Misannotated Multi-Nucleotide Variants in Public Cancer Genomics Datasets Lead to Inaccurate Mutation Calls with Significant Implications

Abstract

Although next-generation sequencing is widely used in cancer to profile tumors and detect variants, most somatic variant callers used in these pipelines identify variants at the lowest possible granularity, single-nucleotide variants (SNV). As a result, multiple adjacent SNVs are called individually instead of as a multi-nucleotide variants (MNV). With this approach, the amino acid change from the individual SNV within a codon could be different from the amino acid change based on the MNV that results from combining SNV, leading to incorrect conclusions about the downstream effects of the variants. Here, we analyzed 10,383 variant call files (VCF) from the Cancer Genome Atlas (TCGA) and found 12,141 incorrectly annotated MNVs. Analysis of seven commonly mutated genes from 178 studies in cBioPortal revealed that MNVs were consistently missed in 20 of these studies, whereas they were correctly annotated in 15 more recent studies. At the BRAF V600 locus, the most common example of MNV, several public datasets reported separate BRAF V600E and BRAF V600M variants instead of a single merged V600K variant. VCFs from the TCGA Mutect2 caller were used to develop a solution to merge SNV to MNV. Our custom script used the phasing information from the SNV VCF and determined whether SNVs were at the same codon and needed to be merged into MNV before variant annotation. This study shows that institutions performing NGS sequencing for cancer genomics should incorporate the step of merging MNV as a best practice in their pipelines. Significance: Identification of incorrect mutation calls in TCGA, including clinically relevant BRAF V600 and KRAS G12, will influence research and potentially clinical decisions.

Pharmacogenomic profile of actionable molecular variants related to drugs commonly used in anesthesia: WES analysis reveals new mutations

“…Recently, it has been recognized that in NGS analyses, MNVs can be miscalled resulting in misannotations and incorrect aminoacid prediction by GATK variant calling. Considering the negative impact on clinical care, novel NGS variant callers that incorporate haplotype information and performs phasing of SNVs have been recommended ( Wang et al, 2020b ; Srinivasan et al, 2021 ).…”

Sync for Genes Phase 5: Computable artifacts for sharing dynamically annotated FHIR‐formatted genomic variants

“…Both FHIR Genomics Operations and GA4GH leverage canonical SPDI format, making annotation of these variant types relatively straightforward. [27][28][29] Our approach has been to leverage variant phase data to combine SNVs into MNVs where possible in order to compute additional potentially relevant annotations.…”

“…Dynamic annotation of MNVs is complicated by the fact that (1) some variant callers will report MNVs whereas others only report component SNVs; (2) knowledge bases may contain MNVs and/or SNVs depending on what was submitted; and (3) bioinformatics tools may predict different molecular consequences for an MNV versus component SNVs. In fact, literature suggests that misannotation of MNVs is common and carries significant clinical implications 27‐29 . Our approach has been to leverage variant phase data to combine SNVs into MNVs where possible in order to compute additional potentially relevant annotations.…”

“…In fact, literature suggests that misannotation of MNVs is common and carries significant clinical implications. 27 , 28 , 29 Our approach has been to leverage variant phase data to combine SNVs into MNVs where possible in order to compute additional potentially relevant annotations.…”

Mass Spectrometry–Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

“…The integration and interpretation of proteogenomic measurements, and ultimately, the test of their value, comes from the development of novel computational approaches. Data integration is first challenged by the fact that data exist on varying scales—genetic mutations are often assigned as discrete types of calls depending on the type of change in the DNA sequence ( 129 , 130 ), while transcript measurements represent absolute changes in the mRNA relative to the length of the transcript and the depth of sequencing ( 131 ), and protein measurements are log ratio values representing the amount of sample measured relative to a standard control ( 132 ). Methods to overcome these challenges depend on the type of analysis at hand: Namely, nonnegative matrix factorization helps identify clusters of samples that behave similarly across scales ( 133 – 135 ) using all types of omic data, differential expression analyses can be performed between experimental conditions, and overlap between those features compared.…”