Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma

Clinical Snapshot

Key Findings

• P Value: p < 0.05 for NR5A1 expression difference between platinum responders vs non-responders

• Effect Size: Mean Pearson correlation r = 0.36 across ~6,400 genes; >300 genes with r > 0.44

• Primary Outcome: Genome-wide gene expression prediction from H&E histopathology images

• Nnt Or Sensitivity: NR5A1 coefficient of variation = 1.486; mean expression 0.263 vs 0.013 in responders vs non-responders

• Confidence Interval: Not consistently reported

Clinical Application

Potentially scalable approach using routine H&E slides, but requires specialized computational infrastructure and validation Could complement existing pathology workflows in Australian cancer centers; would require TGA validation for clinical implementation and consideration for PBS funding of associated testing Patients with high-grade serous ovarian carcinoma requiring molecular stratification for treatment planning

Abstract

BACKGROUND: High-grade serous ovarian cancer (HGSOC) is the most lethal ovarian cancer subtype, responsible for ~ 70% of ovarian cancer-related deaths and marked by late-stage diagnosis and frequent platinum resistance. Although transcriptomic profiling enables molecular stratification and prediction of therapeutic response; routine clinical use of this approach is limited by cost and logistical constraints. Computational pathology analysis offers a scalable alternative by inferring transcriptional states directly from routine hematoxylin and eosin (H&E) whole-slide images (WSIs). METHODS: Paired H&E WSIs and RNA-sequencing data from the TCGA-OV cohort, including 1,371 diagnostic H&E WSIs retrieved for preprocessing and quality control, were used to develop a self-supervised virtual-transcriptomics framework based on Momentum Contrast v2 (MoCo v2) and multi-output Random Forest regression. Model performance was assessed using patient-level five-fold cross-validation. Candidate genes were evaluated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in an independent cohort of 10 HGSOC tumors, including 4 platinum responders and 6 non-responders. RESULTS: The model predicted expression of approximately 6,400 protein-coding genes, achieving a genome-wide mean Pearson correlation of r = 0.36, with more than 300 genes showing stronger image-expression coupling (r > 0.44). RT-qPCR analysis of 18 candidate genes revealed substantial inter-patient heterogeneity. NR5A1 exhibited the highest expression variability (coefficient of variation [CV] = 1.486) and significantly higher expression in platinum-responsive tumors than in non-responders (mean 2⁻ΔCt = 0.263 vs. 0.013; p < 0.05). Exploratory in silico docking and molecular dynamics (MD) analyses suggested structurally stable binding interactions between Steroidogenic Factor-1 (SF-1/NR5A1) and the natural plant compound cubebin. CONCLUSION: This study demonstrates that histological architecture contains measurable transcriptomic information that can support scalable biomarker prioritization from routine diagnostic histology in HGSOC. NR5A1 represents a hypothesis-generating candidate biomarker and structurally tractable target for future experimental studies. Future validation in larger, multi-center cohorts will be essential to confirm model robustness, biological relevance, and potential clinical utility.

References

1. 1.Lingasamy, P., Ostrowska-Leśko, M., Tsakalis, P., Patel, N., Chamatidis, I., Sudhakaran, S. L., Kubik, J., Bobiński, M., Lagaros, N. D., Salumets, A., & Modhukur, V. (2026). Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma. Journal of Ovarian Research. https://doi.org/10.1038/s41419-019-1874-9