Analysis of mutant allele fractions in driver genes in colorectal cancer – biological and clinical insights

Sequencing of tumors is now routine and guides personalized cancer therapy. Mutant allele fractions (MAFs, or the 'mutation dose') of a driver gene may reveal the genomic structure of tumors and influence response to targeted therapies. We performed a comprehensive analysis of MAFs of driver alterations in unpaired primary and metastatic colorectal cancer (CRC) at our institution from 2010 to 2015 and studied their potential clinical relevance. Of 763 CRC samples, 622 had detailed annotation on overall survival in the metastatic setting (OSmet) and 89 received targeted agents matched to KRAS (MEK inhibitors), BRAF (BRAF inhibitors), or PIK3CA mutations (PI3K pathway inhibitors). MAFs of each variant were normalized for tumor purity in the sample (adjMAFs). We found lower adjMAFs for BRAFV600E and PIK3CA than for KRAS, NRAS, and BRAF non-V600 variants. TP53 and BRAFV600E adjMAFs were higher in metastases as compared to primary tumors, and high KRAS adjMAFs were found in CRC metastases of patients with KRAS wild-type primary tumors previously exposed to EGFR antibodies. Patients with RAS- or BRAFV600E -mutated tumors, irrespective of adjMAFs, had worse OSmet. There was no significant association between adjMAFs and time to progression on targeted therapies matched to KRAS, BRAF, or PIK3CA mutations, potentially related to the limited antitumor activity of the employed drugs (overall response rate of 4.5%). In conclusion, the lower BRAFV600E and PIK3CA adjMAFs in subsets of primary CRC tumors indicate subclonality of these driver genes. Differences in adjMAFs between metastases and primary tumors suggest that approved therapies may result in selection of BRAFV600E - and KRAS-resistant clones and an increase in genomic heterogeneity with acquired TP53 alterations. Despite significant differences in prognosis according to mutations in driver oncogenes, adjMAFs levels did not impact on survival and did not help predict benefit with matched targeted agents in the metastatic setting