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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.

ObjectivesThe interaction between the immune system and tumor cells is an important feature for the prognosis and treatment of cancer. Multiplex immunohistochemistry (mIHC) and multiplex immunofluorescence (mIF) analyses are emerging technologies that can be used to help quantify immune cell subsets, their functional state, and their spatial arrangement within the tumor microenvironment.MethodsThe Society for Immunotherapy of Cancer (SITC) convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the optimization and validation of mIHC/mIF assays across platforms.ResultsRepresentative outputs and the advantages and disadvantages of mIHC/mIF approaches, such as multiplexed chromogenic IHC, multiplexed immunohistochemical consecutive staining on single slide, mIF (including multispectral approaches), tissue-based mass spectrometry, and digital spatial profiling are discussed.ConclusionsmIHC/mIF technologies are becoming standard tools for biomarker studies and are likely to enter routine clinical practice in the near future. Careful assay optimization and validation will help ensure outputs are robust and comparable across laboratories as well as potentially across mIHC/mIF platforms. Quantitative image analysis of mIHC/mIF output and data management considerations will be addressed in a complementary manuscript from this task force.

Multiplex immunofluorescence (MIF) staining of tumor sections combined with computational pathology quantifies phenotypic variants of tumor and immune cells and assesses their spatial relationships. Here, we discuss a MIF panel composed of cytokeratin, PD-L1, PD1, CD8, CD68, and Ki67 applied to non-small cell lung cancer (NSCLC) to demonstrate key components of the immune response to this cancer. We also describe a method of whole-slide multiplex imaging and digital multispectral image analysis. Key aspects of marker labeling and digital tissue and cellular classification are highlighted. We then illustrate how digital analysis can measure the spatial relationships among important cell types. This approach is presented in the context of a multidisciplinary team of scientists who together can optimize the combined methods to increase the impact of the study findings. Recommendations are provided to assist others to apply similar methods to further understand the immune response to NSCLC.

Characterization of the location and phenotype of cells in the tumor microenvironment (TME) is important to inform the development and monitoring of anti-cancer therapeutic interventions, especially immunotherapies designed to stimulate the immune system to have an anti-cancer effect. Multiplex immunofluorescence (mIF) imaging is being increasingly employed to simultaneously label multiple cell types and subtypes in the tumor microenvironment, but interpretation of these images to gain a robust understanding of tumor and immune cell interactions remains a complicated and challenging process. The rich phenotypic information contained in mIF images has to be taken into account with the spatial topology of the cells in order to be able to distil potential predictive indicators of patient response to therapies as well as prognostic indicators of outcome. While contemporary computational methods allow pathologists to view aggregated phenotypical information and cell interactions on a limited, generally one-to-one basis, these methods have been largely descriptive and geared toward addressing hypotheses as opposed to holistically leveraging the spatial and phenotypic data into a single predictive model. Additional methods are needed to provide a fuller picture of the spatial structure of the TME as captured in mIF images. In this work, we propose a novel pipeline that uses graphs generated from image analysis results and user-defined distance criteria to represent the tumor cellular microstructure. This graph-based approach complements existing mIF analysis techniques by providing information on the spatial, phenotypic, and morphological features of cells in the context of their neighborhood. These graphs subsequently enable characterization of protein expression in detail, description of interactions between individual cells or cell types and their neighbors, interactive tissue querying, and exploration of the cell-level biodiversity. The graph approach not only allows pathologists to efficiently interrogate data contained in mIF images in a hypothesis-driven manner, but importantly also supports more holistic data-driven approaches which, by leveraging state of the art graph convolutional neural networks to obtain numerical embeddings representing each graph and its nodes, enable additional downstream activities such as cell similarity search, and the development of predictive models for patient outcomes and response to therapies. Citation Format: Jason Hipp, Christopher Innocenti, Zhenning Zhang, Jake Cohen-Setton, Balaji Selvaraj, Michalis Frangos, Carlos Pedrinaci, Michael Surace, Laura Dillon, Khan Baykaner. Leveraging graphs to do novel hypothesis and data-driven research using multiplex immunofluorescence images [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PR-05.

Continued developments in immuno-oncology require an increased understanding of the mechanisms of cancer immunology. The immunoprofiling analysis of tissue samples from formalin-fixed, paraffin-embedded (FFPE) biopsies has become a key tool for understanding the complexity of tumor immunology and discovering novel predictive biomarkers for cancer immunotherapy. Immunoprofiling analysis of tissues requires the evaluation of combined markers, including inflammatory cell subpopulations and immune checkpoints, in the tumor microenvironment. The advent of novel multiplex immunohistochemical methods allows for a more efficient multiparametric analysis of single tissue sections than does standard monoplex immunohistochemistry (IHC). One commercially available multiplex immunofluorescence (IF) method is based on tyramide-signal amplification and, combined with multispectral microscopic analysis, allows for a better signal separation of diverse markers in tissue. This methodology is compatible with the use of unconjugated primary antibodies that have been optimized for standard IHC on FFPE tissue samples. Herein we describe in detail an automated protocol that allows multiplex IF labeling of carcinoma tissue samples with a six-marker multiplex antibody panel comprising PD-L1, PD-1, CD68, CD8, Ki-67, and AE1/AE3 cytokeratins with 4',6-diamidino-2-phenylindole as a nuclear cell counterstain. The multiplex panel protocol is optimized in an automated IHC stainer for a staining time that is shorter than that of the manual protocol and can be directly applied and adapted by any laboratory investigator for immuno-oncology studies on human FFPE tissue samples. Also described are several controls and tools, including a drop-control method for fine quality control of a new multiplex IF panel, that are useful for the optimization and validation of the technique.