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Visualizing DNA Damage Repair Proteins in Patient-Derived Ovarian Cancer Organoids via Immunofluorescence Assays

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IHGAMP: Pan-Cancer HRD Prediction From Routine H&E Whole-Slide Images Using Foundation Models.

Sanwal Ahmad Zafar¹, Liu Chengliang¹, Areeba Ali Khan²

¹School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.

APMIS : Acta Pathologica, Microbiologica, Et Immunologica Scandinavica

|April 24, 2026

Summary

This summary is machine-generated.

IHGAMP, a computational tool, predicts homologous recombination deficiency (HRD) from routine H&E slides, improving cancer therapy selection. This AI-driven approach offers accessible HRD screening, guiding treatment decisions for PARP inhibitors and platinum-based therapies.

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Area of Science:

Computational pathology
Artificial intelligence in oncology
Genomic instability biomarkers

Background:

Homologous recombination deficiency (HRD) is crucial for selecting PARP inhibitors and platinum-based therapies.
Current HRD detection relies on next-generation sequencing, which has limited accessibility and high costs.
There is a need for accessible methods to identify HRD status in cancer patients.

Purpose of the Study:

To develop and validate IHGAMP, a computational framework using vision transformer foundation models, for predicting HRD from H&E whole-slide images.
To assess the generalizability and specificity of IHGAMP across diverse cancer types and independent cohorts.
To evaluate IHGAMP's potential as a screening tool to prioritize patients for confirmatory molecular testing.

Main Methods:

Utilized vision transformer foundation models (OpenCLIP and OpenSlideFM) to analyze H&E whole-slide images.
Trained and tested IHGAMP on the TCGA dataset (8109 patients, 31 cancer types) and validated on seven independent cohorts (927 patients).
Performed pathway specificity and sensitivity analyses to confirm biological relevance and robustness across different HRD definitions.

Main Results:

IHGAMP achieved an AUROC of 0.766 (OpenCLIP) and 0.812 (OpenSlideFM) for HRD prediction on the TCGA test set.
External validation demonstrated generalizability in adenocarcinoma/serous histologies (e.g., CPTAC-LUAD AUROC 0.723) but showed attenuation in squamous histologies.
Analyses confirmed that IHGAMP captures HRD biology, distinguishing it from general genomic instability or single-gene mutations (e.g., BRCA1/2).

Conclusions:

IHGAMP effectively predicts HRD from routine H&E images, offering a more accessible alternative to genomic sequencing.
The framework shows promise for screening and triaging patients, potentially improving the selection of targeted therapies.
Further validation in squamous histologies and broader clinical implementation are warranted.