Abstract
Background
Approximately half of all high-grade serous ovarian carcinomas (HGSCs) have a therapeutically targetable defect in homologous recombination (HR) DNA repair. While there are genomic and transcriptomic methods, developed for other cancers, to identify HR deficient (HRD) samples, there are no gene expression-based tools to predict HR status in HGSC specifically. We have built a HGSC-specific model to predict HR status using gene expression.
Methods
We separated The Cancer Genome Atlas (TCGA) cohort of HGSCs into training (n = 288) and testing (n = 73) sets and labelled each case as HRD or HR proficient (HRP) based on the clinical standard for classification. Using the training set, we performed differential gene expression analysis between HRD and HRP cases. The 2604 significantly differentially expressed genes were used to train a penalised logistic regression model.
Results
IdentifiHR uses the expression of 209 genes to predict HR status in HGSC. These genes preserve the genomic damage signal, capturing known regions of HR-specific copy number alteration which impact gene expression. IdentifiHR is 85% accurate in the TCGA test set and 86% accurate in an independent cohort of 99 samples, taken from primary tumours, ascites and normal fallopian tubes. Further, IdentifiHR is 84% accurate in pseudobulked single-cell HGSC sequencing from 37 patients and outperforms existing expression-based methods to predict HR status, being BRCAness, MutliscaleHRD and expHRD.
Conclusions
IdentifiHR is an accurate model to predict HR status in HGSC. It is available as an open source R package, empowering researchers to robustly classify HR status when only transcriptomic sequencing data is available.
Plain language summary
High-grade serous ovarian cancer (HGSC) is a type of ovarian cancer with very poor outcomes. However, half of HGSCs have faulty DNA repair that can be targeted for treatment if it is identified. Existing methods look at changes in DNA that arise when repair is faulty, but do not consider which genes are actively being used, or are “expressed”, by the cancer. We developed IdentifiHR, a machine learning method to predict DNA repair status using the expression of 209 genes. We tested IdentifiHR on 209 patient samples and found it correctly predicts repair status in about 85–86% of cases, performing better than existing tools on the same patient data. IdentifiHR is released as a software package for public use.
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Data availability
The results published here are in whole or part based upon data generated by The Cancer Genome Atlas, managed by the NCI and NHGRI. Information about TCGA can be found at http://cancergenome.nih.gov. RNA sequencing, gene-level copy number, methylation, SNP and structural variant data collected on the TCGA HGSC cohort, with associated clinical data, are available from the Genomic Data Commons (TCGA project) data portal (https://portal.gdc.cancer.gov/, https://www.cancer.gov/tcga, dbGaP Study Accession: phs000178.v11.p8). AOCS gene expression counts were accessed at the Gene Expression Omnibus (accession: GSE209964). Previously published WGS data are available from the European Genome-phenome Archive (accession: EGAD00001000877). MSKCC gene expression counts were available as a Seurat object on Synpase (SynID: syn51091849). The source data for Fig. 2A, D, E can be found in Supplementary Data 2, for Fig. 3A, B in Supplementary Data 5, for Fig. 3C in Supplementary Data 6, for Fig. 3D, E in supplementary data 7, for Fig. 3F in Supplementary Data 9 and for Fig. 4A–C in Supplementary Data 5. These data are available in the supplementary information and in the IdentifiHR repository, https://github.com/DavidsonGroup/IdentifiHR. All other data supporting the findings of this study, including the source data for all figures, are publicly available.
Code availability
All analyses were carried out in R v4.2.1. Code to reproduce the analysis can be found in the IdentifiHR repository, https://github.com/DavidsonGroup/IdentifiHR.
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Acknowledgements
A.L.W. is supported by a Research Training Program scholarship and is partially funded by a CSL PhD top-up scholarship and a Tour De Cure PhD grant. N.M.D. is funded by NHMRC Investigator Grant [GNT2016547 to N.M.D.] and the Estate of Judith Corrie Philpots. S.J.R. is funded by NHMRC Investigator Grant [GNT2009840 to S.J.R]. We thank Dr Matthew Wakefield for offering insight and expertise in ovarian carcinoma biology. We acknowledge the contributions of Dr Ksenija Nesic and the entire laboratory of Professor Clare Scott at the Walter and Eliza Hall Institute for offering feedback on the complete IdentifiHR model. We offer thanks to Professor James Brenton and members of the Brenton laboratory for discussions surrounding HR and the training of our model. Figures 1 and 3 created, in part, in BioRender. Weir, A. (2025); https://BioRender.com/isms2aw. We thank the many patients who contributed to the data used in this research, and our cancer consumer advisers. We also acknowledge the Wurundjeri people of the Kulin nation as the traditional owners and guardians of the land on which the work was performed.
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A.L.W. and N.M.D. conceived and designed the study. A.L.W. collected, processed, and curated all data, developed the methodology, validated the method and results, wrote the original draft and all subsequent iterations, and produced all tables and visualisations in the study. N.M.D., S.J.R. and C.W.T. supervised the research and revised the manuscript. D.G and A.P. processed and analysed WGS data in the AOCS cohort. S.C.L. and M.L. advised on method development and analysis. All authors contributed to the review of the manuscript. All authors approved the manuscript for submission.
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Weir, A.L., Lee, S.C., Li, M. et al. IdentifiHR predicts homologous recombination deficiency in high-grade serous ovarian carcinoma using gene expression. Commun Med (2026). https://doi.org/10.1038/s43856-026-01387-y
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DOI: https://doi.org/10.1038/s43856-026-01387-y
