Interpreting Mutation Co-Occurrence in Cancer Genomics Under Biological Context

Vollständiger Abstract

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Somatic mutation patterns observed in cancer genomes are widely used to generate hypotheses about functional relationships among cancer genes and signaling pathways. However, mutation co-occurrence and mutual exclusivity are assessed at multiple levels, including cohorts, bulk specimens, lesions, regions, clones, and individual cells, although each observational level supports a different scope of inference. In this structured narrative review, we clarify these inferential boundaries and distinguish marginal from conditional association, as well as negative association from complete mutual exclusivity. A hypothetical numerical example of Simpson’s reversal illustrates how marginal and conditional associations can differ and why negative association with non-zero overlap should be distinguished from complete mutual exclusivity. We then synthesize evidence from bulk, multi-region, phylogenetic, and single-cell analyses to examine spatial and clonal localization, interclonal cooperation, single-cell error and detection power, and genetic versus non-genetic resistance. We also provide a decision guide for method selection and a staged framework for functional validation. Overall, statistical association, physical localization, and functional interaction are related but distinct inferential targets that require different data, assumptions, and forms of validation.