LAUSR

Non-small cell lung cancer (NSCLC) is a complex disease with varying pathological subtypes including adenocarcinomas and squamous cell carcinomas. After diagnosis, tumor staging provides important information about the extent of cancer in the body and anticipated response to treatment. NSCLC patients can have impaired immune responses within the tumor microenvironment (TME), leading to a progression of tumor growth and poorer prognosis. Accurate cell phenotyping combined with spatial profiling of the immune contexture and checkpoint expression, can provide a deeper understanding of complex cellular interactions underpinning the tumor-immune response. The aim of this study was to utilize spatial multiplexed imaging technology and associated data analysis methods to profile the immune contexture, as well as their spatial interactions with the tumor, in a set of tissue cores covering a range of NSCLC subtypes and tumor staging. Formalin-fixed paraffin-embedded (FFPE) NSCLC tissue microarrays (TMA), comprised of n=41 cores containing a range of carcinomas and pathological Tumor-Node-Metastasis (pTNM) stages (I-IV), were stained on a Leica Bond RX™ using the Akoya PhenoCode™ Signature Immuno-contexture Human Protein Panel, which includes markers for CD8, CD68, PD-1, PD-L1, FoxP3, and PanCK as a tumor indicator. Stained TMAs were scanned at 20x magnification on a PhenoImager HT multispectral imaging system. Image analysis was performed using Visiopharm software. Deep learning algorithms were applied to segment tumor and stroma regions of interest (ROI) and to accurately detect and classify specific cell phenotypes. Cell object data files per core were exported for spatial and neighborhood analysis using OracleBio’s proprietary python-based program, PhenoXplore. Immune cell counts, phenotypes and spatial interactions were generated within tumor and stroma ROI per core. Data included total and negative cell phenotype counts, cell density in tumor and stroma, as well as cell distance and neighborhood spatial interactions per core across NSCLC subtypes and stages in the TMA set. The combination of high-quality staining provided by the PhenoCode™ panel, coupled with deep learning quantitative phenotyping and spatial pattern analysis enables detailed characterization of the complex cellular interactions, at both the functional and spatial level, within the TME of NSCLC tissue. Data generated will support a greater understanding of the complex cellular interactions that can contribute to progression of NSCLC and may help guide future precision medicine strategies. Citation Format: Lorcan Sherry, Nicole Couper, Bethany Remeniuk, Karen McClymont, Bei Hopkins, Natalie Monteiro, Gabriel Reines March, Darren Locke. Quantitative spatial profiling of NSCLC subtypes across tumor stages using 6-plex multiplex imaging technology and AI-powered phenotyping analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4633.