CancerVar: An Artificial Intelligence-empowered Platform for Clinical Interpretation of Somatic Mutations in Cancer

Overview

Journal Sci Adv

Specialties Biology
Science

Date 2022 May 11

PMID 35544644

Authors

Quan Li

ZiLin Ren

Kajia Cao

Marilyn M Li

Kai Wang

Yunyun Zhou

Affiliations

Soon will be listed here.

Abstract

Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many previously undocumented mutations may have clinical or functional impacts on cancer but are not systematically interpreted by existing knowledgebases. To address these challenges, we developed CancerVar to facilitate automated and standardized interpretations for 13 million somatic mutations based on the AMP/ASCO/CAP 2017 guidelines. We further introduced a deep learning framework to predict oncogenicity for these variants using both functional and clinical features. CancerVar achieved satisfactory performance when compared to several independent knowledgebases and, using clinically curated datasets, demonstrated practical utility in classifying somatic variants. In summary, by integrating clinical guidelines with a deep learning framework, CancerVar facilitates clinical interpretation of somatic variants, reduces manual work, improves consistency in variant classification, and promotes implementation of the guidelines.

Citing Articles

Exploring somatic mutations in , , and as therapeutic targets in Saudi colorectal cancer patients through massive parallel sequencing and variant classification.

Aljuhani T, Shaik N, Alqawas R, Bokhary R, Al-Mutadares M, Al Mahdi H Front Pharmacol. 2024; 15:1498295.

PMID: 39635441 PMC: 11614610. DOI: 10.3389/fphar.2024.1498295.

Unveiling the Digital Evolution of Molecular Tumor Boards.

Lutz S, DAngelo A, Hammerl S, Schmutz M, Claus R, Fischer N Target Oncol. 2024; 20(1):27-43.

PMID: 39609355 PMC: 11762447. DOI: 10.1007/s11523-024-01109-1.

Development of a Tagmentation-Based Next-Generation Sequencing Clinical Assay as an Alternative to Capillary Electrophoresis-Based Sequencing.

Kuek W, Chai C, Tham W, Ng A, Lau D, Loo J Mol Genet Genomic Med. 2024; 12(11):e70035.

PMID: 39560203 PMC: 11574739. DOI: 10.1002/mgg3.70035.

Assessing expression patterns of PTGR1, a potential biomarker for acylfulven sensitivity in urothelial carcinoma.

Stormoen D, Lehn S, Mouw K, Szallasi Z, Melchior L, Dohn L Sci Rep. 2024; 14(1):27448.

PMID: 39523403 PMC: 11551215. DOI: 10.1038/s41598-024-79334-x.

Reporting of somatic variants in clinical cancer care: recommendations of the Swiss Society of Molecular Pathology.

Christinat Y, Hamelin B, Alborelli I, Angelino P, Barbie V, Bisig B Virchows Arch. 2024; 485(6):1033-1039.

PMID: 39443383 PMC: 11666653. DOI: 10.1007/s00428-024-03951-0.

References

Shuai S, Gallinger S, Stein L . Combined burden and functional impact tests for cancer driver discovery using DriverPower. Nat Commun. 2020; 11(1):734. PMC: 7002750. DOI: 10.1038/s41467-019-13929-1. View

Chakravarty D, Gao J, Phillips S, Kundra R, Zhang H, Wang J . OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol. 2017; 2017. PMC: 5586540. DOI: 10.1200/PO.17.00011. View

Wang K, Li M, Hakonarson H . ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010; 38(16):e164. PMC: 2938201. DOI: 10.1093/nar/gkq603. View

Ng P, Li J, Jeong K, Shao S, Chen H, Tsang Y . Systematic Functional Annotation of Somatic Mutations in Cancer. Cancer Cell. 2018; 33(3):450-462.e10. PMC: 5926201. DOI: 10.1016/j.ccell.2018.01.021. View

Ramchand J, Wallis M, Macciocca I, Lynch E, Farouque O, Martyn M . Prospective Evaluation of the Utility of Whole Exome Sequencing in Dilated Cardiomyopathy. J Am Heart Assoc. 2020; 9(2):e013346. PMC: 7033851. DOI: 10.1161/JAHA.119.013346. View

Bailey M, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A . Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell. 2018; 174(4):1034-1035. PMC: 8045146. DOI: 10.1016/j.cell.2018.07.034. View

Quang D, Chen Y, Xie X . DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2014; 31(5):761-3. PMC: 4341060. DOI: 10.1093/bioinformatics/btu703. View

Patterson S, Liu R, Statz C, Durkin D, Lakshminarayana A, Mockus S . The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies. Hum Genomics. 2016; 10:4. PMC: 4715272. DOI: 10.1186/s40246-016-0061-7. View

Dong X, Liu B, Yang L, Wang H, Wu B, Liu R . Clinical exome sequencing as the first-tier test for diagnosing developmental disorders covering both CNV and SNV: a Chinese cohort. J Med Genet. 2020; 57(8):558-566. PMC: 7418612. DOI: 10.1136/jmedgenet-2019-106377. View

10.

Mao Y, Chen H, Liang H, Meric-Bernstam F, Mills G, Chen K . CanDrA: cancer-specific driver missense mutation annotation with optimized features. PLoS One. 2013; 8(10):e77945. PMC: 3813554. DOI: 10.1371/journal.pone.0077945. View

11.

Bouhaddou M, Eckhardt M, Naing Z, Kim M, Ideker T, Krogan N . Mapping the protein-protein and genetic interactions of cancer to guide precision medicine. Curr Opin Genet Dev. 2019; 54:110-117. PMC: 6746583. DOI: 10.1016/j.gde.2019.04.005. View

12.

He M, Li Q, Yan M, Cao H, Hu Y, He K . Variant Interpretation for Cancer (VIC): a computational tool for assessing clinical impacts of somatic variants. Genome Med. 2019; 11(1):53. PMC: 6708137. DOI: 10.1186/s13073-019-0664-4. View

13.

Micheel C, Sweeney S, LeNoue-Newton M, Andre F, Bedard P, Guinney J . American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange: From Inception to First Data Release and Beyond-Lessons Learned and Member Institutions' Perspectives. JCO Clin Cancer Inform. 2019; 2:1-14. PMC: 6873906. DOI: 10.1200/CCI.17.00083. View

14.

Ng P, Henikoff S . SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003; 31(13):3812-4. PMC: 168916. DOI: 10.1093/nar/gkg509. View

15.

Bailey M, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A . Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell. 2018; 173(2):371-385.e18. PMC: 6029450. DOI: 10.1016/j.cell.2018.02.060. View

16.

Shihab H, Gough J, Cooper D, Stenson P, Barker G, Edwards K . Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat. 2012; 34(1):57-65. PMC: 3558800. DOI: 10.1002/humu.22225. View

17.

Porta-Pardo E, Godzik A . e-Driver: a novel method to identify protein regions driving cancer. Bioinformatics. 2014; 30(21):3109-14. PMC: 4609017. DOI: 10.1093/bioinformatics/btu499. View

18.

Huang L, Fernandes H, Zia H, Tavassoli P, Rennert H, Pisapia D . The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations. J Am Med Inform Assoc. 2016; 24(3):513-519. PMC: 5391733. DOI: 10.1093/jamia/ocw148. View

19.

Sirohi D, Schmidt R, Aisner D, Behdad A, Betz B, Brown N . Multi-Institutional Evaluation of Interrater Agreement of Variant Classification Based on the 2017 Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists Standards and Guidelines for the.... J Mol Diagn. 2019; 22(2):284-293. DOI: 10.1016/j.jmoldx.2019.10.010. View

20.

Reimand J, Bader G . Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Mol Syst Biol. 2013; 9:637. PMC: 3564258. DOI: 10.1038/msb.2012.68. View