Motivation: Complex cancer omics data can be difficult to interpret and analyze with standard statistical methods. We thereby propose an innovative data representation that drastically reduces complexity while improving usability and interpretability for complex cancer phenotype analysis.
Method: Despite recent advances in omics technologies, the robustness of predictive biomarkers in cancer remains severely limited. We hypothesize that this is primarily due to an overemphasis on applying statistical learning methods without taking into consideration the underlying biological processes driving cancer. We therefore propose a new approach based on representing data based on the comparison to a baseline group. This results in a data format that encodes biologically meaningful information and can be easily analyzed. We apply this transformation to publicly available datasets obtained across multiple tumor types using different omics technologies. For each cancer phenotype considered, we cross-validate the learned decision rules using SVMs and random forests and demonstrate that there is no drop in performance despite the use of a simplified data representation. We also apply the Chi-squared test to our simplified data to select genomic features differentially associated with relevant cancer phenotypes. To this end we compare our method to traditional class comparison approaches. Overall, this analysis shows that omics features selected by our method provides equal or better classification performance than standard methods. Further, we show that our simplified data representation filters out much of the biologically irrelevant variation and that the resulting data can be successfully applied to gene set analysis applications, ultimately improving inference on disease phenotypes. For instance, by applying our method to signaling pathways and cancer hallmarks gene sets, we show that our approach can be used to detect dysregulated pathways more efficiently than with traditional methods.
Conclusion: By comparing cancer omics data to a baseline status, we obtain a much simpler data representation that preserves biologically relevant information while eliminating much of the unwanted variance that is often confounding in the analysis of high-dimensional data. Furthermore, data represented using our approach can be easily stored and analyzed, and it is equivalent or superior to traditional data representation methods for predicting clinically relevant cancer phenotypes and detecting biologically relevant cancer pathways.
Citation Format: Wikum Dinalankara, Qian Qe, Lanlan Ji, Yiran Xu, Nicole Pagane, Francisco Lobo, Laurent Younes, Donald Geman, Luigi Marchionni. Divergence analysis with coarse coding of omics data across cancer phenotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4551. doi:10.1158/1538-7445.AM2017-4551