A Weighted Exact Test for Mutually Exclusive Mutations in Cancer

TL;DR

This paper introduces a weighted exact statistical test for detecting mutually exclusive mutations in cancer, improving accuracy and efficiency over existing methods, and effectively analyzing high-mutation-rate cancer data.

Contribution

The authors develop a novel weighted exact test for mutational exclusivity that is more precise and computationally efficient than previous approaches, especially for high-mutation-rate cancers.

Findings

The test identifies mutually exclusive gene sets with fewer false positives.

It outperforms permutation tests in efficiency and significance detection.

Applied to TCGA data, it reveals relevant cancer gene mutations.

Abstract

The somatic mutations in the pathways that drive cancer development tend to be mutually exclusive across tumors, providing a signal for distinguishing driver mutations from a larger number of random passenger mutations. This mutual exclusivity signal can be confounded by high and highly variable mutation rates across a cohort of samples. Current statistical tests for exclusivity that incorporate both per-gene and per-sample mutational frequencies are computationally expensive and have limited precision. We formulate a weighted exact test for assessing the significance of mutational exclusivity in an arbitrary number of mutational events. Our test conditions on the number of samples with a mutation as well as per-event, per-sample mutation probabilities. We provide a recursive formula to compute $p$-values for the weighted test exactly as well as a highly accurate and efficient saddlepoint approximation of the test. We use our test to approximate a commonly used permutation test for exclusivity that conditions on per-event, per-sample mutation frequencies. However, our test is more efficient and it recovers more significant results than the permutation test. We use our Weighted Exclusivity Test (WExT) software to analyze hundreds of colorectal and endometrial samples from The Cancer Genome Atlas, which are two cancer types that often have extremely high mutation rates. On both cancer types, the weighted test identifies sets of mutually exclusive mutations in cancer genes with fewer false positives than earlier approaches.