title: Integrated molecular profiles of invasive breast tumors and ductal carcinoma in situ (DCIS) reveal differential vascular and interleukin signaling.
abstract: We use an integrated approach to understand breast cancer heterogeneity by modeling mRNA, copy number alterations, microRNAs, and methylation in a pathway context utilizing the pathway recognition algorithm using data integration on genomic models (PARADIGM). We demonstrate that combining mRNA expression and DNA copy number classified the patients in groups that provide the best predictive value with respect to prognosis and identified key molecular and stromal signatures. A chronic inflammatory signature, which promotes the development and/or progression of various epithelial tumors, is uniformly present in all breast cancers. We further demonstrate that within the adaptive immune lineage, the strongest predictor of good outcome is the acquisition of a gene signature that favors a high T-helper 1 (Th1)/cytotoxic T-lymphocyte response at the expense of Th2-driven humoral immunity. Patients who have breast cancer with a basal HER2-negative molecular profile (PDGM2) are characterized by high expression of protumorigenic Th2/humoral-related genes (24-38%) and a low Th1/Th2 ratio. The luminal molecular subtypes are again differentiated by low or high FOXM1 and ERBB4 signaling. We show that the interleukin signaling profiles observed in invasive cancers are absent or weakly expressed in healthy tissue but already prominent in ductal carcinoma in situ, together with ECM and cell-cell adhesion regulating pathways. The most prominent difference between low and high mammographic density in healthy breast tissue by PARADIGM was that of STAT4 signaling. In conclusion, by means of a pathway-based modeling methodology (PARADIGM) integrating different layers of molecular data from whole-tumor samples, we demonstrate that we can stratify immune signatures that predict patient survival.
authors: Department of Genetics, Institute for Cancer Research, and Cancer Clinic, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway.
authors: Vessela N
authors: VN
authors: Kristensen
authors: Charles J
authors: CJ
authors: Vaske
authors: Josie
authors: J
authors: Ursini-Siegel
authors: Peter
authors: P
authors: Van Loo
authors: Silje H
authors: SH
authors: Nordgard
authors: Ravi
authors: R
authors: Sachidanandam
authors: Therese
authors: T
authors: Sørlie
authors: Fredrik
authors: F
authors: Wärnberg
authors: Vilde D
authors: VD
authors: Haakensen
authors: Åslaug
authors: Å
authors: Helland
authors: Bjørn
authors: B
authors: Naume
authors: Charles M
authors: CM
authors: Perou
authors: David
authors: D
authors: Haussler
authors: Olga G
authors: OG
authors: Troyanskaya
authors: Anne-Lise
authors: AL
authors: Børresen-Dale
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000098:blood supply* / Q000145:classification / Q000235:genetics* / Q000473:pathology; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000145:classification / Q000235:genetics* / Q000276:immunology / Q000473:pathology; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D023281:Genomics; D006801:Humans; D007378:Interleukins / Q000378:metabolism*; D018655:Lymphocyte Count; D016246:Lymphocytes, Tumor-Infiltrating / Q000276:immunology; D008327:Mammography; D009361:Neoplasm Invasiveness; D011379:Prognosis; D015203:Reproducibility of Results; D015398:Signal Transduction / Q000235:genetics*; D016019:Survival Analysis; D018417:Th1 Cells / Q000276:immunology; D018418:Th2 Cells / Q000276:immunology
chemical_list: D007378:Interleukins