Unveiling Candidate Drivers in Cancer Progression Using Somatic-IV Analysis

Abstract

MotivationIdentifying causal drivers of cancer progression is crucial for developing effective anti-cancer therapies. However, disentangling causality from correlation remains challenging due to confounding factors within the complex genetic and transcriptomic landscapes of cancer. ResultsTo address this challenge, we introduce the Somatic Instrumental Variable analysis (Somatic-IV), which integrates genetic, transcriptomic, and clinical outcome data to identify candidate driver genes likely playing a causal role in disease progression. Somatic-IV estimates genetic-exposure and genetic-outcome associations, utilizing MR-Egger regression to estimate bias-reduced causal effects. We assessed the statistical properties and performance of Somatic-IV through a simulation study and demonstrated its utility in pancreatic adenocarcinoma (PDAC) and colorectal cancer (CRC). Our analysis identified ZBED2 and CSNK2A2 as likely drivers of PDAC and CRC cancer progression, respectively. Somatic-IV is a novel approach for generating target hypotheses and guiding the development of effective therapies for cancer patients. Availability and implementationThe Somatic-IV package is available at: https://github.com/wzhang1984/Somatic_IV_package