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Published on: February 24, 2023
IdentifiHR predicts homologous recombination deficiency in high-grade serous ovarian carcinoma using gene expression
Ashley L Weir1,2, Samuel C Lee3,4,5,6, Mengbo Li3,4
1The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia. weir.a@wehi.edu.au.
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.
Insights
A new gene expression model, IdentifiHR, accurately predicts homologous recombination (HR) DNA repair status in high-grade serous ovarian carcinomas (HGSC). This tool aids researchers in classifying HR deficiency (HRD) using transcriptomic data.
Area of Science:
- Oncology
- Genomics
- Molecular Biology
Background:
- High-grade serous ovarian carcinomas (HGSCs) often exhibit defects in homologous recombination (HR) DNA repair, presenting therapeutic targets.
- Existing genomic and transcriptomic methods for HR status classification are not HGSC-specific.
- There is a need for a gene expression-based tool tailored for predicting HR status in HGSC.
Purpose of the Study:
- To develop and validate a HGSC-specific gene expression model for predicting homologous recombination (HR) deficiency (HRD) status.
- To provide a robust tool for classifying HR status using transcriptomic data in ovarian cancer research.
Main Methods:
- A HGSC cohort from The Cancer Genome Atlas (TCGA) was divided into training (n=288) and testing (n=73) sets.
- Differential gene expression analysis was performed between HR-deficient (HRD) and HR-proficient (HRP) cases in the training set.
- A penalized logistic regression model was trained using 209 differentially expressed genes identified from 2604 significant genes.
Main Results:
- IdentifiHR, a model using 209 genes, predicts HR status in HGSC with 85% accuracy in the TCGA test set.
- The model achieved 86% accuracy in an independent cohort and 84% accuracy in single-cell HGSC sequencing data.
- IdentifiHR outperforms existing expression-based HR status prediction methods like BRCAness, MutliscaleHRD, and expHRD.
Conclusions:
- IdentifiHR is a highly accurate model for predicting HR status in HGSC.
- The open-source R package empowers researchers to reliably classify HR status from transcriptomic data.
