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Head and neck squamous cell carcinoma (HNSCC) is frequently locally advanced, but has a high risk of recurrence after initial treatment. Immune checkpoint blockade with PD-1/PD-L1 inhibitors has revolutionized treatment in the recurrent setting, but resistance to these therapies frequently occurs. Combination of checkpoint inhibitors with the ATR inhibitor ceralasertib could provide an exciting opportunity, with encouraging clinical activity reported in melanoma and non-small cell lung cancer (Kwon et al. 2022; Kim et al. 2022; Besse et al. OA15.05 IASLC 2022 WCLC). This phase 1b trial provided a unique opportunity to understand the immunomodulatory impact of DNA Damage Response (DDR) inhibitors in a clinical setting. NCT03022409 is an open-label, randomized window of opportunity trial where patients with HNSCC were treated with either the PARP inhibitor olaparib (300mg BID) or ceralasertib (160mg BID) for 9-21 days, prior to definitive surgery (or on-treatment biopsy). 21 patients were randomized; n=12 to ceralasertib and n=9 to olaparib, and ceralasertib data will be presented here. Translational endpoints on frozen and fixed tumor biopsies included spatial pharmacokinetics, pharmacodynamic biomarkers, multiplexed fluorescence of tumor infiltrating immune cells and gene expression panels. Blood assessments included cytokine detection, immune cell phenotyping and gene expression. Primary endpoint analysis utilized a bespoke prognostic immune-focused gene signature and secondary endpoint an IHC immunoscore, both aimed to measure if tumors would shift from a ‘cold’ to ‘hot/active’ immune state. We observed an ‘on-drug’ selective suppression of proliferating Ki67+ T-cells (but not total T-cells) in the tumor microenvironment and periphery, followed by a repopulation and median rebound of 63.8% and 187.5% above baseline levels for peripheral helper (CD4+Ki67+; n=8) and cytotoxic (CD8+Ki67+; n=7) T-cells respectively, when ceralasertib dose stopped. IL-12 plasma cytokine levels dropped on ceralasertib treatment and returned to baseline levels ‘off-drug’ in 8/10 patients. Type-I interferon (IFN1) gene expression associated signatures were also upregulated in PBMCs, suggestive of an immune priming effect. 0/2 and 2/4 patients on ceralasertib met their primary and secondary endpoints respectively, however, interpretation is limited due to small numbers of evaluable patients. 1 patient had a grade 3 serious adverse event of chest pain in the ceralasertib arm. There were no unexpected safety findings for either drug and adverse events were generally low grade. The translational data has generated new insights into the immunomodulatory effect of ceralasertib. Further evaluation of the combination of ceralasertib with immune checkpoint blockade is warranted to explore this novel immune mechanism of action. Citation Format: Gemma N. Jones, Sonia Iyer, Marta Milo, Pei-Jen Lou, Melonie A. Nance, Carlos A. Gomez-Roca, Nathan Standifer, Paola Marco-Casanova, Shaan Gill, Michael Surace, Richard Bystry, Maria Alexandrova, Sophie Willis, Jaime Rodriguez-Canales, Andreas Dannhorn, Bienvenu Loembé, Alan Lau, Natalia Lukashchuk, Elizabeth A. Harrington, Elhan Sanai, Emma Dean, Umamaheswar Duvvuri. Immunomodulatory effects of the ATR inhibitor ceralasertib in a window of opportunity biomarker trial in patients with head and neck squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr CT198.

Understanding the complex biology of the tumor microenvironment (TME) is necessary to understand the mechanisms of action of immuno-oncology therapies and to match the right therapies to the right patients. Multiplex immunofluorescence (mIF) is a useful technology that has tremendous potential to further our understanding of cancer patho-biology; however, tools that fully leverage the high dimensionality of this data are still in their infancy. We describe here a novel deep learning pipeline aimed to allow Graph-based Inspection of Tissues via Embeddings, GraphITE. GraphITE transforms mIF data into a graph representation, where unsupervised learning algorithms can be utilised to generate embeddings representing cellular ‘neighbourhoods’. The embeddings can be downprojected and explored for clustering analysis, and patterns can be mapped back to the image as well as interrogated for phenotypical, morphological, or structural distinctiveness. GraphITE supports the extraction of information not only on the phenotypes of individual cells or the relationships between specific cell types, but is able to characterize cell neighborhoods to look for more complex interactions, thereby allowing pathologists and data scientists to explore mIF data sets, uncovering patterns that are otherwise obscured by the high-dimensionality of the data. In this work, we showcase the current setup of the system, going from raw input data all the way to a user friendly exploration tool. Using this tool, we show how the data can be navigated in a way previously not possible.

Introduction: Tertiary 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. 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. Methods: With the aim of gaining insights into TLS biology and evaluating the prognostic role of TLS in Non-Small Cell Lung Carcinoma (NSCLC) according to their multiple features, we developed a TLS multiplex immunofluorescent (mIF) panel that includes T cell (CD3, CD8), B cell (CD20), Follicular Dendritic cell (CD21, CD23) and mature dendritic cell (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). 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. Results: Assessment of the prevalence of each cell phenotype within the tumor tissue and TLS (tumor centre vs invasive margin; tumor epithelium vs stroma), 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 these 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. Conclusion: This 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 NSCLC patient outcomes to evaluate in the clinic. Citation Format: Julie Berthe, Felix Segerer, Emily C. Jennings, Alma Andoni, Marco Testori, Megha Saraiya, Miljenka Vuko, Harald Hessel, Andreas Spitzmüller, Mari Heininen-Brown, Jorge Blando, Felicia Ng, Emma Jones, Sophie Willis, Michael Surace, Rieneke van de Ven, Tanja De Gruijl, Helen Angell. A multi-modal analysis approach leveraging multiplexed spatial phenotyping and multi-omics analysis to better understand the prognostic value of tertiary lymphoid structures in non-small cell 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 1711.

Introduction: Predictive biomarkers of anti‒PD-(L)1 therapies have largely focused on the tumor - T cell axis where tumor cell PD-L1 expression has demonstrated its clinical utility in predicting overall survival (OS) in patients with advanced non-small cell lung cancer (NSCLC). Although, other immune cell subsets were shown to be associated with clinical efficacy, their relative impact and combined effect in predicting improved long-term survival warrant further investigation. Using computational image analysis of multiplex immunofluorescence (mIF) and immunohistochemistry (IHC) immune marker panels, we sought to identify single and combined biomarkers of the tumor immune contexture in association with long-term OS in advanced NSCLC patients treated with Durvalumab. Methods: Pre-treatment tumor samples from advanced NSCLC patients (n = 210) enrolled in durvalumab nonrandomized phase 1/2 trial (10 mg/kg Q2W, CP1108/NCT01693562), were stained using IHC and 6-marker mIF panels to detect markers of immune cells, cell functional state and tertiary lymphoid structure (TLS) (PD-L1, CD8, PD-1, Ki67, CD68, CD20, CD1c, NKp46, CD66b, ICOS, FOXP3). Cell marker density (cells/mm2), distribution and proximity were quantified and analyzed in association with overall survival. Results: Computational image analysis of the tumor immune contexture revealed a greater immune inflamed phenotype, both innate (macrophages, dendritic cells) and adaptive (T and B cells), in NSCLC patients with long OS >2 years compared to those with short OS < 1 year (fold change > 2, p < 0.0001). Patient subgroup with high density of individual immune subsets show a median OS (mOS) of 10-20 months (p < 0.01 high vs. low subgroups) while combined markers of innate and adaptive immune cells show an improved mOS > 2 years (p < 0.001). Specifically, among the key findings, combined biomarkers of CD68+ PD-L1+ macrophages and CD8+ T cells predicts for a significant increase in OS for patients with high vs low marker density (HR = 0.21, 95%CI 0.12 - 0.39, p Conclusion: These findings demonstrate the importance of both tertiary lymphoid structure and high pre-existing innate-adaptive immunity in driving long-term overall survival of durvalumab-treated patients with NSCLC and highlight the need for the development of multiparametric predictive biomarkers beyond tumor-T cell axis. Citation Format: Lina Meinecke, Jorge Blando, Thomas Herz, Michael Surace, Thomas Padel, Monica Azqueta Gavaldon, Harald Hessel, Farzad Sekhavati, Megha Saraiya, Anmarie Boutrin, Karma Da Costa, Jaime Rodriguez Canales, Ashok Gupta, Carl Barrett, Zachary Aaron Cooper, Ikbel Achour. Presence of TLS and combined high densities of PD-L1+ macrophages & CD8+ T cells predict long-term overall survival for patients with advanced NSCLC treated with durvalumab [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 1235.

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.

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.

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.

Background The tumor immune microenvironment (TIME) may hold critical information for developing and optimizing immuno-therapeutic approaches, identifying predictive signatures, and selecting the most adequate treatment option for a given patient. Tissue phenomics facilitates the use of the TIME to derive predictive conclusions. The visual information content in histological sections is systematically converted into numerical readouts using artificial intelligence (AI). Resulting quantitative descriptors, phenes, of detected structures are mined to yield local expression profiles; this spatial data aggregation detects categories of local environments, which are correlated to clinical, genomic or other -omics data to identify relevant cohort subpopulations. Methods Exploration of this technology is illustrated by various examples on different cohorts of NSCLC patients: A categorization of n = 45 non-IO-treated patients with respect to local immune profiles learned via AI in a hypothesis-free scenario was examined. A deep learning based PD-L1 scoring was compared to 3 pathologist’s scoring on n = 40 durvalumab-treated patients using the cutoff 25% of tumor cells staining positive for PD-L1 at any intensity. The predictive value of a digital signature combining cell densities of PD-L1 and CD8+ was tested on n = 163 durvalumab-treated and n = 199 non-IO-treated samples. Results A categorization into biologically interpretable classes learned by AI illustrates the exploratory benefits of tissue phenomics. The scoring algorithm could reproduce survival prediction when compared to pathologist’s visual scoring.The digital signature suggests a predictive value for patient stratification into responders and non-responders for durvalumab, while no prognostic value could be found on the non-IO-treated patients. Kaplan-Meier plots for the 2 latter examples will be presented in the poster. Conclusions Tissue phenomics facilitates the quantitative assessment of the tumor geography and may lead to improved tools for biomarker analysis and diagnosis. Analysis on larger and prospective datasets are to be conducted in the future to strengthen the findings. Clinical trial identification All of these results have been generated retrospectively from samples unrelated to a trial or related to the durvalumab-trial NCT01693562. Legal entity responsible for the study The authors. Funding Boehringer Ingelheim, MedImmune, Definiens AG. Disclosure M. Groher: Full / Part-time employment: Definiens AG. J. Zimmermann: Shareholder / Stockholder / Stock options: AstraZeneca; Full / Part-time employment: Definiens AG. H. Musa: Full / Part-time employment: Boehringer Ingelheim. A. Ackermann: Full / Part-time employment: Boehringer Ingelheim. M. Surace: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. J. Rodriguez-Canales: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca. M. Rebelatto: Shareholder / Stackeholder / Stock options: AstraZenec LLC; Full / Part-time employment: AstraZeneca LLC. K. Steele: Shareholder / Stockholder / Stock options, Full / Part-time employment: AstraZeneca; Spouse / Financial dependant: Arcellx LLC. A. Kapil: Full / Part-time employment: Definiens AG. N. Brieu: Shareholder / Stockholder / Stock options, Full / Part-time employment: Definiens AG. L. Rognoni: Full / Part-time employment: Definiens AG. F. Segerer: Full / Part-time employment: Definiens AG. A. Spitzmuller: Full / Part-time employment: Definiens AG. T. Tan: Full / Part-time employment: Definiens AG. A. Schape: Full / Part-time employment: Definiens AG. G. Schmidt: Full / Part-time employment: Definiens AG; Shareholder / Stockholder / Stock options: AstraZeneca.