Curated multiple sequence alignment for the Adenomatous Polyposis Coli (APC) gene and accuracy of in silico pathogenicity predictions.

Alexander D Karabachev,David J Hermel,Marc S Greenblatt,Dana Solcz,Marcy E Richardson,Dylan J Martini,Indra Neil Sarkar,Tina Pesaran,Obul Reddy Bandapalli

doi:10.1371/journal.pone.0233673

Abstract

Computational algorithms are often used to assess pathogenicity of Variants of Uncertain Significance (VUS) that are found in disease-associated genes. Most computational methods include analysis of protein multiple sequence alignments (PMSA), assessing interspecies variation. Careful validation of PMSA-based methods has been done for relatively few genes, partially because creation of curated PMSAs is labor-intensive. We assessed how PMSA-based computational tools predict the effects of the missense changes in the APC gene, in which pathogenic variants cause Familial Adenomatous Polyposis. Most Pathogenic or Likely Pathogenic APC variants are protein-truncating changes. However, public databases now contain thousands of variants reported as missense. We created a curated APC PMSA that contained >3 substitutions/site, which is large enough for statistically robust in silico analysis. The creation of the PMSA was not easily automated, requiring significant querying and computational analysis of protein and genome sequences. Of 1924 missense APC variants in the NCBI ClinVar database, 1800 (93.5%) are reported as VUS. All but two missense variants listed as P/LP occur at canonical splice or Exonic Splice Enhancer sites. Pathogenicity predictions by five computational tools (Align-GVGD, SIFT, PolyPhen2, MAPP, REVEL) differed widely in their predictions of Pathogenic/Likely Pathogenic (range 17.5-75.0%) and Benign/Likely Benign (range 25.0-82.5%) for APC missense variants in ClinVar. When applied to 21 missense variants reported in ClinVar and securely classified as Benign, the five methods ranged in accuracy from 76.2-100%. Computational PMSA-based methods can be an excellent classifier for variants of some hereditary cancer genes. However, there may be characteristics of the APC gene and protein that confound the results of in silico algorithms. A systematic study of these features could greatly improve the automation of alignment-based techniques and the use of predictive algorithms in hereditary cancer genes.

Highlights

Multi-gene panel testing is routine for identifying hereditary cancer susceptibility, leading to increased detection of pathogenic mutations, which can improve clinical management
Data Availability Statement: The data underlying the results presented in the study are either within the manuscript or available through ClinVar at the Computational algorithms are often used to assess pathogenicity of Variants of Uncertain Significance (VUS) that are found in disease-associated genes
We encountered a number of challenges to the simple automated assembly of a meaningful Adenomatous Polyposis Coli (APC) Protein Multiple Sequence Alignments (PMSA), including: a

Summary

Introduction

Multi-gene panel testing is routine for identifying hereditary cancer susceptibility, leading to increased detection of pathogenic mutations, which can improve clinical management. The ClinVar database at the National Center for Biotechnology Information at the United States National Library of Medicine provides a freely accessible archive of variants with assertions regarding the pathogenicity of each variant with the indicated phenotype from submitting laboratories and expert panels [3] The classification of these VUS represents a major challenge in clinical genetics. The PMSA must be of high quality and sample enough species to provide reliable data [5, 6] These in silico methods have been validated for relatively few hereditary cancer genes in which pathogenic missense variants are not rare (BRCA1/2, the mismatch repair [MMR] genes, TP53, a few others) [5, 7,8,9,10,11]. The American College of Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for evaluating the pathogenicity of variants in Mendelian disease genes, including general rules for the use of in silico tools [13]

Objectives

Methods

Results

Conclusion