Characterization Of Microenvironment Research Articles

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.

T cell proliferation-related subtypes, prognosis model and characterization of tumor microenvironment in head and neck squamous cell carcinoma

BackgroundThirty-three synthetic driver genes of T-cell proliferation have recently been identified through genome-scale screening. However, the tumor microenvironment (TME) cell infiltration, prognosis, and response to immunotherapy mediated by multiple T cell proliferation-related genes (TRGs) in patients with head and neck squamous cell carcinoma (HNSC) remain unclear. MethodsThis study examined the genetic and transcriptional changes in 771 patients with HNSC by analyzing the TRGs from two independent datasets. Two different subtypes were analyzed to investigate their relationship with immune infiltrating cells in the TME and patient prognosis. The study also developed and validated a risk score to predict overall survival (OS). Furthermore, to enhance the clinical utility of the risk score, an accurate nomogram was constructed by combining the characteristics of this study. ResultsThe low-risk score observed in this study was associated with high levels of immune checkpoint expression and TME immune activation, indicating a favorable OS outcome. Additionally, various factors related to risk scores were depicted. ConclusionThrough comprehensive analysis of TRGs in HNSC, our study has revealed the characteristics of the TME and prognosis, providing a basis for further investigation into TRGs and the development of more effective immunotherapy and targeted therapy strategies.

T cell proliferation-related subtypes, prognosis model and characterization of tumor microenvironment in head and neck squamous cell carcinoma

T cell proliferation-related subtypes, prognosis model and characterization of tumor microenvironment in head and neck squamous cell carcinoma

Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.

The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single-cell datasets and using inferCNV, we identified and analysed tumour cell clusters to explore their heterogeneity and changes in abundance throughout LUAD progression. We applied gene set variation analysis (GSVA), pseudotime analysis, scMetabolism, and Cytotrace scores to study biological functions, metabolic profiles and stemness traits. A predictive model for prognosis, based on key cluster marker genes, was developed using CoxBoost and plsRcox (CPM), and validated across multiple cohorts for its prognostic prediction capabilities, tumour microenvironment characterization, mutation landscape and immunotherapy response. We identified nine distinct tumour cell clusters, with Cluster 6 indicating an early developmental stage, high stemness and proliferative potential. The abundance of Clusters 0 and 6 increased from AAH to IAC, correlating with prognosis. The CPM model effectively distinguished prognosis in immunotherapy cohorts and predicted genomic alterations, chemotherapy drug sensitivity, and immunotherapy responsiveness. Key gene S100A16 in the CPM model was validated as an oncogene, enhancing LUAD cell proliferation, invasion and migration. The CPM model emerges as a novel biomarker for predicting prognosis and immunotherapy response in LUAD patients, with S100A16 identified as a potential therapeutic target.

In Situ Structural Characterization of Cardiomyocyte Microenvironment by Multimodal STED Microscopy

Within the myocardium, cardiomyocytes reside in a complex and dynamic extracellular matrix (ECM) consisting of a basement membrane (BM) and interstitial matrix. The interactions between cardiomyocytes and the myocardial ECM play a critical role in maintaining cardiac geometry and function throughout cardiac development and in adult hearts. Understanding how the structural changes of the myocardial ECM affect cardiomyocyte function requires knowledge of pericellular structures. These structures are of a size beyond the resolution of conventional optical microscopy. Here, we demonstrated multi-scale and multi-aspect characterization of the cardiomyocyte microenvironment in myocardial tissue sections using multimodal stimulated emission depletion (STED) microscopy. Second harmonic generation and autofluorescence facilitated multiplexed imaging, enabling the interpretation of protein distribution in 3D. STED imaging modality revealed BM structures of cardiomyocytes and myocardial capillaries at the subdiffractional level. Moreover, meaningful measurements retrieved from acquired images, such as sarcomere length and capillary density, enabled quantitative assessment of myocardial structures.

Efficacy and biomarker analysis of phase 2 (P2) and window-of-opportunity (WoO) cohorts of patients with recurrent glioblastoma (rGBM) treated with ST101, an inhibitor of the transcription factor C/EBPβ.

2011 Background: CCAAT/enhancer-binding protein β (C/EBPβ) is an important transcription factor in GBM cell maintenance and is associated with worse outcomes and resistance to chemotherapy through transcription of genes associated with cell cycle and DNA repair. C/EBPβ is also required for maintenance of immunosuppressive tumor-associated macrophages. ST101 is a first-in-class antagonist of C/EBPβ that promotes selective GBM cell death without impacting normal cell viability (Darvishi, 2022), and repolarization of macrophages toward an immunostimulatory program. In an all comers P1 study, ST101 was well-tolerated, showed significant tumor uptake, ~40 hr plasma half-life, and signal of efficacy (1PR +8SD). With strong rationale targeting C/EBPβ in GBM and evidence that ST101 crosses the BBB in preclinical biodistribution studies, cohorts in rGBM were explored. Methods: Pts with ≤2 recurrences of GBM were enrolled in a P2 cohort (n=30) and treated with 500mg QW. A WoO cohort of 6 pts with rGBM received 2-4 doses of ST101 500mg QW prior to surgery and resumed ST101 after surgery until progression (NCT04478279). Endpoints for the two cohorts included response, PFS and OS while the WoO cohort also included tissue analyses (GBM subtypes, tumor microenvironment (TME) characterization, ST101 tumor penetration, and C/EBPβ target engagement). Clinical data is available for both cohorts and tissue analysis is available for the WoO cohort. Results: Data is a snapshot as of February 1, 2024. ST101 was safe and well-tolerated in both cohorts with no discontinuations due to AEs. Pts in the P2 study show 30% DCR (1PR + 8SD), 20% 6-mo PFS and 37% OS at 1 year. Median duration of treatment is 3.4 mos with 2 pts stable and still on study for 10+ and 16+ mos. In the WoO study, efficacy from post-surgery MRI showed 67% DCR (2 PR + 2 SD) and ~50% 6-mo PFS. Median OS cannot be assessed at this early stage. The median duration of treatment is 6.02 mos with 2 pts still on study for 7+ and 10+ mos. Tissue compared before and after ST101 exposure shows ST101 uptake and C/EBPβ target engagement by IHC, and pathologic evidence of treatment effect, such as geographic necrosis, in 5/6 pts. Evaluation of tumor and TME characteristics in post-ST101 treatment tissue will be presented. Conclusions: Monotherapy ST101 in pts with rGBM has an excellent safety profile and results in comparable outcomes to current 2nd line treatments. ST101 crosses the BBB, engages its target, and potentially induces treatment-related necrosis. These encouraging clinical activity, safety, and tissue findings, as well as preclinical data showing additive activity in combination with lomustine, support advancing ST101 to a randomized, placebo-controlled study comparing ST101 + lomustine vs. lomustine + placebo in rGBM. We aim to initiate this study in Q3 2024. Clinical trial information: NCT04478279 .

Abstract PO5-24-11: An integrated approach for comprehensive molecular and tumor microenvironment characterization of invasive lobular carcinoma

Abstract Introduction: Although recent studies have shown that invasive lobular carcinoma (ILC) is associated with worse long-term outcomes compared to invasive breast cancer of no special type (NST), they are treated in a similar manner. Therefore, classification approaches to help understand the molecular characteristics associated with the different BC subtypes are needed to personalize therapy. Here, we describe an integrated analytical approach to comprehensively characterize ILC samples. Methods: We collected genomic and transcriptomic data for 16,087 ILC and NST samples, with clinical and pathological annotation, from 59 datasets and used an internally developed algorithm based on clustering performed after gene expression analysis to classify them into 5 intrinsic subtypes: Basal, Luminal A (LumA), Luminal B (LumB), HER2 high, and HER2 low. We performed gene set enrichment analyses and utilized the DESeq2 algorithm to perform differential expression analysis of RNA-seq data. We performed molecular grade subtyping using methods described by Antysheva et al. (Cancer Res., 2022). Samples that were listed as ILC or had an inactivated CDH1 gene by mutation, deletion or low expression (< −2.5 MAD after median scaling) were defined as having an ILC histomolecular subtype. Using methods described by Bagaev et. al. (Cancer Cell, 2021), 29 functional gene expression signatures were selected, and unsupervised dense Louvain clustering was performed to identify TME subtypes. The Mann-Whitney U test was used for statistical analysis. The tertiary lymphoid structures (TLS) gene signature was evaluated using methods described by Sautès-Fridman et al. (Nat. Rev. Cancer, 2019). Results: In the ILC samples (n=1,887), we identified LumA (46%), LumB (40%), Basal (8%,), HER2 high (3%), and HER2 low (3%) subtypes. In contrast, the NST samples were classified as LumB (41%), LumA (25%), Basal (19%), HER2 high (9%), and HER2 low (5%). Molecular grade subtyping revealed that 67% of ILC samples were low grade and 33% were high grade, compared to NST where 55% were high grade and 45% were low grade. Notably, among high-grade ILC, LumB (64%) and Basal (23%) subtypes were predominant. Key differentially expressed genes included downregulated CDH1 and upregulated MMP23B, IGF1, and TFAP2B across LumA and LumB ILC samples. Further analysis revealed downregulation of TACSTD2 and CDH3 in the Basal subtype for ILC samples compared to NST samples. Transcriptomic-based TME subtyping revealed distinct TME distributions across the BC subtypes and molecular grades for ILC samples (Table 1). In high-grade ILC LumB samples, 38% had a Desert (D; predictive of poor immunotherapy response) and 39% had an Immune-Enriched (IE; predictive of favorable immunotherapy response) TME. In low-grade ILC LumB samples we found that while 51% had a D TME, only 14% had an IE TME. We found an overrepresentation of the TLS signature [Solid TLS] in ILC compared to NST samples, and higher matrix remodeling and CAF signatures in LumA compared to the LumB subtype among low-grade ILC samples. Samples with high CD274 expression levels had higher percentages of T cells and M1 macrophages, and LumA samples had significantly higher (p < 0.001) CD274 expression compared to the other BC subtypes. Conclusion: These results provide in-depth molecular and tumor microenvironment characterization of ILC. Further optimization of this analytical approach could lead to the development of more effective therapeutic strategies. Table 1. ILC subtypes classification Citation Format: Jason Mouabbi, Konstantin Chernyshov, Oleg Baranov, Vladimir Kushnarev, Polina Turova, Anna Butusova, Sofia Menshikova, Jessica Brown, Nikita Kotlov, Patrick Clayton, Krystle Nomie, Nathan Fowler, Debu Tripathy. An integrated approach for comprehensive molecular and tumor microenvironment characterization of invasive lobular carcinoma [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-24-11.

DNA methylation regulator-based molecular subtyping and tumor microenvironment characterization in hepatocellular carcinoma.

Although recent studies have reported the regulation of the immune response in hepatocellular carcinoma (HCC) through DNA methylation, the comprehensive impact methylation modifications on tumor microenvironment characteristics and immunotherapy efficacy has not been fully elucidated. In this research, we conducted a comprehensive assessment of the patterns of DNA methylation regulators and the profiles of the tumor microenvironment (TME) in HCC, focusing on 21 specific DNA methylation regulators. We subsequently developed a unique scoring system, a DNA methylation score (DMscore), to assess the individual DNA methylation modifications among the three distinct methylation patterns for differentially expressed genes (DEGs). Three distinct methylation modification patterns were identified with distinct TME infiltration characteristics. We demonstrated that the DMscore could predict patient subtype, TME infiltration, and patient prognosis. A low DMscore, characterized by an elevated tumor mutation burden (TMB), hepatitis B virus (HBV)/hepatitis C virus (HCV) infection, and immune activation, indicates an inflamed tumor microenvironment phenotype with a 5-year survival rate of 7.8%. Moreover, a low DMscore appeared to increase the efficacy of immunotherapy in the anti-CTLA-4/PD-1/PD-L1 cohort. In brief, this research has enhanced our understanding of the correlation between modifications in DNA methylation patterns and the profile of the tumor microenvironment in individuals diagnosed with HCC. The DMscore may serve as an alternative biomarker for survival and efficacy of immunotherapy in patients with HCC.

Tumor Immune Microenvironment and Its Clinicopathological and Prognostic Associations in Canine Splenic Hemangiosarcoma.

Tumor cells can induce important cellular and molecular modifications in the tissue or host where they grow. The idea that the host and tumor interact with each other has led to the concept of a tumor microenvironment, composed of immune cells, stromal cells, blood vessels, and extracellular matrix, representing a unique environment participating and, in some cases, promoting cancer progression. The study of the tumor immune microenvironment, particularly focusing on the role of tumor-infiltrating lymphocytes (TILs), is highly relevant in oncology due to the prognostic and therapeutic significance of TILs in various tumors and their identification as targets for therapeutic intervention. Canine splenic hemangiosarcoma (HSA) is a common tumor; however, its immune microenvironment remains poorly understood. This retrospective study aimed to characterize the histological and immunohistochemical features of 56 cases of canine splenic HSA, focusing particularly on tumor-infiltrating lymphocytes (TILs). We assessed the correlations between the lymphocytic response, the macroscopic and histological characteristics of the tumor, and the survival data. Our study demonstrated that FoxP3 distribution was associated with tumor-related death and survival, while the CD20 count was associated with metastasis. This study provides an in-depth characterization of the tumor immune microenvironment in canine splenic HSA and describes potential prognostic factors.

A Novel Disulfidptosis-related lncRNAs Prognostic Signature for Prognosis Predicting and Immune Microenvironment Characterization in Breast Cancer.

Breast cancer (BRCA) is one of the leading causes of cancer-related death in women. The improvement of the BRCA risk assessment method is of positive clinical significance. Although many clues showed the potential role of disulfidptosis in BRCA as a novel type of programmed cell death, whether disulfidptosis is involved in BRCA tumorigenesis remains unclear. We used LASSO-univariate Cox analysis and multivariate Cox analysis to identify six disulfidptosis-related lncRNAs (DPLs) that correlated with BRCA clinical outcome and confirmed that these DPLs were independent prognostic factors for BRCA (YTHDF3-AS1, AC002398.1, AL451085.2, AC092718.4, AC097662.1 and AC053503.5). The BRCA risk prognosis model was subsequently established based on these DPLs. After verifying the model reliability in predicting prognosis, immune infiltration and somatic mutation analysis showed significant differences in the immune microenvironment and mutation of DPLs by risk stratification. Immunotherapy response and drug resistance analysis suggest the reference value of DPLs in clinical individualized therapy. The abnormal expressions of selected DPLs were further validated by the BRCA cell line experiment. Our results shed new light on the role of DPLs in BRCA.

Abstract 5485: Spatial characterization of the immunosuppressive tumor microenvironment in estrogen receptor positive breast cancer at single cell level

Abstract We characterized immune cell exclusion in estrogen receptor positive (ER+) breast cancer through the study of spatial relationships among various subsets of cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), and other immune cells using a novel in-situ spatial transcriptomic technology. ER+ breast cancer is the most common breast cancer subtype. While endocrine therapy and immune checkpoint inhibitors have considerably reduced relapse and mortality for breast cancer patients, tumor resistance to treatment remains a major challenge. The tumor microenvironment (TME) plays an important role in this mechanism of resistance. Our previous spatial proteomic analysis of ER+ breast FFPE samples demonstrated the intricate relationship between tumor and immunosuppressive cells such as CAFs and TAMs via the CXCR4-CXCL12 axis associated with the exclusion of CD8 T cells from the TME. However, the limited number of antibodies in the panel made it challenging to investigate the diversity of those CAF and TAM populations and better understand their contribution to T cell exclusion. For this purpose, we further characterized the TME of 4 human ER+ breast cancer FFPE cores using the Xenium in-situ transcriptomic platform with a breast cancer-focused panel of 280 genes from 10x Genomics plus 100 additional genes selected from literature and publicly available single cell datasets to delineate CAF and TAM sub-populations. Image analysis enabled the quantification and spatial mapping of the target genes within each cell boundary. Cells were manually annotated through unsupervised clustering of over 1 million cells. The cell phenotyping analysis allowed us to annotate major cell types including tumor cells, macrophages, T cells and fibroblasts. With our curated panel, we further characterized CAF and TAM sub-populations by identifying myofibroblastic CAFs (myCAFs), inflammatory CAFs (iCAFs), and antigen-presenting CAFs (apCAFs) in addition to M1 and M2-like macrophages as reported in the literature. We then performed spatial neighborhood analysis based on the similarity of the cell type composition in the vicinity of each cell. The results showed that myCAFs and M2-like macrophages are found in proximity to tumor cells and may play a role in T-cell exclusion. In addition, iCAFs mainly express CXCL12, a chemokine known to attract immunosuppressive cells such as regulatory T cells and M2-like macrophages as well as repel cytotoxic T cells from infiltrating the tumor via the CXCR4-CXCL12 axis. Overall, our work shows the potential of spatial transcriptomics to elucidate the spatial distribution of cells and their contribution to the immunosuppressive TME, which may enable researchers to improve understanding of tumor biology and drug target discovery efforts. Citation Format: Julien Tessier, Sandra Curras-Alonso, Joon Sang Lee, Fabien Delahaye, Hong Ma, Katie Malley, Dinesh Bangari, Donald Jackson, Angela Hadjipanayis. Spatial characterization of the immunosuppressive tumor microenvironment in estrogen receptor positive breast cancer at single cell level [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 5485.

Abstract 7433: CGC-hosted comprehensive pipeline for spatial transcriptomics analysis

Abstract Spatial transcriptomics has been experiencing significant growth in the past years. Inspired by two widely adopted methodologies, particularly in situ hybridization and next-generation sequencing, with an emphasis on the single-cell RNA sequencing (scRNA-seq), this hybrid method facilitates whole transcriptome profiling while preserving spatial context at high resolutions, thus allowing the exploration of novel insights in cancer research. We introduce the highly configurable solution for sequencing-based technologies that allow comprehensive spatial analysis. The pipeline is available on NCI-funded Cancer Genomics Cloud (CGC) platform, powered by Seven Bridges. The CGC platform provides a collaborative cloud base computation infrastructure that collocates computation, over 1000 bioinformatics workflows, and 4+ PB data to researchers, making the analysis of the Cancer Research data Commons (CRDC) datasets accessible from any environment. This pipeline is developed using reliable, widely adopted packages and can process datasets generated by several leading technologies. It comprises a set of common steps including quality control, data preprocessing, dimensionality reduction, cluster identification, detection of spatially variable features, and integration with scRNA-Seq reference. The pipeline is highly configurable, allowing for various settings to be modified and optimized for result refinement. Additionally, it allows the selection of only specific components of the pipeline to be executed. The key steps of the pipeline are visually represented, allowing researchers to get a detailed insight into results. Here, we demonstrate a typical flow of spatial transcriptomics analysis on publicly available datasets using the developed pipeline. We illustrate the impact of pipeline settings on the analysis outcomes. Furthermore, we identify spatially variable genes whose expressions show a distinct localization within the tissue. Finally, we perform the data integration to predict the cell type composition within the determined spatial domains. Spatial transcriptomics analysis is a powerful method that has highly improved critical aspects of cancer research, such as the characterization of tumor microenvironment, the discovery of novel biomarkers and the clarification of drug resistance mechanisms. The ongoing evolution of this method is expected to play vital role in our deep understanding of complex spatial relationships within tissues. This CGC-hosted workflow is developed to contribute to the promising advancements. Citation Format: Miona Rankovic, Nevena Vukojicic, Nevena Ilic Raicevic, Vida Matovic, Divya Sain, Jack DiGiovanna, Brandi Davis-Dusenbery. CGC-hosted comprehensive pipeline for spatial transcriptomics analysis [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 7433.

Abstract 3977: Neoadjuvant chemotherapy and radiation shape the local and regional adaptive antitumor immune response in lung squamous cell carcinomas

Abstract Chemotherapy (CT) and radiotherapy (RT) are known for their stimulating as well as inhibiting effects on the anti-tumor immune response. This study aimed to understand how CT and RT change the local immune microenvironment of the tumor and the regional immune response in draining lymph nodes. Evaluations were performed on lung squamous cell carcinomas (LUSC) with a special focus on tertiary lymphoid structures (TLS) and lymph follicles. A cohort of 21 untreated LUSC (naiveLUSC) and 21 matched neoadjuvantly treated LUSC (neoLUSC) was compiled. Digitized H&E whole slide images of the tumor and of two representative probes of a proximal and distal lymph node were assessed. A pathologist blinded to the groups assigned tumors to one of three immunophenotypes (inflamed, excluded, desert) by visual analysis. Tertiary lymphoid structures (TLS) in the tumor area and lymph follicles in the regional lymph nodes were counted and characterized according to their maturation status (with/without germinal center). RNAseq analyses were performed for a subset of tumors. NeoLUSC tended to more frequently have a desert immune phenotype (6/21 versus 1/21, p=0.0931). The mean number of TLS was significantly reduced in neoLUSC compared to naiveLUSC (4 versus 9, p=0.0179). Additionally, neoLUSC had a significantly lower likelihood for the presence of mature TLS with germinal centers (3/21 versus 9/21, p=0.0327). While the mean number of lymph follicles in regional lymph nodes was comparable between neoLUSC and naiveLUSC, neoLUSC had a significantly reduced number of mature lymph follicles (20% versus 41% of lymph follicles, p=0.0376) in distant lymph nodes and showed a similar, but less pronounced trend in proximal lymph nodes. In general, the amount of TLS correlated with the amount of lymph follicles in naiveLUSC patients (p=0.0154), but not in neoLUSC patients. Across the complete cohort, long-term survivors (>2 years) tended to have higher numbers of lymph follicles in lymph nodes than short term survivors (1.6 versus 1.1 lymph follicles/mm2, p=0.0805). RNAseq analysis revealed a significant upregulation of inflammatory gene sets and downregulation of cell cycle associated gene sets in neoLUSC as compared to naiveLUSC. This study demonstrates that CT and RT are potent, but multimodal influencers of the local tumor immune microenvironment as well as the regional immune response of the draining lymph nodes. While gene expression results underline the inflammation promoting effect of CT and RT, histological evaluations suggest that CT and RT negatively affect the adaptive anti-tumor immune response by reducing the development and maturation of TLS and lymph follicles in the lymph nodes. Next steps include an artificial intelligence based cell characterization and spatial evaluation of the tumor immune microenvironment to further unveil functional changes induced by CT and RT. Citation Format: Rosemarie Krupar, Justin Nieder, Johannes Braegelmann, Mareike Haarmann, Torsten Goldmann, Sharon Ruane, Timo Milbich, Cornelius Boehm, Lukas Ruff, Julika Ribbat-Idel, Christian Watermann, Sven Perner, Christiane Kuempers. Neoadjuvant chemotherapy and radiation shape the local and regional adaptive antitumor immune response in lung squamous cell carcinomas [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 3977.

Abstract 5075: Transcriptomic landscape of tumor microenvironment in gastric cancer in treatment with different types of cancer drugs

Abstract Gastric cancer (GC) in the fifth-leading types of cancer and the third-leading cause of death from cancer. Characterization of tumor microenvironment (TME) in GC is important not only to comprehensively understand tumor biology in GC but also to develop a novel cancer therapeutics targeting TME. In this study, we conducted multi-omics analyses of single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (bulk RNA-seq), and whole exome sequencing (WES) using biopsied tumor samples through endoscopy from 23 GC patients: 3 treatment-naïve patients, 7 patients in treatment with chemotherapy (Chemo), 5 patients in combination treatment with chemotherapy and immune checkpoint blockade (Chemo+CPI), 3 patients in combination treatment with chemotherapy and Her2-antibody targeted therapy (Chemo+Her2i), and 5 patients in third-line or later treatment. We initially classified the GC samples into 4 molecular subtypes (CIN, GS, MSI, and EBV) using datasets of WES, bulk RNA-seq and scRNA-seq, showing that our samples were mainly composed of CIN (n=7) and GS (n=16) types. In scRNA-seq, we revealed that drug treatments profoundly modulated not only proportions of TME components but also their molecular characteristics. For instance, Chemo+CPI treated patients showed higher proportion of CD8+ cytotoxic T cells with activation and/or exhaustion markers. Combination treatments with Chemo+Her2i, in addition, resulted in higher Trail and Estrogen signaling pathways in epithelial cells of TME components. These findings suggest that interventions with certain cancer therapeutic drugs could substantially convert transcriptome landscape and reconstitute GC TME components. High-resolution of GC TME analyses should provide a clue for developing a next generation of cancer therapeutic drugs. Citation Format: Hiroki Yamashita, Jason Guoqiang Zhang, Chiaki Mashima, Ryo Kamata, Tomoko Yamamori, Izuma Nakayama, Akihito Kawazoe, Kohei Shitara, Tomonori Yano, Qunli Xu, Akihiro Ohashi, Takeshi Saito. Transcriptomic landscape of tumor microenvironment in gastric cancer in treatment with different types of cancer drugs [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 5075.

Abstract 5503: Ultrahigh-plex spatial phenotyping of head and neck cancer tissue uncovers multiomic signatures of immunotherapy response

Abstract Background Targeted immune checkpoint inhibitors (ICI) with anti-PD-1/PD-L1 therapy offer durable treatment of mucosal head and neck squamous cell cancer (HNSCC), in both human papillomavirus-positive (HPV+) and negative (HPV-) patients. However, currently available biomarker signatures for targeted ICI therapies have limited predictive value. Our recent ultrahigh-plex profiling of HNSCC tissue with 100+ cancer hallmarks of tumor and immunobiology uncovered distinct spatial domains that serve as defining factors for clinical response and resistance. Methods Our unbiased analysis of whole-slide metastatic HNSCC tumors is from a clinical cohort of patients treated with Pembrolizumab/Nivolumab. The cohort consisted of patients with a range of outcomes from complete vs partial vs progressive disease responses to ICI therapy. We first characterized the tumor microenvironment using our ultrahigh-plex protein panel with 100+ antibodies on the PhenoCycler®-Fusion platform. To expand upon our biomarker discovery, we included multiomic cancer hallmarks with a multimodal protein/RNA detection panel. Targeted spatial RNA detection was performed to complement and augment the microenvironment characterization achieved by our protein panel. To further consolidate the multiomic data, we leveraged MaxFuse, a state-of-the art computational framework that integrates multimodal spatial and single-cell expression data. Results Our multiomic spatial phenotyping uncovered diverse tumor regions, each with distinct biomarker expression that is reflected across modalities including protein, RNA, and metabolic activity, indicating regions likely associated with resistance to immunotherapy. Our multiomic data integration also revealed spatial signatures associated with different tissue compartments, such as the tumor and non-tumor associated tertiary lymphoid structures. Conclusions We demonstrate a multi-pronged approach that incorporated both novel experimental and computational techniques for elucidating tumor microenvironment in HNSCC tissue prior to ICI-based immunotherapy. Our multiomic approach provides deeper characterization of the HNSCC at the transcriptomic and proteomic level incorporating depth across the entire transcriptome and single-cell spatial resolution of key protein determinants for predicting and furthering our understanding of immunotherapy response to ICI therapy. Citation Format: Aditya Pratapa, Lydia Hernandez, Bassem Ben Cheikh, Niyati Jhaveri, Arutha Kulasinghe. Ultrahigh-plex spatial phenotyping of head and neck cancer tissue uncovers multiomic signatures of immunotherapy response [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 5503.

Bioinformatic Analysis of m6A Regulator-Mediated RNA Methylation Modification Patterns and Immune Microenvironment Characterization in Endometriosis.

Epigenetic regulation plays an essential role in immunity and inflammation in endometriosis. In this study, we aimed to explore differences in m6A regulators between endometriosis patients and normal women and analyze the effect of m6A modification on immune and inflammatory microenvironment. The samples for analysis were downloaded from the Gene Expression Omnibus database, including ectopic endometrium (EC), eutopic endometrium (EU), and normal eutopic endometrium (NM) samples from non-endometriosis women. The validation process involved utilizing our previous RNA-sequencing data. Subsequently, a correlation analysis was performed to ascertain the relationship between m6A and the inflammatory microenvironment profile, encompassing infiltrating immunocytes, immune-inflammation reaction gene sets, and human leukocyte antigen genes. LASSO analyses were used to develop risk signature. The findings of this study indicate that the m6A regulators FTO were observed to be significantly up-regulated, while YTHDF2, CBLL1, and METTL3 were down-regulated in endometriosis tissues. The CIBERSORT analysis revealed that the local inflammatory microenvironment of ectopic lesions plays a crucial role in the development of endometriosis. Notably, M2 macrophages exhibited a significant difference between the EC and NM groups. Moreover, M2 macrophages demonstrated a positive correlation with FTO (0.39) and a negative correlation with CBLL1 (-0.35). Furthermore, consistent clustering of EC and EU samples resulted in the identification of three distinct cell subtypes. Among different cell subtypes, significant differences were in immunoinfiltrating cells, plasma cells, naive CD4 T cells, memory activated CD4 T cells, gamma delta T cells, resting NK cells and activated NK cells but not in macrophages. Furthermore, the identification of various compounds capable of targeting these m6A genes was achieved. In conclusions, our integrated bioinformatics analysis results demonstrated that m6A-related genes METTL3, CBLL1 and YTHDF2 may be useful biomarkers for endometriosis in ectopic endometrium. The potential therapeutic approach of targeting m6A regulators holds promise for the treatment of endometriosis.

Abstract P06: Characterization of the Immune Tumor Microenvironment in a Diverse Cohort of Patients with Multiple Myeloma

Abstract Multiple myeloma (MM) is the most common blood cancer in African American (AA) individuals. AAs have an increased risk of developing Monoclonal Gammopathy of Undetermined Significance (MGUS) and MM compared to non-Hispanic White (NHW) individuals. While several studies have shown a pivotal role for the immune tumor microenvironment (iTME) in MM pathogenesis, the composition of the iTME in AAs with MM is under investigated. Viably frozen whole bone marrow (BM) aspirates from 128 individuals (53 AA and 75 NHW) with either MM (n=65), MGUS (n=28), smoldering MM (SMM, n=13), or healthy controls (n=22) were evaluated by single cell RNA-sequencing (scRNA-seq) or mass cytometry (CyTOF). We characterized 34 (14 AA and 20 NHW) by scRNA-seq and 94 (39 AA and 55 NHW) by CyTOF. CyTOF samples were stained with Maxpar Direct Immune Profiling Assay Kit (Fluidigm), acquired on a Helios mass cytometer and analyzed in Cytobank. Data were analyzed by logistic equivalent regression model with p<0.05 for significance. ScRNA-seq samples were processed as in Yao, 2022. Data were normalized using scvi-tools and clustered by Uniform manifold approximation and projection (UMAP). Differentially expressed markers between clusters were identified using Model-based Analysis of Single-cell Transcriptomics (MAST) in Seurat R package at FDR < 0.05 and log2 fold change > 0.55. ScRNA-seq of the CD45+ non-tumor lymphocytes revealed 17 immune populations (HSC, erythroblast, MDSC, GMP, CD1c+ DC, CD4+ and CD8+ T cell, CD14+ M1 macrophage, CD16+monocyte, monocyte, pDC, naïve B, pre-B, pro-B, memory B, NK/MAIT). CD3E/D+cells were clustered into naïve/central memory (CCR7+, LEF1+, SELL+), memory (GZMK+), effector (GZMB+, GZMH+, FGFBP2+), g/d (TRDC+), MAIT (SLC4A10+, KLRB1+) and IFN CD8+ T cells (IFIT1+, IFIT2+,IFIT3+). Disease progression was associated with increases in median CD4+ and CD8+ GZMK+ T cells, memory B cells and a reduction in all monocyte clusters. Within the diseased cohort in comparison to NHW patients, AA patients had increased CD3+ T cells (CD4 and CD8+) (AA 57.5% vs. NHW 44.3%) and a reduction in overall monocytes (AA 10.5% vs. 26.5%). These results were validated by CyTOF, which confirmed an increase in CD3+ T cells (AA 53.7% vs. NHW 35.7%, p<0.001) including increases in CD8+ (AA 7.0% vs. NHW 3.2%, p<0.05) and CD4+ terminal effector T cells (AA 5.7% vs. NHW 2.7%, p<0.05) and a reduction in monocytes (AA 18.3% vs. NHW 26.6%) among AAs. After adjusting for age and sex, AAs retained an increase in T cells (OR 1.59 (95%CI 1.2-2.01, p=0.00023)) and a reduction in monocytes (OR 0.88 (95%CI 0.78-1, p=0.045)). Despite the increase in CD8+ T cells in AA patients, we identified reduced GZMK+ T cells and reductions in exhaustion markers (TIGIT, LAG3, TOX) in CD8+ T cells in comparison to NHWs. Nevertheless, the distribution of the immune populations was similar between healthy AA and NHW individuals. Our multiomics approach to characterize the iTME identified significant differences among AA compared to NHW patients with MM. Citation Format: Suganti Shivaram, Hongwei Tang, Huihuang Yan, Shulan Tian, Guyu Qin, Emilie Anderson, Neeraj Sharma, Abiola Bolarinwa, Cinthya Zepeda-Mendoza, Stacey Lehman, Felicia Gomez, Amit Mitra, Surendra Dasari, Taxiarchis Kourelis, Shaji Kumar, Linda Baughn. Characterization of the Immune Tumor Microenvironment in a Diverse Cohort of Patients with Multiple Myeloma [abstract]. In: Proceedings of the Blood Cancer Discovery Symposium; 2024 Mar 4-6; Boston, MA. Philadelphia (PA): AACR; Blood Cancer Discov 2024;5(2_Suppl):Abstract nr P06.

Abstract B061: Multiparametric single-cell characterization of the immune tumor microenvironment in ovarian carcinomas

Abstract B061: Multiparametric single-cell characterization of the immune tumor microenvironment in ovarian carcinomas

Abstract A078: In vivo tumor micro-environment characterization: preliminary results of an ovarian cancer patients-derived co-colture organoids platform

Abstract Introduction: Most patients with ovarian cancer (OC) are diagnosed at advanced stage. Current standard of care is based on surgery and platinum-based chemotherapy (CHT) in association with target therapies according to molecular characteristics (BRCA status and Homologous Recombination Repair (HRR) status). However, 15-20% progressed to first line CHT and mechanisms of resistance remain far from being completely known. In this scenario the role of tumoral microenvironment (TME) represents an open question. Methods: We designed a study to establish OC patients-derived co-colture organoids (ID PICTURE, IRB approval 4531). Both peripheral Blood Mononuclear Cells (PBMC) and isolated immune infiltrating cells from tumor tissue were planned to be used. The first endpoint of the study was the feasibility of this approach including (i) establishment OC patients-derived organoids (ii) PBMC isolation from whole blood and characterization (iii) immune infiltrate isolation from tumor tissue and characterization. For the latter, the evaluation of the immune phenotype was carried out by cytofluorimetry analyses, through target cells labelling with monoclonal antibodies conjugates with fluorescent molecules (CD45, CD19, CD3, CD4, CD8, 7AAD). Correlation between tumor immune infiltrate and mutational and clinical findings was also accomplished. Results: From January 2022 to May 2023, 36 patients with EOC, 92% with high grade serous OC and 95% with an advanced stage (from stage III according to FIGO, all high tumor load according to the Fagotti’s score) were enrolled. 23 patients (64%) were BRCA wild type while HRR status was available only for 17 patients of whom 28% were deficient. Considering the model feasibility, 5 organoids were obtained, and 8 and 20 million immune cells were isolated from tissue and peripheral blood, respectively. The immune infiltrate analysis was performed on 21 samples obtained from primary debulking surgery and 15 from diagnostic laparoscopies. 75% were treated with carboplatin paclitaxel (weekly or q21); among patients undergoing neoadjuvant treatment, 3 had a complete response (CRS3). The infiltration of B and T lymphocytes was described in all patients: 73% were positive for CD3, 20% for CD45, 7% for CD19. The lymphocytes T characterization was available for 7 patients with a median value of CD4+ 51.3, CD8+ 44.4, CD8+CD4+ 2,6 and CD8-CD4- 1.6. A trend for statistically significant difference was observed for CD45+ in stage IV vs stage I-III (p=0.021) and in CD19+ B cells in BRCA mutant vs BRCA WT (p=0.026). Finally, CD3+ T cells showed a significantly higher concentration in the 4 patients experiencing progression of disease at 6 months follow-up (p=0.014). Conclusion: These preliminary results suggest that patients-derived organoids co-cultured with tumor immune infiltrate could be challenging because of small numbers of overall cell content. Nevertheless, a correlation between specific components of B and T cells and molecular features and cancer progression deserves further investigation. Citation Format: Floriana Camarda, Clara Di Mario, Camilla Nero, Barbara Tolusso, Stefano Alivernini, Francesca Sillano, Sonia Mastrogiovanni, Tina Pasciuto, Elisa Gremese, Eleonora Cesari, Alessandra Ciucci, Claudio Sette, Giovanni Scambia. In vivo tumor micro-environment characterization: preliminary results of an ovarian cancer patients-derived co-colture organoids platform [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A078.

Molecular classification and tumor microenvironment characteristics in pheochromocytomas

Pheochromocytomas (PCCs) are rare neuroendocrine tumors that originate from chromaffin cells in the adrenal gland. However, the cellular molecular characteristics and immune microenvironment of PCCs are incompletely understood. Here, we performed single-cell RNA sequencing (scRNA-seq) on 16 tissues from 4 sporadic unclassified PCC patients and 1 hereditary PCC patient with Von Hippel-Lindau (VHL) syndrome. We found that intra-tumoral heterogeneity was less extensive than the inter-individual heterogeneity of PCCs. Further, the unclassified PCC patients were divided into two types, metabolism-type (marked by NDUFA4L2 and COX4I2) and kinase-type (marked by RET and PNMT), validated by immunohistochemical staining. Trajectory analysis of tumor evolution revealed that metabolism-type PCC cells display phenotype of consistently active metabolism and increased metastasis potential, while kinase-type PCC cells showed decreased epinephrine synthesis and neuron-like phenotypes. Cell-cell communication analysis showed activation of the annexin pathway and a strong inflammation reaction in metabolism-type PCCs and activation of FGF signaling in the kinase-type PCC. Although multispectral immunofluorescence staining showed a lack of CD8+ T cell infiltration in both metabolism-type and kinase-type PCCs, only the kinase-type PCC exhibited downregulation of HLA-I molecules that possibly regulated by RET, suggesting the potential of combined therapy with kinase inhibitors and immunotherapy for kinase-type PCCs; in contrast, the application of immunotherapy to metabolism-type PCCs (with antigen presentation ability) is likely unsuitable. Our study presents a single-cell transcriptomics-based molecular classification and microenvironment characterization of PCCs, providing clues for potential therapeutic strategies to treat PCCs.