Abstract:
INTRODUCTION: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.
journal_name
Mol Oncoljournal_title
Molecular oncologyauthors
Livshits A,Git A,Fuks G,Caldas C,Domany Edoi
10.1016/j.molonc.2015.04.006subject
Has Abstractpub_date
2015-08-01 00:00:00pages
1471-83issue
7eissn
1574-7891issn
1878-0261pii
S1574-7891(15)00094-0journal_volume
9pub_type
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