Abstract LB-311: Combinatorial logic over breast cancer control modules predicts survival and chemotherapy response of ISPY1 breast cancer patients (CALGB 150007/150012; ACRIN 6657)
Authors: Denise Wolf, Marc Lenburg, Christina Yau, Aaron Boudreau, Laura Esserman, Nola Hylton, Laura Van 't Veer
Journal: Cancer Research (2011)

Abstract Background: Breast cancer is a heterogeneous disease. Clinically useful predictors for breast cancer management may therefore need to consider the variety of molecular processes that can impact chemotherapy response and survival. With the growing database of gene expression data, it is now possible to deduce co-expression modules active in breast cancer subpopulations. Viewed from a combinatorial perspective supporting ‘OR’ and ‘AND’ reasoning, the activities of these modules might provide insights into breast cancer biology and form the basis for improved predictors. Methods: We assembled 74 breast-cancer related gene expression datasets containing ∼5,500 samples altogether. Per dataset, we identified genes with bimodal expression analyzed using mixture-model clustering to ultimately find gene groups that are consistently co-regulated across multiple datasets. We scored the AFFY U133 ISPY1 dataset (117 pts) for module expression, and explored the relationships between module state, chemotherapy response, patient outcome, receptor status, intrinsic subtype, and other signatures. To derive logical functions consisting of nested ‘AND’ and ‘OR’ gates relating module expression to clinical variables, we discretized the module scores and applied a methodology in the spirit of Karnaugh maps for digital circuit design. Results: Our meta-analysis identified 11 modules ranging in size from ∼5–200 genes. The expression level of some modules were associated with known molecular markers such as intrinsic subtype or wound signature scores, whereas others – three immune related modules, two ECM related modules, a mixed immune/ECM module, and a small module encoding histones – do not. Disease free survival of patients with triple negative disease is predicted (p=1.93e-08) by the logical function fDFS_TN=[ER_module&amp;gt;LOW] OR [TN_Immune_ECM=HIGH] OR [(AURK/PROLIF_module=HIGH) AND ((Histone_module&amp;gt;LOW) OR (TN_Immune_ECM=HIGH)], where TN_Immune_ECM is a composite function over the three immune modules and the immune/ECM hybrid module. The best predictor of chemo-sensitivity for triple negatives is a hybrid over subtype and module state, fRCB01_TN=Subtype_basal AND TN_immune_ECM, correctly classifying 95% of the RCB01 sensitives and 90% of the RCBIII resistants. Conclusion: In triple negatives, high proliferation can be rescued by immune/ECM upregulation, though neither immune rescue nor low RCB is necessary for survival if the estrogen module is even slightly upregulated or if the histone module is high. Similar analyses of ER/Pr+ and Her2+ patients demonstrate receptor-subtype specificity in the logic predicting response. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-311. doi:10.1158/1538-7445.AM2011-LB-311