scholarly journals Pan-cancer landscape of homologous recombination deficiency

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Luan Nguyen ◽  
John W. M. Martens ◽  
Arne Van Hoeck ◽  
Edwin Cuppen

Abstract Homologous recombination deficiency (HRD) results in impaired double strand break repair and is a frequent driver of tumorigenesis. Here, we develop a genome-wide mutational scar-based pan-cancer Classifier of HOmologous Recombination Deficiency (CHORD) that can discriminate BRCA1- and BRCA2-subtypes. Analysis of a metastatic (n = 3,504) and primary (n = 1,854) pan-cancer cohort reveals that HRD is most frequent in ovarian and breast cancer, followed by pancreatic and prostate cancer. We identify biallelic inactivation of BRCA1, BRCA2, RAD51C or PALB2 as the most common genetic cause of HRD, with RAD51C and PALB2 inactivation resulting in BRCA2-type HRD. We find that while the specific genetic cause of HRD is cancer type specific, biallelic inactivation is predominantly associated with loss-of-heterozygosity (LOH), with increased contribution of deep deletions in prostate cancer. Our results demonstrate the value of pan-cancer genomics-based HRD testing and its potential diagnostic value for patient stratification towards treatment with e.g. poly ADP-ribose polymerase inhibitors (PARPi).

Author(s):  
Luan Nguyen ◽  
John Martens ◽  
Arne Van Hoeck ◽  
Edwin Cuppen

AbstractHomologous recombination deficiency (HRD) results in impaired double strand break repair and is a frequent driver of tumorigenesis. Here, we developed a genome-wide mutational scar-based pan-cancer Classifier of HOmologous Recombination Deficiency (CHORD) that can discriminate BRCA1- and BRCA2-subtypes. Analysis of a metastatic (n=3,504) and primary (n=1,854) pan-cancer cohort revealed HRD was most frequent in ovarian and breast cancer, followed by pancreatic and prostate cancer. Biallelic inactivation of BRCA1, BRCA2, RAD51C or PALB2 was the most common genetic cause of HRD, with RAD51C and PALB2 inactivation resulting in BRCA2-type HRD. While the specific genetic cause of HRD was cancer type specific, biallelic inactivation was predominantly associated with loss-of-heterozygosity (LOH), with increased contribution of deep deletions in prostate cancer. Our results demonstrate the value of pan-cancer genomics-based HRD testing and its potential diagnostic value for patient stratification towards treatment with e.g. poly ADP-ribose polymerase inhibitors (PARPi).


2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15664-e15664
Author(s):  
Joshua SK Bell ◽  
Aarti Venkat ◽  
Jerod Parsons ◽  
Catherine Igartua ◽  
Benjamin D. Leibowitz ◽  
...  

e15664 Background: Homologous recombination deficiency (HRD) is the primary biomarker for sensitivity to PARP inhibitors, but identifying the genetic and transcriptomic characteristics that fully capture all HRD patients has remained difficult. For example, DNA-based approaches are limited to patients with pathogenic mutations and loss of heterozygosity (LOH) events, and can fail to properly classify patients with variants of unknown significance. To capture more dynamic cellular processes that arise immediately upon induction of HRD through silencing or loss of BRCA 1/2, a more integrated approach that includes both RNA and DNA based models is necessary. Methods: Using DNA sequencing we developed a genome-wide LOH score that combines pathogenic mutation status and LOH at the BRCA1/2 loci, and the proportion of bases sequenced in the Tempus xT panel that undergo LOH. We also developed three independent RNA-based models to predict BRCA deficiency: 1) An elastic net transcriptome model to predict DNA-based HRD scores derived from exome and SNP array data for each tumor type represented in TCGA; 2) A logistic model to detect BRCA1 promoter hypermethylation from the transcriptome in TCGA data; 3) A model that leveraged the mSigDB annotated gene sets to conduct single sample gene set enrichment analysis (ssGSEA) on Tempus-sequenced patients, selecting over a hundred gene sets that were predictive of BRCA-deficiency. These 4 features were combined to develop a stacked, linear-regression model to distinguish BRCA-intact from BRCA-deficient patients. Results: We found that the genome-wide LOH score alone is predictive of BRCA deficiency. However, our integrated model was highly accurate at distinguishing between BRCA-intact and BRCA-deficient patients and outperformed any single RNA- or DNA-based model. Using this model, we identified many patients that are likely to respond to PARP inhibitors that would have been overlooked using RNA or DNA-based inferences alone. Conclusions: Our approach highlights the strength of integrating diverse molecular features to refine diagnosis and enable oncologists to deliver the most effective therapies to patients.


Author(s):  
Adam B. Weiner ◽  
Yang Liu ◽  
Matthew McFarlane ◽  
Pushpinder S. Bawa ◽  
Eric V. Li ◽  
...  

2014 ◽  
Author(s):  
Sean J. Leith ◽  
Susan E. Kuruvilla ◽  
Jason Moffat ◽  
Ann F. Chambers ◽  
Eva A. Turley ◽  
...  

2014 ◽  
Author(s):  
Andrea M. Marquard ◽  
Aron C. Eklund ◽  
Zhigang C. Wang ◽  
Andrea L. Richardson ◽  
Zoltan Szallasi ◽  
...  

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Ieva Rauluseviciute ◽  
Finn Drabløs ◽  
Morten Beck Rye

Abstract Background Prostate cancer (PCa) has the highest incidence rates of cancers in men in western countries. Unlike several other types of cancer, PCa has few genetic drivers, which has led researchers to look for additional epigenetic and transcriptomic contributors to PCa development and progression. Especially datasets on DNA methylation, the most commonly studied epigenetic marker, have recently been measured and analysed in several PCa patient cohorts. DNA methylation is most commonly associated with downregulation of gene expression. However, positive associations of DNA methylation to gene expression have also been reported, suggesting a more diverse mechanism of epigenetic regulation. Such additional complexity could have important implications for understanding prostate cancer development but has not been studied at a genome-wide scale. Results In this study, we have compared three sets of genome-wide single-site DNA methylation data from 870 PCa and normal tissue samples with multi-cohort gene expression data from 1117 samples, including 532 samples where DNA methylation and gene expression have been measured on the exact same samples. Genes were classified according to their corresponding methylation and expression profiles. A large group of hypermethylated genes was robustly associated with increased gene expression (UPUP group) in all three methylation datasets. These genes demonstrated distinct patterns of correlation between DNA methylation and gene expression compared to the genes showing the canonical negative association between methylation and expression (UPDOWN group). This indicates a more diversified role of DNA methylation in regulating gene expression than previously appreciated. Moreover, UPUP and UPDOWN genes were associated with different compartments — UPUP genes were related to the structures in nucleus, while UPDOWN genes were linked to extracellular features. Conclusion We identified a robust association between hypermethylation and upregulation of gene expression when comparing samples from prostate cancer and normal tissue. These results challenge the classical view where DNA methylation is always associated with suppression of gene expression, which underlines the importance of considering corresponding expression data when assessing the downstream regulatory effect of DNA methylation.


Sign in / Sign up

Export Citation Format

Share Document