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 (HR repair),{gamma} H2AX foci (double strand breaks) and DAPI (nuclear localization) in both cultured cell lines and in a single formalin-fixed, paraffin-embedded (FFPE) patient sample. 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. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/639329v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@154d43eorg.highwire.dtl.DTLVardef@3a6fa6org.highwire.dtl.DTLVardef@782daborg.highwire.dtl.DTLVardef@1fe6c28_HPS_FORMAT_FIGEXP M_FIG C_FIG