate "performance metrics," such as AUC, time to result, shelf life, etc. as relevant.
  • PSV Positive predictive value
  • Raw expression data specifically refers to expression data prior to normalization.
  • raw expression data are the data obtained from the image processing of the scanned hybridized microarray.
  • “Risk” in the context of the present invention relates to the probability that an event will occur over a specific time period, as in the responsiveness to treatment, and can mean a subject's "absolute” risk or “relative” risk.
  • Absolute risk can be measured with reference to either actual observation post-measurement for the relevant time cohort, or with reference to index values developed from statistically valid historical cohorts that have been followed for the relevant time period.
  • Relative risk refers to the ratio of absolute risks of a subject compared either to the absolute risks of low risk cohorts or an average population risk, which can vary by how clinical risk factors are assessed.
  • Odds ratios the proportion of positive events to negative events for a given test result, are also commonly used (odds are according to the formula p/(l-p) where p is the probability of event and (1- p) is the probability of no event) to no-conversion.
  • Risk evaluation in the context of the present invention encompasses making a prediction of the probability, odds, or likelihood that an event or disease state may occur, the rate of occurrence of the event or conversion from one disease state.
  • Risk evaluation can also comprise prediction of future clinical parameters, traditional laboratory risk factor values, or other indices of cancer, either in absolute or relative terms in reference to a previously measured population.
  • the methods of the present invention may be used to make continuous or categorical measurements of the responsiveness to treatment thus diagnosing and defining the risk spectrum of a category of subjects defined as being at responders or non- responders. In the categorical scenario, the invention can be used to discriminate between normal and other subject cohorts at higher risk for responding. Such differing use may require different biomarker/calibrator marker combinations and individualized panels, mathematical algorithms, and/or cut-off points, but be subject to the same aforementioned measurements of accuracy and performance for the respective intended use.
  • sample in the context of the present invention is a biological sample isolated from a subject and can include, by way of example and not limitation, tissue biopies, whole blood, serum, plasma, blood cells, endothelial cells, lymphatic fluid, ascites fluid, interstitital fluid (also known as "extracellular fluid” and encompasses the fluid found in spaces between cells, including, inter alia, gingival crevicular fluid), bone marrow, cerebrospinal fluid (CSF), saliva, mucous, sputum, sweat, urine, or any other secretion, excretion, or other bodily fluids.
  • tissue biopies whole blood, serum, plasma, blood cells, endothelial cells, lymphatic fluid, ascites fluid
  • interstitital fluid also known as "extracellular fluid” and encompasses the fluid found in spaces between cells, including, inter alia, gingival crevicular fluid
  • bone marrow also known ascites fluid
  • CSF cerebrospinal fluid
  • Specificity is calculated by TN/(TN+FP) or the true negative fraction of non- disease or normal subjects.
  • Signal intensity refers to a measured quantity indicative of the expression level of a gene.
  • the signal intensity is proportional to the amount of transcript or the amount of protein translated from a gene.
  • the signal may be light emitted by a fluorescence or luminescent process, or radiation and/or particles emitted by a radioactive label or dye (quantum dots).
  • Statistical significance can be determined by any method known in the art. Commonly used measures of significance include the p-value, which presents the probability of obtaining a result at least as extreme as a given data point, assuming the data point was the result of chance alone. A result is often considered highly significant at a p-value of 0.05 or less.
  • a "subject" in the context of the present invention is preferably a mammal.
  • the mammal can be a human, non-human primate, mouse, rat, dog, cat, horse, or cow, but are not limited to these examples. Mammals other than humans can be advantageously used as subjects that represent animal models of cancer.
  • a subject can be male or female.
  • TN is true negative, which for a disease state test means classifying a non-disease or normal subject correctly.
  • TP is true positive, which for a disease state test means correctly classifying a disease subject.
  • the present invention provides methods of normalizing gene expression levels.
  • RNA or protein expression levels are usually normalized per total amount of RNA or protein in the sample and an endogenous control gene, which is typically a house-keeping gene.
  • the present invention is based in par on using an internal reference standard (i.e., calibrator marker) for normalization of quantifiable protein detection in cells.
  • the calibrator marker is a robust biomarker analyte of the same class (i.e., protein) to that for which it is being held as a control, with consistent levels of expression across tissues but with a dynamic range commensurate with the assay and treatment conditions.
  • the actual measurement of levels (i.e., expression levels) or amounts of the biomarkers and calibrator markers can be determined at the protein or nucleic acid level using any method known in the art. For example, at the nucleic acid level, Northern and Southern hybridization analysis, as well as ribonuclease protection assays using probes which specifically recognize one or more of these sequences can be used to determine gene expression.
  • amounts of biomarkers and calibrator markers can be measured using reverse- transcription-based PCR assays (RT-PCR), e.g., using primers specific for the differentially expressed sequence of genes or by branch-chain RNA amplification and detection methods by Panomics, Inc.
  • Amounts of biomarkers and calibrator markers can also be determined at the protein level, e.g., by measuring the levels of peptides encoded by the gene products described herein, or subcellular localization or activities thereof using technological platform such as for example AQUA.
  • Such methods are well known in the art and include, e.g., immunoassays based on antibodies to proteins encoded by the genes, aptamers or molecular imprints. Any biological material can be used for the detection/quantification of the protein or its activity.
  • a suitable method can be selected to determine the activity of proteins encoded by the marker genes according to the activity of each protein analyzed.
  • the biomarkers and calibrator markers proteins, polypeptides, mutations, and polymorphisms thereof can be detected in any suitable manner, but is typically detected by contacting a sample from the subject with an antibody which binds the biomarkers and calibrator markers protein, polypeptide, mutation, or polymorphism and then detecting the presence or absence of a reaction product.
  • the antibody may be monoclonal, polyclonal, chimeric, or a fragment of the foregoing, as discussed in detail above, and the step of detecting the reaction product may be carried out with any suitable immunoassay.
  • the sample from the subject is typically a biological fluid as described above, and may be the same sample of biological fluid used to conduct the method described above.
  • Immunoassays carried out in accordance with the present invention may be homogeneous assays or heterogeneous assays.
  • the immunological reaction usually involves the specific antibody, a labeled analyte, and the sample of interest.
  • the signal arising from the label is modified, directly or indirectly, upon the binding of the antibody to the labeled analyte.
  • Both the immunological reaction and detection of the extent thereof can be carried out in a homogeneous solution.
  • Immunochemical labels which may be employed include free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, or coenzymes.
  • the reagents are usually the sample, the antibody, and means for producing a detectable signal.
  • Samples as described above may be used.
  • the antibody can be immobilized on a support, such as a bead (such as protein A and protein G agarose beads), plate or slide, and contacted with the specimen suspected of containing the antigen in a liquid phase.
  • the support is then separated from the liquid phase and either the support phase or the liquid phase is examined for a detectable signal employing means for producing such signal.
  • the signal is related to the presence of the analyte in the sample.
  • Means for producing a detectable signal include the use of radioactive labels, fluorescent labels, or enzyme labels.
  • an antibody which binds to that site can be conjugated to a detectable group and added to the liquid phase reaction solution before the separation step.
  • the presence of the detectable group on the solid support indicates the presence of the antigen in the test sample.
  • suitable immunoassays are oligonucleotides, immunoblotting, immunofluorescence methods,
  • Exemplary antibodies include monoclonal Ab 3F9-1F7 (SNRPA), monoclonal Ab E3000Y (PABPNl), monoclonal Ab 3F7 (HNPRM), monoclonal Ab 2B6 (HNPRM), monoclonal Ab 6D7 (ZNF207),polyclonal Ab F-21 (SNRPA), Genway anti-polyclonal Ab (SNRPA), polyclonal Ab to SnRNP70 (Genway), polyclonal Ab G-17 (PABPNl), polyclonal anti-PABP2 (Atlas), monoclonal Ab Y-39 (ZNF207)
  • Antibodies can be conjugated to a solid support suitable for a diagnostic assay (e.g., beads such as protein A or protein G agarose, microspheres, plates, slides or wells formed from materials such as latex or polystyrene) in accordance with known techniques, such as passive binding.
  • Antibodies as described herein may likewise be conjugated to detectable labels or groups such as radiolabels (e.g., 35 S, 125 I, 131 I), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine) in accordance with known techniques.
  • a diagnostic assay e.g., beads such as protein A or protein G agarose, microspheres, plates, slides or wells formed from materials such as latex or polystyrene
  • Antibodies as described herein may likewise be conjugated to detectable labels or groups such as radiolabel
  • Highly sensitivity antibody detection strategies may be used that allow for evaluation of the antigen-antibody binding in a non- amplified configuration.
  • antibodies may be conjugated to oligonucleotides, andfollowed by Polymerase Chain Reaction and a variety of oligonucleotide detection methods.
  • Antibodies can also be useful for detecting post-translational modifications of proteins, polypeptides, mutations, and polymorphisms, such as tyrosine phosphorylation, threonine phosphorylation, serine phosphorylation, glycosylation (e.g., O-GlcNAc).
  • Such antibodies specifically detect the phosphorylated amino acids in a protein or proteins of interest, and can be used in immunoblotting, immunofluorescence, and ELISA assays described herein. These antibodies are well-known to those skilled in the art, and commercially available.
  • Post- translational modifications can also be determined using metastable ions in reflector matrix- assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF) (Wirth, U. et al. (2002) Proteomics 2(10): 1445-51).
  • these processes may be coupled to localization of the protein, such that a re-localization process is monitored, and the biomarker is evaluated in a relative fashion exhibited by the constancy or change to the ratio of the protein in different compartments.
  • biomarker and calibrator marker proteins polypeptides, mutations, and polymorphisms known to have enzymatic activity, the activities can be determined in vitro using enzyme assays known in the art.
  • Such assays include, without limitation, kinase assays, phosphatase assays, reductase assays, among many others. Modulation of the kinetics of enzyme activities can be determined by measuring the rate constant KM using known algorithms, such as the Hill plot, Michaelis-Menten equation, linear regression plots such as Lineweaver-Burk analysis, and Scatchard plot.
  • sequence information provided by the database entries for the biomarkers and calibrator markers sequences expression of the biomarkers and calibrator markers equences can be detected (if present) and measured using techniques well known to one of ordinary skill in the art.
  • sequences within the sequence database entries corresponding to biomarkers and calibrator markers sequences, or within the sequences disclosed herein can be used to construct probes for detecting biomarker and calibrator marker RNA sequences in, e.g., Northern blot hybridization analyses or methods which specifically, and, preferably, quantitatively amplify specific nucleic acid sequences.
  • sequences can be used to construct primers for specifically amplifying the sequences in, e.g., amplification-based detection methods such as reverse-transcription based polymerase chain reaction (RT-PCR).
  • amplification-based detection methods such as reverse-transcription based polymerase chain reaction (RT-PCR).
  • RT-PCR reverse-transcription based polymerase chain reaction
  • sequence comparisons in test and reference populations can be made by comparing relative amounts of the examined DNA sequences in the test and reference cell populations.
  • RNA levels can be measured at the RNA level using any method known in the art. For example, Northern hybridization analysis using probes which specifically recognize one or more of these sequences can be used to determine gene expression. Alternatively, expression can be measured using reverse-transcription-based PCR assays (RT- PCR), e.g., using primers specific for the differentially expressed sequences. RNA can also be quantified using, for example, other target amplification methods (e.g., TMA, SDA, NASBA), or signal amplification methods (e.g., bDNA), and the like.
  • RT-PCR reverse-transcription-based PCR assays
  • RNA can also be quantified using, for example, other target amplification methods (e.g., TMA, SDA, NASBA), or signal amplification methods (e.g., bDNA), and the like.
  • metabolites can be measured.
  • the term "metabolite” includes any chemical or biochemical product of a metabolic process, such as any compound produced by the processing, cleavage or consumption of a biological molecule (e.g., a protein, nucleic acid, carbohydrate, or lipid).
  • Metabolites can be detected in a variety of ways known to one of skill in the art, including the refractive index spectroscopy (RI), ultra-violet spectroscopy (UV), fluorescence analysis, radiochemical analysis, near-infrared spectroscopy (near-IR), nuclear magnetic resonance spectroscopy (NMR), light scattering analysis (LS), mass spectrometry, pyrolysis mass spectrometry, nephelometry, dispersive Raman spectroscopy, gas chromatography combined with mass spectrometry, liquid chromatography combined with mass spectrometry, matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) combined with mass spectrometry, ion spray spectroscopy combined with mass spectrometry, capillary electrophoresis, NMR and IR detection.
  • RI refractive index spectroscopy
  • UV ultra-violet spectroscopy
  • fluorescence analysis radiochemical analysis
  • radiochemical analysis near-inf
  • circulating calcium ions can be detected in a sample using fluorescent dyes such as the Fluo series, Fura-2A, Rhod-2, among others.
  • Methods of the invention involve analysis of gene expression levels in a biological sample.
  • a biological sample may contain material obtained cells or tissues, e.g., a cell or tissue lysate or extract. Extract may contain material enriched in sub-cellular elements such as that from the Golgi complex, mitochondria, lysosomes, the endoplasmic reticulum, cell membrane, and cytoskeleton, etc.
  • the biological sample contains materials obtained from a single cell.
  • Bio samples can come from a variety of sources.
  • biological samples may be obtained from whole organisms, organs, tissues, or cells from different stages of development, differentiation, or disease state, and from different species (human and non-human, including bacteria and virus).
  • the samples may represent different treatment conditions (e.g., test compounds from a chemical library), tissue or cell types, or source (e.g., blood, urine, cerebrospinal fluid, seminal fluid, saliva, sputum, stool), etc.
  • Tissues may be obtained from a subject using any of the methods known in the art.
  • a "subject" refers to a human or animal, including all mammals such as primates (particularly higher primates), sheep, dog, rodents (e.g., mouse or rat), guinea pig, goat, pig, cat, rabbit, and cow.
  • the subject is a human.
  • the subject is an experimental animal or animal suitable as a disease model.
  • a "tissue" sample from a subject may be a biopsy specimen sample, a normal or benign tissue sample, a cancer or tumor tissue sample, a freshly prepared tissue sample, a frozen tissue sample, a formalin fixed paraffin embedded sample, a primary cancer or tumor sample, or a metastasis sample.
  • Exemplary tissues include, but are not limited to, epithelial, connective, muscle, nervous, heart, lung, brain, eye, stomach, spleen, bone, pancreatic, kidney, gastrointestinal, skin, uterus, thymus, lymph node, colon, breast, prostate, ovarian, esophageal, head, neck, rectal, testis, throat, thyroid, intestinal, melanocytic, colorectal, liver, gastric, and bladder tissues.
  • Cells may be obtained, e.g., from cell culture or breakdown of tissues.
  • the biological sample is derived from a cell line, optionally, treated with an agent whose effect on gene expression is evaluated.
  • the sample is a tissue or a biological fluid of a subject (e.g., a mammal, (e.g., a rodent or a primate, e.g., human)).
  • the biological sample is divided into replicates (e.g., duplicates, triplicates, etc.) in which the expression levels are measured.
  • the sample may be derived from the same source and split into replicates just prior to measuring the expression levels.
  • Replicate samples may be analyzed in a serial or parallel manner.
  • Gene expression levels for the same gene may be measured in replicates, and the final gene expression level expressed as an average or a mean of the replicates, or an otherwise calculated level representing multiple samples.
  • expression levels of two or more genes are measured in separate replicates individually.
  • the expression levels of at least some genes may be measured in the same reaction volume, e.g., multiplexing.
  • a plurality of genes being measured comprises at least one biomarker of a disease, including a disease type or subtype.
  • the term "disease” includes a pathologic or otherwise abnormal condition identifiable by altered gene expression levels.
  • a biomarker is a gene whose expression correlates with the presence of a specified disease or condition. Such a disease or condition may be due to a pathogen, e.g., virus, fungus, bacteria, or a toxin.
  • a disease or condition may be of any type, e.g., malignancy, immunological disorder, cardiovascular, or neurological.
  • cancers being evaluated may include, for example, cancers of colon, breast, prostate, skin, bladder, or lung as well as lymphoma, leukemia, etc.
  • Numerous biomarkers for various diseases and conditions are known (see, e.g., Biomarkers in Breast Cancer (Cancer Drug Discovery and Development), Humana Press; 1 edition, 2005); Biomarkers of Disease: An Evidence-Based Approach; Cambridge University Press; 1 edition, 2002).
  • the cancer markers used are DNA Repair and DNA Damage Response Markers.
  • Biomarkers associated with disease states include those biomarkers listed on Tables 3-5.
  • methods of the invention are used for differentiation between disease types or subtypes by evaluating two or more biomarkers specific to one or more disease types or subtypes.
  • the methods may include evaluation of 2, 3, 4, 5, 10, 25, 50, 100 or more biomarkers of disease types or subtypes.
  • Imaging Hardware RGB Vs. Multispectral Approaches
  • RGB detectors can introduce significant problems when one is trying to achieve quantification and inter-instrument precision. There are a number of ways that variation arises. For example, color values can vary significantly with the color temperature of the illumination source, different color-correction routines in camera firmware can play a role in the exact color values that are reported out, and different camera chips have differing spectral responsiveness. Some cameras employ automatic gain control or related circuitry designed to "optimize" image quality, with unpredictable effects on resulting images.
  • Spectral imaging microscopy represents a technological advance over visual or RGB- camera-based analyses. By acquiring a stack of images at multiple wavelengths, spectral imaging allows the determination of precise optical spectra at every pixel location. With this spatially resolved spectral information in hand, it is possible to enhance the utility of IHC and ISH stains, and even the standard biologic stains used in surgical pathology. There are a number of ways to perform spectral imaging, reviewed in (24,35).
  • OD (absorbance) units are dimensionless and logarithmic: so that zero absorbance means all photons transmitted; an OD of 1.0 absorbs 90% of all photons, and an OD of 2.0 absorbs 99% of all potentially detected photons.
  • IHC stains can individually generate signals of 1 OD. Accordingly, having 2 or more dense and overlapping stains can result in virtually black deposits from which little or no useful spectral or quantitative data can be recovered. This, plus the lesser dynamic range achievable with IHC vs. fluorescence -based approaches may mean that immunofluorescence may be preferable or necessary for some applications. Nevertheless, IHC has some practical advantages over immunofluorescence, including the fact that pathologists prefer it largely because it allows integration of 'phenotypic' features in the IHC stain with the traditional morphologic features, long the 'gold standard' for diagnosis.
  • hybridization signals can be combined with IHC.
  • the performance and thus absolute and relative clinical usefulness of the invention may be assessed in multiple ways as noted above.
  • the invention is intended to provide accuracy in clinical diagnosis and prognosis and predicting response to treatment.
  • the accuracy of a diagnostic or prognostic test, assay, or method concerns the ability of the test, assay, or method to distinguish between subjects responsive to chemotherapeutic treatment and those that are not, is based on whether the subjects have an "effective amount” or a "significant alteration" in the levels of a BIOMARKER.
  • BIOMARKERS By “effective amount” or “significant alteration,” it is meant that the measurement of an appropriate number of BIOMARKERS (which may be one or more) is different than the predetermined cutoff point (or threshold value) for that BIOMARKER(S) and therefore indicates that the subject responsiveness to therapy for which the BIOMARKER(S) is a determinant.
  • the difference in the level of BIOMARKER between normal and abnormal is preferably statistically significant. As noted below, and without any limitation of the invention, achieving statistical significance, and thus the preferred analytical and clinical accuracy, generally but not always requires that combinations of several BIOMARKERS be used together in panels and combined with mathematical algorithms in order to achieve a statistically significant BIOMARKER index.
  • an "acceptable degree of diagnostic accuracy” is herein defined as a test or assay (such as the test of the invention for determining the clinically significant presence of BIOMARKERS, which thereby indicates the presence of cancer and/or a risk of having a metastatic event) in which the AUC (area under the ROC curve for the test or assay) is at least 0.60, desirably at least 0.65, more desirably at least 0.70, preferably at least 0.75, more preferably at least 0.80, and most preferably at least 0.85.
  • a "very high degree of diagnostic accuracy” it is meant a test or assay in which the AUC (area under the ROC curve for the test or assay) is at least 0.80, desirably at least 0.85, more desirably at least 0.875, preferably at least 0.90, more preferably at least 0.925, and most preferably at least 0.95.
  • ROC and AUC can be misleading as to the clinical utility of a test in low disease prevalence tested populations (defined as those with less than 1% rate of occurrences (incidence) per annum, or less than 10% cumulative prevalence over a specified time horizon).
  • absolute risk and relative risk ratios as defined elsewhere in this disclosure can be employed to determine the degree of clinical utility.
  • Populations of subjects to be tested can also be categorized into quartiles by the test's measurement values, where the top quartile (25% of the population) comprises the group of subjects with the highest relative risk for therapeutic unresponsiveness, and the bottom quartile comprising the group of subjects having the lowest relative risk for therapeutic unresponsiveness
  • the top quartile (25% of the population) comprises the group of subjects with the highest relative risk for therapeutic unresponsiveness
  • the bottom quartile comprising the group of subjects having the lowest relative risk for therapeutic unresponsiveness
  • values derived from tests or assays having over 2.5 times the relative risk from top to bottom quartile in a low prevalence population are considered to have a "high degree of diagnostic accuracy," and those with five to seven times the relative risk for each quartile are considered to have a "very high degree of diagnostic accuracy.”
  • values derived from tests or assays having only 1.2 to 2.5 times the relative risk for each quartile remain clinically useful are widely used as risk factors for a disease; such is the case with total cholesterol and for many inflammatory biomark
  • a health economic utility function is an yet another means of measuring the performance and clinical value of a given test, consisting of weighting the potential categorical test outcomes based on actual measures of clinical and economic value for each.
  • Health economic performance is closely related to accuracy, as a health economic utility function specifically assigns an economic value for the benefits of correct classification and the costs of misclassification of tested subjects.
  • As a performance measure it is not unusual to require a test to achieve a level of performance which results in an increase in health economic value per test (prior to testing costs) in excess of the target price of the test.
  • BIOMARKERS In general, by defining the degree of diagnostic accuracy, i.e., cut points on a ROC curve, defining an acceptable AUC value, and determining the acceptable ranges in relative concentration of what constitutes an effective amount of the BIOMARKERS of the invention allows for one of skill in the art to use the BIOMARKERS to identify, diagnose, or prognose subjects with a pre-determined level of predictability and performance.
  • BIOMARKER results into indices useful in the practice of the invention.
  • indices may indicate, among the various other indications, the probability, likelihood, absolute or relative chance of responding to chemotherapy. This may be for a specific time period or horizon, or for remaining lifetime risk, or simply be provided as an index relative to another reference subject population.
  • model and formula types beyond those mentioned herein and in the definitions above are well known to one skilled in the art.
  • the actual model type or formula used may itself be selected from the field of potential models based on the performance and diagnostic accuracy characteristics of its results in a training population.
  • the specifics of the formula itself may commonly be derived from BIOMARKER results in the relevant training population.
  • such formula may be intended to map the feature space derived from one or more BIOMARKER inputs to a set of subject classes (e.g. useful in predicting class membership of subjects as normal, responders and non-responders), to derive an estimation of a probability function of risk using a Bayesian approach (e.g. the risk of cancer or a metastatic event), or to estimate the class- conditional probabilities, then use Bayes' rule to produce the class probability function as in the previous case.
  • subject classes e.g. useful in predicting class membership of subjects as normal, responders and non-responders
  • Bayesian approach e.g. the risk of cancer or a meta
  • Preferred formulas include the broad class of statistical classification algorithms, and in particular the use of discriminant analysis.
  • the goal of discriminant analysis is to predict class membership from a previously identified set of features.
  • LDA linear discriminant analysis
  • features can be identified for LDA using an eigengene based approach with different thresholds (ELD A) or a stepping algorithm based on a multivariate analysis of variance (MANOVA). Forward, backward, and stepwise algorithms can be performed that minimize the probability of no separation based on the Hotelling-Lawley statistic.
  • Eigengene-based Linear Discriminant Analysis is a feature selection technique developed by Shen et al. (2006). The formula selects features (e.g. biomarkers) in a multivariate framework using a modified eigen analysis to identify features associated with the most important eigenvectors. "Important” is defined as those eigenvectors that explain the most variance in the differences among samples that are trying to be classified relative to some threshold.
  • a support vector machine is a classification formula that attempts to find a hyperplane that separates two classes.
  • This hyperplane contains support vectors, data points that are exactly the margin distance away from the hyperplane.
  • the dimensionality is expanded greatly by projecting the data into larger dimensions by taking non-linear functions of the original variables (Venables and Ripley, 2002).
  • filtering of features for SVM often improves prediction.
  • Features e.g., biomarkers
  • KW non-parametric Kruskal-Wallis
  • a random forest (RF, Breiman, 2001) or recursive partitioning (RPART, Breiman et al., 1984) can also be used separately or in combination to identify biomarker combinations that are most important. Both KW and RF require that a number of features be selected from the total.
  • RPART creates a single classification tree using a subset of available biomarkers.
  • normalization of biomarker results using either common mathematical transformations such as logarithmic or logistic functions, as normal or other distribution positions, in reference to a population's mean values, etc. are all well known to those skilled in the art.
  • Clinical Parameters such as age, gender, race, or sex
  • specific formula are used solely on subjects within a class or continuously combining a Clinical Parameter as an input.
  • analyte-based biomarkers can be combined into calculated variables which are subsequently presented to a formula.
  • an overall predictive formula for all subjects, or any known class of subjects may itself be recalibrated or otherwise adjusted based on adjustment for a population's expected prevalence and mean biomarker parameter values, according to the technique outlined in D'Agostino et al, (2001) JAMA 286: 180-187, or other similar normalization and recalibration techniques.
  • Such epidemiological adjustment statistics may be captured, confirmed,
  • numeric result of a classifier formula itself may be transformed postprocessing by its reference to an actual clinical population and study results and observed endpoints, in order to calibrate to absolute risk and provide confidence intervals for varying numeric results of the classifier or risk formula.
  • Figure 9 indicates a series of R plots to depict relative patient order rank change within the cohort as a whole for specific biomarkers.
  • the rank ordering of patients from non-calibrated patient Q-score data is plotted against patient rank order ratiometrics generated from a Q-score of the test biomarker of interest normalized to a candidate calibrator of interest, here SnRNP70.
  • R values indicate an estimate on the amount of rank ordering rearrangement of patients occurs by normalized patient data relative to raw Q-scores. For several biomarkers stained and assessed, the R values for normalized and non-normalized patient data are listed below.
  • SnRNP70 is expressed consistently across patient specimens in our research and vetting of this candidate calibrator, we established that a Q-score ⁇ 200 (0-1000 scale), indicates a sample that is likely to have been compromised and should be removed from further analyses. In the case of this study, this effectively led to the elimination of 11 patients (from 551 total [1.98%] with assessable SnRNP70).
  • Table 6 below shows representative data from 2 biomarkers, PARP1 and MSH2, and how the statistical significance is affected by calibration of the sample set compared to statistical analysis of non-calibrated biomarker data with regards to patient outcomes.
  • the analysis cohort for each biomarker is restricted to patients for which there are Q-scores generated for both test as well as calibrator marker. Therefore, the same patients and biomarker scores are being assessed on a pre-normalized and post-normalized basis.
  • Cox proportional hazard modelings were conducted on patient data derived from binary cut-offs of Q-scores or normalized ratiometric Q-scores based on median values. Analyses were carried out without the inclusion of clinical variable and then by inclusion of significant variable and finally by inclusion of all adjustment variable. Data was analyzed with all NSCLC grouped as a single cohort and then by separation of all NSCLC into constituent histological subgroupings of squamous cell carcinoma (SCC) and adenocarcinoma.
  • SCC squamous cell
  • Chemotherapy in Lung Cancer Affects the Expression of Certain Biomarkers Including ERCCl.
  • Platinum chemotherapy may select for tumor cells with aggressive phenotype, and affect expression of ERCCl that could have predictive value

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Abstract

L'invention concerne un procédé pour l'utilisation d'un marqueur de calibration interne pour la normalisation de la détection de protéines quantifiables dans des cellules. Cet agent de calibration interne servira ostensiblement de recenseur de l'intégrité immunitaire de l'épitope. De manière optimale, ce sera une substance à analyser biomarqueur robuste de la même classe (c'est-à-dire une protéine) que celle pour laquelle il est retenu comme témoin, avec des niveaux constants d'expression à travers les tissus mais avec une gamme dynamique en accord avec l'analyse et les états du traitement. Alors qu'une expression stable dans un contexte biologique est une nécessité, il présentera un niveau d'expression dynamique adapté en raison de la variation technique par rapport à un marqueur d'essai pour lequel il servira de substitut.
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WO2017107639A1 (fr) * 2015-12-25 2017-06-29 中国科学院广州能源研究所 Dispositif de table de refroidissement-chauffe à haute pression, pour observation in situ de processus cinétique de réaction microscopique d'hydrate et procédé d'utilisation
WO2017151989A1 (fr) * 2016-03-02 2017-09-08 Flagship Biosciences, Inc. Procédé d'attribution de facteurs de normalisation de tissu pour analyse d'image numérique
WO2024215582A1 (fr) * 2023-04-14 2024-10-17 Ventana Medical Systems, Inc. Réalité de terrain de quantification d'agrégat de signaux

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US6077665A (en) * 1996-05-07 2000-06-20 The Board Of Trustees Of The Leland Stanford Junior University Rapid assay for infection in neonates
US7905134B2 (en) * 2002-08-06 2011-03-15 The Regents Of The University Of California Biomarker normalization
WO2007100652A2 (fr) * 2006-02-22 2007-09-07 Edison Pharmaceuticals, Inc. Variants à chaînes latérales d'agents thérapeutiques ayant une activité oxydoréductrice pour le traitement de maladies mitochondriales et d'autres conditions et pour la modulation de biomarqueurs énergétiques
US20090062624A1 (en) * 2007-04-26 2009-03-05 Thomas Neville Methods and systems of delivering a probability of a medical condition
US9240043B2 (en) * 2008-09-16 2016-01-19 Novartis Ag Reproducible quantification of biomarker expression

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* Cited by examiner, † Cited by third party
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WO2017107639A1 (fr) * 2015-12-25 2017-06-29 中国科学院广州能源研究所 Dispositif de table de refroidissement-chauffe à haute pression, pour observation in situ de processus cinétique de réaction microscopique d'hydrate et procédé d'utilisation
WO2017151989A1 (fr) * 2016-03-02 2017-09-08 Flagship Biosciences, Inc. Procédé d'attribution de facteurs de normalisation de tissu pour analyse d'image numérique
US10424061B2 (en) 2016-03-02 2019-09-24 Flagship Biosciences, Inc. Method for assigning tissue normalization factors for digital image analysis
WO2024215582A1 (fr) * 2023-04-14 2024-10-17 Ventana Medical Systems, Inc. Réalité de terrain de quantification d'agrégat de signaux

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