Abstract 6584: The ‘AstroPath' platform for spatially resolved, single cell analysis of the tumor microenvironment (TME) using multispectral immunofluorescence (mIF)

Abstract

Abstract Background: Multidimensional, spatially resolved analyses of immune and tumor cells within the TME of patients treated with checkpoint inhibitors will provide clinically translatable mechanistic insights and potentiate biomarker discovery. To achieve this goal, information from pathology specimens needs to be captured at a single cell level with high fidelity and in meaningfully sized cohorts. To date, efforts have been limited by inadequate tissue sampling and previously unrecognized errors in staining, imaging and data analysis. Here we describe the ‘AstroPath' platform, where strategies from the field of astronomy were adapted to study pathology specimens and generate large high quality mIF data. Methods: Potential error was identified and addressed at each stage of 6-plex (PD-1, PD-L1, FoxP3, CD163, CD8, tumor marker) mIF assay development. Whole slides from formalin-fixed paraffin embedded tissue specimens were stained with the optimized assay and imaged using a multispectral microscope (Vectra 3.0) with 20% overlap of high power fields (HPFs). The overlaps were used to quantify and correct optical lens distortion, HPF alignment, and illumination variation. Errors from cell segmentation algorithms, batch-to-batch staining variation, and HPF sampling were also addressed. The resultant mIF data were organized and analyzed using a large, relational database. Results: The optimized mIF assay captured equivalent signal compared to gold standard chromogenic immunohistochemistry and 2x more signal for PD-1, PD-L1 and FoxP3 compared to the manufacturer's recommended protocol. Errors and corrections for imaging included: pixel alignment error reduced from ~10 to <0.5 pixels at edges of HPFs; illumination variation reduced from 10% to 3% per HPF; over-counting of larger cells, e.g. tumor cells, reduced by ~25% using custom cell segmentation and ‘multi-pass' phenotyping algorithms; and batch-to-batch variation reduced by ~50% by normalizing to tissue controls. Correction of these errors that would otherwise be compounded at each stage, allowed for more accurate and reliable cell type and marker intensity comparisons across samples. Lastly, the entire slide rather than select HPFs were imaged, resulting in ~100x more HPFs analyzed per slide. The whole slide imaging approach also corrected for other potential source of errors, i.e., sampling error due to tumor heterogeneity and operator-dependent field selection. Conclusion: Here we present an end-to-end pathology workflow with rigorous quality control for creating quantitative, spatially resolved mIF datasets using lessons derived from the field of astronomy. Such approaches will vastly improve standardization and scalability of mIF technologies, enabling cross-site comparisons and eventual clinical translation as biomarker discovery platforms or standard diagnostic tests. Citation Format: Sneha Berry, Nicolas Giraldo, Benjamin Green, Elizabeth Engle, Haiying Xu, Aleksandra Ogurtsova, Daphne Wang, Julie E. Stein, Peter Nguyen, Suzanne Topalian, Angelo DeMarzo, Drew M. Pardoll, Robert A. Anders, Tricia R. Cottrell, Alexander S. Szalay, Janis M. Taube. The ‘AstroPath' platform for spatially resolved, single cell analysis of the tumor microenvironment (TME) using multispectral immunofluorescence (mIF) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6584.