Applying PySCMGroup to Breast Cancer Biomarkers Discovery

Background. The identification of biomarkers associated with triple-negative breast cancer (TNBC) is still an active area of research due to the complexity of finding robust biomarkers associated with the disease. Previous methods have attempted to tackle the problem from a mono-perspective view by analyzing each omics individually in the search of biomarkers. The majority of these methods mainly focus on gene expression analysis since their impact on the phenotype is easier to measure and possibly more direct. However, it is common understanding that genes belong to pathways and tend to work together within various metabolic, regulatory, and signalling pathways. Hence, in this work, we tackled the TNBC biomarker discovery problem as a multi-omic pathway-based problem by efficiently combining the biological knowledge from multiple pathways using a novel machine learning algorithm. The proposed algorithm, called GroupSCM, is an extension of the Set Covering Machine (SCM) that incorporate the pathway features as priors. Results. Although the GroupSCM performed similarly to the SCM, metric-wise, it helps identify new biomarkers not previously found by the SCM. By leveraging the pathway priors, the GroupSCM was able to uncover two miRNAs: hsa-mir-18a and hsa-mir-190b, already known to be associated with various cancers including breast cancer and yet to be linked to the Triple-Negative Breast Cancer phenotype. Conclusion. The addition of priors to the SCM leads to interpretable, complete and sparser models which are easier to analyze in vivo settings. It also provides insight into the omics interaction by highlighting the miRNAs and epigenome contribution to the prediction task.