title: Pathway-based personalized analysis of breast cancer expression data.
abstract: BACKGROUND
  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.
authors.affiliation: Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel. Electronic address: annaliv@gmail.com.
authors.forename: Anna
authors.initials: A
authors.lastname: Livshits
authors.affiliation: Cancer Research UK Cambridge Research Institute, Cambridge CB2 0RE, UK; Department of Oncology, University of Cambridge, Cambridge CB2 2XZ, UK. Electronic address: Anna.Git@cruk.cam.ac.uk.
authors.forename: Anna
authors.initials: A
authors.lastname: Git
authors.affiliation: Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel. Electronic address: Garold.fuks@weizmann.ac.il.
authors.forename: Garold
authors.initials: G
authors.lastname: Fuks
authors.affiliation: Cancer Research UK Cambridge Research Institute, Cambridge CB2 0RE, UK; Department of Oncology, University of Cambridge, Cambridge CB2 2XZ, UK. Electronic address: Carlos.Caldas@cruk.cam.ac.uk.
authors.forename: Carlos
authors.initials: C
authors.lastname: Caldas
authors.affiliation: Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel. Electronic address: eytan.domany@weizmann.ac.il.
authors.forename: Eytan
authors.initials: E
authors.lastname: Domany
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling; D018734:Genes, erbB-2; D006801:Humans; D057285:Precision Medicine; D011960:Receptors, Estrogen / Q000235:genetics; D015203:Reproducibility of Results
chemical_list: D011960:Receptors, Estrogen
keywords: Breast cancer; PKA pathways; Pathway-based analysis