MIRACS: An Unsupervised Multi-Omics Strategy for Identifying Patterns of Therapeutic Resistance in Cancer

Abstract

[Abstract] Drug resistance is a major challenge in cancer therapy, often driven by poorly characterized molecular states. We developed MIRACS (Multiomic Integration for Resistance Association via Correlation Strategy), an unsupervised multi-omics framework to identify resistance programs across diverse cancer types and drug classes. Transcriptomic and proteomic profiles from 370 cell lines (22 tumor types, DepMap) were decomposed into 30 latent factors using Multi-Omics Factor Analysis (MOFA). Correlating these factors with drug response data from the PRISM dataset revealed over 700 significant factor–mechanism of action (MoA) associations across 10 cancer types. Among these, a molecular program emerged as a cross-lineage resistance program linked to five drug classes - including topoisomerase, Bcr-Abl kinase, Aurora kinase, DNA synthesis, and HSP inhibitors - across eight cancer types. Functional analyses of gene and protein loadings highlighted stress response and inflammatory pathways (p53, NF-κB, TNF-α, JAK–STAT) and transcriptional regulators ATF6 and IRF1, consistent with a pre-existing stress-adapted state. These results demonstrate that integrating multi-omics with drug perturbation data can uncover convergent, lineage-independent resistance programs, providing a systematic approach to identify targetable vulnerabilities across cancers.