Abstract 3754: Predicting cancer phenotypes with mechanism-driven multi-omics data integration

Over the past decade technological advances have enabled molecular profiling of human cancers across distinct genomic domains and other “omes”. The availability of such multi-omics datasets has in turn enabled the discovery of cancer subtypes characterized by distinct molecular patterns within and across different data modalities. Despite promising beginnings and the wealth of data, most efforts so far have focused on the discovery of new molecular taxonomies, enumerating novel cancer subtypes, and only subsequently projecting them into a biological context by leveraging knowledge on genetic and epigenetic variations, genomic alterations, gene expression patterns, and, in general, cell pathophysiology. A paradigmatic approach to omics-based cancer classification usually entails the i) discovery of novel molecular subtypes; (ii) the biological contextualization of such subtypes and their correlation with clinical phenotypes; and (iii) the development of predictors to detect these subtypes. Nevertheless, the direct clinical utility of such taxonomies is less evident. Some of the molecular subtypes, for instance, might not portend any different clinical behavior, or the underlying molecular pathways might not be actionable. Ultimately, existing biological knowledge enters the analysis only a posteriori to characterize and “label” the novel subtypes, rather than being leveraged a priori to guide the discovery process itself. To overcome such nearly universal absence of mechanistic underpinnings for the omics-derived signatures and develop clinically useful biomarkers, we have proposed to develop mechanistic predictive models by incorporating gene network and signaling pathway information directly into the statistical learning process used to detect the cancer phenotypes. Unlike the paradigm described above, we used omics data and prior biological information to directly detect and predict the phenotypes. We now further extend this concept and leverage biological knowledge also to constrain multi-omics data integration, by implementing predictive rules that mechanistically aggregate measurements across distinct genomic modalities, reproducing the natural flow of biological information in the cell: from genome to phenotype, through epigenome, transcriptome and proteome. To illustrate our approach and its impact on computational learning and cancer classification, we analyze clinically relevant cancer phenotypes using independent training and testing data. To this end we build our novel predictors using the Top Scoring Pair (TSP) algorithm, a two-gene parameter-free classifier, and its multi-pair extension kTSP. We then compare the classification performance of predictors derived from a single omics modality to those constructed by integrating multi-omics data according to mechanistic and biologically meaningful rules, revealing increased accuracy with the integrated classifiers. Citation Format: Luigi Marchionni, Donald Geman. Predicting cancer phenotypes with mechanism-driven multi-omics data integration. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3754. doi:10.1158/1538-7445.AM2015-3754