Abstract 4623: Single-cell spatial proteomic analysis of the tumor microenvironment in treatment-naive NSCLC samples with immunotherapy treatment and response data
Authors: Cumberbatch Marie , Ivison Geoffrey , Lam Amy , Mayer Aaron , Bhagat Milan 
Published: June 2023

Abstract
The tumor microenvironment (TME) represents a complex network comprising a variety of cell types including immune, stromal, and extracellular elements in addition to malignant tumor cells. Growing evidence suggests that the spatial relationships of these components influence tumor progression and response/resistance to immunotherapeutic agents. However, the precise mechanisms driving this relationship to clinical outcomes remains poorly understood. This is in part because generating comprehensive spatial datasets has only recently been made possible due to innovations in instrumentation, laboratory techniques, and data storage and processing. Thus, there has been little integration between various types of -omics datasets with spatial data. Here, we present a workflow for analyzing multi-omic data of the TME with the aim of providing mechanistic insights into signatures of response to immunotherapy. The workflow starts with data generation and proceeds through cell segmentation, quality filtering, phenotype assignment through unsupervised clustering, analysis of pairwise interactions and higher order multicellular structures (i.e., “neighborhoods”), and finally association with patient clinical and molecular data allowing for meaningful comparisons to be made. This analysis can be performed through the Enable Medicine Cloud Platform, a framework that facilitates the association of a multitude of data types to samples, rapid processing of multiplex images to deliver single cell spatial proteomic data, and graphical interfaces for each analysis step. To demonstrate the utility of this workflow, we generated spatial proteomic data for a tissue microarray (TMA) comprising a cohort of treatment naïve NSCLC samples (n=42) with second-line immune checkpoint inhibitor-treatment and clinical follow-up information. The spatial proteomics data was generated with the Akoya PhenoCycler-Fusion multiplexed tissue imaging platform, with an optimized panel of 51 oligonucleotide-conjugated antibodies covering a wide variety of immune, epithelial, stromal, and malignant cell markers developed by Enable Medicine. This proteomic data was then integrated with GeoMX DSP, bulk RNAseq, and clinical information using the Enable Platform. This framework for analysis allows for the comprehensive integration of both clinical and biological parameters to enable biomarker discovery and insight into signatures of response to immunotherapy. Acknowledgement: Pasteur Hospital, 30 Voie Romaine, 06000 Nice, France for NSCLC tumor samples.
Citation Format: Marie Cumberbatch, Geoffrey Ivison, Amy Lam, Aaron Mayer, Milan Bhagat. Single-cell spatial proteomic analysis of the tumor microenvironment in treatment-naive NSCLC samples with immunotherapy treatment and response data. [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 4623.