High prevalence of cancer‐associated TP53 variants in the gnomAD database: A word of caution concerning the use of variant filtering

Abstract

The 1,000 genome project, the Exome Aggregation Consortium (ExAC) or the Genome Aggregation database (gnomAD) datasets, were developed to provide largescale reference data of genetic variations for various populations to filter out common benign variants and identify rare variants of clinical importance based on their frequency in the human population. Using a TP53 repository of 80,000 cancer variants, as well as TP53 variants from multiple cancer genome projects, we have defined a set of certified oncogenic TP53 variants. This specific set has been independently validated by functional and in silico predictive analysis. Here we show that a significant number of these variants are included in gnomAD and ExAC. Most of them correspond to TP53 hotspot variants occurring as somatic and germline events in human cancer. Similarly, disease-associated variants for five other tumor suppressor genes, including BRCA1, BRCA2, APC, PTEN, and MLH1, have also been identified. This study demonstrates that germline TP53 variants in the human population are more frequent than previously thought. Furthermore, population databases such as gnomAD or ExAC must be used with caution and need to be annotated for the presence of oncogenic variants to improve their clinical utility.

Comprehensive assessment of TP53 loss of function using multiple combinatorial mutagenesis libraries

“…A full analysis of the 2018 release of the database was recently published in collaboration with the TCGA and will not be reiterated here. The new release of the database (100K_UMD TP53 database, October 2019) includes more than 100,000 TP53 mutations and a novel feature developed for the analysis of TP53 variants, the cancer shared dataset (CSD), which comprises only variants highly likely to be pathogenic 15 (see " Methods "). The CSD includes 258 TP53 SNS common to four different large independent sequencing datasets (see " Methods ").…”

“…For the present analysis, we developed upon the CSD, a specific TP53 benchmark dataset that includes only highly likely pathogenic variants 15 . As variant recurrence is one of the strongest indicators of pathogenicity, using four independent sources of TP53 variants, each deploying a different analysis methodology, should negate methodological issues for the specific selection of pathogenic variants.…”

“…Unfortunately, both sets of data are generally not accurate. We have previously shown that gnomAD is heavily contaminated by pathogenic TP53 variants due to the high frequency of de novo mutations in the human population 15 . Similarly, as shown in previous studies, cancer mutation databases can include passenger mutations, unidentified low frequency SNP and artifactual data 30 , 31 .…”

Annotation of Human Exome Gene Variants with Consensus Pathogenicity

“…Among the many types of annotations that can be assigned to each sequenced NGS variant, of particular interest are those predicting if a variant has a benign or deleterious phenotypic effect. However, causative relationships between genes and phenotypes are very complex, depending on many connections and influences, and there are major challenges in obtaining the associations [2]. In the following paragraphs, we briefly overview the state-of-the-art approaches in annotating with phenotypic effects, focusing mostly on an array of predicted pathogenicity scores, and on population allele frequencies, as it is also very popular for estimation of pathogenicity.…”

Annotation of Human Exome Gene Variants with Consensus Pathogenicity

“…With the growing availability of NGS technology in medical diagnostics, quantitatively exemplified by 68,000 genetic tests currently offered in clinics according to Genetests [1], the amount of experimentally sequenced data on human genetic variation in both healthy and patient populations is rapidly increasing. Accurate and exhaustive variant annotation is important for every application of NGS technology, including development of therapies, selection of effective individualized therapy, and comparing multiple samples in clinical studies, to mention a few; however, currently there are major challenges associated with sequencing data analysis and interpretation [2]. While most of the prevalent or common variants (about one million of currently sequenced variants) have been annotated as benign (neutral) based on their high frequency of occurrence in the healthy population [3], the remaining majority are ultra-rare Variants of Unknown Significance (VUS).…”