Abstract
Elucidating interactions between cancer cells and their microenvironment is a key goal of cancer research with implications for understanding cancer evolution and improving immunotherapy. Previous studies used association-based approaches to infer relationships in transcriptomic data, but could not infer the direction of interaction. Here we present a causal modeling approach that infers directed interactions between signaling pathway activity and immune activity by anchoring the analysis on somatic genomic changes. Our approach integrates copy number profiles, transcriptomic data, image data and a protein-protein interaction network to infer directed relationships. As a result, we propose 11 novel genomic drivers of T cell phenotypes in the breast cancer tumour microenvironment and validate them in independent cohorts and orthogonal data types. Our framework is flexible and provides a generally applicable way to extend association-based analysis in other cancer types and to other data and clinical parameters.
