TMBur: a distributable tumor mutation burden approach for whole genome sequencing.
Immune checkpoint inhibitors
Tumor mutation burden
Whole genome and transcriptome analysis (WGTA)
Journal
BMC medical genomics
ISSN: 1755-8794
Titre abrégé: BMC Med Genomics
Pays: England
ID NLM: 101319628
Informations de publication
Date de publication:
07 09 2022
07 09 2022
Historique:
received:
04
04
2022
accepted:
01
09
2022
entrez:
7
9
2022
pubmed:
8
9
2022
medline:
11
9
2022
Statut:
epublish
Résumé
Tumor mutation burden (TMB) is a key characteristic used in a tumor-type agnostic context to inform the use of immune checkpoint inhibitors (ICI). Accurate and consistent measurement of TMB is crucial as it can significantly impact patient selection for therapy and clinical trials, with a threshold of 10 mutations/Mb commonly used as an inclusion criterion. Studies have shown that the most significant contributor to variability in mutation counts in whole genome sequence (WGS) data is differences in analysis methods, even more than differences in extraction or library construction methods. Therefore, tools for improving consistency in whole genome TMB estimation are of clinical importance. We developed a distributable TMB analysis suite, TMBur, to address the need for genomic TMB estimate consistency in projects that span jurisdictions. TMBur is implemented in Nextflow and performs all analysis steps to generate TMB estimates directly from fastq files, incorporating somatic variant calling with Manta, Strelka2, and Mutect2, and microsatellite instability profiling with MSISensor. These tools are provided in a Singularity container downloaded by the workflow at runtime, allowing the entire workflow to be run identically on most computing platforms. To test the reproducibility of TMBur TMB estimates, we performed replicate runs on WGS data derived from the COLO829 and COLO829BL cell lines at multiple research centres. The clinical value of derived TMB estimates was then evaluated using a cohort of 90 patients with advanced, metastatic cancer that received ICIs following WGS analysis. Patients were split into groups based on a threshold of 10/Mb, and time to progression from initiation of ICIs was examined using Kaplan-Meier and cox-proportional hazards analyses. TMBur produced identical TMB estimates across replicates and at multiple analysis centres. The clinical utility of TMBur-derived TMB estimates were validated, with a genomic TMB ≥ 10/Mb demonstrating improved time to progression, even after correcting for differences in tumor type (HR = 0.39, p = 0.012). TMBur, a shareable workflow, generates consistent whole genome derived TMB estimates predictive of response to ICIs across multiple analysis centres. Reproducible TMB estimates from this approach can improve collaboration and ensure equitable treatment and clinical trial access spanning jurisdictions.
Sections du résumé
BACKGROUND
Tumor mutation burden (TMB) is a key characteristic used in a tumor-type agnostic context to inform the use of immune checkpoint inhibitors (ICI). Accurate and consistent measurement of TMB is crucial as it can significantly impact patient selection for therapy and clinical trials, with a threshold of 10 mutations/Mb commonly used as an inclusion criterion. Studies have shown that the most significant contributor to variability in mutation counts in whole genome sequence (WGS) data is differences in analysis methods, even more than differences in extraction or library construction methods. Therefore, tools for improving consistency in whole genome TMB estimation are of clinical importance.
METHODS
We developed a distributable TMB analysis suite, TMBur, to address the need for genomic TMB estimate consistency in projects that span jurisdictions. TMBur is implemented in Nextflow and performs all analysis steps to generate TMB estimates directly from fastq files, incorporating somatic variant calling with Manta, Strelka2, and Mutect2, and microsatellite instability profiling with MSISensor. These tools are provided in a Singularity container downloaded by the workflow at runtime, allowing the entire workflow to be run identically on most computing platforms. To test the reproducibility of TMBur TMB estimates, we performed replicate runs on WGS data derived from the COLO829 and COLO829BL cell lines at multiple research centres. The clinical value of derived TMB estimates was then evaluated using a cohort of 90 patients with advanced, metastatic cancer that received ICIs following WGS analysis. Patients were split into groups based on a threshold of 10/Mb, and time to progression from initiation of ICIs was examined using Kaplan-Meier and cox-proportional hazards analyses.
RESULTS
TMBur produced identical TMB estimates across replicates and at multiple analysis centres. The clinical utility of TMBur-derived TMB estimates were validated, with a genomic TMB ≥ 10/Mb demonstrating improved time to progression, even after correcting for differences in tumor type (HR = 0.39, p = 0.012).
CONCLUSIONS
TMBur, a shareable workflow, generates consistent whole genome derived TMB estimates predictive of response to ICIs across multiple analysis centres. Reproducible TMB estimates from this approach can improve collaboration and ensure equitable treatment and clinical trial access spanning jurisdictions.
Identifiants
pubmed: 36071521
doi: 10.1186/s12920-022-01348-z
pii: 10.1186/s12920-022-01348-z
pmc: PMC9450342
doi:
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
190Informations de copyright
© 2022. The Author(s).
Références
Bioinformatics. 2016 Aug 1;32(15):2243-7
pubmed: 27153582
J Immunother Cancer. 2021 Mar;9(3):
pubmed: 33653800
JAMA Oncol. 2021 May 01;7(5):739-743
pubmed: 33599686
Clin Cancer Res. 2021 Jan 1;27(1):202-212
pubmed: 33020056
Nat Genet. 2018 Sep;50(9):1271-1281
pubmed: 30150660
Nat Commun. 2020 Sep 21;11(1):4748
pubmed: 32958763
PLoS One. 2017 May 11;12(5):e0177459
pubmed: 28494014
Front Genet. 2020 Dec 01;11:612515
pubmed: 33335541
Cold Spring Harb Mol Case Stud. 2015 Oct;1(1):a000570
pubmed: 27148575
Fly (Austin). 2012 Apr-Jun;6(2):80-92
pubmed: 22728672
Cell. 2017 Nov 16;171(5):1042-1056.e10
pubmed: 29056344
Science. 2018 Feb 2;359(6375):582-587
pubmed: 29217585
Bioinformatics. 2018 Sep 1;34(17):i884-i890
pubmed: 30423086
Genome Med. 2017 Apr 19;9(1):34
pubmed: 28420421
Br J Cancer. 2004 Jul 19;91(2):355-8
pubmed: 15188009
Ann Oncol. 2019 Mar 1;30(3):397-404
pubmed: 30475950
Oncogenesis. 2021 Jan 8;10(1):3
pubmed: 33419967
Nucleic Acids Res. 2021 Jan 8;49(D1):D884-D891
pubmed: 33137190
Bioinformatics. 2016 Apr 15;32(8):1220-2
pubmed: 26647377
Genes Chromosomes Cancer. 2019 Aug;58(8):578-588
pubmed: 30664300
Nat Cancer. 2020 Apr;1(4):452-468
pubmed: 35121966
Nature. 2019 Nov;575(7781):210-216
pubmed: 31645765
Science. 2018 Oct 12;362(6411):
pubmed: 30309915
Nat Biotechnol. 2017 Apr 11;35(4):316-319
pubmed: 28398311
Front Oncol. 2021 Jun 07;11:683419
pubmed: 34164344
Nat Methods. 2018 Aug;15(8):591-594
pubmed: 30013048
Ann Oncol. 2021 Dec;32(12):1626-1636
pubmed: 34606929
Bioinformatics. 2009 Aug 15;25(16):2078-9
pubmed: 19505943
Expert Opin Biol Ther. 2019 Mar;19(3):169-171
pubmed: 30652499
Bioinformatics. 2010 Mar 15;26(6):841-2
pubmed: 20110278
Bioinformatics. 2014 Apr 1;30(7):1015-6
pubmed: 24371154