An automated tissue staining and quantitative digital image analysis pipeline for quantification of DNA damage repair at the single-cell level.

Background: The repair of double-stranded breaks in DNA occurs through homologous recombination (HR) or nonhomologous enjoining (NHEJ) pathways. Germline mutations in DNA damage repair enzymes reduce efficiency of DNA damage repair (DDR) and increase cancer risk, but also the sensitivity to DNA damaging agents, such as cisplatinum and PARP1 inhibitors. Because the regulation of DDR occurs through protein-protein interactions and post-translational modifications, genomic methods are insufficient to identify cancers with defective DDR pathways. The formation of DDR protein complexes underlies strict spatial regulation. Therefore, the measurement of DDR efficiency requires in situ detection of DDR foci by visualizing the location of multiple proteins within the nucleus. This can be accomplished by multiantibody immunofluorescent staining combined with digital image analysis. The main goal of the project is to develop tissue staining assays for protein complexes involved in DDR using 5 antibodies per slide and quantitative digital image analysis pipelines to integrate data from consecutive slides in order to establish a DDR index of a patient’s prostate cancer. Methods: We developed 2 fully automated 5-plex fluorescent (RPA32, RAD51, Ku80, XRCC4, pH2AX) (53BP1, pDNA-PK, PARP1, PAR, AR) antibody protocols on the Ventana Discovery autostainer. The fluorescent assays were combined with an automated cytokeratin (CK), Ki-67, and Geminin chromogenic staining protocol on the exact same tissue. The panels were applied to a TMA of high-grade prostate cancer and for comparison to a bladder and a rectal cancer TMA slide. Slides were imaged after the fluorescent portion of the assay was completed and subsequently processed for staining with CK (yellow) and Ki-67 (teal) and Geminin (brown). Chromogenic and fluorescent images were coregistered. To integrate images from adjacent slides, a transformation matrix was applied to distort the images in order to maximize the overlap of nuclear DAPI stains. The nuclear staining of each antibody was quantified through intensity, area, and texture measurements and compared in nuclei positive and negative for pH2AX. After histogram normalization and subtraction of background and autofluorescence, the amount of colocalized pixels between antibody pairs was determined. Results: We have demonstrated feasibility of automated multiplex staining protocols that increase the reproducibility and future clinical utility of DDR assays. Specific staining of individual antibodies was identified as a punctate nuclear staining pattern in a fraction of cancer cells, and multiple parameters of the staining pattern (intensity, stained area, subnuclear distribution) were quantified using digital image analysis. DDR occurred in high-grade and low-grade prostate cancer as determined by the percentage of cancer cells that stained positive for pH2AX. pH2AX staining was also noticed in normal epithelium (weakly proliferative), stroma (nonproliferative), and inflammatory cells (proliferative). At least 5 nuclei from each compartment (tumor, normal, stroma, and inflammation) per TMA core were selected for analyses based on pH2AX positivity. Using a consistent image analysis pipeline to set staining intensity thresholds, overlapping pixels between RPA32-RAD51, Ku80-XRCC4, 53BP1-pDNAPK, and the overlap of pixels of each protein complex with AR were determined. The intracase and intercase variance of DDR complexes was compared for each cell type (cancer, normal epithelium, stroma, and inflammation) and correlated with the amount of cell proliferation. Conclusion: We demonstrate feasibility of measuring DDR complexes in single nuclei. The assay has the potential to be used in a CLIA-certified laboratory and the quantitative measurements from the multiplex DDR assays can be compared to those from other OMICS platforms, such as mutation burden and copy number alterations. Funding: Precision Medicine initiative and Translational Research Core at Cedars-Sinai Medical Center. Citation Format: Joshua Saylor, Eric Weterings, Nathan Ellis, James Hinton, Esteban Roberts, Anne Cress, Beatrice Knudsen. An automated tissue staining and quantitative digital image analysis pipeline for quantification of DNA damage repair at the single-cell level [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B095.