Open AccessPathway‐based personalized analysis of breast cancer expression data
Author(s) -
Livshits Anna,
Git Anna,
Fuks Garold,
Caldas Carlos,
Domany Eytan
Publication year - 2015
Publication title -
molecular oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.332
H-Index - 88
eISSN - 1878-0261
pISSN - 1574-7891
DOI - 10.1016/j.molonc.2015.04.006
Subject(s) - breast cancer , cancer , biological pathway , computational biology , deregulation , biology , gene , subclass , pathway analysis , cancer research , gene expression , oncology , bioinformatics , medicine , immunology , genetics , antibody , macroeconomics , economics
Most analyses of high throughput cancer data represent tumors by “atomistic” single‐gene properties. Pathifier, a recently introduced method, characterizes a tumor in terms of “coarse grained” pathway‐based variables. Methods We applied Pathifier to study a very large dataset of 2000 breast cancer samples and 144 normal tissues. Pathifier uses known gene assignments to pathways and biological processes to calculate for each pathway and tumor a Pathway Deregulation Score (PDS). Individual samples are represented in terms of their PDSs calculated for several hundred pathways, and the samples of the data set are analyzed and stratified on the basis of their profiles over these “coarse grained”, biologically meaningful variables. Results We identified nine tumor subtypes; a new subclass (comprising about 7% of the samples) exhibits high deregulation in 38 PKA pathways, induced by overexpression of the gene PRKACB. Another interesting finding is that basal tumors break into two subclasses, with low and high deregulation of a cluster of immune system pathways. High deregulation corresponds to higher concentrations of Tumor Infiltrating Lymphocytes, and the patients of this basal subtype have better prognosis. The analysis used 1000 “discovery set” tumors; our results were highly reproducible on 1000 independent “validation” samples. Conclusions The coarse‐grained variables that represent pathway deregulation provide a basis for relevant, novel and robust findings for breast cancer. Our analysis indicates that in breast cancer reliable prognostic signatures are most likely to be obtained by treating separately different subgroups of the patients.