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BackgroundTertiary Lymphoid Structures (TLS) are highly organized ectopic lymphoid structures found in inflamed or tumor tissues, acting as sites of lymphoid recruitment and immune activation. A high TLS density within the tumor is commonly associated with an increased prognostic effect of TILs and with an improved disease free survival and overall survival for patients.1 However, the existence of conflicting studies suggest that multiple TLS features should be taken into account when assessing their prognostic value, such as their location, cellular composition, maturation stage and spatial organisation, as those may affect their functionalities.2MethodsWith the aim of gaining insights into TLS biology and evaluating the prognostic role of TLS in Non-Small Cell Lung Carcinoma according to their multiple features, we developed a TLS multiplex immunofluorescent (mIF) panel that includes T cells (CD3, CD8), B cells (CD20), Follicular Dendritic cells (CD21, CD23) and mature dendritic cells (DC-LAMP) markers. We deployed this panel across a cohort of primary tumors from NSCLC patients (n=408) and established a mIF image analysis workstream to assess the status and spatial location of each cell within the tissue. A H&E staining of the same tissue section was performed to evaluate mIF spatial data in relation to the tumor context. Additional multi-omics assessments were conducted across the same cohort including; whole exome sequencing, NanoString transcriptomics, and immunohistochemistry (e.g. PD-L1, FOXP3, NKp46, LKB1, CTLA4). We have leveraged clinical metadata, including demographics (e.g. age, sex, smoking status) and clinical risk factors (e.g. stage, grade, Standard of Care treatment) with clinical follow up (e.g. OS, PFS) for prevalence analysis, novel biomarker identification, and survival association.ResultsAssessment of the prevalence of each cell phenotype within the tumor tissue and TLS, the cell-cell interactions, the distance between each cell type, and the distance of non-TLS immune cells to the closest TLS will be described, demonstrating the different types of lymphoid aggregates and TLS and their functional status. An integrative analysis combining spatial biology data with multi-omics and clinical data will be presented evaluating the prognostic value of TLS composition, maturation status and spatial organization, in correlation with additional biomarkers and clinical characteristics.ConclusionsThis exploratory study using cutting-edge technologies enables us to better understand how TLS orchestrate an organised anti-tumour response, defining TLS spatial biomarker signatures, TLS gene signatures, and TLS features associated with patient outcomes to evaluate in the clinic.ReferencesMarie-Caroline Dieu-Nosjean, Jérémy Goc, Nicolas A Giraldo, Catherine Sautès-Fridman, Wolf Herman Fridman. Tertiary lymphoid structures in cancer and beyond. Trends Immunol 2014;35(11):571–580.Catherine Sautès-Fridman, Florent Petitprez, Julien Calderaro, Wolf Herman Fridman. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat Rev Cancer 2019;19(6):307–325.Ethics ApprovalThe study was approved by AstraZeneca.

BackgroundPredictive biomarkers for response to IO therapies remain insufficient. Although multiplex immunofluorescence has the potential to provide superior biomarkers, the information garnered from these studies is frequently underleveraged. Due to the large number of markers that must be analyzed (6 - 40 +), and the complexity of the spatial information, the number of hypotheses is large and must be tested systematically and automatically. GraphITE (Graphs-based Investigation of Tissues with Embeddings) is a novel method of converting multiplex IF image analysis results into embeddings, numerical vectors which represent the phenotype of each cell as well as the immediate neighborhood. This allows for the clustering of embeddings based on similarity as well as the discovery of novel predictive biomarkers based on both the spatial and multimarker data in multiplex IF images. Here we demonstrate initial observations from deployment of GraphITE on 564 commercially-sourced NSCLC and HNSCC resections stained with a multiplex IF panel containing CD8, PDL1, PD1, CD68, Ki67, and CK.Methods4 μm FFPE tumor sections were stained with CD8, PDL1, PD1, CD68, Ki67, and CK at Akoya Biosciences using OPAL TSA-linked fluorophores and imaged on a Vectra Polaris. Images were analyzed by Computational Biology (AstraZeneca). Graphs were built by mapping each cell in the mIF image as a node, using the X, Y coordinates and connecting nodes with edges according to distance. 64-dimensional embeddings were generated using Deep Graph InfoMax (DGI).1 Embeddings are downprojected to 2 dimensions using UMAP.2. Details are available in the preprint of the GraphITE methods manuscript.3ResultsA single downprojection was developed using embeddings from 158 HNSCC and 406 NSCLC cases. 60–80 distinct clusters were observed, some of which contained embeddings from both indications and others which were exclusive to one indication. Exclusive clusters describe tissue neighborhoods observed only in one indication. Drivers of cluster exclusivity included increased cell density in HNSCC as compared to NSCLC both in PD-L1- tumor centers with few infiltrating lymphocytes as well as in PD-L1- macrophagedominated neighborhoods. HNSCC and NSCLC embeddings were more colocalized in PD-L1+ tumor centers and in tumor stroma with high CD8+ or CD68+ immune cell content and high PD-L1+ expression.ConclusionsThis study demonstrates the utility and potential of the GraphITE platform to discriminate between and describe both unique and common neighborhood-level features of the tumor microenvironment. Deploying GraphITE across multiple indications effectively leverages spatial heterogeneity and multimarker information from multiplex IF panels.References1. Veličković P, Fedus W, Hamilton WL, Liò P, Bengio Y, DevonHjelm R. Deep Graph Infomax. 2018. arxiv:1809.10341 [stat.ML].2. McInnes L, Healy J, Melville J. UMAP: Uniform manifold approximationand projection for dimension reduction. 2020; arxiv:1802.03426 [stat.ML].3. Innocenti C, Zhang Z, Selvaraj B, Gaffney I, Frangos M, Cohen-Setton J, Dillon LAL, Surace MJ, Pedrinaci C, Hipp J, Baykaner K. An unsupervised graph embeddings approach to multiplex immunofluorescence image explorationbioRxiv 2021.06.09.447654; doi: https://doi.org/10.1101/2021.06.09.447654Ethics ApprovalThe study was approved by AstraZeneca.