Composition Of Tumor Microenvironment Research Articles

Overcoming resistance to immune checkpoint blockade in liver cancer - stronger together?

Primary liver cancer, represented mainly by hepatocellular carcinoma (HCC) and intrahepatic cholangiocellular carcinoma (CCA), is one of the most common and deadliest tumors worldwide. While surgical resection or liver transplantation are the best option in early disease stages, these tumors often present in advanced stages and systemic treatment is required to improve survival time. The emergence of immune checkpoint inhibitor therapy has had a positive impact especially on the treatment of advanced cancers, thereby establishing immunotherapy as part of first-line treatment in HCC and CCA. Nevertheless, low response rates reflect on the usually cold or immunosuppressed tumor microenvironment of primary liver cancer. In this review, we aim to summarize mechanisms of resistance leading to tumor immune escape with a special focus on the composition of tumor microenvironment in both HCC and CCA, also reflecting on recent important developments in ICI combination therapy. Furthermore, we discuss how combination of immune checkpoint inhibitors with established primary liver cancer treatments (e.g. multikinase inhibitors and chemotherapy) as well as more complex combinations with state-of-the-art therapeutic concepts may re-shape the tumor microenvironment, leading to higher response rates and long-lasting anti-tumor immunity for primary liver cancer patients.

Analysis of tumor microenvironment composition in vestibular schwannomas: insights into NF2-associated and sporadic variations and their clinical correlations

ObjectiveVestibular schwannomas (VS), benign tumors stemming from the eighth cranial nerve’s Schwann cells, are associated with Merlin gene mutations, inflammation, and the tumor microenvironment (TME), influencing tumor initiation, maintenance, and potential neural dysfunction. Understanding TME composition holds promise for systemic therapeutic interventions, particularly for NF2-related schwannomatosis.MethodologyA retrospective analysis of paraffin-embedded tissue from 40 patients (2013-2020), evenly divided by neurofibromatosis type 2 status, with further stratification based on magnetic resonance imaging (MRI) progression and hearing function. Immunohistochemistry assessed TME components, including T-cell markers (CD4, CD8, CD25), NK cells (CD7), and macrophages (CD14, CD68, CD163, CCR2). Fiji software facilitated image analysis.ResultsT-cell markers (CD4, CD8, CD7) exhibited low expression in VS, with no significant NF2-associated vs. sporadic distinctions. Macrophage-related markers (CD14, CD68, CD163, CCR2) showed significantly higher expression (CD14: p = 0.0187, CD68: p < 0.0001, CD163: p = 0.0006, CCR2: p < 0.0001). CCR2 and CD163 significantly differed between NF2-associated and sporadic VS. iNOS, an M1-macrophage marker, was downregulated. CD25, a regulatory T-cell marker, correlated significantly with tumor growth dynamics (p = 0.016).DiscussionImmune cells, notably monocytes and macrophages, crucially contribute to VS pathogenesis in both NF2-associated and sporadic cases. Significant differences in CCR2 and CD163 expression suggest distinct immune responses. Regulatory T-cells may serve as growth dynamic markers. These findings highlight immune cells as potential biomarkers and therapeutic targets for managing VS.

Characterization of innate lymphoid cell subsets infiltrating melanoma and epithelial ovarian tumors

ABSTRACT The innate lymphoid cell (ILC) family is composed of heterogeneous innate effector and helper immune cells that preferentially reside in tissues where they promote tissue homeostasis. In cancer, they have been implicated in driving both pro- and anti-tumor responses. This apparent dichotomy highlights the need to better understand differences in the ILC composition and phenotype within different tumor types that could drive seemingly opposite anti-tumor responses. Here, we characterized the frequency and phenotype of various ILC subsets in melanoma metastases and primary epithelial ovarian tumors. We observed high PD-1 expression on ILC subsets isolated from epithelial ovarian tumor samples, while ILC populations in melanoma samples express higher levels of LAG-3. In addition, we found that the frequency of cytotoxic ILCs and NKp46+ILC3 in tumors positively correlates with monocytic cells and conventional type 2 dendritic cells, revealing potentially new interconnected immune cell subsets in the tumor microenvironment. Consequently, these observations may have direct relevance to tumor microenvironment composition and how ILC subset may influence anti-tumor immunity.

Autophagy in glioblastoma: A mechanistic perspective.

Glioblastoma (GBM) is one of the most lethal malignancies in humans. Even after surgical resection and aggressive radio- or chemotherapies, patients with GBM can survive for less than 14 months. Extreme inter-tumor and intra-tumor heterogeneity of GBM poses a challenge for resolving recalcitrant GBM pathophysiology. GBM tumor microenvironment (TME) exhibits diverse heterogeneity in cellular composition and processes contributing to tumor progression and therapeutic resistance. Autophagy is such a cellular process; that demonstrates a cell-specific and TME context-dependent role in GBM progression, leading to either the promotion or suppression of GBM progression. Autophagy can regulate GBM cell function directly via regulation of survival, migration, and invasion, or indirectly by affecting GBM TME composition such as immune cell population, tumor metabolism, and glioma stem cells. This review comprehensively investigates the role of autophagy in GBM pathophysiology.

A vascularized breast cancer spheroid platform for the ranked evaluation of tumor microenvironment-targeted drugs by light sheet fluorescence microscopy

Targeting the supportive tumor microenvironment (TME) is an approach of high interest in cancer drug development. However, assessing TME-targeted drug candidates presents a unique set of challenges. We develop a comprehensive screening platform that allows monitoring, quantifying, and ranking drug-induced effects in self-organizing, vascularized tumor spheroids (VTSs). The confrontation of four human-derived cell populations makes it possible to recreate and study complex changes in TME composition and cell-cell interaction. The platform is modular and adaptable for tumor entity or genetic manipulation. Treatment effects are recorded by light sheet fluorescence microscopy and translated by an advanced image analysis routine in processable multi-parametric datasets. The system proved to be robust, with strong interassay reliability. We demonstrate the platform’s utility for evaluating TME-targeted antifibrotic and antiangiogenic drugs side-by-side. The platform’s output enabled the differential evaluation of even closely related drug candidates according to projected therapeutic needs.

Abstract 5501: Novel whole slide imaging modes for imaging mass cytometry unveil extensive cellular heterogeneity in human glioma

Abstract Gliomas present a complex form of cancer that is challenging to diagnose and treat, with a median survival of just over one year after diagnosis for primary glioblastoma (GBM). GBM can occur as multi-lesion, remote, or diffuse tumors, and often contains a tumor microenvironment (TME) that is devoid of peripheral immune cells. Additional hallmark features of GBM include necrosis, hemorrhage, and pseudo palisades, making it a highly heterogeneous disease requiring further investigation. Identification of the cellular and spatial level composition of the TME is vital for interpretation of GBM disease origin, progression, prediction, and treatment. A 40-plus-marker neuro-oncology Imaging Mass Cytometry™ (IMC™) antibody panel was used to determine the cellular and structural landscape of the brain TME. We applied the panel on a tissue microarray (TMA) containing dozens of human glioma cores and uncovered the spatial distribution of over 40 distinct molecular markers. We performed imaging using two new features of the Hyperion XTi™ Imaging System that provide whole slide scanning capabilities. Ultrafast preview mode was applied to rapidly screen tumor cores for expression signatures associated with tumor immuno-oncology processes. This enabled biomarker-guided selection of areas in tumor tissue that were imaged at higher resolution and analyzed using single-cell analysis. In parallel, a high-throughput tissue mode was applied to perform a detailed scan of the brain tumor TMA followed by pixel-based analysis to unravel the spatial composition of the TME. Using tissue mode imaging, we successfully mapped the spatial location of cell populations making up human gliomas, such as neurons, astrocytes, microglia, and oligodendrocytes, across the entire TMA. Various tumor cell phenotypes, resident and infiltrating cells, and resting and activated microglia were detected across all TMA cores. Subsequent single-cell analysis of select regions of interest provided a quantitative assessment of the cellular composition of the brain TME. We classified the distinct states of neurons and quantified myeloid and lymphoid immune cell infiltration across normal, astrocytoma, and GBM tissues. Striking cellular and protein heterogeneity was observed between acquired cores, indicating the complexity of each case that is impossible to capture with low-plex visualization techniques. Therefore, IMC is a highly relevant tool capable of quantitative spatial evaluation of the high-plex protein composition in the brain TME without the complications of autofluorescence, tissue degradation, and spectral overlap. Empowered by the neuro-oncology panel and new whole slide imaging modes, IMC accelerates neurological research and provides insights into the spatial complexity of gliomas and other tumors. For research purpose only. Not for use in diagnostic procedures. Citation Format: Nick Zabinyakov, Qanber Raza, Thomas D. Pfister, Nikesh Parsotam, David Howell, Liang Lim, Christina Loh. Novel whole slide imaging modes for imaging mass cytometry unveil extensive cellular heterogeneity in human glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5501.

Abstract 1530: Tumor cell line-derived xenograft subtype shapes tumor microenvironment composition in BRGSF-HIS mice

Abstract The relevance of preclinical models has vastly improved with mice bearing a human immune system, especially in the context of immunotherapy. BRGSF (Balb/C Rag2−/−, IL2Rγ−/−, SIRPαNOD and Flt3−/−) is a highly immunodeficient mouse featuring reduced murine myeloid cells. BRGSF mice reconstituted with human cord blood CD34+ cells (BRGSF-HIS) develop functional lymphoid and myeloid compartments. This engraftment is stable for over a year (Labarthe et al., 2019) and mice do not develop GvHD. Myeloid compartment can be transiently boosted with exogenous human Flt3L injections. In contrary to other models overexpressing human cytokines to develop human myeloid cells, Flt3L-treated BRGSF-HIS mice do not show side effects. BRGSF-HIS mice are permissive to mouse and human cancer cell lines engraftment and represent a valuable preclinical model to study cancer development and test novel therapeutics. Effect of Flt3L-induced boost on myeloid compartment is seen in a non-small cell lung cancer (A549) tumor model. A single boost prior to tumor cell inoculation associates with increased tumor-infiltrating T-cells (mainly CD8+ T-cells) and myeloid cells, and reduced tumor-infiltrating NK cells in the tumor microenvironment (TME). Human triple negative cancer cell (TNBC) MDA-MB-231 and human pancreatic adenocarcinoma HPAFII cell lines are widely used in cancer research and drug development. We show that upon implantation in BRGSF-HIS mice, in vivo growth of human cell line-derived xenograft (CDX) is CD34+ donor independent and the TME composition varies according to cell line used. As seen in TNBC patients (Zheng et al., 2021); TME of MDA-MB-231 bearing BRGSF-HIS mice is enriched in myeloid cells, mostly CD206+/CD163+ M2 macrophages. CD163-expressing M2 macrophages represent the main tumor-associated macrophages, and are known to promote breast cancer initiation, angiogenesis, invasion, and metastasis by generating an immunosuppressive TME. Interestingly, TME composition evolves over time, with increased T cells frequency in later stage (tumor volume over 1000 mm3 compared to 500 mm3) depicting cancer cell plasticity. Conversely, TME of HPAFII bearing mice is mainly composed of T cells enabling T cells-based therapy. Treatment of BRGSF-HIS mice bearing HPAFII tumor cells with combotherapy based on CD3xTAA + CD28xTAA efficiently reduces tumor growth in vivo compared to vehicle-treated mice or mice injected with CD3xTAA alone. Induced systemic immunomodulation is observed in the TME where increased numbers of both CD4 and CD8 T cells are observed upon combotherapy. Remarkably, while no CD34+ donor effect is observed in CDX growth kinetic, response to treatment appears to be donor dependent with apparition of “weak” and “good” responders. This heterogeneity of response mimics what is observed in clinic and enables further investigations of treatment mode of action and immune escape mechanisms. Citation Format: Perrine Martin-Jeantet, Siham Hedir, Fabiane Sonego, Gaëlle Martin, Yacine Cherifi, Kader Thiam. Tumor cell line-derived xenograft subtype shapes tumor microenvironment composition in BRGSF-HIS mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1530.

Abstract 1587: Multimodal spatial transcriptomics uncover distinct tumor microenvironment states and cell-cell communication networks in molecular pancreatic cancer subtypes

Abstract The tumor microenvironment (TME) presents a complex ecosystem comprising a diversity of immune and stromal cell populations that influence tumor progression, drug delivery and therapy outcome. High levels of inter- and intra-tumor heterogeneity in TME state are driven by molecular tumor phenotypes. Pancreatic ductal adenocarcinoma (PDAC), one of the most lethal cancer types, displays a high genetic heterogeneity along with an immunosuppressive TME. However, it remains largely elusive how distinct TME states are mechanistically influenced, and which molecular processes dictate the emergence of different modes of immunosuppression within the TME of molecular PDAC subtypes.Here, we performed a systematic analysis of functional associations between the major molecular PDAC subtypes, the classical and mesenchymal subtype, and their TME states. We describe the TME composition and complex cell-cell communication networks within PDAC subtypes using a multimodal integration of spatial transcriptomics (ST) with complementary approaches, such as scRNA-seq, MS-based Secretomics and multiplexed histocytometry. We generated ST data sets (Visium) of a tumor cohort derived from a Kras-driven PDAC mouse model. To analyze TME composition as well as spatial niches and cell type communities, we computationally enhanced the spot-resolution of ST datasets, followed by cell type deconvolution and cell-cell communication analysis to identify subtype-specific TME communities and communication networks.To this end, we generated a large resource of PDAC mouse models which represent molecular subtypes of the disease and mimic the heterogeneity of TME states found in human PDAC cohorts. This analysis revealed that a set of subtype-specific secreted factors shape the immunosuppressive PDAC TME via direct and indirect communication networks with immunosuppressive myeloid and T cells in molecular PDAC subtypes. Multimodal ST analysis delineated spatial subtype-specific TME communities as well as spatial communication patterns. We functionally investigate the tumor cell intrinsic regulation of the identified subtype-specific secreted factors, providing potential therapeutic vulnerabilities for more effective combinatorial subtype-specific therapies including immunotherapeutic approaches. Citation Format: Stefanie Baerthel, Chiara Falcomatà, Vanessa Goelling, Daniele Lucarelli, Rushin Gindra, Constantin Schmitt, Jonathan Swietlik, Felix Meissner, Marc Schmidt-Supprian, Dieter Saur. Multimodal spatial transcriptomics uncover distinct tumor microenvironment states and cell-cell communication networks in molecular pancreatic cancer subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1587.

Abstract 4215: Single-cell proteomic characterization of the tumor microenvironment in humanized NOG-EXL patient-derived xenograft (PDX) models

Abstract Introduction: CD34+ hematopoietic stem cell (HSC) derived humanized mice engineered to express human cytokine transgenes are promising models for the evaluation of novel immunotherapeutic treatments. While flow cytometry-based analyses have confirmed the presence of many human immune cell populations within humanized mice bearing patient-derived tumor xenografts (PDX), comprehensive high-dimensional immunophenotyping of the tumor microenvironment (TME) of these models remains limited. Beyond this, the relative influence of PDX vs. donor CD34+ HSC populations on TME composition is poorly understood. Here, we characterize the quantity and diversity of immune cell subpopulations in the TME, bone marrow, and spleen of humanized NOG-EXL (huNOG-EXL) mice using Cytometry by Time-Of-Flight (CyTOF) data with > 60 features. Methods: We performed CyTOF analysis on 250 dissociated samples from primary tumors (n = 15; n = 6 head and neck cancer, n = 6 ovarian cancer, n = 3 renal cell carcinoma), as well as xenograft (n = 59), spleen (n = 88), and bone marrow (n = 88) samples from huNOG-EXL mice (Taconic). Each sample was profiled with two partially overlapping antibody panels; an innate immunity-focused panel (40 features), and an adaptive immunity-focused panel (40 features). Results: CyTOF data were collected from over 10 million live immune cells for each of the adaptive and innate antibody panels. Unbiased clustering of single-cell marker expression revealed clusters corresponding to all major lymphoid and myeloid cell types. Therapeutically relevant cell states were identified including exhausted CD8+ T cells marked by PD-1 and Tim-3 expression, and PD-L1+ macrophages. Hierarchical clustering of bone marrow and spleen samples suggested that systemically, immune composition was not driven by tumor-intrinsic factors alone with many samples clustering on the basis of CD34+ stem cell donor ID. Conversely, clustering PDX samples revealed strong consistency in xenografts originating from the same primary tumor sample regardless of the chosen stem cell donor. The immune composition of primary tumor samples showed greater similarity with matched PDX samples versus unmatched PDX samples, further supporting that tumor-intrinsic factors drive immune composition in xenografts. Conclusions: We provide a high-resolution characterization of the immune phenotypes present in huNOG-EXL PDX models across three cancer types. These data captured substantial heterogeneity in the immune composition of humanized xenograft samples, and we demonstrate that immune composition in humanized xenografts is strongly driven by tumor-intrinsic features. Overall, this resource data supports the use of these models for immuno-oncology studies with future work focused on functionally validating specific immune cell populations of interest. Citation Format: Daniel Stueckmann, Jalna Meens, Sally Zhang, Shirley Hui, Sujana Sivapatham, Sephane Chevrier, Jennifer Pfeil, Lisa Martin, Maria Komisarenko, Philip Dube, Susan Prendeville, Hartland Jackson, Antonio Finelli, Gary Bader, Bernd Bodenmiller, Keith A. Lawson, Laurie Ailles. Single-cell proteomic characterization of the tumor microenvironment in humanized NOG-EXL patient-derived xenograft (PDX) models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4215.

Abstract 4944: Divergent proteogenomic gene expression is driven by microenvironment across tumor types

Abstract Solid tumors develop through a complex series of genome and downstream expression alterations that interact with the local tissue microenvironment, leading to a transformed cell and malignant phenotype. With massive multi-omic data, recent proteogenomics studies have jointly analyzed RNA and protein expression to uncover new gene regulatory relationships; however, many of these studies focused on single tumor types and lack a generalizable view of joint RNA and protein expression. Here, we present the first pan-cancer analysis of sample-wise RNA to protein correlation (SRPC). We re-analyzed over 1,000 tumors across 10 tumor types from published proteogenomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) Research Network. We identified a wide range of SRPC across all tumors (ρ range: -0.08 to 0.70, median: 0.45). We then analyzed tumor pathologic and molecular characteristics versus the range of SRPC. High SRPC tumors had high tumor purity by pathology review, by somatic DNA whole exome sequencing, and by RNA and protein purity scores. In contrast, low SRPC tumors had high immune cell and stromal cell expression scores as inferred by either protein or RNA based signatures. We observed marked differences in cell type populations estimated by expression in different SRPC tumor tranches (low SRPC: macrophages, endothelial cells, cancer-associated fibroblasts; high SRPC: CD4+ Th2 cells). Tumors expressed signaling pathways depending on their SRPC (low SRPC: hypoxia, inflammatory response, and KRAS signaling; high SRPC: DNA repair and E2F targets). SRPC also stratifies tumors with distinct somatic DNA alterations (low SRPC: HRAS and NF2; high SRPC: TP53, MEN1, high Tumor Mutational Burden and high DNA chromosomal instability). Finally, we found that cancer driver genes displayed divergent gene-wise RNA to protein correlations (GRPC) among tumor types with a median absolute deviation interquartile range of 0.1 - 0.2, suggesting tumor-type-specific regulation. In summary, divergent RNA and protein expression is driven, in part, by tumor microenvironment composition across tumor types. Tumors with a diverse cellular microenvironment display a summation of RNA and protein expression resulting from this cell type diversity leading to a low SRPC, while tumors predominated by tumor cells display coordinated RNA and protein expression levels resulting from a pure clonal or cell type leading to a high SRPC. This first deep analysis into sample-wise RNA to protein correlation represents a large proteogenomic community resource for informing biomarker analysis by modality and by tranches of tumor microenvironment. The views expressed in this abstract are solely of the authors and do not reflect the official policy of the Departments of Army/Navy/Air Force, Department of Defense, USUHS, HJF, or U.S. Government. Citation Format: Joseph LaMorte, Nicholas Bateman, Thomas Conrads, Robert F. Browning, Craig D. Shriver, Robert L. Kortum, Matthew D. Wilkerson. Divergent proteogenomic gene expression is driven by microenvironment across tumor types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4944.

Abstract 1517: Tc1 cytotoxic T-cells and Th1 helper T-cell rich tumor microenvironment is a hallmark of MSI colorectal cancers

Abstract Background: Microsatellite instability (MSI-h) is a strong biomarker to predict response to immune checkpoint therapy and patient’s outcome in colorectal cancer. Although enrichment of distinct T-cell subpopulations, such as type 1 cytotoxic T-cells (Tc1) or type 1 helper T-cells (Th1), have been identified to impact patient’s outcome and response to immune checkpoint therapy, only little is known about the underlying changes in the composition of the immune tumor microenvironment. Material and Methods: To assess the density, composition, degree of functional marker expression, and spatial interplay of T-cell subpopulations in 93 MSI and 1153 microsatellite stable (MSS) colorectal cancers, a tissue microarray as well as large sections were stained with antibodies directed against CD3, CD4, CD8, FOXP3, T-bet, GATA3, RORyT, BCL6, CD27, CD56, TIM3, PD-1, CTLA-4 Granzym B, and Ki67 using our BLEACH&STAIN multiplex fluorescence immunohistochemistry approach. A deep learning-based framework comprising two different convolutional neuronal networks (U-Net and DeepLabv3+) was used for image analysis. Results: The composition of Type 1 (T-bet+), Type 2 (GATA3+), Type 17 (RORyT+), NKT-like (CD56+), regulatory (FOXP3+), and follicular (BCL6+) cytotoxic (CD3+CD8+) or helper (CD3+CD4+) T-cells showed marked differences between MSI and MSS patients. For instance, the density and proportion of Tc1 as well as Th1 was significantly higher (p<0.001 each) in MSI compared to MSS patients. In contrast, the density and composition of Tregs, Th2, Th17 T-cells was significantly lower in MSI compared to MSS patients in the intraepithelial tumor compartment. In addition, among the MSI colorectal cancers, NKT-cells showed the highest level of Granzyme B that was significantly higher than in MSS patients (p<0.05). The degree of immune checkpoint expression (i.e., TIM3, PD-1, CTLA-4) was significantly higher in MSI compared to MSS patients for most T-cell subpopulations (p<0.05 each). The additional analysis of 10 large sections confirmed the observations from the TMA analysis and revealed that the alterations in the T-cell composition seen in the center of the tumor largely reflect the T-cell composition at the invasive margin in both MSI and MSS patients. Conclusion: This study identified a higher proportion of Tc1 cytotoxic T-cells and Th1 helper T-cells accompanied by a paucity of regulatory T-cell subpopulations, Th17 and Th2 T- helper cells in MSI colorectal cancers compared to MSS patients. Citation Format: Tim Mandelkow, Zhihao Huang, Elena Bady, Jan H. Müller, Guido Sauter, Julia Ebner, Maximilian Lennartz, Natalia Gorbokon, Ronald Simon, Martina Kluth, Claudia Hube-Magg, Sarah Minner, Niclas C. Blessin. Tc1 cytotoxic T-cells and Th1 helper T-cell rich tumor microenvironment is a hallmark of MSI colorectal cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1517.

Abstract 7398: Unveiling the tumor microenvironment of hepatocellular carcinoma using AI trained by spatial transcriptomics: A preliminary study to predict response to immunotherapy

Abstract Background. Immune checkpoint inhibitors (ICIs), particularly targeting the PD-1 pathway, are promising in treating hepatocellular carcinoma (HCC). However, their variable effectiveness among individuals calls for a better understanding of the tumor microenvironment (TME) and reliable predictive biomarkers. Here, we employ spatial transcriptomics (ST) to develop a deep-learning model that aims to investigate the TME in HCC using Hematoxylin and eosin (H&E) staining images. Our focus is on identifying cell types within the TME that are linked to the response to ICI in a cohort of patients treated with nivolumab. Methods. Our study leveraged 21 Visium ST datasets from HCC to develop a model aimed at inferring the cellular composition of TME from H&E images that matched with ST data. The comprehensive TME cell types enrichment score, encompassing Tumor Endothelial Cells (TECs), Tumor-Associated Macrophages (TAMs), B cells, T cells, and Cancer-Associated Fibroblasts (CAFs) in each spot, was calculated by the CellDART algorithm, a method that maps cell type by integrating with a reference scRNA-seq data. The model was trained in a patch-wise manner, utilizing a pre-trained ResNetRS50 as the backbone model. For the internal validation, Spearman correlation coefficients were evaluated for predicted cell types using H&E, and the trained model was correlated with cell type enrichment scores estimated by ST datasets of 4 samples independent of the training set. The model was applied to H&E images of 16 patients with HCC who underwent nivolumab treatment. The model predicted the 5 cell types enrichment score in the tumor and tumor-adjacent normal liver tissues. Results. The internal validation set results demonstrate robust Spearman correlations between our model predictions and actual cellular compositions in the TME. Specifically, we observe strong correlations for T cells (rho = 0.66), TAMs (rho=0.46), CAFs (rho=0.32), B cells (rho=0.30), and TECs (rho=0.24). When we applied the model to the H&E images of the cohort of patients who underwent nivolumab, immunotherapy non-responders showed a significantly higher predicted enrichment score of CAFs in tumor-adjacent regions (Mann-Whitney test, p<.01). Upon stratifying patients based on stromal CAF prediction, those with low stromal CAF levels exhibited better overall survival (Log-rank test, p<.05). Conclusions. We present a deep learning model to analyze TME in HCC solely on H&E images trained by ST data. Our findings showed a potential relationship between CAFs in tumor-adjacent regions and non-responders to nivolumab treatment in HCC. These insights underscore the potential to predict nivolumab treatment responders using H&E images combined with the deep learning model. This approach could provide a significant advancement in personalized treatment strategies for HCC patients. Citation Format: Dongjoo Lee, Haenara Shin, Seungho Cook, Daeseung Lee, Hongyoon Choi, Won-Mook Choi, Changhoon Yoo, Kwon Joong Na. Unveiling the tumor microenvironment of hepatocellular carcinoma using AI trained by spatial transcriptomics: A preliminary study to predict response to immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7398.

Abstract 4969: Investigating digital pathology tumor microenvironment features for immunotherapy outcome in patients with advanced non-small cell lung cancer

Abstract Introduction The composition of the tumor microenvironment (TME) has been shown to influence response to immunotherapy (IO). In this study, we investigated digital pathology TME features of IO outcomes among patients with non-squamous, non-small cell lung cancer (NSCLC) within a real-world dataset. Methods From the nationwide de-identified Flatiron Health-Foundation Medicine NSCLC clinico-genomic database, a cohort of 50 EGFR/ALK-negative, non-squamous NSCLC patients treated with first-line pembrolizumab monotherapy (mono-IO) or pembrolizumab in combination with carboplatin/cisplatin and pemetrexed (chemo-IO) that had available whole slide images of H&E-stained tissue resection specimens were included. The de-identified data originated from approximately 280 US cancer clinics (~800 sites of care). AI-powered TME characterization models developed by PathAI (Boston, MA; commercially available as PathExplore™) were deployed on scanned H&E slides to extract a panel of cell- and tissue-level human interpretable features (HIFs). Cox proportional hazards regression was used to identify digital pathology features or HIFs associated with overall survival. Results 17 HIFs were associated with better survival and 19 HIFs were associated with worse survival among patients treated with mono-IO (p < 0.05). Lymphocyte features were among the HIFs associated with favorable survival, including density of lymphocyte cells in cancer epithelium (hazard ratio (HR) per unit standard deviation (SD) = 0.46 [0.24, 0.88], p-value = 0.02) and proportion of lymphocyte cells relative to total immune cells in cancer epithelium (HR per unit SD = 0.61 [0.39, 0.95], p-value = 0.03). Cell-level features associated with worse survival included density of fibroblast cells in stroma (HR per unit SD = 1.51 [1.01, 2.25], p-value = 0.05). These associations remained after adjusting for tumor mutational burden (TMB) (N = 29 low, 21 high) and PD-L1 status (N = 0 negative, 1 low-positive, 17 high-positive, 32 unknown). Additionally, similar associations were not observed among patients treated with chemo-IO. High lymphocyte density was associated with better survival compared to low lymphocyte density in mono-IO treated patients (adjusted HR per unit SD = 0.36 [0.18, 0.74], p-value = 0.01) but not in chemo-IO treated patients (adjusted HR per unit SD = 1.10 [0.76, 1.60], p-value = 0.6). Conclusion These results indicate that the composition of TME assessed via digital pathology may have utility in identifying NSCLC patients that will respond to first-line immune checkpoint inhibitors beyond the established IO biomarkers. Citation Format: Ericka M. Ebot, Kuei-Ting Chen, Mikayla Biggs, Douglas I. Lin, Julia A. Elvin, Garrett M. Frampton, James J. Pao. Investigating digital pathology tumor microenvironment features for immunotherapy outcome in patients with advanced non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4969.

Abstract 86: Deep immuno-profiling of syngeneic tumor mouse models for preclinical studies

Abstract The identification of the major cellular players involved in the progression of a type of cancer is a key step for the success of new immunotherapies for personalized medicine. Immune cells play critical functions in cancer, and mice with intact immune systems are vital to understanding tumor immunology. It is however a daunting challenge as complex relationships interplay between tumor cells and the immune system. Each component of innate immunity or adaptive immunity, such as T lymphocytes, macrophages or neutrophils, may be directed to a pro- or anti-tumor function. In order to cope with the complexity of the tumor microenvironment, it is necessary to use an experimental approach capable of characterizing the heterogeneity of the cell types present in the tumor, the evolution of their relative proportion and their fine-tuned functional specificity. This approach leads to the identification of new cellular biomarkers of different stages of tumor progression in order to identify new targets for therapeutic time-window of and improve cancer diagnosis To decipher the impact of immunotherapy treatments involved in the anti-tumor response, cellular phenotyping of leukocytes infiltrating a tumor but also those present in peripheral organs is necessary. Essentially based on extracellular labeling, this primary screen aims to quantify the different cell populations present in a syngenic tumor models on B6/N or BalbC genetical backgrounds. In order to increase our understanding in the precise mode of action of anti-PD1 treatment at the cellular level in sensitive and unsensitive models, we investigated immunophenotypes and responses to immune checkpoint inhibitor (ICI) of several hallmark syngeneic tumor models (MC38, CT26, B16F10, B16-OVA, RENCA, EMT6) in immunocompetent mouse models by flow and mass cytometry. We compared growth kinetics and profiled the immune cell composition of tumor microenvironment (TME), draining lymph node (dLN) and blood in order to establish immune-phenotypic cell signatures that correlates with treatment efficacy. Supervised, unsupervised and integrative data analysis as well as visualization tools are used to identify significant changes across different experimental settings. Our results indicate that each model possesses a unique tumor-immune infiltrate profile that can be modulated with immunotherapies. Overall, these studies provide an important resource of highly-characterized syngeneic tumor model and highlight the importance of tumor immune landscape variance across models that will drive selecting the most appropriate model to test novel immunotherapeutic agents and enhance our translation of knowledge from syngeneic models to human tumors. Citation Format: Anais Joachim, Emilie Maturin, Marielle Mello, Lilia Hadjem, Magali Grange, Olivier Deas, Ana Zarubica, Bernard Malissen, Hervé Luche, François Romagne, Stéphanie Blanchin. Deep immuno-profiling of syngeneic tumor mouse models for preclinical studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 86.

Abstract 2335: MultiNMF: multiview factorization for joint modeling of spatial multi-omics and histology images

Abstract An increasing number of cancer research studies employ spatially resolved transcriptomics (SRT) to investigate the composition of tumor microenvironment in a cancer type of interest. These studies have defined tumor microenvironment (TME) states and spatial domains based on clustering spatial gene expression patterns in SRT in an unbiased manner, yet a more thorough delineation of TME states requires the incorporation of the tumor’s histology image. Here, we develop MultiNMF, a multiview factorization approach that is suitable for cancer research studies where joint profiles of spatial multi-omics and tumor histology images are available. We apply MultiNMF to analyze a set of TNBC SRT primary tumor samples and reveal TME states in the stromal, epithelial, and immune enriched compartments, defined by distinct histomorphological features. We further illustrate the ability of the approach to extend to paired spatial ATAC-seq and histology dataset that is recently published on HER2 breast cancer. MultiNMF thus permits an automated and data-driven decomposition of SRT and spatial ATAC data supported by histomorphological evidence. Context In SRT by 10X Visium, the hematoxylin eosin (H&E) staining image is automatically aligned to the slide where the tissue section is mounted. Image patches can be easily extracted from areas under the SRT barcoded spots. For these images, I will apply a pre-trained convolutional neural network (CNN) model to extract high dimensional features from spot images. The image features can be added as a second view after gene expression view for joint analysis under MultiNMF. Results MultiNMF can factorize the SRT data into components well-supported by histological evidence. It has identified T-cell infiltrating regions, and EMT-enriched regions with distinct immune and stroma morphological characteristics. The T-cell infiltration neighbors a region that has an appearance of necrosis according to the pathologist evaluation. These domains further demonstrate enrichments of modules with motif enrichment (known as regulons) according to SCENIC analysis. Analysis of spatial ATAC illustrates not only HER2 but also its amplicon amplification. From MultiNMF components of spatial ATAC, we derive regulatory regions for T-cell/B-cell marker genes. Overall, MultiNMF is a novel model that has not been applied to SRT field. It provides an multiomic extension to the traditional NMF. Citation Format: William Bowie, Stacy Wang, Benjamin Strope, Qian Zhu. MultiNMF: multiview factorization for joint modeling of spatial multi-omics and histology images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2335.

Abstract 6781: Maintaining the tumor microenvironment: Serum-free cell culture for an advanced 3D in vitro tissue model

Abstract Cancer research has made significant progress in recent years, especially in the field of immunotherapy that uses immune modulators. A significant portion of these novel therapeutic strategies focuses on manipulating the tumor microenvironment (TME). The TME is a complex structure that comprises various cellular elements, including cancer cells, immune cells, and structural components, such as secreted factors and extracellular matrix proteins. However, replicating the human TME in experimental murine models has proved challenging. Therefore, researchers have started to develop ex vivo cultivation protocols of tumor tissues, particularly precision-cut slices (PCS), that offer a three-dimensional representation of the TME. It is essential to select the best culture conditions to ensure the preservation of the TME that closely resembles the in vivo conditions. To further this research, a serum-free cell culture medium has been developed to propagate primary epithelial tumor cells originating from pancreatic, renal, and ovarian tissues. The optimized media formulation was successfully employed in the derivation and expansion of tumor cell lines, sourced from both primary and xenotransplanted tumors. An additional serum-free cell culture medium has been designed to cultivate primary lung cancer cells. Suitable culturing conditions have been introduced that maintain TME composition ex vivo cultured PCS prepared from fresh non-small cell lung carcinoma (NSCLC) tissue. Lung TumorMACS Medium and a serum-containing medium were compared, and it was found that the cellular composition of PCS cultivated in Lung TumorMACS was comparable to the original composition. At the same time, PCS cultivated in Lung TumorMACS showed a lower expression of stress genes, compared to the serum-containing alternative. Citation Format: Alina Siebenmorgen, Christian Wöhle, Katharina Lamfried, Lilian Martinez Carrera, Bianca Heller, Katharina Lupar, Giovanna Bergamini, Daniel Poeckel, Olaf Hardt, Benjamin Theek. Maintaining the tumor microenvironment: Serum-free cell culture for an advanced 3D in vitro tissue model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6781.

Abstract 7399: Development of a reproducible AI-based spatial biomarker of the tumor immune infiltrate on H&E slides

Abstract Background: The immune infiltrate in the tumor microenvironment (TME) has been shown to be associated with prognosis in several studies. However, a comprehensive assessment of the TME composition is not performed in routine practice due to the lack of standardized methods and tools for quantification. The aim of our study is to develop a reproducible, artificial intelligence (AI)-based, spatial biomarker of the tumor related immune response to predict patients outcome on routine Hematoxylin & Eosin (H&E) slides. To the best of our knowledge, this is the first study that implements a robust H&E pipeline to assess, across multiple cohorts, the prognostic power and the added-value of a spatial marker in comparison to non-spatial measures such as the overall lymphocytes density on the slide. Methods: We trained AI models on both publicly available (NuCLS and Lizard) and proprietary datasets to automatically detect cells and tissue types on H&E stained Whole Slide Images of cancers from different origins. We used these models on TCGA COAD and TCGA BRCA and computed spatial measures related to the lymphocyte densities in the tumor core versus the invasive margin as well as the immune infiltration in the tumor stroma (in between tumor islets) versus in the tumor islets themselves. Univariate and multivariate survival analysis is performed through repeated cross-validation. A log-rank test on the stratified groups is performed for each evaluated marker, using the median value as cutoff. Preliminary results on TCGA-COAD and TCGA-BRCA show that spatial markers of the tumor-related immune response are individually associated with overall survival, reinforcing findings from prior studies emphasizing the importance of the spatial relationship between tumor and immune cells. Multivariate analysis with both linear and non-linear models is currently ongoing across selected cohorts. Citation Format: Valentina Di Proietto, Jean El-Khoury, Benjamin Adjadj, Lucie Gillet, Ulysse Marteau, Kathryn Schutte, Katharina Von Loga. Development of a reproducible AI-based spatial biomarker of the tumor immune infiltrate on H&E slides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7399.

Abstract 2941: A novel gene signature predicting fibroblast composition in the TME allows for an improved selection of patient-derived xenograft (PDX) models for preclinical studies

Abstract The Tumor MicroEnvironment (TME) plays a pivotal role in driving tumor progression, metastasis, and therapeutic resistance through diverse mechanisms. Notably, the TME influences the response to therapy by regulating immune surveillance evasion in tumor cells. Fibroblasts can play a significant role in resistance to therapy. In the context of cancer cancer-associated fibroblasts (CAFs) in the TME have been associated with resistance to various therapeutic approaches. CAFs can contribute to therapy resistance through multiple mechanisms, including extracellular matrix remodeling, immune modulation, angiogenesis and paracrine signaling. Understanding the intricate interactions between fibroblasts and cancer cells is crucial for developing more effective therapeutic strategies and overcoming resistance in cancer treatment. In the pre-clinical stages of therapy testing, the use of clinically and biologically relevant models is essential. However, current preclinical tumor models often lack a comprehensive characterization of the TME. To address this limitation, Champions Oncology utilized transcriptomic data from its exclusive TumorGraft models bank to develop a signature predicting TME composition. Validation involved confirming the predicted TME composition through histological analysis of Patient-Derived Xenograft (PDX) models grown in humanized mice. In an immune-competent host, the Tumor Microenvironment (TME) of the model more accurately replicates the conditions observed in cancer patients. PDX) models are recognized as accurate and clinically relevant for pre-clinical studies. Consequently, Champions Oncology built its pre-clinical research on a deeply characterized PDX bank, employing multi-omic datasets, including WES,RNAseq, proteomics, phospho-proteomics, kinase activity, and patient treatment history. To identify molecular signatures predictive of fibroblast presence, Champions analyzed the data with xCell3 computational method. Concurrently, mice were humanized, and high and low infiltration predicted PDXs models were implanted. Tissues were collected and stained for fibroblast populations. Immunohistochemistry analyses confirmed a strong correlation between the infiltration predicted by the molecular signature and positive staining. The application of this method to Champions' model bank provided a comprehensive understanding of the TME profile, culminating in the definition of a gene signature predictive of fibroblast infiltration. Histological analysis validated the computational findings, creating a TME atlas of the PDX bank. This data support the use of the pre-defined molecular signature, facilitating the pairing of ideal model systems with appropriate humanized hosts for future preclinical studies. Citation Format: Mara Gilardi, Gilad Silberberg, Hsiu-Wen Tsai, Stefano Cairo, Clare Killick-Cole, Michael Ritchie. A novel gene signature predicting fibroblast composition in the TME allows for an improved selection of patient-derived xenograft (PDX) models for preclinical studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2941.

Abstract 5357: The tumor-intrinsic RNA binding protein HuR is essential for anti-tumor immunity in PDAC

Abstract Immunotherapies have shown limited effectiveness in pancreatic ductal adenocarcinoma (PDAC), despite having success in other cancers. The RNA-binding protein HuR is known to play a critical role in the oncogenesis of PDAC by regulating key mRNA transcripts. Our lab has shown that tumor-intrinsic HuR is overexpressed in PDAC and regulates the tumor microenvironment composition, altering multiple tumor cytokines to release. Thus, we hypothesized that HuR plays a role in immune evasion of PDAC. We disrupted the locus encoding HuR (Elavl1) using CRISPR Cas9 in Kras-p53 mutant-driven (KPC) murine PDAC to assess its function in tumor immune evasion. Here, we report that HuR, a known pro-survival factor for PDAC, has a role in tumor immune evasion by suppressing T cell infiltration and T cell activation. Specifically, HuR-knockout (KO) KPC tumors grew slower compared to KPC wildtype (WT) tumors in an orthotopically implanted model (p-value < 0.0001), even though human PDAC cell lines with HuR-WT and HuR-KO grew at the same rate in NRG mice (in an immune-compromised environment). Accordingly, HuR-KO tumors had more T cell infiltration and activated T cells compared to HuR-WT tumors (p-value = 0.0047). Importantly, T cell depletion (both CD4+ and CD8+) partially rescued the size of HuR-KO tumors. Next, we rescued the expression of HuR in the HuR-KO cell line, and the re-expression partially rescued the tumor size in vivo. This observation validates a HuR-mediated dependent mechanism. Collectively, these data support the hypothesis that HuR suppresses T cell activation and prevents T cells from infiltrating into the PDAC microenvironment, leading to PDAC immune evasion. Ongoing experiments will transduce KPC cell lines with ovalbumin (OVA) antigen, which will selectively activate CD8+ OT-I T cells through their transgenic TCR, to measure the ability of KPC HuR-WT or HuR-KO OVA cells in directly activating CD8+ OT-I T cells. We will also investigate HuR's impact on immune cells' cytokine release and how that will impact the cytotoxicity of T cells. These findings support the ongoing concept that HuR plays an important pro-survival role in PDAC in vivo, and targeting strategies against HuR being developed (e.g., siHuR nano-therapies and small molecule inhibitors) could potentially enhance PDAC sensitivity to immune-based anti-cancer therapies, such as checkpoint blockade and T cell transfer. Citation Format: Yifei Guo, Jennifer M. Finan, Alexandra Q. Bartlett, Jonathan R. Brody, Robert Eil. The tumor-intrinsic RNA binding protein HuR is essential for anti-tumor immunity in PDAC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5357.

Abstract 3063: Defining oncogene-specific metabolic vulnerabilities in a mouse model of hepatocellular carcinoma

Abstract Despite the continued success of advanced cancer therapies, hepatocellular carcinoma (HCC) represents a substantial challenge to current treatment modalities that are not sufficiently effective for the management of advanced disease. Metabolic reprogramming is a hallmark of cancer and many oncogenic signaling pathways impact on cellular metabolism. Here, we are investigating changes in metabolism in different oncogene-driven mouse models of liver cancer to identify selective metabolic vulnerabilities associated with HCC that could be implicated in tumor initiation and progression to be exploited for cancer therapy. Transposon-based vectors coding for different combinations of oncogenes, either Myc and Akt1Myr (CaMIA) or Myc and NrasG12V (CaMIN), were transduced by hydrodynamic tail vein injected into C57BL/6 mice. The resulting liver tumors and control livers were subjected to a comprehensive profiling of polar metabolites (metabolomics) and lipids (lipidomics and total fatty acids) as well as changes in gene expression (transcriptomics). Despite similar overall survival, the two models display marked differences in the presentation of the tumors, with CaMIA showing larger aggressive tumors, while CaMIN livers contain numerous small tumor nodules. Both models also showed evidence for the deregulation of intermediate metabolism, indicating activation of aerobic glycolysis and deregulation of glutamine metabolism. Lipidomic analysis revealed substantial lipidome remodeling in both models. Oncogene-specific alterations included elevated levels of lysophosphatidylcholine (LPC) in CaMIN tumors, while CaMIA tumors displayed high levels of ether phospholipids compared to CaMIN and normal liver. Moreover, the fraction of polyunsaturated fatty acids (PUFAs) was enhanced in lipids from CaMIN tumors, while monounsaturated fatty acids (MUFAs) were enriched in CaMIA tumors. Furthermore, tumors from both genotypes produced higher levels of oxylipins, a class of lipid mediators involved in signaling and inflammation, compared to normal liver. Transcriptome analysis confirmed these metabolic alterations and revealed evidence for enhanced inflammatory signaling in CaMIN. In addition, analysis of tumor microenvironment composition disclosed specific differences in stromal components, indicative of immune evasion through checkpoint activation. Experiments in primary murine liver cancer cell lines confirmed oncogene-specific metabolic rearrangements and suggest elevated sensitivity towards inhibitors of lipid remodeling. Future experiments will investigate the role of altered lipid metabolism in tumor development and immune evasion in liver cancer. Citation Format: Kamal Al-Shami, Marteinn Snaebjornsson, Felix Vogel, Lisa Schlicker, Phillip Pöller, Daniel Dauch, Almut Schulze. Defining oncogene-specific metabolic vulnerabilities in a mouse model of hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3063.