Analysis of shared variants between cancer biospecimens.

Purpose: Mutational data from multiple solid and liquid biospecimens of a single patient is often integrated to track cancer evolution. However, there is no accepted framework to resolve if individual samples from the same individual share variants due to common identity versus coincidence.
Experimental Design: Utilizing 8,000 patient tumors from The Cancer Genome Atlas (TCGA) across 33 cancer types, we estimated background rates of co-occurrence rates of mutations between discrete pairs of samples across cancers and by cancer type. We developed a mutational profile similarity score (MPS) that uses a large background database to produce confidence estimates that two tumors share a unique, related molecular profile. The MPS algorithm was applied to randomly paired tumor profiles, including patients who underwent repeat solid tumor biopsies sequenced with MSK-IMPACT (n=53,113). We also evaluated the MPS in sample pairs from single patients with multiple cancers (n=2,012), as well as patients with plasma and solid-tumor variant profiles (n=884 patients).
Results: In unrelated tumors, nucleotide-specific variants are shared in 1.3% (cancer-type agnostic) and in 10-13% (cancer-type specific) of cases. The mutational profile similarity (MPS) method contextualized shared variants to specify whether patients had a single cancer versus multiple distinct cancers. When multiple tumors were compared from the same patient, and an initial clinicopathologic diagnosis was discordant with molecular findings, the MPS anticipated future diagnosis changes in 28% of examined cases.
Conclusions: Use of a novel shared variant framework can provide information to clarify the molecular relationship between compared biospecimens with minimal required input.