Abstract 1713: Delineation of cell subpopulations and cell cell interactions to determine correlations between drug response and tumor microenvironment in early-stage lung cancer

Abstract

Abstract Purpose The tumor microenvironment (TME) is an integral player in cancer initiation, tumor progression, response and resistance to anti-cancer therapy. Understanding the complex interactions of tumor immune architecture has therefore become increasingly desirable to guide patient selection. Conventional studies that underestimate the potential value of the spatial architecture of the TME are unable to completely elucidate its complexity. To overcome these limitations, we used quantitative image analysis based on multiplexed immunohistochemistry and deep learning technologies to interrogate complex information from the tumor microenvironment and find predictive insights into treatment response for non-small cell lung cancer (NSCLC). Method We used tumor tissues samples from resected NSCLC (adenocarcinoma and squamous cell carcinoma) tumors treated with adjuvant pembrolizumab therapy. Patient frozen tissue sections were prepared and stained using a panel of 20 immune or tumor-related antibodies. Different metal tags were assigned to each antibody. The stained cells were then detected and analyzed by the Hyperion™ Imaging System. Further data analysis was performed using proprietary workflow to identify which cells are influencing the treatment response and how they are spatially distributed relative to each other. Results Tumor samples of NSCLC with similar clinical characteristics at the T1N0M0 had different cells types, including CD4+ T cells, CD8+ T cells, CD20+ B cells, CD14+ macrophages, CD56+ natural killer cells, granulocytes, etc. More immunosuppressive cells were found in the sample of patient that did not respond to immunotherapy illustrating that differences in the tumor microenvironment of samples may contribute to different patients’ resistance to immunotherapy. Moreover, unsupervised clustering analysis reveal cell type signatures that are relevant to the prognoses of NSCLC. Conclusion High-multiplexed proteomics analyses using the Hyperion imaging system help identify and analyze the tumor microenvironment heterogeneity of NSCLC and explain the factors leading to different prognoses for NSCLC patients despite the same TNM stage. Furthermore, additional information for screening drugs for targeted therapy was found, that could greatly improve NSCLC treatments’ individualization. Citation Format: Corinne Ramos, Journe Anne Sophie, Amandine Gerstenberg, Fabien Pamelard, Jonathan Stauber. Delineation of cell subpopulations and cell cell interactions to determine correlations between drug response and tumor microenvironment in early-stage lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1713.