A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer

Overview

Journal Int J Mol Sci

Publisher MDPI

Specialties Biochemistry
Chemistry
Molecular Biology

Date 2024 Aug 10

PMID 39125613

Authors

Rossano Atzeni

Matteo Massidda

Enrico Pieroni

Vincenzo Rallo

Massimo Pisu

Andrea Angius

Affiliations

Soon will be listed here.

Abstract

Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol.

References

Li H, Durbin R . Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25(14):1754-60. PMC: 2705234. DOI: 10.1093/bioinformatics/btp324. View

Wang Q, Zhai Y, Dai J, Li K, Deng Q, Han Z . SAMD9L inactivation promotes cell proliferation via facilitating G1-S transition in hepatitis B virus-associated hepatocellular carcinoma. Int J Biol Sci. 2014; 10(8):807-16. PMC: 4115192. DOI: 10.7150/ijbs.9143. View

Bian X, Shi D, Xing K, Zhou H, Lu L, Yu D . AMD1 upregulates hepatocellular carcinoma cells stemness by FTO mediated mRNA demethylation. Clin Transl Med. 2021; 11(3):e352. PMC: 7989706. DOI: 10.1002/ctm2.352. View

Fang H, Bergmann E, Arora K, Vacic V, Zody M, Iossifov I . Indel variant analysis of short-read sequencing data with Scalpel. Nat Protoc. 2016; 11(12):2529-2548. PMC: 5507611. DOI: 10.1038/nprot.2016.150. View

Goode D, Hunter S, Doyle M, Ma T, Rowley S, Choong D . A simple consensus approach improves somatic mutation prediction accuracy. Genome Med. 2013; 5(9):90. PMC: 3978449. DOI: 10.1186/gm494. View

Leiserson M, Wu H, Vandin F, Raphael B . CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer. Genome Biol. 2015; 16:160. PMC: 4531541. DOI: 10.1186/s13059-015-0700-7. View

Larson D, Harris C, Chen K, Koboldt D, Abbott T, Dooling D . SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics. 2011; 28(3):311-7. PMC: 3268238. DOI: 10.1093/bioinformatics/btr665. View

Ling S, Hu Z, Yang Z, Yang F, Li Y, Lin P . Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution. Proc Natl Acad Sci U S A. 2015; 112(47):E6496-505. PMC: 4664355. DOI: 10.1073/pnas.1519556112. View

Lawrence M, Stojanov P, Polak P, Kryukov G, Cibulskis K, Sivachenko A . Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013; 499(7457):214-218. PMC: 3919509. DOI: 10.1038/nature12213. View

10.

Ellrott K, Bailey M, Saksena G, Covington K, Kandoth C, Stewart C . Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. Cell Syst. 2018; 6(3):271-281.e7. PMC: 6075717. DOI: 10.1016/j.cels.2018.03.002. View

11.

Hwang S, Kim E, Lee I, Marcotte E . Systematic comparison of variant calling pipelines using gold standard personal exome variants. Sci Rep. 2015; 5:17875. PMC: 4671096. DOI: 10.1038/srep17875. View

12.

Forbes S, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H . COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2014; 43(Database issue):D805-11. PMC: 4383913. DOI: 10.1093/nar/gku1075. View

13.

Ainscough B, Barnell E, Ronning P, Campbell K, Wagner A, Fehniger T . A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data. Nat Genet. 2018; 50(12):1735-1743. PMC: 6428590. DOI: 10.1038/s41588-018-0257-y. View

14.

Kim S, Jacob L, Speed T . Combining calls from multiple somatic mutation-callers. BMC Bioinformatics. 2014; 15:154. PMC: 4035752. DOI: 10.1186/1471-2105-15-154. View

15.

Sahraeian S, Liu R, Lau B, Podesta K, Mohiyuddin M, Lam H . Deep convolutional neural networks for accurate somatic mutation detection. Nat Commun. 2019; 10(1):1041. PMC: 6399298. DOI: 10.1038/s41467-019-09027-x. View

16.

Miller C, White B, Dees N, Griffith M, Welch J, Griffith O . SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLoS Comput Biol. 2014; 10(8):e1003665. PMC: 4125065. DOI: 10.1371/journal.pcbi.1003665. View

17.

Cibulskis K, Lawrence M, Carter S, Sivachenko A, Jaffe D, Sougnez C . Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013; 31(3):213-9. PMC: 3833702. DOI: 10.1038/nbt.2514. View

18.

Sherry S, Ward M, Kholodov M, Baker J, Phan L, Smigielski E . dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2000; 29(1):308-11. PMC: 29783. DOI: 10.1093/nar/29.1.308. View

19.

Mayakonda A, Lin D, Assenov Y, Plass C, Koeffler H . Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018; 28(11):1747-1756. PMC: 6211645. DOI: 10.1101/gr.239244.118. View

20.

Mroz E, Tward A, Tward A, Hammon R, Ren Y, Rocco J . Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the Cancer Genome Atlas. PLoS Med. 2015; 12(2):e1001786. PMC: 4323109. DOI: 10.1371/journal.pmed.1001786. View