Abstract 4707: Quantifying cellular composition using laser microdissection regions of interest via automated digital image analysis of co-registered tissue thin sections
Authors: Lorcan Sherry, Mark Anderson, Allison Hunt, Dave Mitchell, Julie Oliver, Thomas Conrads, Nicholas Bateman
Journal: Cancer Research (2019)

Abstract Low purity tumors often portend poor disease outcome. Understanding the compositional heterogeneity of complex tumors, such as high-grade serous ovarian cancers (HGSOC) is an area of intense study. Laser microdissection affords decoupled molecular profiling analyses of tumor and non-tumor cells not otherwise possible using routine methods. Automated documentation of LMD collection activities are needed to support the development of high stringency quality control and quality assurance (QA/QC) procedures. This study was conducted to demonstrate that histology combined with digital image analysis can be utilized to quantify tumor and stroma cell content within LMD regions of interest (ROI). A single frozen high grade serous ovarian cancer tissue specimen was acquired under an IRB-approved protocol and thin sectioned onto charged glass or polyethylene naphthalate (PEN) membrane slides followed by staining with hematoxylin and eosin H&amp;E. Tumor and stromal cell populations were harvested from membrane slides in a locoregional manner by LMD (LMD7, Leica) at ~150 µm intervals. Digital whole slide images (WSI) (Aperio ScanScope XT slide scanner, Leica) of HGSOC tissue were provided for analysis consisting of matched reference H&amp;E sections and serial sections following enrichment of tumor (n=15) or stromal cell populations (n=3). Image analysis was performed by OracleBio using the Indica Labs HALO™ platform. Classification algorithms were developed and applied to each LMD image to identify the ‘Dissection Area’ and ‘All Remaining Tissue’ as two separate ROIs. A separate cytonuclear algorithm was developed using the H&amp;E stained sections to detect cell nuclei. Each LMD image was co-registered with their respective H&amp;E image to overlay classified ROI before analysis for nuclei detection. Dissected ROIs revealed median tumor cell areas were 27.5% and median stroma cell areas were ~50% of the total tissue area observable across sections. These were consistent with histopathological estimates of tumor (~36%, R2=0.91) and stromal (~52%, R2=0.96) cellularity across sections. Data generated from co-registered post-LMD dissected Area ROI within H&amp;E sections serial to the LMD tissues highlighted that the median number of nuclei per sample within tumor (n=15) and stroma (n=3) ROI was 7,527 ± 665 and 3,470 ± 476 per mm2 respectively. The average size of nuclei detected within tumor and stroma ROI was 24.2 ± 1 and 21.4 ± 0.5µm2 respectively; a difference of ~12% between these cell types. This study highlights the successful use of histology combined with digital image analysis to quantify tumor and stroma cell numbers within HGSOC tissue subjected to LMD. Furthermore, proteogenomic analyses of LMD-recovered tumor and stromal cells will support further understanding of potential correlations between these cell types within the tumor microenvironment of HGSOC. Citation Format: Lorcan Sherry, Mark Anderson, Allison Hunt, Dave Mitchell, Julie Oliver, Thomas Conrads, Nicholas Bateman. Quantifying cellular composition using laser microdissection regions of interest via automated digital image analysis of co-registered tissue thin sections [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4707.