Validation of genomic and transcriptomic models of homologous recombination deficiency in a real-world pan-cancer cohort

Abstract

Background With the introduction of DNA-damaging therapies into standard of care cancer treatment, there is a growing need for predictive diagnostics assessing homologous recombination deficiency (HRD) status across tumor types. Following the strong clinical evidence for the utility of DNA-sequencing-based HRD testing in ovarian cancer, and growing evidence in breast cancer, we present analytical validation of the Tempus HRD-DNA test. We further developed, validated, and explored the Tempus HRD-RNA model, which uses gene expression data from 16,750 RNA-seq samples to predict HRD status from formalin-fixed paraffin-embedded tumor samples across numerous cancer types. Methods Genomic and transcriptomic profiling was performed using next-generation sequencing from Tempus xT, Tempus xO, Tempus xE, Tempus RS, and Tempus RS.v2 assays on 48,843 samples. Samples were labeled based on their BRCA1, BRCA2 and selected Homologous Recombination Repair pathway gene (CDK12, PALB2, RAD51B, RAD51C, RAD51D) mutational status to train and validate HRD-DNA, a genome-wide loss-of-heterozygosity biomarker, and HRD-RNA, a logistic regression model trained on gene expression. Results In a sample of 2058 breast and 1216 ovarian tumors, BRCA status was predicted by HRD-DNA with F1-scores of 0.98 and 0.96, respectively. Across an independent set of 1363 samples across solid tumor types, the HRD-RNA model was predictive of BRCA status in prostate, pancreatic, and non-small cell lung cancer, with F1-scores of 0.88, 0.69, and 0.62, respectively. Conclusions We predict HRD-positive patients across many cancer types and believe both HRD models may generalize to other mechanisms of HRD outside of BRCA loss. HRD-RNA complements DNA-based HRD detection methods, especially for indications with low prevalence of BRCA alterations.

Genomic Scar Score: A robust model predicting homologous recombination deficiency based on genomic instability

“…Another category detects the gene expression profile. 31 This category has higher sample quality requirements compared with measuring DNA levels, and its performance is also unstable given the nature of RNA. The third category detects CNV features, including the GSS, Myriad HRD 14 and Foundation HRD.…”

“…These methods require whole exome sequencing or whole genome sequencing data to achieve accurate mutation profiles. Another category detects the gene expression profile 31 . This category has higher sample quality requirements compared with measuring DNA levels, and its performance is also unstable given the nature of RNA.…”

Prediction of homologous recombination deficiency from cancer gene expression data

“…Some gene expression-based HRD prediction tests have previously been studied. 28 , 30 – 32 However, the HRD status in these studies was determined by the presence of pathogenic variants in HRD-related genes such as BRCA1/2 . Conversely, our prediction model used an HRD score to determine the HRD status.…”

Molecular Testing for Diagnostics, Prognostication, and Treatment Stratification in Cancers

“…Inherently, though, all of these approaches suffer from the weakness that they are looking at the past, not current behavior of the tumor, and that they may represent a phenotype that is no longer expressed. To counteract this, an expression-based approach has been developed to explore the current HRD status 55 . With more than 20 different assay developers creating HRD assays, harmonization is essential to ensure that clinically valid results reach patients.…”