Pan-cancer association of DNA repair deficiencies with whole-genome mutational patterns
Abstract
DNA repair deficiencies in cancers may result in characteristic mutational patterns, as exemplified by deficiency of BRCA1/2 and efficacy prediction for PARP-inhibitors. We trained and evaluated predictive models for loss-of-function (LOF) of 145 individual DDR genes based on genome-wide mutational patterns, including structural variants, indels, and base-substitution signatures. We identified 24 genes whose deficiency could be predicted with good accuracy, including expected mutational patterns for BRCA1/2, MSH3/6, TP53, and CDK12 LOF variants. CDK12 is associated with tandem-duplications, and we here demonstrate that this association can accurately predict gene deficiency in prostate cancers (area under the ROC curve=0.97). Our novel associations include mono- or biallelic LOF variants of ATRX, IDH1, HERC2, CDKN2A, PTEN, and SMARCA4, and our systematic approach yielded a catalogue of predictive models, which may provide targets for further research and development of treatment, and potentially help guide therapy.
Data availability
This study is based on analyses of human germline and cancer somatic variant data. The data sets were generated and made available by the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and from the Hartwig Medical Foundation (HMF). The majority of the data cannot be publicly accessed as it includes protected personal data, including germline variants, which cannot be made publicly available. However, accession to the underlying data sets can be achieved through applications to ICGC/TCGA and HMF as described below.The public parts of the PCAWG data set are available at https://dcc.icgc.org/releases/PCAWG, whereas controlled files may be accessed through applications to gbGaP and DACO, which should include a project proposal, as instructed on this site https://docs.icgc.org/pcawg/data/. The ICGC study ID of the project is EGAS00001001692.The HMF data used in this project may be found by accession code DR-044 and can be obtained by submitting an application with a project proposal to the Hartwig Medical Foundation (https://www.hartwigmedicalfoundation.nl/en).Non-personal summary data have been supplied in supplementary tables S1 to S9:Supplementary Table 1: All included tumours and their primary tumour locationsSupplementary Table 2: 736 DDR genes, hg19 coordinates and the number ofpathogenic events across 6,065 cancer genomesSupplementary Table 3: All SBS signature contributions, indels counts, and1104 SV counts, per sample; zip-compressed; tab-separated values (.tsv), may be opened in Microsoft ExcelSupplementary Table 4: All SBS signature contributions, indels counts, andSV counts, per sample, log-transformed and scaled to z-scores; zip-compressed; tab-separated values (.tsv), may be opened in Microsoft ExcelSupplementary Table 5: Proposed Etiologies of base substitution signaturesSupplementary Table 6: All models (n=535)Supplementary Table 7: Pathogenic events in each of the 535 LOF-setsSupplementary Table 8: Shortlisted models (n=48)Supplementary Table 9: Correlation between features in shortlisted modelsSupplementary Table 10: Survival analysis for the shortlisted modelsThe third-party software used for data analysis includes:Pathogenicity annotation using CADD annotation software, which may be accessed at https://cadd.gs.washington.eduSignature analysis using Signature Tools Lib, which has been installed from the GitHub: https://github.com/Nik-Zainal-Group/signature.tools.libCode that we developed locally for the analysis can be accessed at:https://github.com/SimonGrund/DDR_Predict
Article and author information
Author details
Funding
Novo Nordisk Fonden (NNF15OC0016662)
- Eva R Hoffmann
Cancer Research UK (C23210/A7574)
- Eva R Hoffmann
Danmarks Frie Forskningsfond (8021-00419B)
- Jakob Skou Pedersen
Kræftens Bekæmpelse (R307-A17932)
- Jakob Skou Pedersen
Aarhus Universitets Forskningsfond (AUFF-E-2020-6-14)
- Jakob Skou Pedersen
Sundhedsvidenskabelige Fakultet, Aarhus Universitet (PhD stipend)
- Simon Grund Sørensen
Sundhed, Region Midtjylland (A2972)
- Gustav Alexander Poulsgaard
Danmarks Grundforskningsfond (DNRF115)
- Eva R Hoffmann
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: We analysed data generated and made available by the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) as well as the Hartwig Medical Foundation (HMF). The research conforms to the principles of the Helsinki Declaration.
Copyright
© 2023, Sørensen et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,689
- views
-
- 323
- downloads
-
- 3
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Cancer Biology
Glioblastomas are aggressive brain tumors with dismal prognosis. One of the main bottlenecks for developing more effective therapies for glioblastoma stems from their histologic and molecular heterogeneity, leading to distinct tumor microenvironments and disease phenotypes. Effectively characterizing these features would improve the clinical management of glioblastoma. Glucose flux rates through glycolysis and mitochondrial oxidation have been recently shown to quantitatively depict glioblastoma proliferation in mouse models (GL261 and CT2A tumors) using dynamic glucose-enhanced (DGE) deuterium spectroscopy. However, the spatial features of tumor microenvironment phenotypes remain hitherto unresolved. Here, we develop a DGE Deuterium Metabolic Imaging (DMI) approach for profiling tumor microenvironments through glucose conversion kinetics. Using a multimodal combination of tumor mouse models, novel strategies for spectroscopic imaging and noise attenuation, and histopathological correlations, we show that tumor lactate turnover mirrors phenotype differences between GL261 and CT2A mouse glioblastoma, whereas recycling of the peritumoral glutamate-glutamine pool is a potential marker of invasion capacity in pooled cohorts, linked to secondary brain lesions. These findings were validated by histopathological characterization of each tumor, including cell density and proliferation, peritumoral invasion and distant migration, and immune cell infiltration. Our study bodes well for precision neuro-oncology, highlighting the importance of mapping glucose flux rates to better understand the metabolic heterogeneity of glioblastoma and its links to disease phenotypes.
-
- Cancer Biology
- Medicine
A doctoral-level internship program was developed at the University of North Carolina at Chapel Hill with the intent to create customizable experiential learning opportunities for biomedical trainees to support career exploration, preparation, and transition into their postgraduate professional roles. We report the outcomes of this program over a 5-year period. During that 5-year period, 123 internships took place at over 70 partner sites, representing at least 20 academic, for-profit, and non-profit career paths in the life sciences. A major goal of the program was to enhance trainees’ skill development and expertise in careers of interest. The benefits of the internship program for interns, host/employer, and supervisor/principal investigator were assessed using a mixed-methods approach, including surveys with closed- and open-ended responses as well as focus group interviews. Balancing stakeholder interests is key to creating a sustainable program with widespread support; hence, the level of support from internship hosts and faculty members were the key metrics analyzed throughout. We hypothesized that once a successful internship program was implemented, faculty culture might shift to be more accepting of internships; indeed, the data quantifying faculty attitudes support this. Furthermore, host motivation and performance expectations of interns were compared with results achieved, and this data revealed both expected and surprising benefits to hosts. Data suggests a myriad of benefits for each stakeholder group, and themes are cataloged and discussed. Program outcomes, evaluation data, policies, resources, and best practices developed through the implementation of this program are shared to provide resources that facilitate the creation of similar internship programs at other institutions. Program development was initially spurred by National Institutes of Health pilot funding, thereafter, successfully transitioning from a grant-supported model, to an institutionally supported funding model to achieve long-term programmatic sustainability.