Evidence map›Paper›PMID 41535393›Full record

ArticleCommunications medicine2026

IdentifiHR predicts homologous recombination deficiency in high-grade serous ovarian carcinoma using gene expression.

Ashley L Weir, Samuel C Lee, Mengbo Li, Ahwan Pandey, Chin Wee Tan, Dale W Garsed, Susan J Ramus, Nadia M Davidson

Abstract read
In one paragraph

Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ashley L WeirThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia. weir.a@wehi.edu.au.ORCID http://orcid.org/0000-0002-3072-2616
Samuel C LeeThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.ORCID http://orcid.org/0000-0002-6805-4051
Mengbo LiThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.ORCID http://orcid.org/0000-0002-9666-5810
Ahwan PandeyPeter MacCallum Cancer Centre, Melbourne, VIC, Australia.
Chin Wee TanThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.ORCID http://orcid.org/0000-0001-9695-7218
Dale W GarsedPeter MacCallum Cancer Centre, Melbourne, VIC, Australia.ORCID http://orcid.org/0000-0003-1223-0121
Susan J RamusSchool of Clinical Medicine, UNSW Medicine and Health, University of NSW Sydney, Sydney, NSW, Australia.ORCID http://orcid.org/0000-0003-0005-7798
Nadia M DavidsonThe Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia. davidson.n@wehi.edu.au.ORCID http://orcid.org/0000-0002-8461-7467

Funding

Department of Health | National Health and Medical Research Council (NHMRC) GNT2009840Department of Health | National Health and Medical Research Council (NHMRC) GNT2016547
6 · The paper itself

Abstract

backgroundApproximately 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.

methodsWe 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.

resultsIdentifiHR 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.

conclusionsIdentifiHR 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.

Identifiers

PMID41535393
PMCPMC12910048

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.