title: Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer.
abstract: The molecular diversity of breast cancer makes it impossible to identify prognostic markers that are applicable to all breast cancers. To overcome limitations of previous multigene prognostic classifiers, we propose a new dynamic predictor: instead of using a single universal training cohort and an identical list of informative genes to predict the prognosis of new cases, a case-specific predictor is developed for each test case. Gene expression data from 3,534 breast cancers with clinical annotation including relapse-free survival is analyzed. For each test case, we select a case-specific training subset including only molecularly similar cases and a case-specific predictor is generated. This method yields different training sets and different predictors for each new patient. The model performance was assessed in leave-one-out validation and also in 325 independent cases. Prognostic discrimination was high for all cases (n = 3,534, HR = 3.68, p = 1.67 E-56). The dynamic predictor showed higher overall accuracy (0.68) than genomic surrogates for Oncotype DX (0.64), Genomic Grade Index (0.61) or MammaPrint (0.47). The dynamic predictor was also effective in triple-negative cancers (n = 427, HR = 3.08, p = 0.0093) where the above classifiers all failed. Validation in independent patients yielded similar classification power (HR = 3.57). The dynamic classifier is available online at http://www.recurrenceonline.com/?q=Re_training. In summary, we developed a new method to make personalized prognostic prediction using case-specific training cohorts. The dynamic predictors outperform static models developed from single historical training cohorts and they also predict well in triple-negative cancers.
authors.affiliation: MTA TTK Lendület Cancer Biomarker Research Group, Budapest, Hungary; 2nd Department of Pediatrics, Semmelweis University Budapest, 1094, Budapest, Tűzoltó utca 7-9, Hungary; MTA-SE Pediatrics and Nephrology Research Group, Bókay u. 53, H-1083, Budapest, Hungary.
authors.forename: Balázs
authors.initials: B
authors.lastname: Győrffy
authors.forename: Thomas
authors.initials: T
authors.lastname: Karn
authors.forename: Zsófia
authors.initials: Z
authors.lastname: Sztupinszki
authors.forename: Boglárka
authors.initials: B
authors.lastname: Weltz
authors.forename: Volkmar
authors.initials: V
authors.lastname: Müller
authors.forename: Lajos
authors.initials: L
authors.lastname: Pusztai
mesh_terms: D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008875:Middle Aged; D011379:Prognosis; D059467:Transcriptome / Q000235:genetics*; D064726:Triple Negative Breast Neoplasms / Q000235:genetics* / Q000473:pathology*
keywords: breast cancer; gene expression; survival