Sense-antisense gene pairs for patient stratification, prognosis, and therapeutic biomarkers identification

Patent metadata

Field	Value
Publication number	US-2016259883-A1
Application number	US-201415030370-A
Country	US
Kind code	A1
Filing date	Oct 20, 2014
Priority date	Oct 18, 2013
Publication date	Sep 8, 2016
Grant date	—

Abstract

The present invention relates to a method of identification of clinically and genetically distinct sub-groups of patients subject to a medical condition, particularly breast, lung, and colon cancer patients using a composition of respective gene expression values for certain gene pairs. Sense-antisense gene pairs (SAGPs) which are relevant for a medical condition and the disease prognosis are used by the method to generate statistical models based on the expression values of the SAGPs. SAGPs for which the statistical models are found to have high value in prognosis of the variation of medical condition and the diseases are selected and integrated in the prognostic signature including specified parameters (e.g. cut-off values) of the prognostic model. It further relates to using respective gene expression values for these genes to predict patient′ risk groups (in context of patient's survival or/and disease progression) and to using the predicted groups for identification of patient risk, and specific and robust prognostic biomarkers with mechanistic interpretations of biological changes (associated with the gene signatures) appropriating for an implementation of therapeutic targeting.

First claim

1 . A computerized method of identifying candidate biomolecules relevant to a medical condition, the candidate biomolecules being putative clinical biomarkers for prognosis of, or putative therapeutic targets for treating, the medical condition, the method comprising:
for each subject k of a set of K subjects suffering from the medical condition, receiving subject data which indicates (i) for each gene pair i, j of a plurality of sense-antisense gene pairs (SAGPs), corresponding gene expression values y i,k , y j,k of subject k; and (ii) a survival time and survival event of subject k; identifying, using said subject data, a prognostic subset of said SAGPs which optimally stratifies the subjects into low-risk and high-risk disease progression subgroups; comparing gene expression values of each gene in the low-risk and high-risk subgroups which have been stratified by said prognostic subset of SAGPs, to identify a set of prognostic genes which are differentially expressed between the low-risk and high-risk subgroups; and identifying one or more predefined biologically-related categories of genes which are over-represented in the set of differentially expressed prognostic genes, wherein the candidate biomolecules comprise genes or gene products belonging to said over-represented categories.

2 . A computerized method according to claim 1 , wherein the set of K subjects comprises a plurality of independent cohorts of subjects.

3 . A computerized method according to claim 2 , wherein said differentially expressed prognostic genes are identified by:
for each cohort, identifying a cohort-specific set of genes which is differentially expressed in said cohort, to thereby obtain a plurality of cohort-specific sets; and finding the intersection of the cohort-specific sets to obtain the set of differentially expressed genes.

4 . A computerized method according to any one of claims 1 to 3 , wherein genes in respective predefined categories of biologically-related genes are related by one or more of:
cellular localization, biological process, molecular function, or biological pathway.

5 . A computerized method according to any one of the preceding claims, wherein identifying the prognostic subset of SAGPs comprises:
generation of a statistical partition model (SPM) for each of each SAGPs using said subject data; obtaining data characterizing the statistical significance of the SPMs; and identifying of a subset of said SAGPs using the data characterizing the statistical significance.

6 . A computerized method according to claim 5 ,
the method comprising for each SAGP: (i) defining a plurality of trial values for each of two cut-off values c i and c j ; (ii) for each of a plurality of angles α, for each subject, and for each of the trial cut-off values c i and c j : (a) comparing the expression values to a respective pair of lines in a two-dimensional space spanned by the expression values to obtain comparison data indicating on which side of the pair of lines the expression values for the corresponding subject lie, the pair of lines being formed using the cut-off values c i and c j , each of the lines having angle α to a direction in the space indicating increasing values of a corresponding one of the expression values; and (b) generating at least one SPM based on the comparison data; and (iii) selecting the one of the SPMs (‘the maximally predictive SPM’) which has the maximal statistical value in predicting the survival times of the subjects.

7 . A computerized method according to claim 6 in which for each of the plurality of angles α, and for each subject, and for each of the trial cut-off values c i and c j , a plurality of statistical partition models of survival prognosis of the patients are constructed based on a plurality of respective designs, each design representing a respective combination of possibilities for realizations of the comparison data.

8 . A computerized method according to claim 7 in which the comparison data for a given subject, a given angle α, a given said subject, and a given pair of trial cut-off values c i and c j , takes one of four possibilities:
A: indicating that both the corresponding expression values lie on a first side of the lines;
B: indicating that a first of the expression values lies on the first side of a first of the lines, and the second value lies on a second side of the second of the lines;
C: indicating that the first of the expression values lies on a second side of the first of the lines, and the second value lies on the first side of the second of the lines; and
D: indicating that both expression values lie on the second side of the lines;
and the plurality of designs include:
a first design indicating whether the subjects' expression level values are within regions A or D, rather than B or C;
a second design indicating whether the subjects' expression level values are within regions A, B or C, rather than D;
a third design indicating whether the subjects' expression level values are within regions A, C or D, rather than B;
a fourth design indicating whether the subjects' expression level values are within regions B, C or D, rather than A;
a fifth design indicating whether the subjects' expression level values are within regions A, B or D, rather than C;
a sixth design indicating whether the subjects' expression level values are within regions A or C, rather than B or D;
a seventh design indicating whether the subjects' expression level values are within regions A or B, rather than C or D.

9 . A computerized method according to any of claims 6 to 8 , comprising selecting the subset of the gene pairs for which the corresponding selected models are of maximal statistical significance of the survival prognosis model.

10 . A computerized method according to claim 9 further including i) a step of determining for each gene of the selected gene pairs the statistical significance of the expression level of the individual genes of the survival prognosis model, and ii) a step of selecting of the gene pairs for which the statistical significance of the maximally predictive SPM is higher than a threshold of the statistical significance of the individual genes of the gene pair.

11 . A computerized method of clinical outcome prognosis in a subject having a medical condition, the method comprising:
receiving data representing parameters of one or more statistical partition models (SPMs) said SPMs being configured to stratify a cohort of subjects having the medical condition into subgroups, said parameters representing, for each gene pair of one or more sense-antisense gene pairs (SAGPs), a pair of lines in a two-dimensional space spanned by respective expression level values of respective genes i, j in the gene pair, the pair of lines being formed using two cut-off values c i and c j , and each of the lines having a non-zero angle α to each of two axis directions in the space indicating increasing values of a corresponding one of the expression level values; receiving expression level data representing expression levels in the subject of genes of one or more selected SAGPs; and for each SAGP of the selected SAGPs, comparing the expression levels to the pair of lines for the SAGP to obtain comparison data indicating on which side of the pair of lines the expression values for the subject lie, thereby obtaining a prediction of a subgroup to which the subject belongs.

12 . A computerized method according to claim 11 , wherein the SAGPs comprise one or more of the gene pairs listed in Table 1A.

13 . A computerized method according to claim 11 or claim 12 , wherein the medical co

Assignees

• Agency Science Tech & Res

Inventors

• Grinchuk Oleg
• Motakis Efthimios
• Yenamandra Surya Pavan
• Kuznetsov Vladimir Andreevich

Classifications

• G16B40/20

  Supervised data analysis · CPC title

• C12Q2600/158

  Expression markers · CPC title

• C12Q2600/118

  Prognosis of disease development · CPC title

• G16C20/60

  In silico combinatorial chemistry · CPC title

• C12Q1/6886

  for cancer (immunoassay for cancer G01N33/575) · CPC title