Automated machine learning profiling with MAP-HR for quantifying homologous recombination foci in patient samples.

Abstract

Accurate visualization and quantification of homologous recombination (HR)-associated foci in readily available patient samples are critical for identifying patients with HR deficiency (HRD) when they present for care to guide polyADP ribose polymerase (PARP) inhibitors (PARPi) or platinum-based therapies. Immunofluorescence (IF) assays have the potential to accurately visualize DNA repair processes as punctate foci within the nucleus. To ensure precise HRD assessment, we developed MAP-HR, (Machine-learning Assisted Profiling of Homologous Recombination), a scalable machine-learning (ML) analysis platform to enable effective patient triage and therapeutic decision-making. This workflow integrates high-resolution four-channel IF imaging and automated analysis of Geminin (cell cycle states), RAD51 foci (HR repair), γH2AX foci (double strand breaks) and DAPI (nuclear localization) in both cultured cell lines and in a single formalin-fixed, paraffin-embedded (FFPE) patient samples. Using a spinning disk confocal microscope, we optimized imaging parameters to improve resolution and signal-to-noise ratio. Our MAP-HR pipeline uses nested nuclei and segmentation of foci to analyze the HR status of each cell, unlike competing bulk or single-foci marker assays, allowing evaluation of HR functional heterogeneity across and within patient biopsies. This approach facilitates robust comparisons of HR and foci-based processes across diverse cell populations and patient tissues, enabling scalable, translational research.