Pan-cancer association of DNA repair deficiencies with whole-genome mutational patterns
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eLife assessment
This paper will be of interest to researchers in the field of genomic medicine and cancer mutagenesis. It presents predictive models with potential clinical applications that can identify patients with specific gene dysfunction based on characteristic patterns of mutation. The key findings are well supported.
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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 DNA damage response 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 receiver operator characteristic 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.
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eLife assessment
This paper will be of interest to researchers in the field of genomic medicine and cancer mutagenesis. It presents predictive models with potential clinical applications that can identify patients with specific gene dysfunction based on characteristic patterns of mutation. The key findings are well supported.
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Reviewer #1 (Public Review):
In this manuscript, the authors present a suite of statistical models that can identify patterns of somatic single base mutational signatures (SBS), insertions, deletions, and structural rearrangements predictive of DNA damage response (DDR) gene deficiency. A similar approach (HRDetect) has already proved successful in identifying BRCA1/BRCA2 deficiency in breast cancers and other tumor types. To generate their models, Sørensen and colleagues consider over 700 DDR gene deficiencies across more than 6,000 patients enrolled through the Hartwig Medical Foundation and PCAWG studies. The authors also consider the full set of COSMIC SBS reference signatures. The models recapitulate known associations between BRCA1/2,TP53, and CDK12 and mutational patterns but also characterize previously undescribed associations …
Reviewer #1 (Public Review):
In this manuscript, the authors present a suite of statistical models that can identify patterns of somatic single base mutational signatures (SBS), insertions, deletions, and structural rearrangements predictive of DNA damage response (DDR) gene deficiency. A similar approach (HRDetect) has already proved successful in identifying BRCA1/BRCA2 deficiency in breast cancers and other tumor types. To generate their models, Sørensen and colleagues consider over 700 DDR gene deficiencies across more than 6,000 patients enrolled through the Hartwig Medical Foundation and PCAWG studies. The authors also consider the full set of COSMIC SBS reference signatures. The models recapitulate known associations between BRCA1/2,TP53, and CDK12 and mutational patterns but also characterize previously undescribed associations involving ATRX, PTEN, CDKN2A, and SMARCA4. Many of these novel models generalize across different tumour types and also primary and metastatic cancers. The authors also consider negative coefficient features in their models which is worthwhile and present hypotheses supporting how negative features might arise. One of the further strengths of the study is its innovative reuse of large-scale cancer genome data sets leading to predictive models with potential use for clinical intervention and demonstrating the potential of using WGS mutational signatures to guide cancer treatment. Many of the findings and observations presented in the paper have the collateral potential to enhance our understanding of the aetiology of SBS signatures. While I don't think the paper presents any major conceptual and technical advances in terms of methodology, the manuscript is important, interesting, and timely.
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Reviewer #2 (Public Review):
Sørensen and colleagues performed a comprehensive analysis aiming to find how DNA repair genes shape mutational patterns. They take advantage of the Hartwig Medical Foundation (HMF) and TCGA/ICGC databases which have germline and somatic molecular data. These molecular data layers are used as input features for the predictive models of DNA damage response (DDR) gene deficiency.
Of note, the project is of interest to oncology in the sense of unveiling new genes to be further investigated as a therapeutic candidate target in cancer.
This paper brings statistical modelling based on LASSO regression coupled with appropriate metrics for unbalanced data sets. Their finds recapitulate known DDR-associate genes but novel genes that can be explored in animal models or functional assays with cell lines.
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