Joshua SK Bell, Aarti Venkat, Jerod Parsons, Catherine Igartua, Benjamin D. Leibowitz, Robert Tell, and Kevin White
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.
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