Causal Interactions from Proteomic Profiles: Molecular Data Meets Pathway Knowledge

Abstract

We present a computational method to infer causal mechanisms in cell biology by analyzing changes in high-throughput proteomic profiles on the background of prior knowledge captured in biochemical reaction knowledge bases. The method mimics a biologist's approach of explaining changes in data using prior knowledge in their memory and in the literature, but at the scale of hundreds of thousands of reactions. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. We first demonstrate the validity of the method on cell line perturbation experiments. In discovery mode we then identify 1) differential signaling within breast cancer and ovarian cancer, 2) subtype-specific signaling in these cancers, and 3) recurrent signaling relations across 32 cancer types. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at causalpath.org.