WO2019068087A1 - Système de prédiction de réponse à une thérapie anticancéreuse et ses méthodes d'utilisation - Google Patents

Système de prédiction de réponse à une thérapie anticancéreuse et ses méthodes d'utilisation Download PDF

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WO2019068087A1
WO2019068087A1 PCT/US2018/053751 US2018053751W WO2019068087A1 WO 2019068087 A1 WO2019068087 A1 WO 2019068087A1 US 2018053751 W US2018053751 W US 2018053751W WO 2019068087 A1 WO2019068087 A1 WO 2019068087A1
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nucleic acid
expression
acid sequences
subject
probability score
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PCT/US2018/053751
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Eytan Ruppin
Noam AUSLANDER
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University Of Maryland, College Park
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    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K16/00—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies
    • C07K16/18—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans
    • C07K16/28—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
    • C07K16/2803—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants against the immunoglobulin superfamily
    • C07K16/2818—Immunoglobulins [IGs], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants against the immunoglobulin superfamily against CD28 or CD152
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the disclosure relates to methods and a system for predicting components of protein expression, or interrelated immune genes, the protein activity levels of such genes, which are used to establish a prognosis for a subject, predict the likelihood of a subject to respond to a therapy for treatment of a disease or disorder, and/or predict improved therapies for treatment of as disease or disorder.
  • Immune checkpoint blockade (ICB) therapy provides remarkable clinical gains, where melanoma is at the forefront of its success.
  • melanoma is at the forefront of its success.
  • NB neuroblastoma
  • NB is the first pediatric cancer with an FDA-approved immunotherapy (Dinutuximab), a monoclonal antibody targeting the disialoganglioside GD2 that is expressed in NB, melanoma, and other tumors 11 ' 12.
  • FDA-approved immunotherapy Dinutuximab
  • the disclosure features a method of identifying antigen-specific immune activity in a subject or population of subjects, comprising (a) selecting, from the subject or the population diagnosed with cancer, at least a first pair of nucleic acid sequences comprising a first nucleic acid sequence and a second nucleic sequence, wherein the first nucleic acid sequence and the second nucleic acid sequence encode a immune checkpoint protein or variant thereof; (b) calculating the ratio of expression of the first nucleic acid sequence over the expression of the second nucleic acid sequence in a sample from the subject; (c) assigning a probability score to at least the first pair of nucleic acid sequences based upon the ratio of expression of at least the first pair of nucleic acid sequences; and (d) identifying antigen-specific immune activity of the subject or the population of subjects based upon the ratio of expression of the first and the second nucleic acid sequences.
  • the pair of nucleic acid sequences is chosen from two of the nucleic acid sequences that are at least 70% homologous to nucleic acid sequences encoding: VISTA, HVEM, PDL1, CD40, CD80, CD137L, BTLA, CD27, CD28, CD267, OX40L, CD86, CD200, CTLA4, CD200R, TTM3, and PD1.
  • the method further comprises the step of determining the average probability score over at least 5 pairs of nucleic acid sequences and wherein the step of predicting the subject or population's responsiveness to the therapy is based upon the average probability score assigned over the at least 5 pairs of nucleic acid sequences.
  • the method further comprises the step of determining the average probability score over at least 10 pairs of nucleic acid sequences and wherein the step of predicting the subject or population's responsiveness to the therapy is based upon the average probability score assigned over each of the at least 10 pairs of nucleic acid sequences.
  • the step of selecting at least one pair of nucleic acid sequences comprises selecting at least ten pairs of nucleic acid sequences, wherein the step of calculating the ratio of expression comprises calculating the ratio of mRNA or protein expression over each of the at least ten pairs of nucleic acid sequences, wherein the step of assigning a probability score comprises assigning a probability each of the a least ten pairs of nucleic acid sequences and subsequently calculating an average probability score, and wherein the step of identifying antigen- specific immune activity comprises comparing the average probability score to a threshold value of a probability score associated with a control subject, such that, if the average probability score of the subject is at or above the threshold value of a control subject, then the subject is characterized as having high antigen- specific immune activity; and, if the average probability score of the subject falls below the threshold value, the subject is characterized as not having a high antigen- specific immune activity.
  • the step of assigning a probability score further comprises (i) assigning a probability score of 1 if the ratio of expression of the at least one pair of nucleic acid sequences is at or above 1, (ii) assigning a probability score of 0 if the ratio of expression of the at least one pair of nucleic acid sequences is below 1; (iii) calculating an average probability score over each pair of nucleic acid sequences; and wherein the step of predicting antigen- specific immune activity further comprises comparing the average probability score to a threshold value, such that, if the average probability score is above the threshold value, then subject is characterized as having high antigen- specific immune activity; and, if the average probability score of the subject falls below the threshold value, the subject is characterized as not having a high antigen- specific immune activity.
  • the threshold value is from about 0.50 to about 0.95. In one embodiment, the threshold value is from about 0.69 to about 0.90. In one embodiment, the method further comprises a step of quantifying, or acquiring data comprising the quantity of, mRNA or protein expression of one or more nucleic acid sequences in a sample comprising one or a plurality of cancer cells.
  • the sample is a human tissue sample comprising a tissue from a brushing, biopsy, or surgical resection of a subject.
  • the sample comprises a cell that is freshly obtained, formalin fixed, alcohol-fixed and/or paraffin embedded.
  • the sample comprises one or more skin cells.
  • the sample comprises one or a plurality of cancer cells that are melanoma cells.
  • the step of identifying antigen- specific immune activity of the subject or the population of subjects based upon the ratio of expression of the first and the second nucleic acid sequences comprising comparing expression of at least the first pair of nucleic acid sequences in a subject or population of subjects diagnosed with cancer with expression of the first pair of nucleic acid sequences in a control subject or control population of subjects such that if the ratio of expression of the at least first pair of nucleic acid sequences in the subject is statistically higher than the ratio of expression of the first pair of nucleic acid sequences in the control subject, then the subject is characterized as having high level of antigen-specific immunity or an increased survival rate under immune checkpoint blockage therapy as compared to the control subject.
  • the step of comparing expression of at least the first pair of nucleic acid sequences in a subject or population of subjects with expression of the first pair of nucleic acid sequences in a control subject or control population of subjects comprises performing a Kaplan-Meier test using a two-sided log-rank test.
  • the step of calculating the ratio of expression of the first nucleic acid sequence over the expression of the second nucleic acid sequence in a sample from the subject comprises calculating the expression of each nucleic acid sequence with Formula:
  • expi(x) and exp j (x) denote the quantification of expression of genes i and j in the sample x; and wherein the expression of the first nucleic acid sequence i and the second nucleic acid sequence j are calculated by measuring RNA expression in the sample by immunohistochemistry, quantitative PCR, or fluorescence analysis.
  • the disclosure features a method of predicting responsiveness to immune checkpoint therapy of a subject or of a population of subjects diagnosed or suspected of having cancer comprising (a) selecting, from the subject or the population diagnosed with cancer, at least a first pair of nucleic acid sequences comprising a first nucleic acid sequence and a second nucleic sequence, wherein the first nucleic acid sequence and the second nucleic acid sequence encode a immune checkpoint protein or variant thereof and wherein the first or the second nucleic acid sequence encodes a protein or variant thereof associated with anti-immune checkpoint blockage therapy; (b) calculating the ratio of expression of the first nucleic acid sequence over the expression of the second nucleic acid sequence in a sample from the subject; (c) assigning a probability score to at least the first pair of nucleic acid sequences based upon the ratio of expression of at least the first pair of nucleic acid sequences; and (d) predicting responsiveness to the therapy in the subject or the population of subjects based upon the ratio of
  • the step of selecting at least one pair of nucleic acid sequences comprises selecting at least ten pairs of nucleic acid sequences, wherein the step of calculating the ratio of expression comprises calculating the ratio of mRNA or protein expression over each of the at least ten pairs of nucleic acid sequences, wherein the step of assigning a probability score comprises assigning a probability each of the a least ten pairs of nucleic acid sequences and subsequently calculating an average probability score, and wherein the step of predicting responsiveness to the therapy comprises comparing the average probability score to a threshold value of a probability score associated with a control subject, such that, if the average probability score of the subject is at or above the threshold value of a control subject, then the subject is characterized as being responsive to the therapy; and, if the average probability score of the subject falls below the threshold value, the subject is characterized as being non-responsive to the therapy.
  • the step of assigning a probability score further comprises (i) assigning a probability score of 1 if the ratio of expression of the at least one pair of nucleic acid sequences is at or above 1, (ii) assigning a probability score of 0 if the ratio of expression of the at least one pair of nucleic acid sequences is below 1; (iii) calculating an average probability score over each pair of nucleic acid sequences; and wherein the step of predicting the responsiveness to therapy further comprises comparing the average probability score to a threshold value, such that, if the average probability score is above the threshold value, then subject is characterized as being responsive to the therapy; and, of the average probability score falls below the threshold value, then the subject is characterized as being nonresponsive to the therapy.
  • the threshold value is from about 0.50 to about 0.95. In one embodiment, the threshold value is from about 0.69 to about 0.85. In one embodiment, the step of calculating the ratio of expression of the further comprises normalizing expression of the first pair of nucleic acid sequences by the ratio of expression of a pair of nucleic acid sequences unassociated with anti-CTLA-4 or anti-PDl blockade therapy used as a negative control. In one embodiment, the step of assigning a probability score comprises assigning a 1 to at least the first pair of nucleic acid sequences if the ratio of expression of the pair is 1 or greater and assigning a 0 to at least the first pair of nucleic acid sequences if the ratio of expression of the pair is less than 1.
  • the sample is a human tissue sample comprising a tissue from a brushing, biopsy, or surgical resection of a subject.
  • the sample comprises a cell that is freshly obtained, formalin fixed, alcohol-fixed and/or paraffin embedded.
  • the sample comprises one or more skin cells.
  • the sample comprises one or more cancer cells.
  • the cancer cell is a melanoma cell.
  • the subject is diagnosed or suspected of having melanoma.
  • the cancer is metastatic cancer derived from the skin.
  • the pair of nucleic acid sequences is chosen from two of the nucleic acid sequences that are at least 70% homologous to nucleic acid sequences encoding: VISTA, HVEM, PDL1, CD40, CD80, CD137L, BTLA, CD27, CD28, CD267, OX40L, CD86, CD200, CTLA4, CD200R, TTM3, and PD1.
  • the method further comprises the step of determining the average probability score over at least 5 pairs of nucleic acid sequences and wherein the step of predicting the subject or population's responsiveness to the therapy is based upon the average probability score assigned over the at least 5 pairs of nucleic acid sequences.
  • the method further comprises the step of determining the average probability score over at least 10 pairs of nucleic acid sequences and wherein the step of predicting the subject or population's responsiveness to the therapy is based upon the average probability score assigned over each of the at least 10 pairs of nucleic acid sequences. In one embodiment, the method further comprises the step of determining the average probability score over at least 15 pairs of nucleic acid sequences and wherein the step of predicting the subject or population's responsiveness to the therapy is based upon the average probability score assigned over each of the at least 15 pairs of nucleic acid sequences. In one embodiment, the method further comprises the step of acquiring mRNA or protein expression quantities from at least one sample of the subject.
  • the method further comprises quantifying the number of CD4+ cells and CD8+ cells in the sample.
  • the step of predicting responsiveness to the therapy comprises correlating expression of at least the first pair of nucleic acid sequences with antigen-specific immunity in the subject or the population of subjects comprises comparing expression of at least the first pair of nucleic acid sequences in a subject or population of subjects with expression of the first pair of nucleic acid sequences in a control subject or control population of subjects that have an low level of antigen-specific immunity or a decreased survival rate due to the cancer while taking the therapy.
  • the step of comparing expression of at least the first pair of nucleic acid sequences in a subject or population of subjects with expression of the first pair of nucleic acid sequences in a control subject or control population of subjects that have an low level of antigen- specific immunity or a decreased survival rate due to the cancer comprises performing a Kaplan-Meier test using a two-sided log-rank test.
  • the step of calculating the ratio of expression of the first nucleic acid sequence over the expression of the second nucleic acid sequence in a sample from the subject comprises calculating the expression of each nucleic acid sequence with Formula:
  • expi(x) and exp j (x) denote the quantification of expression of genes i and j in the sample x.
  • the expression of genes i and j are calculated by measuring RNA expression in the sample by immunohistochemistry, quantitative RT-PCR, or microarray analysis.
  • the subject or population of subjects comprises data collected while the subject or population of subjects is exposed to therapy for no more than about 90 days.
  • the therapy is an antibody comprising a CDR sequence programmed death receptor- 1 (PD-1), an antibody comprising a CDR sequence that binds cytotoxic T lymphocyte-associated protein 4 (CTLA-4), or a combination thereof.
  • PD-1 CDR sequence programmed death receptor- 1
  • CTL-4 cytotoxic T lymphocyte-associated protein 4
  • the method is a computer- implemented method, the method comprising: in a system configured to perform statistical analysis comprising at least one processor and a memory, performing statistical analysis or calculating a probability score of any of steps of the method.
  • the step of calculating the probability score or performing the statistical analysis, by the at least one processor comprises: setting, by the at least one processor, a predetermined value, stored in the memory, that corresponds to a threshold value above which a nucleic acid sequence pair is correlated the immune activity or responsiveness of the subject to the therapy; calculating, by the at least one processor, the probability score, wherein calculating the probability score comprises receiving subject or population expression quantities of at least a first and second nucleic acid sequences in the at least one pair of nucleic acid sequences, normalizing the expression quantities against a control quantity, conducting one or a plurality of statistical tests from the expression quantities, and assigning a probability score based upon a comparison of an outcome of the statistical tests and the threshold value.
  • the disclosure features a method of predicting a prognosis and/or a clinical outcome of a subject or population of subjects suffering from cancer comprising: (a) selecting, from the subject or the population diagnosed with cancer, at least a first pair of nucleic acid sequences comprising a first nucleic acid sequence and a second nucleic sequence, wherein the first nucleic acid sequence and the second nucleic acid sequence encode a immune checkpoint protein or variant thereof; (b) calculating a ratio of expression of the first nucleic acid sequence over the expression of the second nucleic acid sequence in a sample from the subject; (c) assigning a probability score to at least the first pair of nucleic acid sequences based upon the ratio of expression of at least the first pair of nucleic acid sequences; and (d) predicting responsiveness to the therapy in the subject or the population of subjects based upon the ratio of expression of the first and the second nucleic acid sequences.
  • the step of selecting at least one pair of nucleic acid sequences comprises selecting at least ten pairs of nucleic acid sequences, wherein the step of calculating the ratio of expression comprises calculating the ratio of mRNA or protein expression over each of the at least ten pairs of nucleic acid sequences, wherein the step of assigning a probability score comprises assigning a probability each of the a least ten pairs of nucleic acid sequences and subsequently calculating an average probability score, and wherein the step of predicting responsiveness to the therapy comprises comparing the average probability score to a threshold value of a probability score associated with a control subject, such that, if the average probability score of the subject is at or above the threshold value of a control subject, then the subject is characterized as having a positive clinical outcome; and, if the average probability score of the subject falls below the threshold value, the subject is characterized as having a poor clinical outcome.
  • the step of assigning a probability score further comprises (i) assigning a probability score of 1 if the ratio of expression of the at least one pair of nucleic acid sequences is at or above 1, (ii) assigning a probability score of 0 if the ratio of expression of the at least one pair of nucleic acid sequences is below 1 ; (iii) calculating an average probability score over each pair of nucleic acid sequences; and wherein the step of predicting the responsiveness to therapy further comprises comparing the average probability score to a threshold value, such that, if the average probability score is above the threshold value, then subject is characterized as having a positive clinical outcome; and, of the average probability score falls below the threshold value, then the subject is characterized as having a poor clinical outcome.
  • the threshold value is from about 0.50 to about 0.95. In one embodiment, the threshold value is from about 0.69 to about 0.85. In one embodiment, the step of calculating the ratio of expression of the further comprises normalizing expression of the first pair of nucleic acid sequences by the ratio of expression of a pair of nucleic acid sequences unassociated with anti-CTLA-4 or anti-PDl blockade therapy used as a negative control. In one embodiment, the step of assigning a probability score comprises assigning a 1 to at least the first pair of nucleic acid sequences if the ratio of expression of the pair is 1 or greater and assigning a 0 to at least the first pair of nucleic acid sequences if the ratio of expression of the pair is less than 1.
  • the sample is a human tissue sample comprising a tissue from a brushing, biopsy, or surgical resection of a subject.
  • the sample comprises a cell that is freshly obtained, formalin fixed, alcohol-fixed and/or paraffin embedded.
  • the sample comprises one or more skin cells or cells derived form the skin.
  • the sample comprises one or more skin cancer cells.
  • the subject is diagnosed with melanoma.
  • the cancer is metastatic melanoma.
  • the pair of nucleic acid sequences is chosen from two of the nucleic acid sequences that are at least 70% homologous to nucleic acid sequences encoding: VISTA, HVEM, PDL1, CD40, CD80, CD137L, BTLA, CD27, CD28, CD267, OX40L, CD86, CD200, CTLA4, CD200R, TIM3, and PD1.
  • the method is a computer-implemented method, the method comprising: in a system configured to perform statistical analysis comprising at least one processor and a memory, performing statistical analysis or calculating a probability score of any of steps (a), (b), (c).
  • the step of calculating the probability score or performing the statistical analysis, by the at least one processor comprises: setting, by the at least one processor, a threshold value, stored in the memory, that corresponds to a probability score above which the first pair of nucleic acid sequences is correlated to positive clinical outcome; calculating, by the at least one processor, the probability score, wherein calculating the probability score comprises analyzing a ratio of mRNA and/or protein expression associated with at least one pair of nucleic acid sequences associated with anti-immune checkpoint blockage therapy in a sample from a subject; conducting one or a plurality of statistical tests from the information associated with a disease or disorder; and assigning a probability score related to prognosis of the disease or disorder based upon a comparison of outcomes from the statistical tests and the threshold value.
  • the disclosure features a method of selecting or optimizing a therapy for treatment of a cancer responsive to immune checkpoint blockage therapy, the method comprising: (a) analyzing information from a subject or population of subjects associated with a disease or disorder comprising a step selecting at least a first pair of nucleic acids comprising a first and second nucleic acid; (i) wherein expression of the first nucleic acid sequence and second nucleic acid sequence contributes to antigen- specific immune response against one or more cancer cells; and/or (ii) wherein mRNA or protein expression of both nucleic acid sequences contributes at least partially to a clinical outcome more positive than control subject or control population; and (b) comparing expression of at least the first pair of nucleic acid sequences with a survival rate associated with a disease or disorder in a control population of subjects; and (c) assigning a probability score to the expression of at least the first pair of nucleic acid sequences based upon the survival rate of the subject or population of subjects diagnosed or suspected as having
  • the step of comparing comprises determining the average probability score over at least 15 pairs of nucleic acid sequences and the step of predicting the subject or population's responsiveness to the therapy is based upon the average probability score assigned over each of the at least 15 pairs of nucleic acid sequences.
  • the method is a computer- implemented method, the method comprising: in a system configured to perform statistical analysis comprising at least one processor and a memory, performing statistical analysis or calculating a probability score of any of steps (a), (b), (c), and/or (d) by the processor.
  • the step of calculating the probability score or performing the statistical analysis, by the at least one processor comprises: setting, by the at least one processor, a predetermined value, stored in the memory, that corresponds to a probability score above which the first pair of nucleic acid sequence is correlated to effectiveness of a therapy; calculating, by the at least one processor, the probability score, wherein calculating the probability score comprises analyzing information associated with a disease or disorder of the subject or the population of subjects; and conducting one or a plurality of statistical tests from the information associated with the cancer; and assigning a probability score related to effectiveness of a therapy based upon a comparison of outcomes from the statistical tests.
  • the disclosure features a computer program product encoded on a computer-readable storage medium comprising instructions for: (a) analyzing information from a subject or population of subjects associated with a disease or disorder comprising a step selecting at least a first pair of nucleic acids comprising a first and second nucleic acid; (i) wherein expression of the first nucleic acid sequence and second nucleic acid sequence contributes to antigen- specific immune response against one or more cancer cells; and/or (ii) wherein mRNA or protein expression of both nucleic acid sequences contributes at least partially to a clinical outcome more positive than control subject or control population; and (b) comparing a ratio of expression of at least the first pair of nucleic acid sequences with a survival rate associated with a disease or disorder in a control population of subjects; and (c) assigning a probability score to the expression of at least the first pair of nucleic acid sequences based upon the survival rate of the subject or population of subjects diagnosed with or suspected as having cancer; and (d)
  • the computer program product further comprises instructions for: setting a threshold value that corresponds to a probability score above which the ratio of mRNA or protein expression of the first pair of nucleic acid sequence is correlated to effectiveness of treating a cancer; calculating the probability score, wherein calculating the probability score comprises analyzing information associated with a disease or disorder of the subject or the population of subjects; and conducting one or a plurality of statistical tests from the information associated with a disease or disorder; and assigning a probability score related to effectiveness or ineffectiveness of a therapy based upon a comparison of outcomes from the statistical tests.
  • the cancer is melanoma.
  • the therapy is immune checkpoint blockade therapy.
  • the computer program product further comprises the step of calculating the ratio of expression of the first nucleic acid sequence over the expression of the second nucleic acid sequence in a sample from the subject with Formula:
  • expi(x) and exp j (x) denote the quantification of expression of genes i and j in the sample x; and wherein the expression of the first nucleic acid sequence i and the second nucleic acid sequence j are calculated by measuring RNA expression in the sample by immunohistochemistry, quantitative PCR, or fluorescence analysis.
  • the disclosure features a system comprising a computer program product encoded on a computer-readable storage medium comprising instructions for: (a) analyzing information from a subject or population of subjects associated with a disease or disorder comprising a step selecting at least a first pair of nucleic acids comprising a first and second nucleic acid; (i) wherein expression of the first nucleic acid sequence and second nucleic acid sequence contributes to antigen- specific immune response against one or more cancer cells; and/or (ii) wherein mRNA or protein expression of both nucleic acid sequences contributes at least partially to a clinical outcome more positive than control subject or control population; and (b) comparing a ratio of expression of at least the first pair of nucleic acid sequences with a survival rate associated with a disease or disorder in a control population of subjects; and (c) assigning a probability score to the expression of at least the first pair of nucleic acid sequences based upon the survival rate of the subject or population of subjects diagnosed with or suspected as having cancer
  • FIG. 1A Boxplots showing IMPRES of high vs low immune response in test and validation datasets of non-ICB treated melanoma patients 14 ; P-values are computed via a one-sided Rank-sum test. Boxplots center lines indicate medians, box edges represent the interquartile range, whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the '+' symbol.
  • FIG. IB Kaplan-Meier survival curves of patients with high versus low IMPRES (computed over the combined test and validation datasets 14 ). The median IMPRES is used to define the "Low IMPRES" and "High IMPRES" subgroups.
  • FIG. 1C Upper Panel: Heatmaps showing the enrichment P-values for CDPs that are up (light gray) or down (dark gray) regulated in responders versus non-responders across the anti-PD-1 (encapsulated in the left rectangle) and the anti-CTLA-4 melanoma datasets 1 ' 3 ' 4 ' 6 (right rectangle).
  • the lower Panel displays the enrichment P-values for these CDPs in high immune response vs other subtypes in non-ICB treated melanoma, and in spontaneous regression vs non- spontaneous regression in the NB dataset.
  • FIG. 1C Upper Panel: Heatmaps showing the enrichment P-values for CDPs that are up (light gray) or down (dark gray) regulated in responders versus non-responders across the anti-PD-1 (encapsulated in the left rectangle) and the anti-CTLA-4 melanoma datasets 1 ' 3 ' 4 ' 6 (right rectangle).
  • the lower Panel displays the enrichment P-values for
  • FIG. 2A Receiver Operating Characteristic (ROC) curves quantifying
  • FIG. 2B ROC curves for the MGH dataset of ICB response (with 10 patients treated with anti-CTLA-4 and 31 patients treated with anti-PD-1) and for the aggregate datasets including all 297 samples, the 216 samples of patients treated with anti-PD-1 and 81 with anti-CTLA-4.
  • FIG. 2C Bar plots showing the prediction accuracy and error types for different IMPRES thresholds (where a positive label corresponds to a 'responder' prediction) on the aggregate compendium of 297 patients included in all 11 datasets studied. The dashed line represents the total number of responders.
  • FIG. 2D Precision/recall evaluation of IMPRES on the same aggregate compendium.
  • the Y-axis displays the precision/recall as a function of the number of 'responder' predictions made (shown on the X-axis, obtained by decreasing the classification threshold, whose value is also displayed in italic font). Prediction performance in terms of specificity and sensitivity values is provided in Supp. Table 5.
  • FIG. 2E-FIG. 2F Kaplan Meier survival curves for the ICB treatment datasets 1 ' 6 , with high vs. low IMPRES scores (using the median IMPRES as a threshold differentiating between the high and low groups). The P-values are computed via a two-sided log-rank test.
  • FIG. 2G - FIG. 2H Boxplots comparing progression free survival between low vs.
  • FIG. 3A AUC of IMPRES and other published predictors across 9 publicly available ICB treatment datasets grouped by treatment type and stage (pre and on stands for before and during ICB treatment).
  • the one-sided Rank-sum P-values comparing the performance of each predictor evaluated to that of IMPRES over all datasets are presented (P- value of 0.002 is achieved when IMPRES AUC is larger than that obtained by the other predictor for all 9 datasets, and 0.004 when it is larger for 8/9 datasets).
  • Bar center is defined by the mean and error bars via SD.
  • FIG. 3C A network representation of the 15 pairwise features comprising IMPRES. Each node represents an immune checkpoint gene and each edge describes a pairwise relation (an IMPRES feature). The direction of edge A -> B denotes that the higher expression of A vs. that of B is associated with better patients' response.
  • FIG. 3D Clustogram (with average linkage function) of the individual predictive power of the 15 IMPRES features (based on their expression ratios) in each of the melanoma treatment datasets studied (the color scaling denotes the AUC obtained using each individual ratio as a response predictor, ranging from 0 to 1).
  • FIG. 3D Clustogram (with average linkage function) of the individual predictive power of the 15 IMPRES features (based on their expression ratios) in each of the melanoma treatment datasets studied (the color scaling denotes the AUC obtained using each individual ratio as a response predictor, ranging from 0 to 1).
  • 3E Scatter plots showing the correlation between CIBERSORT- inferred CD8+ T cells abundance (X-axis) and the gene expression ratios of two IMPRES features that are significantly associated with it (Y-axis); CD40/PD1 (upper panel) and PD1/OX40L lower panel). The Spearman p and associated P-values are shown for each ICB response data 1 ' 3 ' 4 ' 6 individually (on the right) and for all four datasets together (in the plot).
  • FIG. 4A PCA analysis of the full transcriptomics of all NB samples (left panel: 176 that are clinically considered 'high risk' NB and 322 that are not; right panel: 181 that are clinically considered 'favorable disease course' NB (i.e. patients patient survived without chemotherapy for at least 1000 days) and 91 that are not (i.e., patient died despite intensive chemotherapy).
  • FIG. 4B Bar plot showing the AUCs for predicting spontaneous NB regression (Y-axis) resulting when selecting features for different score f binomial P- value thresholds (X-axis) when using the samples in the cluster defined by PC2+PC3>0 (dark gray) and when using all relevant samples (light gray). The number above each bar corresponds to the number of features selected with each P-value threshold.
  • FIG. 4C ROC curve depicting IMPRES predictive performance for predicting spontaneous regression on 108 NB samples including 92 spontaneously regressing patients and 16 progressing ones.
  • FIG. 5 Heatmap showing the fold change of CIBERSORT inferred immune cell abundances between NB patients with versus without spontaneous regression (row 1), ICB melanoma responders versus non-responders (row 2-5) ⁇ and high immune response versus other subtypes in non-ICB treated melanoma patients (row 6) 5 . Entries bearing statistically significant differences include the corresponding P-values (using one-sided Rank-sum test). Four immune cell abundances are significantly up-regulated in regressing NB, from which 2 overlap with the 4 that are up-regulated in ICB responders in the Riaz et al.
  • FIG. 6A and FIG. 6B are Precision/recall evaluation of IMPRES on the aggregate compendium of anti-PD-1 and anti-CTLA-4, respectively.
  • the Y-axis displays the precision and recall of the response as a function of the number of 'responder' predictions made (shown on the X-axis, obtained by decreasing the classification threshold, whose value is also displayed in italic font).
  • FIG. 6C Survival prediction by IMPRES: Kaplan Meier survival curves for the Hugo et al6 data., using the median IMPRES score to define the Low and High groups, with the resulting one-sided log-rank P-value.
  • FIG. 7A Scatter plots showing the correlation between CD8+, CD4+ T cells abundances (X-axis) inferred via CIBERSORT for different melanoma ICB response datasets (excluding nanostring datasets), and IMPRES scores (Y-axis).
  • FIG. 7B ROC curves denoting the accuracy of predicting ICB response from CIBERSORT inferred abundances of naive B cells, CD8+ and CD4+ T cells, for each of the RNA-seq melanoma ICB-treated datasets.
  • FIG. 7C Similar ICB response prediction ROC curves, but this time resulting from the most predictive CIBERSORT inferred abundances ratios.
  • FIG. 8A and FIG. 8D are Receiver Operating Characteristic (ROC) curves quantifying the prediction accuracy across numerous publicly available ICB response datasets 1 ⁇ 7 ⁇ 2 ⁇ 4 ⁇ 8 ⁇ 3, obtained via features (checkpoints binary ratios) selected analyzing the combined dataset from Hugo et al. and Van Allen et al. 1 ' 2 and the combined dataset from Riaz et al. and Hugo et al.2 ' 3 , respectively.
  • FIG. 8B and FIG. 8E are boxplots showing the scores obtained via training on the combined data from Riaz et al. and Hugo et al. 2 ' 3 and on the combined dataset from Hugo et al and Van Allen et al. 1 ' 2 for high vs.
  • ROC Receiver Operating Characteristic
  • FIG. 8C and FIG. 8F are Kaplan Meier survival curves for the combined datasets of non ICB -treated melanoma test and validation sets 5 , using the median of these inferred scores to define the Low and High groups, with the resulting log-rank P-values.
  • FIG. 9A ROC curves quantifying the ICB response prediction AUCs obtained for numerous publicly available ICB response datasets 1 ⁇ 7 ⁇ 2 ⁇ 4 ⁇ 8 ⁇ 3, based on the reduced, 11 -features score (the features remaining after applying feature reduction to IMPRES features, listed in Supp. Table 9).
  • FIG. 10A-FIG. IOC are boxplots comparing IMPRES scores of different melanoma subtypes (each subplot compares one subtype against all others, compared via two-sided rank-sum test) for the pre anti-PD-1, on anti-PD-1 and all samples from Riaz et al. .
  • MU is mucosal melanoma
  • OC/OV is Ocular/Uveal melanoma
  • CU is Cutaneous melanoma and OT stands for Other subtypes'. Boxplots center lines indicate medians, box edges represent the interquartile range, whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the '+' symbol.
  • FIG. 11 Bar plot showing the number of randomly selected signatures (Y- axis) whose ICB response predictive power achieves AUCs higher than a given threshold (X- axis); the histogram presents the results for 1000 randomly generated predictors, each based on 15 immune gene binary relations that are randomly formed from the original space of 28 immune checkpoint genes (Supp. Table 1).
  • FIG. 12 Scatter plots visualizing the results of a PCA analysis using IMPRES ratios to describe each sample, for each ICB response data and for the integration of all data together. The variance explained by each PC is shown in the X, Y and Z-axes of each plot.
  • FIG. 13A and FIG. 13B are bar plots showing the mean and standard deviation of IMPRES scores (FIG. 13A) and mutation counts (FIG. 13B) for each cancer type, computed over the pertaining samples in the TCGA collection.
  • FIG. 13C is a scatter plot showing the correlation between mean IMPRES scores (X-axis) and mean mutational counts (Y-axis) across the different cancer types (TCGA). The Spearman rank correlation between the two scores is almost 0.8. Circle size corresponds to sample size of each cancer type.
  • FIG. 14 is a flow chart showing the steps of the software method used to identify and process the nucleic acid sequence selected for assignment of a probability score correlated to the repsosiveness to the therapy by the subject.
  • amino acid refers to a molecule containing both an amino group and a carboxyl group bound to a carbon which is designated the a-carbon.
  • Suitable amino acids include, without limitation, both the D- and L-isomers of the naturally-occurring amino acids, as well as non-naturally occurring amino acids prepared by organic synthesis or other metabolic routes.
  • a single “amino acid” might have multiple sidechain moieties, as available per an extended aliphatic or aromatic backbone scaffold.
  • amino acid as used herein, is intended to include amino acid analogs including non-natural analogs.
  • biopsy means a cell sample, collection of cells, or bodily fluid removed from a subject or patient for analysis.
  • the biopsy is a bone marrow biopsy, punch biopsy, endoscopic biopsy, needle biopsy, shave biopsy, incisional biopsy, excisional biopsy, or surgical resection.
  • bodily fluid means any fluid from isolated from a subject including, but not necessarily limited to, blood sample, serum sample, urine sample, mucus sample, saliva sample, and sweat sample.
  • the sample may be obtained from a subject by any means such as intravenous puncture, biopsy, swab, capillary draw, lancet, needle aspiration, collection by simple capture of excreted fluid.
  • immune checkpoint blockade therapy is a pharmaceutical composition comprising an antibody or antibody fragment that binds PD-1, CTLA-4 and/or a combination thereof.
  • immune checkpoint protein is an amino acid sequence encoded by one or more nucleic acid sequences that activate the directly or indirectly effect the activation of the immune system within a subject.
  • the immune checkpoint protein comprises an amino acid sequence encoded by one of the following nucleic acid sequences or a nucleic acid variant that comprises at least about 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% sequence identity to the following:
  • PDL-1 1 gctttctatt caagtgcctt ctgtgtgtgc acatgtgtaa tacatatctg ggatcaaagc
  • CD28 1 taaagtcatc aaaacaacgt tatatcctgt gtgaaatgct gcagtcagga tgccttgtgg
  • HVEM 1 tccttcatac cggcccttcc cctcggcttt gcctggacag ctcctgcctc ccgcagggcc
  • CD27 1 cggaagggga agggggtgga ggttgctgct atgagagaga aaaaaaac agccacaata
  • PD-1 1 agtttccctt ccgctcacct ccgcctgagc agtggagaag gcggcactct ggtggggctg
  • CD137L 1 aaaaagcggc gcgctgtgtc ttcccgcagt ctctcgtcat ggaatacgcc tctgacgctt
  • TIM-3 1 atgttttcac atcttccctt tgactgtgtc ctgctgctgc tgctgctact acttacaagg
  • OX40L 1 ccgcaaggaa aacccagact ctggcgacag cagagacgag gatgtgcgtg ggggctcggc (SEQ ID NO: 61 ggctgggccg cgggccgtgt gcggctctgc tcctctggg cctggggctg agcaccgtga 12) 121 cggggctcca ctgtgtcggg gacacctacc ccagcaacga ccggtgctgc cacgagtgca
  • CTLA4 1 cttctgtgtg tgcacatgtg taatacatat ctgggatcaa agctatctat ataaagtcct
  • CD200 1 gtcagtttcc ccagcggtca cctttgaaaa gggaaaatg tctgaaata gacaaagctg (SEQ ID NO: 61 aatataaaca tcatttaatt ccccccacac agacagcctc cgctcctgtg agggcgtggg 15) 121 gaaaacggag tgggagaagg gggctagcga ggaggaagag gcgggaggtg cggcaggggc
  • disease or disorder is any one of a group of ailments capable of causing an negative health in a subject by: (i) expression of one or a plurality of mutated nucleic acid sequences in one or a plurality of amino acids; or (ii) aberrant expression of one or a plurality of nucleic acid sequences in one or a plurality of amino acids, in each case, in an amount that causes an abnormal biological affect that negatively affects the health of the subject.
  • the disease or disorder is chosen from: cancer of the adrenal gland, bladder, bone, bone marrow, brain, spine, breast, cervix, gall bladder, ganglia, gastrointestinal tract, stomach, colon, heart, kidney, liver, lung, muscle, ovary, pancreas, parathyroid, penis, prostate, salivary glands, skin, spleen, testis, thymus, thyroid, or uterus.
  • a disease or disorder is a hyperproliferative disease.
  • hyperproliferative disease means a cancer chosen from: lung cancer, bone cancer, CMML, pancreatic cancer, skin cancer, cancer of the head and neck, cutaneous or intraocular melanoma, uterine cancer, ovarian cancer, rectal cancer, cancer of the anal region, stomach cancer, colon cancer, breast cancer, testicular, gynecologic tumors (e.g., uterine sarcomas, carcinoma of the fallopian tubes, carcinoma of the endometrium, carcinoma of the cervix, carcinoma of the vagina or carcinoma of the vulva), Hodgkin's disease, cancer of the esophagus, cancer of the small intestine, cancer of the endocrine system (e.g., cancer of the thyroid, parathyroid or adrenal glands), sarcomas of soft tissues, cancer of the urethra, cancer of the penis, prostate cancer, chronic or acute leukemia, solid tumors of childhood, lymphocytic lymphomas, cancer of the bladder
  • the terms "electronic medium” mean any physical storage employing electronic technology for access, including a hard disk, ROM, EEPROM, RAM, flash memory, nonvolatile memory, or any substantially and functionally equivalent medium. In some
  • the software storage may be co-located with the processor implementing an embodiment of the invention, or at least a portion of the software storage may be remotely located but accessible when needed.
  • the terms "information associated with the disease or disorder” means any information related to a disease or disorder necessary to perform the method described herein or to run the software identified herein.
  • the information associated with a disease or disorder is any information from a subject that can be used or is used as a parameter or variable in the input of any analytical function performed in the course of performing any method disclosed herein.
  • the information associated with the disease or disorder is selected from: DNA or RNA expression levels of a subject or population of subjects, amino acid expression levels of a subject or population of subjects, whether or not the subject or population is taking a therapy for a condition, the age of a subject or population of subjects, the gender of a subject or population of subjects; or whether and, if so, how much or how long a subject or population of subjects has been diagnosed with a cancer disclosed herein, and/or exposed to an environmental condition, drug or biologic.
  • inhibitors or “antagonists” of a given protein refer to modulatory molecules or compounds that, e.g., bind to, partially or totally block activity, decrease, prevent, delay activation, inactivate, desensitize, or down regulate the activity or expression of the given protein, or downstream molecules regulated by such a protein.
  • Inhibitors can include siRNA or antisense RNA, genetically modified versions of the protein, e.g., versions with altered activity, as well as naturally occurring and synthetic antagonists, antibodies, small chemical molecules and the like.
  • Assays for identifying other inhibitors can be performed in vitro or in vivo, e.g., in cells, or cell membranes, by applying test inhibitor compounds, and then determining the functional effects on activity.
  • nucleic acid refers to a molecule comprising two or more linked nucleotides.
  • Nucleic acid and nucleic acid molecule are used interchangeably and refer to oligoribonucleotides as well as oligodeoxyribonucleotides.
  • the terms also include polynucleosides (i.e., a polynucleotide minus a phosphate) and any other organic base containing nucleic acid.
  • the organic bases include adenine, uracil, guanine, thymine, cytosine and inosine.
  • the nucleic acids may be single or double stranded.
  • the nucleic acid may be naturally or non-naturally occurring.
  • Nucleic acids can be obtained from natural sources, or can be synthesized using a nucleic acid synthesizer (i.e., synthetic). Isolation of nucleic acids are routinely performed in the art and suitable methods can be found in standard molecular biology textbooks. (See, for example, Maniatis' Handbook of Molecular Biology.)
  • the nucleic acid may be DNA or RNA, such as genomic DNA, mitochondrial DNA, mRNA, cDNA, rRNA, miRNA, PNA or LNA, or a combination thereof, as described herein.
  • the term nucleic acid sequence is used to refer to expression of genes with all or part of their regulatory sequences operably linked to the expressible components of the gene.
  • the expression of genes is analyzed for genetic interactions.
  • genetic interactions are analyzed by identifying pairs of a first gene and a second gene whose expression or activity contributes to the modulation of the lethality or likelihood of a subject from which the information associated with a disease or disorder is obtained.
  • the nucleic acid pair (comprising a first and second nucleic acid) is a pair of microRNAs, shRNAs, amino acids or nucleic acid sequences defined with presence of only partial regulatory sequences operably linked to the expressible components of a gene.
  • nucleic acid derivatives or synthetic sequences may enable complementarity as between natural expression products (such as mRNA) and the synthetic sequences to block protein translation of products for validation of software analysis and corroboration with biological assays.
  • a nucleic acid derivative is a non-naturally occurring nucleic acid or a unit thereof.
  • Nucleic acid derivatives may contain non-naturally occurring elements such as non-naturally occurring nucleotides and non- naturally occurring backbone linkages.
  • Nucleic acid derivatives according to some aspects of this invention may contain backbone modifications such as but not limited to phosphorothioate linkages, phosphodiester modified nucleic acids, combinations of phosphodiester and phosphorothioate nucleic acid, methylphosphonate, alkylphosphonates, phosphate esters, alkylphosphonothioates, phosphoramidates, carbamates, carbonates, phosphate triesters, acetamidates, carboxymethyl esters, methylphosphorothioate, phosphorodithioate, p-ethoxy, and combinations thereof.
  • the backbone composition of the nucleic acids may be homogeneous or heterogeneous.
  • Nucleic acid derivatives according to some aspects of this invention may contain substitutions or modifications in the sugars and/or bases.
  • some nucleic acid derivatives may include nucleic acids having backbone sugars which are covalently attached to low molecular weight organic groups other than a hydroxyl group at the 3' position and other than a phosphate group at the 5' position (e.g., an 2'-0-alkylated ribose group).
  • Nucleic acid derivatives may include non-ribose sugars such as arabinose.
  • Nucleic acid derivatives may contain substituted purines and pyrimidines such as C-5 propyne modified bases, 5-methylcytosine, 2-aminopurine, 2-amino-6- chloropurine, 2,6-diaminopurine, hypoxanthine, 2-thiouracil and pseudoisocytosine.
  • a nucleic acid may comprise a peptide nucleic acid (PNA), a locked nucleic acid (LNA), DNA, RNA, or a co-nucleic acids of the above such as DNA-LNA co-nucleic acid.
  • the term "probability score" refers to a quantitative value given to the output of any one or series of algorithms that are disclosed herein.
  • the probability score is determined by application of one or plurality of algorithm disclosed herein by: setting, by the at least one processor, a predetermined value, stored in the memory, that corresponds to a threshold value above which the first pair of nucleic acid sequence is correlated to an interaction event, the ineffectiveness or effectiveness of a therapy, the resistance of a therapy, and/or the prognosis of the subject or population of subjects suffering from a disease or disorder; calculating, by the at least one processor, the probability score, wherein calculating the probability score comprises: (i) analyzing information associated with a disease or disorder of the subject or the population of subjects; and (ii) conducting one or a plurality of statistical tests from the information associated with a disease or disorder; and (iii) assigning a probability score related to an interaction event, the ineffectiveness or effectiveness of a therapy, the resistance
  • the threshold value is a calculated area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve.
  • the threashold value is an AUC calculation that is at least about 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 or greater in its ROC curve.
  • the AUC calculation contributes to the probability score associated with any of the steps of any of the methods disclosed herein and is from about 0.5 to about 1.5. In some embodiments, the AUC calculation contributes to the probability score associated with any of the steps of any of the methods disclosed herein and is from about 0.55 to about 1.5. In some embodiments, the AUC calculation contributes to the probability score associated with any of the steps of any of the methods disclosed herein and is from about 0.6 to about 1.5. In some embodiments, the AUC calculation contributes to the probability score associated with any of the steps of any of the methods disclosed herein and is from about 0.65 to about 1.5.
  • the AUC calculation contributes to the probability score associated with any of the steps of any of the methods disclosed herein and is from about 0.70 to about 1.5. In some embodiments, the AUC calculation contributes to the probability score associated with any of the steps of any of the methods disclosed herein and is from about 0.75 to about 1.5. In some embodiments, the AUC calculation contributes to the probability score associated with any of the steps of any of the methods disclosed herein and is from about 0.80 to about 1.5. In some embodiments, the AUC calculation contributes to the probability score associated with any of the steps of any of the methods disclosed herein and is from about 0.85 to about 1.5.
  • the AUC calculation contributes to the probability score associated with any of the steps of any of the methods disclosed herein and is from about 0.90 to about 1.5. In some embodiments, the AUC calculation contributes to the probability score associated with any of the steps of any of the methods disclosed herein and is from about 0.7 to about 0.8.
  • peptide As used herein, the terms “peptide,” “polypeptide” and “protein” are used interchangeably and refer to two or more amino acids covalently linked by an amide bond or non-amide equivalent.
  • the peptides of the disclosure can be of any length.
  • the peptides can have from about two to about 100 or more residues, such as, 5 to 12, 12 to 15, 15 to 18, 18 to 25, 25 to 50, 50 to 75, 75 to 100, or more in length.
  • peptides are from about 2 to about 18 residues in length.
  • the peptides of the disclosure also include 1- and d-isomers, and combinations of 1- and d-isomers.
  • the peptides can include modifications typically associated with posttranslational processing of proteins, for example, cyclization (e.g., disulfide or amide bond), phosphorylation, glycosylation, carboxylation, ubiquitination, myristylation, or lipidation.
  • cyclization e.g., disulfide or amide bond
  • phosphorylation e.g., glycosylation, carboxylation, ubiquitination, myristylation, or lipidation.
  • the term "prognosing” means determining the probable course and/or clinical outcome of a disease.
  • probes may be used in concert with any of the devices, computer program product or methods disclosed herein.
  • the term "probe” refers to any molecule that may bind or associate, indirectly or directly, covalently or non-covalently, to any of the nucleic acid sequences disclosed herein or any immune checkpoint proteins or variants thereof encoded by the nucleic acid sequences disclosed herein and whose association or binding is detectable using the methods disclosed herein.
  • the probe is a fluorogenic, fluorescent, or chemiluminescent probe, an antibody, or an absorbance-based probe.
  • an absorbance-based probe for example the chromophore pNA (para-nitroanaline), may be used as a probe for detection and/or quantification of a protease disclosed herein.
  • the probe comprises an amino acid sequence that naturally binds to any of the nucleic acid sequences disclosed herein or any immune checkpoint proteins or variants thereof encoded by the nucleic acid sequences, including those variants that are at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 87%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99% homologous to amino acids encoded by SEQ ID NOs: 1 through 15 below.
  • a probe may be a complementary nucleic acid sequence that is comprises at least about 4, 5, 6, 7, 8, 9, or 10 or more nucleic acids complementary to any of the nucleic acid sequences disclosed herein or variants thereof that are at least 70% homologous to any of SEQ ID NO: 1 through 15.
  • a probe may be immobilized, adsorbed, or otherwise non- covalently bound to a solid surface, such that upon exposure to any of the nucleic acid sequences disclosed herein or any immune checkpoint proteins or variants thereof encoded by the nucleic acid sequences for a time period sufficient to perform a detectable reaction.
  • cleavage of a substrate bound to the any of the nucleic acid sequences disclosed herein or any immune checkpoint proteins or variants thereof encoded by the nucleic acid sequences, non-covalently or covantly causes a biological change in the nature or chemical availability of one or more probes such that cleavage enables detection of the reaction product.
  • the step of detecting comprises use of FRET
  • cleavage of the substrate disclosed herein causes one of the chromophore to emit a fluorescent light under exposure to a wavelength sufficient to activate such a fluorescent molecule.
  • the intensity, length, or amplitude of a wavelength emitted from fluorescent marker can be measured and is, in some embodiments, proportional to the presence, absence or quantity of any of the nucleic acid sequences disclosed herein or any immune checkpoint proteins or variants thereof encoded by the nucleic acid sequences present in the sample, reaction vessel comprising the sample, thereby the quantity of enzyme can be determined from detection of the intensity of or fluorescence at a known wavelength of light.
  • an "activity-based probe,” as used herein, refers to a certain embodiment of probe comprising a small molecule that binds to or has affinity for a molecule such as a substrate that binds any of the nucleic acid sequences disclosed herein or any immune checkpoint proteins or variants thereof encoded by the nucleic acid sequences in the presence of such nucleic acid sequences disclosed herein or any immune checkpoint proteins or variants thereof encoded by the nucleic acid sequences, such that its bound or unbound state confers an activity readout detectable by PCR, fluorescence, absorbance or any other detection means.
  • the activity-based probe covalently or non- covalently binds to any of the nucleic acid sequences disclosed herein or any immune checkpoint proteins or variants thereof encoded by the nucleic acid sequences.
  • the binding of the activity-based probe modifies the physical or biological activity of the any of the nucleic acid sequences disclosed herein or any immune checkpoint proteins or variants thereof encoded by the nucleic acid sequences.
  • the activity-based probe can be fluorescent or chemiluminescent.
  • the activity-based probe has a measurable activity of one value if any of the nucleic acid sequences disclosed herein or any immune checkpoint proteins or variants thereof encoded by the nucleic acid sequences are inactive and another measurable activity if in an activated state.
  • fluorogenic and fluorescent probe refer to any molecule (dye, quantum dot, peptide, or fluorescent marker) that emits a known and/or detectable wavelength of light upon exposure to a known wavelength of light.
  • substrates or peptides with known cleavage sites recognizable by any of the nucleic acid sequences disclosed herein or any immune checkpoint proteins or variants thereof encoded by the nucleic acid sequences are covalently or non-covalently attached to a fluorogenic probe.
  • the attachment of the fluorogenic probe to the substrate creates a chimeric molecule capable of a fluorescent emission or emissions upon exposure of the substrate to the immune checkpoint proteins or variants and the known wavelength of light, such that exposure to the immune checkpoint proteins or variants thereof creates a reaction product which is quantifiable in the presence of a fluorimeter.
  • light from the fluorogenic probe is fully quenched upon exposure to the known wavelength of light before enzymatic cleavage of the substrate and the fluorogenic probe emits a known wavelength of light, the intensity of which is quantifiable by absorbance readings or intensity levels in the presence of a fluorimeter and after enzymatic cleavage of the substrate.
  • the fluorogenic probe is a coumarin-based dye or rhodamine-based dye with fluorescent emission spectra measureable or quantifiable in the presence of or exposure to a predetermined wavelength of light.
  • the fluorogenic probe comprises rhodamine.
  • the fluorogenic probe comprises rhodamine- 100.
  • Coumarin-based fluorogenic probes are known in the art, for example in US Pat Nos. 7,625,758 and 7,863,048, which are herein incorporated by reference in their entireties.
  • the fluorogenic probes are a component to, covalently bound to, non-covalently bound to, intercalated with one or a plurality of substrates to any of the immune checkpoint proteins or variants disclosed herein.
  • the fluorogenic probes are chosen from ACC or AMC.
  • the fluorogenic probe is a fluorescein molecule.
  • the fluorogenic probe is capable of emitting a resonance wave detectable and/or quantifiable by a fluorimeter after exposure to one or a plurality of immune checkpoint proteins or variants disclosed herein.
  • Fluorescence microscopy which uses the fluorescence to generate an image, may be used to detect the presence, absence, or quantity of a fluorescent probe.
  • fluorescence microscopy comprises measuring fluorescence resonance energy transfer (FRET) within a FRET -based assay.
  • FRET fluorescence resonance energy transfer
  • a "chemiluminescent probe” refers to any molecule (dye, peptide, or chemiluminescent marker) that emits a known and/or detectable wavelength of light as the result of a chemical reaction. Chemiluminescence differs from fluorescence or phosphorescence in that the electronic excited state is the product of a chemical reaction rather than of the absorption of a photon. Non-limiting examples of chemiluminescent probes are luciferin and aequorin molecules.
  • a chemiluminescent molecule is covalently or non-covalently attached to a nucleic acid or protein encoded by the nucleic acid disclosed herein, such that the excited electronic state can be quantified to determine directly that an enzyme, such as an immune checkpoint protein, is in a reaction vessel, or, indirectly, by quantifying the amount of reaction product was produced after activation of the probe on the substrate or a portion of the substrtate.
  • sample refers to a biological sample obtained or derived from a source of interest, as described herein.
  • a source of interest comprises an organism, such as an animal or human.
  • a biological sample comprises biological tissue or fluid.
  • a biological sample may be or comprise bone marrow; blood; blood cells; ascites; tissue or fine needle biopsy samples; cell-containing body fluids; free floating nucleic acids; sputum; saliva; urine; cerebrospinal fluid, peritoneal fluid; pleural fluid; feces; lymph; gynecological fluids; skin swabs; vaginal swabs; oral swabs; nasal swabs; washings or lavages such as a ductal lavages or broncheoalveolar lavages; aspirates; scrapings; bone marrow specimens; tissue biopsy specimens; surgical specimens; feces, other body fluids, secretions, and/or excretions; and/or cells therefrom, etc.
  • a biological sample is or comprises bodily fluid.
  • a sample is a "primary sample" obtained directly from a source of interest by any appropriate means.
  • a primary biological sample is obtained by methods selected from the group consisting of biopsy (e.g., fine needle aspiration or tissue biopsy), surgery, collection of body fluid (e.g., blood, lymph, feces etc.), etc.
  • body fluid e.g., blood, lymph, feces etc.
  • sample refers to a preparation that is obtained by processing (e.g., by removing one or more components of and/or by adding one or more agents to) a primary sample. For example, filtering using a semi-permeable membrane.
  • Such a “processed sample” may comprise, for example nucleic acids or proteins extracted from a sample or obtained by subjecting a primary sample to techniques such as amplification or reverse transcription of mRNA, isolation and/or purification of certain components, etc. in some embodiments, the methods disclosed herein do not comprise a processed sample.
  • Representative biological samples include, but are not limited to: blood, a component of blood, a portion of a tumor, plasma, serum, saliva, sputum, urine, cerebral spinal fluid, cells, a cellular extract, a tissue specimen, a tissue biopsy, or a stool specimen.
  • a biological sample is whole blood and this whole blood is used to obtain measurements for a biomarker profile.
  • a biological sample is tumor biopsy and this tumor biopsy is used to obtain measurements for a biomarker profile.
  • a biological sample is some component of whole blood. For example, in some embodiments some portion of the mixture of proteins, nucleic acid, and/or other molecules (e.g., metabolites) within a cellular fraction or within a liquid (e.g., plasma or serum fraction) of the blood.
  • the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in monocytes that are isolated from the whole blood.
  • the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in red blood cells that are isolated from the whole blood.
  • the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in platelets that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in neutrophils that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in eosinophils that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in basophils that are isolated from the whole blood.
  • the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in lymphocytes that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in monocytes that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from one, two, three, four, five, six, or seven cell types from the group of cells types consisting of red blood cells, platelets, neutrophils, eosinophils, basophils, lymphocytes, and monocytes.
  • a biological sample is a tumor that is surgically removed from the patient, grossly dissected, and snap frozen in liquid nitrogen within twenty minutes of surgical resection.
  • a "score" is a numerical value that may be assigned or generated after normalization of the value based upon the presence, absence, or quantity of substrates or enzymes disclosed herein. In some embodiments, the score is normalized in respect to a control score.
  • the term "subject” is used throughout the specification to describe an animal from which a sample is taken.
  • the animal is a human.
  • the term “patient” may be interchangeably used.
  • the term “patient” will refer to human patients suffering from a particular disease or disorder.
  • the subject may be a human suspected of having or being identified as at risk to develop a type of cancer more severe or invasive than initially diagnosed.
  • the subject may be diagnosed as having at resistance to one or a plurality of treatments to treat a disease or disorder afflicting the subject.
  • the subject is suspected of having or has been diagnosed with stage I, II, III or greater stage of cancer.
  • the subject may be a human suspected of having or being identified as at risk to a terminal condition or disorder.
  • the subject may be a mammal which functions as a source of the isolated sample of biopsy or bodily fluid.
  • the subject may be a non-human animal from which a sample of biopsy or bodily fluid is isolated or provided.
  • the term "mammal" encompasses both humans and non-humans and includes but is not limited to humans, non-human primates, canines, felines, murines, bovines, equines, and porcines.
  • any therapy or combination of therapies is a predetermined amount calculated to achieve the desired effect, i.e., to improve and/or to decrease one or more symptoms of a disease or disorder.
  • the activity contemplated by the present methods includes both medical therapeutic and/or prophylactic treatment, as appropriate.
  • the specific dose of a compound administered according to this invention to obtain therapeutic and/or prophylactic effects will, of course, be determined by the particular circumstances surrounding the case, including, for example, the compound administered, the route of administration, and the condition being treated.
  • the compounds are effective over a wide dosage range and, for example, dosages per day will normally fall within the range of from 0.001 to 10000 mg/kg, more usually in the range of from 0.01 to 1 mg/kg.
  • a therapeutically effective amount of compound of embodiments of this disclosure is typically an amount such that when it is administered in a physiologically tolerable excipient composition, it is sufficient to achieve an effective systemic concentration or local concentration in the tissue.
  • the therapeutically effective amount is the amount of one or more active agents sufficient to treat or prevent cancers disclosed herein.
  • the therapeutically effective amount is the amount of one or more active agents sufficient to treat or prevent melanoma or a cancer derived from melanoma.
  • the therapeutically effective amount is the amount of one or more active agents sufficient to treat or prevent metastatic melanoma.
  • threshold value refers to the quantitative value above which or below which a probability value is considered statistically significant as compared to a control set of data.
  • the threshold value is the quantitative value that is about 20%, 15%, 10%, 5%, 4%, 3%, 2%, or 1% below the greatest probability score assigned to a nucleic acid pair after the probability score is calculated by input of information associated with a disease or disorder into one or more of the statistical tests provided herein.
  • Treatment can mean protecting of an animal from a disease or disorder through means of preventing, suppressing, repressing, or completely eliminating the disease or symptom of a disease or disorder.
  • Preventing the disease involves administering a therapy (such as immune checkpoint blockade therapy, vaccine, antibody, biologic, gene therapy with or without viral vectors, small chemical compound, etc.) to a subject or population of subjects prior to onset of the disease or disorder.
  • Suppressing the disease involves administering a therapy to a subject or population of subjects after induction of the disease but before its clinical appearance.
  • Repressing the disease involves administering a therapy of to a subject or population of subjects after clinical appearance of the disease.
  • the term "web browser” means any software used by a user device to access the internet.
  • the web browser is selected from: Internet Explorer®, Firefox®, Safari®, Chrome®, SeaMonkey®, K-Meleon, Camino, OmniWeb®, iCab, Konqueror, Epiphany, OperaTM, and WebKit®.
  • Human or non-human variants of the enzymes above are contemplated by the methods, systems, and devices disclosed herein. Variants of these enzymes include sequences that are at least 70% homologous to the human sequences above. As used herein, the term “variants" is intended to mean substantially similar sequences.
  • a variant comprises a nucleic acid molecule having deletions (i.e., truncations) at the 5' and/or 3' end; deletion and/or addition of one or more nucleotides at one or more internal sites in the native polynucleotide; and/or substitution of one or more nucleotides at one or more sites in the native polynucleotide.
  • a "native" nucleic acid molecule or polypeptide comprises a naturally occurring nucleotide sequence or amino acid sequence, respectively.
  • conservative variants include those sequences that, because of the degeneracy of the genetic code, encode the amino acid sequence of one of the polypeptides of the disclosure.
  • Variant nucleic acid molecules also include synthetically derived nucleic acid molecules, such as those generated, for example, by using site-directed mutagenesis but which still encode a protein of the disclosure.
  • variants of a particular nucleic acid molecule of the disclosure will have at least about 70%, 75%, 80%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to that particular polynucleotide as determined by sequence alignment programs and parameters as described elsewhere herein.
  • Variants of a particular nucleic acid molecule of the disclosure can also be evaluated by comparison of the percent sequence identity between the polypeptide encoded by a variant nucleic acid molecule and the polypeptide encoded by the reference nucleic acid molecule. Percent sequence identity between any two polypeptides can be calculated using sequence alignment programs and parameters described elsewhere herein.
  • the percent sequence identity between the two encoded polypeptides is at least about 70%, 75%, 80%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity.
  • the term "variant" protein is intended to mean a protein derived from the native protein by deletion (so-called truncation) of one or more amino acids at the N-terminal and/or C-terminal end of the native protein; deletion and/or addition of one or more amino acids at one or more internal sites in the native protein; or substitution of one or more amino acids at one or more sites in the native protein.
  • Variant proteins encompassed by the present disclosure are biologically active, that is they continue to possess the desired biological activity of the native protein as described herein. Such variants may result from, for example, genetic polymorphism or from human manipulation.
  • Biologically active variants of a protein of the disclosure will have at least about 70%, 75%, 80%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to the amino acid sequence for the native protein as determined by sequence alignment programs and parameters described elsewhere herein.
  • a biologically active variant of a protein of the disclosure may differ from that protein by as few as 1-15 amino acid residues, as few as about 1 to about 10, such as 6-10, as few as about 20, 15, 10, 9, 8, 7, 6, 5, as few as 4, 3, 2, or even 1 amino acid residue.
  • the proteins or polypeptides of the disclosure may be altered in various ways including amino acid substitutions, deletions, truncations, and insertions. Methods for such manipulations are generally known in the art.
  • amino acid sequence variants and fragments of the proteins can be prepared by mutations in the nucleic acid sequence that encode the amino acid sequence recombinantly.
  • the disclosure further relates to a computer program product encoded on a computer-readable storage medium that comprises instructions for performing any of the methods described herein.
  • the disclosure relates to any of the disclosed methods on a system or software that accesses the internet.
  • One application of such computers, computer program products, systems and methods is the identification of specific conditions for which a given chemical agent or one or more pharmaceutical drugs would provide effective therapeutic treatment.
  • the present invention provides systems and methods for identifying genetic expression profiles of specific cancers for which currently available chemical agents, pharmaceutical drugs, or other therapies of interest, such as immune checkpoint blockage therapy would provide either effective to treatment or ineffective due to resistance of treatment.
  • the present disclosure also provides systems and methods for identifying genetic expression profiles of specific cancer susceptible to immune checkpoint blockage therapy for which currently available chemical agents, pharmaceutical drugs, would provide a therapeutically effective amount of a treatment or an adjuvant treatment.
  • the therapy is immune checkpoint blockage therapy.
  • the disclosure relates to a system or computer program product either integrated into a device or self-contained and, in either case, operably accessible to a database of expression information (mRNA expression or protein expression) of one or a plurality of nucleic acid sequences that are immune checkpoint proteins or variants thereof.
  • the computer program product of the disclosure comprises instructions execute any one or plurality of steps in any of the disclosed methods.
  • the computer program product optionally integrated into a device, comprises instruction for executing the steps comprising: (a) analyzing information from a subject or population of subjects associated with a disease or disorder comprising a step selecting at least a first pair of nucleic acids comprising a first and second nucleic acid; (i) wherein expression of the first nucleic acid sequence and second nucleic acid sequence contributes to antigen- specific immune response against one or more cancer cells; and/or (ii) wherein mRNA or protein expression of both nucleic acid sequences contributes at least partially to a clinical outcome more positive than control subject or control population; and
  • the disclosure provides systems and methods for defining and analyzing genetic profiles for at least one or two specific disease states (such as skin cancer); (2) identifying a therapy of interest (e.g., one or more chemical agents or one or more pharmaceutical drugs comprising immune checkpoint blockage therapy) known to be therapeutically effective in treating the specific disease state whose expression signature is defined by accessing and inputting information associated with the disease state or disorder from a database, (3) defining a discrimination set of genetic interactions that are
  • the disclosure also relates to a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system, including, in some embodiments, the aforementioned steps (1), (2), (3), and/or (4).
  • the computer program product is a component, at least in operative
  • a processor of a device capable of measuring of expression levels of at least one or a plurality of nucleic acid sequences associated with immune checkpoint blockage therapy.
  • the disclosure relates to a method of quantifying the levels of antigen- specific immune activity in a subject with cancer or suspected of having cancer, a method of predicting clinical outcome or a method of predicting the responsiveness of a subject to immune checkpoint blockade therapy, wherein (a) the levels of antigen- specific immunity are the levels of antigen- specific immunity against the cancer or suspected cancer; (b) the predicting of clinical outcome; and/or (c) the predicting of responsiveness of a subject to a therapy comprises calculating a ratio of expression of nucleic acid sequences associated with or that are immune checkpoint proteins or variants expressed in a sample from a subject.
  • the ration of expression of the nucleic acids are a pair of a first and a second nucleic acid sequence, the first and second nucleic acid sequence comprising a sequence of at least about 70% homologous to any of SEQ ID NO: l, SEQ ID ⁇ :2 5 SEQ ID NO:3, SEQ ID NO:4 5 SEQ ID NO:5 5 SEQ ID NO:6 5 SEQ ID NO:7, SEQ ID NO:8, SEQ ID NO:9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, and/or SEQ ID NO: 15.
  • any of the mtethods disclosed herein comprise the step of acquiring, measuring or quantifying the expression of any one or plurality of immune checkpoint proteins or variants thereof prior to the step of calculating a ratio of expression of the nucleic acid sequences associated with immune checkpoint proteins or variants therof.
  • the value of expression for a first nucleic acid sequence or variant thereof is divided by the value of expression of a second nucleic acid sequence or variant therof.
  • the step of calculating the ratio of expression comprises generating a pair of immune checkpoint proteins or variants thereof comprises normalizing the values of expression of the first and second nucleic acids or respective variants thereof prior to dividing their values.
  • the pair of genes expressing an immune check point protein or variants thereof comprises a first nucleic acid sequence (z) encoding an immune checkpoint protein selected from SEQ ID NO. 1 through SEQ ID NO: 15 or variants thereof and the second, different, nucleic acid sequence (j) encoding an immune checkpoint protein or variant thereof is also selected from SEQ ID NO. 1 through SEQ ID NO: 15 or variants thereof.
  • Calulcation of any ratio of the mRNA or protein expression of the nucleic acids can be performed by the following formula I: where expi(x) and exp j (x) denote the expression of checkpoint genes i and j in sample x.
  • the average number Fi,j(x) can be calculated by adding the Fi,j(x) of each pair of nucleic acids and dividing that value by the number of pairs chosen to perform the analysis. In some embodiments, there are no fewer than 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 or more pairs of nucleic acids encoding encoding an immune checkpoint protein selected from SEQ ID NO. 1 through SEQ ID NO: 15 or variants thereof.
  • Fi,i(x) level over the values of each ratio of nucleic acids is at or great than about 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, then that average value of Fi,j(x) can be used confirm that the subject has an antigen-specific immune response against the cancer that is characterized as high and thus predictive of the subject's good clinical outcome and/or responsiveness to immune checkpoint blockade therapy.
  • any of the methods disclosed herein can be any combination of two mRNA or protein expression values normalized against a control level of expression chosen from: SEQ ID NO: l through SEQ ID NO: 15 or a variant of any of the same comprising at least 70% sequence identity to any of SEQ ID NO: l through SEQ ID NO: 15.
  • any of the methods disclosed herein further comprise a step of determining the number of immune cells in a sample.
  • any of the methods disclosed herein further comprise the step of determining the number of immune cells in a sample comprises quantifying the number of CD8+ and CD4+ T cells.
  • the numbers of CD8+ or CD4+ T cells can be used to correlate the level of antigen-specific immune activity against one or a plurality of cancer cells in the subject or can be used to correlate the likelihood that the subject responds to immune checkpoint blockade (ICB) therapy.
  • ICB immune checkpoint blockade
  • the information may be then be used by a physician treating the subject to prescribe the subject ICB therapy or inform the subject that he or she has a good clinical outcome relative to the clinical outcome of a subject who is not responsive to an ICB therapy.
  • a good clinical outcome for a subject on ICB therapy may be about 30% relative improvement in 4-year overall survival rate (p ⁇ 0.001), as compared to subject noton the ICB therapy.
  • a good clincial outcome or a subject that responds to ICB thraepy demontsrates an improved median and 4-year OS of 16.9 months (95CI 15.6-19.3; vs. 7.7 months, 95CI 7.2-8.4) and 32.4% (95CI 29.5-35.3; vs. 21.0%, 95CI 19.6-22.2, all p ⁇ 0.001), respectively.
  • the disclosure also relates to a method of treating a subject in need thereof diagnosed or suspected of having melanoma, the subject having skin cancer and the method comprises: (a) calculating the ratio of expression of a first nucleic acid sequence over the expression of a second nucleic acid sequence in a sample from the subject; (b) assigning a probability score to at least the first pair of nucleic acid sequences based upon the ratio of expression of at least the first pair of nucleic acid sequences; and (c) identifying antigen- specific immune activity of the subject or the population of subjects based upon the ratio of expression of the first and the second nucleic acid sequences, wherein, if the ratio of expression of the first as compared to the second nucleic acid has an average score of above about 6, 7, 8, or 9 or greater, the subject is identified as having a high level of antigen- specific immune activity against the cancer; wherein the first nucleic acid sequence and the second nucleic acid sequence encode an immune checkpoint protein or variant thereof.
  • the method of treatment further comprises the step of determining the average probability score over at least about 5, 10, 11, 12, 13, 14, or 15 or more pairs of nucleic acid sequences and wherein the step of predicting the subject or population's responsiveness to the therapy is based upon the average probability score assigned over the at least 5, 10, 11, 12, 13, 14, or 15 pairs of nucleic acid sequences; and wherein, in some embodiments, the methods further comprise the step of administering to the subject in need thereof a pharmaceutical composition comprising a therapeutically effective amount of an immune checkpoint blockage therapy if the subject is identified as being responsive to ICB therapy or having a cancer susceptible to ICB therapy.
  • the methods further comprise a step of quantifying the levels of mRNA or protein expression of two or more immune checkpoint proteins or variants thereof in a sample from the subject, wherein the subject is diagnosed with having cancer or exhibiting one or more symptoms of cancer.
  • Detection of mRNA or protein levels are known in the art and include in situ hybridization using antibodies capable of binding protein expressed by the nucleic acids disclosed herein, microarray analysis, PCR, RT-PCR, quantitative and semi-quantitiative PCR, fluorescence and/or absorbance readings of labels on the nucleic acids.
  • the disclosure relates to methods of identifying a level of antigen- specific immunity in a subject against at least one cancer cell in the subject comprising determining the level of RNA or protein expression of a first and second nucleic acid sequence in a sample from the subject, selecting the first and second nucleic acid sequences to form at least a first pair of nucleic acid sequences, calculating a score for relative expression as between the first and second nucleic acid sequence, an identifying the subject as having a high level of antigen-specific immunity against the at least one cancer cell if the score exceeds a threshold value related to a level of antigen-specific immunity in a control subject.
  • the methods of identifying a level of antigen-specific immunity in a subject against at least one cancer cell in the subject comprises selecting at least a first, second, third, fourth, fifth, and/or sixth nucleic acid sequence or more, and selecting at least a first, second, third or more pairs of the nucleic acid sequences.
  • the pair of nucleic acid sequences comprise two nucleic acid sequences associated with anti-CTLA-4 or anti- PD1 blockade therapy.
  • the methods disclosed herein comprises selecting, 1, 2, ,3 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more pairs of nucleic acid sequences, and a score corresponding to the relative expression ratio of the nucleic acid sequences in the is calculated.
  • the levels of antigen- specific immunity in the subject is based upon relative protein expression ratio of at least the first and second nucleic acid sequences. In some embodiments, the first and/or second nucleic acid sequences are based upon the expressible portion of nucleic acid sequences identified herein responsible for mRNA expression. In some embodiments, the first and/or second nucleic acid sequences are based upon the expressible portion of nucleic acid sequences identified herein responsible for protein expression. In some embodiments, the antigen- specific immunity is an antigen- specific immunity against a skin cell expession one or more tumor associated antigens. In some embodiments, the antigen-specific immunity in the subject is the level of immunity raised against one or a plurality of skin cancer cells.
  • the antigen- specific immunity in the subject is the level of immunity raised against a one or a plurality of tumor cells. In some embodiments, the antigen- specific immunity in the subject is the level of immunity raised against a one or a plurality of melanoma tumor cells. In some embodiments, the first or the second nucleic acid sequence encodes a protein or variant thereof associated with anti-immune checkpoint blockage therapy
  • the methods disclosed herein also relate to methods of determining a level of antigen-specific immunity in a subject diagnosed with cancer comprising calculating relative levels of expression of one or more pairs of nucleic acid sequences from a sample of the subject, calculating a probability score for each pair of nucleic acid sequences, and determining an average probability score over each pair of nucleic acids sequences, and correlating the average probability score to a threshold value such that if the score if at or higher than the threshold value, the subject is characterized as having a high level of antigen- specific immunity against the cancer of the subject and, if the average probability score is below the threshold value, the subject is characterized as not having a high antigen- specific immunity against the cancer of the subject.
  • the methods disclosed herein comprise determining relative ratio of expression of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more pairs of nucleic acid sequences that are immune checkpoint proteins or variants thereof and then calculating an average probability score over each pair of the nucleic acid sequences.
  • Any pair of the disclosed nucleic acid sequences are possible in each embodiment, including any pairs disclosed in Supp. Table 8.
  • the first nucleic acid of the pair is the left-hand name of each pair disclosed in the columns of Supp. Table 8 and the second nucleic acid is the right-hand of name of each pair disclosed in Supp. Table 8.
  • the threshold values of any of the methods disclosed herein may be calculated by measuring the AUC of an ROC curve.
  • ROC curve generation is known in the art.
  • US Patent No. 7552035 which is incrorpoated by reference in its entirety, details a method of generating the curves with an acquired dataset.
  • the step of calculating a probability score comprises generating an ROC curve and then calculating AUC for a given dataset.
  • the AUC may reflect the threshold value or the average AUC over several caluclations of expression of individual pairs (e.g. 5, 10, 11, 12, 13, 14 or 15 or more pairs) of nucleic acid sequence disclosed herein.
  • the present disclosure provides a method and system to determine the ability of a model to discriminate between normal operations or a fault situation may be evaluated using Receiver Operating Characteristic (ROC) curve analysis.
  • the step of calculating a probability score comprises performing an ROC analysis, calculating AUC and assigning a score based upon the AUC measurement in respect to the ratio of expression of each pair of nucleic acids analyzed and disclosed herein.
  • the step of assigning a probability score can comprise, in such cases, comparing the average AUC values to a threshold value and characterizing the subject as being a responder to ICB therapy if the average AUC value is at or above the threshold value and characterizing the subject as not being a responder of ICB therapy if the average AUC value is lower than the threshold value.
  • the subject has metastatic cancer. When the results of a particular model are considered in two populations, one population without operating condition (e.g. responsive to ICB therapy), the other population operating with a fault (non-responsive to ICB therapy), there will rarely be a perfect separation between the two groups.
  • the disclosure also relates to a method of determining the level of antigen- specific immunity of a subject to a cancer comprising correlating the one or plurality of probability scores to a particular clinical outcome.
  • being characterized as having a high antigen- specific immunity against the cancer is predictive of or correlated to the likelihood that the subject will respond to therapy, such as immune checkpoint blockade therapy. If the subject is characterized as being responsive to the therapy, the relative levels of each probability score can be used simultaneously to predict whether the prognosis of the subject is improved. Conversely if the subject is characterized as not having a high level of antigen-specific immunity, then the relative levels of each probability score can be used simultaneously to predict whether the prognosis of the subject is not improved.
  • the disclosure also relates to a method of detecting expression of immune checkpoint proteins or transcripts of the same in a sample of a subject with cancer or suspected as having cancer, the method comprising:
  • the method comprises at least 5, 10, 11, 12, 13, 14, or 15 pairs of nucleic acid sequence combinations from the set of nucleic acids chosen from: SEQ ID NO: l through SEQ ID NO: 15.
  • the pairs of nucleic acids are those pairs identified in Supp. Table 8, whereby the first nucleic acid appears first in the pair and the second nucleic acid sequence appears second in the pair disclosed in Supp.
  • the step of assigning a probability score comprises subjecting the one or more ratio of expression calculations for each nucleic acid pair to ROC analysis and then calculating the AUC of the ROC curve for each nucleic acid pair; and subsequently calculating the average AUC for the entire set of pair of nucleic acids.
  • the step of assigning further comprises assigning a 1 to any AUC calculation that is at about a value of 1 and assigning a 0 to any AUC calculation less than 1, and, if the average assigned value of the AUC is from about 1 or greater, the method comprises classifying the subject as having a high level of immune activity as compared to a control subject; and, if the average assigned value of the AUC is from about 0.99 or less, the method comprises classifying the subject as not having a high level of antigen- specific immune activity as compared to a control subject.
  • high levels of antigen-specific immune activity is correlated to the subject being a responder to ICB therapy and not having high levels of immune activity is correlated to the subject be a non-repsonder to ICB therapy.
  • the ratios of expression of at least one or several pairs of nucleic acid sequences is calculated by the Formula of page 45 of the application.
  • the step may be accomplished by RNA sequencing, fluorescence, semi-quantitiative or quantitative PCR, absorbance measurements or the like.
  • the probe may be an antibody or an antibody probe that binds specifically to any of the proteins or variants thereof encoded by SEQ ID NO: l through 15.
  • components and/or units of the devices described herein may be able to interact through one or more communication channels or mediums or links, for example, a shared access medium, a global communication network, the Internet, the World Wide Web, a wired network, a wireless network, a combination of one or more wired networks and/or one or more wireless networks, one or more communication networks, an a-synchronic or asynchronous wireless network, a synchronic wireless network, a managed wireless network, a non-managed wireless network, a burstable wireless network, a non-burstable wireless network, a scheduled wireless network, a non-scheduled wireless network, or the like.
  • a shared access medium for example, a shared access medium, a global communication network, the Internet, the World Wide Web, a wired network, a wireless network, a combination of one or more wired networks and/or one or more wireless networks, one or more communication networks, an a-synchronic or asynchronous wireless network, a synchronic wireless network, a managed wireless network
  • computing may refer to operation(s) and/or process(es) of a computer, a computing platform, a computing system, or other electronic computing device, that manipulate and/or transform data represented as physical (e.g., electronic) quantities within the computer's registers and/or memories into other data similarly represented as physical quantities within the computer's registers and/or memories or other information storage medium that may store instructions to perform operations and/or processes.
  • data represented as physical (e.g., electronic) quantities within the computer's registers and/or memories into other data similarly represented as physical quantities within the computer's registers and/or memories or other information storage medium that may store instructions to perform operations and/or processes.
  • Some embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment including both hardware and software elements. Some embodiments may be implemented in software, which includes but is not limited to firmware, resident software, microcode, or the like.
  • some embodiments may take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system.
  • a computer-usable or computer-readable medium may be or may include any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • the medium may be or may include an electronic, magnetic, optical, electromagnetic, InfraRed (IR), or semiconductor system (or apparatus or device) or a propagation medium.
  • a computer-readable medium may include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a Random Access Memory (RAM), a Read-Only Memory (ROM), a rigid magnetic disk, an optical disk, or the like.
  • RAM Random Access Memory
  • ROM Read-Only Memory
  • optical disks include Compact Disk-Read-Only Memory (CD-ROM), Compact Disk-Read/Write (CD- R/W), DVD, or the like.
  • a data processing system suitable for storing and/or executing program code may include at least one processor coupled directly or indirectly to memory elements, for example, through a system bus.
  • the memory elements may include, for example, local memory employed during actual execution of the program code, bulk storage, and cache memories which may provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution.
  • I/O devices including but not limited to keyboards, displays, pointing devices, etc.
  • I/O controllers may be coupled to the system either directly or through intervening I/O controllers.
  • network adapters may be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices, for example, through intervening private or public networks.
  • modems, cable modems and Ethernet cards are demonstrative examples of types of network adapters. Other suitable components may be used.
  • Some embodiments may be implemented by software, by hardware, or by any combination of software and/or hardware as may be suitable for specific applications or in accordance with specific design requirements. Some embodiments may include units and/or sub-units, which may be separate of each other or combined together, in whole or in part, and may be implemented using specific, multi-purpose or general processors or controllers. Some embodiments may include buffers, registers, stacks, storage units and/or memory units, for temporary or long-term storage of data or in order to facilitate the operation of particular implementations .
  • Some embodiments may be implemented, for example, using a machine- readable medium or article which may store an instruction or a set of instructions that, if executed by a machine, cause the machine to perform a method and/or operations described herein.
  • Such machine may include, for example, any suitable processing platform, computing platform, computing device, processing device, electronic device, electronic system, computing system, processing system, computer, processor, or the like, and may be implemented using any suitable combination of hardware and/or software.
  • the machine- readable medium or article may include, for example, any suitable type of memory unit, memory device, memory article, memory medium, storage device, storage article, storage medium and/or storage unit; for example, memory, removable or non-removable media, erasable or non-erasable media, writeable or re-writeable media, digital or analog media, hard disk drive, floppy disk, Compact Disk Read Only Memory (CD-ROM), Compact Disk Recordable (CD-R), Compact Disk Re- Writeable (CD-RW), optical disk, magnetic media, various types of Digital Versatile Disks (DVDs), a tape, a cassette, or the like.
  • any suitable type of memory unit for example, any suitable type of memory unit, memory device, memory article, memory medium, storage device, storage article, storage medium and/or storage unit; for example, memory, removable or non-removable media, erasable or non-erasable media, writeable or re-writeable media, digital or analog media, hard disk drive, floppy disk, Compact Dis
  • the instructions may include any suitable type of code, for example, source code, compiled code, interpreted code, executable code, static code, dynamic code, or the like, and may be implemented using any suitable high-level, low-level, object-oriented, visual, compiled and/or interpreted programming language, e.g., C, C++, Java, BASIC, Pascal, Fortran, Cobol, assembly language, machine code, or the like.
  • code for example, source code, compiled code, interpreted code, executable code, static code, dynamic code, or the like
  • suitable high-level, low-level, object-oriented, visual, compiled and/or interpreted programming language e.g., C, C++, Java, BASIC, Pascal, Fortran, Cobol, assembly language, machine code, or the like.
  • kits contain software and/or software systems, such as those described herein.
  • the kits may comprise microarrays comprising a solid phase, e.g., a surface, to which probes are hybridized or bound at a known location of the solid phase.
  • these probes consist of nucleic acids of known, different sequence, with each nucleic acid being capable of hybridizing to an RNA species or to a cDNA species derived therefrom.
  • the probes contained in the kits of this invention are nucleic acids capable of hybridizing specifically to nucleic acid sequences derived from RNA species in cells collected from subject of interest.
  • any of the disclosed methods comprise a step of obtaining or providing information associated with a disease or disorder.
  • the step of obtaining or providing comprises isolating a sample from a subject or population of subjects and, optionally performing a genetic screen to obtain expression data or nucleic acid sequence activity data which can then be analyzed with other disclosed steps as compared to a control subject or control population of subjects.
  • data or information associated with a subject or population of subjects may be obtained by an individual patient and scored across any or all of the steps disclosed herein by comparing the analysis to information associated with a disease or disorder from a control subject or control population of subjects.
  • the disease is cancer.
  • the data or information associated with a disease is taken from any of the data provided in https://gdc-portal.nci.nih.gov, an NIH database of clinical data, which is hereby incorporated by reference in its entirety. Any of the data from the website may be analyzed across one or a plurality of conditions including cancer types disclosed on within the NIH database.
  • kits of the invention also contains one or more databases described above, encoded on computer readable medium, and/or an access authorization to use the databases described above from a remote networked computer.
  • a kit of the invention further contains software capable of being loaded into the memory of a computer system such as the one described above. The software contained in the kit of this invention, is essentially identical to the software described above.
  • HVEM A 1 1 1 1 6 https://www.ncbi.nlm.nih.gOv/pmc/articles/P
  • TNFSF1 A 0 1 1 1 5 https://www.ncbi.nlm.nih.gOv/pmc/articles/P 4 1 MC3786574/
  • exp t (x) and exp j (x) denote the expression of genes i and j in sample x.
  • CDPs Consistently differentially expressed immune pathways in melanoma ICB responders
  • IMPRES checkpoint pairs features. This leads to a binary vector of length 15 for each sample. The total number of Ts in this vector denotes the sample's IMPRES score (ranging between 0 and 15). High scores predict good response.
  • IMPRES score ranging between 0 and 15.
  • RN A- sequencing of 31 anti-PD-1 pre- and on-treatment tumor specimens, and 10 anti-CTLA-4 pre- and on- treatment metastatic tumor specimens (for which the response is known) derived from patients with metastatic melanoma (up to 90 days from treatment start) was conducted as previously described in Jenkins et al 28 (Supp. Table 9). These patients were enrolled in clinical trials at Massachusetts General Hospital. Clinical trial registration numbers at ClinicalTrials.gov are NCT01714739; NCT02083484; NCT01543698; NCT01072175; NCT00949702; NCT01783938; NCT01006980.
  • Table 1 summarizes the response annotations and criteria used for establishing them in the original study.
  • the response classification of each patient in each of the publicly available studies and the MGH dataset is not shown but in N. Alexander, et al. Nature Medicine, 2018, incorporated by reference in its entirety.
  • Van Allen et al. 1 ' 3 (anti-PD-1 and anti-CTLA-4 datasets, respectively).
  • the final feature set is also selected in the same manner (using similar definition of score(f) and selecting features with binomial p-value ⁇ 0.05).
  • the predictors' performance is evaluated using the nine publicly available melanoma datasets analyzed to evaluate IMPRES (see Example 2).
  • the Cytolytic activity 15 and PDL1 expression based predictors are applied in a straightforward manner, analogous to that of IMPRES as they do not require additional training.
  • making predictions using gene signatures reported in specific studies in the literature requires training on every specific dataset tested (using cross validation), aiming to identify their maximal performance levels.
  • SVMs Support Vector Machines
  • Each such SVM predictor is built using the genes in the specific signatures on which it is based as its feature set. This is performed with linear kernels using 100 repetitions of a five-fold cross validation process, where in each fold the training set and test set are randomly selected.
  • the AUC presented for each predictor is the mean AUC overall repetitions (Supp. Table 6, upper panel).
  • PCA Principle Component Analysis
  • PCA principle component analysis
  • PCI (explaining 7.2% of the variance) shows differences in clinical annotations of the samples (between those considered 'high risk' vs 'not high risk' and those considered 'favorable disease course' vs. 'not favorable disease course', Figure 4A).
  • PC2 and PC3 (explaining 3.8% and 2.3% of the variance, respectively) reveal two different clusters that are not associated with any clinical features.
  • expi(x) and exp j (x) denote the expression of checkpoint genes i and j in sample x.
  • PD-1 CD137L [00120] To choose the features that best separate spontaneous regression from no spontaneous regression samples in the NB data, we use a hill climbing aggregative feature selection procedure. This procedure starts from an empty set and incrementally adds the best discriminating feature at each step in a greedy manner, until no further improvement in the prediction accuracy is obtained. More formally, we repeat the following procedure with 500 iterations:
  • RNA-seq RNA-sequencing
  • the prediction of spontaneous regression of a tumor sample from its expression data is simply made by counting the number of predictive feature pairs that are fulfilled (true) in that sample given its transcriptomics data. This number, ranging from 0-15, denotes its EVImuno- PREdictive Score (IMPRES), with higher scores predicting spontaneous regression (Methods; Supp. Table 2). The resulting predictor obtains an accuracy of 0.9 (in terms of the Area Under the Receiver Operator Curve (AUC)) in the NB dataset ( Figure 4, Methods).
  • CDPs Consistently Differentially expressed Pathways
  • IMPRES achieves AUCs of 0.81 and 0.97 on the anti-PD-1 and anti-CTLA-4 samples respectively ( Figure 2B). It maintains its predictive accuracy when evaluating the aggregate collection of the datasets studied above (a total of 297 samples, Figure 2B).
  • Figure 2C shows the number of true/false positives (responders) and true/false negatives (non-responders) obtained on this aggregated data at different IMPRES score classification thresholds, manifesting the well-known tradeoff between precision and recall (Figure 2D, Figure 6A-B, Supp. Table 5).
  • Higher IMPRES scores are also associated with improved overall survival and progression-free survival (PFS) in ICB treated melanoma patients (Methods, Figure 2E-H, Figure 6C).
  • IMPRES is constructed only once from the NB data and never trained on any melanoma dataset. Thus, it is markedly less prone to over-fitting, a paramount concern regarding standard cancer transcriptomics predictors 16 18 .
  • CD27 agonist plus anti-PD- 1 recapitulates the effects of CD4+ T helper cells on tumor control, while the combination of a CD27 agonist plus anti-CTLA-4 did not improve tumor control 20.
  • IMPRES' high predictive performance is mainly due to two key conjectures: (a) key immune mechanisms underlining spontaneous regression in NB are shared with those determining response to ICB in melanoma, and (b) those may be captured by specific pairwise relations of immune checkpoint genes' expression. Building on these assumptions leads to a predictor of response to checkpoint therapy that is significantly superior to existing predictors and displays robust performance across many different melanoma datasets. From a translational standpoint, we show that IMPRES can correctly capture almost all true responders while misclassifying less than half of the non-responders.

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Abstract

La présente invention concerne des méthodes de prédiction de la réactivité à une thérapie chez des sujets ou des populations de sujets atteints d'un cancer. L'invention concerne des méthodes de prédiction de l'effet probable de traitements du mélanome ou d'une combinaison de traitements sur la base de l'expression de protéines de molécules immunostimulatrices. L'invention concerne également un logiciel destiné à exécuter les étapes décrites dans la description ainsi que des procédés mis en œuvre par ordinateur.
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WO2021030293A1 (fr) * 2019-08-09 2021-02-18 Arizona Board Of Regents On Behalf Of The University Of Arizona Méthodes de surveillance ou de prédiction de réponse à des immunothérapies pour un cancer gynécologique

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WO2021030293A1 (fr) * 2019-08-09 2021-02-18 Arizona Board Of Regents On Behalf Of The University Of Arizona Méthodes de surveillance ou de prédiction de réponse à des immunothérapies pour un cancer gynécologique
CN111931421A (zh) * 2020-08-07 2020-11-13 合肥工业大学 基于秩次相关的因果结构图的燃气轮机故障预测方法
CN111931421B (zh) * 2020-08-07 2023-09-29 合肥工业大学 基于秩次相关的因果结构图的燃气轮机故障预测方法

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