Abstract
Intratumoral heterogeneity is an important clinical challenge because low burden clones expressing specific genetic alterations drive therapeutic resistance mechanisms. We have developed CAVE (cancer-associated variant enrichment), a gene-agnostic computational tool to identify specific enrichment of low-burden cancer driver variants in next-generation sequencing (NGS) data. For this study, CAVE was applied to TP53 in chronic lymphocytic leukemia (CLL) as a cancer model. Indeed, as TP53 mutations are part of treatment decision-making algorithms and low-burden variants are frequent, there is a need to distinguish true variants from background noise. Recommendations have been published for reliable calling of low-VAF variants of TP53 in CLL and the assessment of the background noise for each platform is essential for the quality of the testing. CAVE is able to detect specific enrichment of low-burden variants starting at variant allele frequencies (VAFs) as low as 0.3%. In silico TP53 dependent and independent analyses confirmed the true driver nature of all these variants. Orthogonal validation using either ddPCR or NGS analyses of follow-up samples confirmed variant identification. CAVE can be easily deployed in any cancer-related NGS workflow to detect the enrichment of low-burden variants of clinical interest.