Features Of Tumor Microenvironment Research Articles

Stroma-infiltrating T cell spatiotypes define immunotherapy outcomes in adolescent and young adult patients with melanoma

The biological underpinnings of therapeutic resistance to immune checkpoint inhibitors (ICI) in adolescent and young adult (AYA) melanoma patients are incompletely understood. Here, we characterize the immunogenomic profile and spatial architecture of the tumor microenvironment (TME) in AYA (aged ≤ 30 years) and older adult (aged 31–84 years) patients with melanoma, to determine the AYA-specific features associated with ICI treatment outcomes. We identify two ICI-resistant spatiotypes in AYA patients with melanoma showing stroma-infiltrating lymphocytes (SILs) that are distinct from the adult TME. The SILhigh subtype was enriched in regulatory T cells in the peritumoral space and showed upregulated expression of immune checkpoint molecules, while the SILlow subtype showed a lack of immune activation. We establish a young immunosuppressive melanoma score that can predict ICI responsiveness in AYA patients and propose personalized therapeutic strategies for the ICI-resistant subgroups. These findings highlight the distinct immunogenomic profile of AYA patients, and individualized TME features in ICI-resistant AYA melanoma that require patient-specific treatment strategies.

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis.

Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have failed to achieve good therapeutic effects. In this article, single-cell transcriptome sequencing (scRNA-seq) data from six patients downloaded from the GEO database were adopted to describe the tumor microenvironment (TME) of ccRCC, including its T cells, tumor-associated macrophages (TAMs), endothelial cells (ECs), and cancer-associated fibroblasts (CAFs). Based on the differential typing of the TME, we identified tumor cell-specific regulatory programs that are mediated by three key transcription factors (TFs), whilst the TF EPAS1/HIF-2α was identified via drug virtual screening through our analysis of ccRCC's protein structure. Then, a combined deep graph neural network and machine learning algorithm were used to select anti-ccRCC compounds from bioactive compound libraries, including the FDA-approved drug library, natural product library, and human endogenous metabolite compound library. Finally, five compounds were obtained, including two FDA-approved drugs (flufenamic acid and fludarabine), one endogenous metabolite, one immunology/inflammation-related compound, and one inhibitor of DNA methyltransferase (N4-methylcytidine, a cytosine nucleoside analogue that, like zebularine, has the mechanism of inhibiting DNA methyltransferase). Based on the tumor microenvironment characteristics of ccRCC, five ccRCC-specific compounds were identified, which would give direction of the clinical treatment for ccRCC patients.

Abstract LB313: Immunosuppressive effect of tumor-infiltrating IL-22 receptor-expressing myeloid cells in early-stage pancreatic ductal adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a poor survival rate, largely due to the lack of diagnostic biomarker for early detection. Here, we found enriched IL-10R2+/IL-22R1+ myeloid cells in peripheral blood and specifically PDAC tissues respectively using xenograft/orthotropic PDAC mouse models. The subcutaneous injection of PDAC cell line in mouse showed a positive correlation of pancreatic tumor growth with increased IL-10R2+/IL-22R1+ myeloid cells by flow cytometry. We also verified that IL-10R2+CD45+ and IL-22R1+CD45+ myeloid cells were infiltrated in tumor tissues after 2 weeks and 3 weeks of injection in orthotropic murine PDAC model. Notably, the IL-10R2+IL-22R1+ cells were highly detected in tumor tissues after 7 days of injection, an earlier time point of PDAC progression. To define whether these cells have immunosuppressive functions to contribute further PDAC progression, IL-10R2+/IL-22R1+ myeloid cells were sorted from splenocytes after 3 days of tumor implantation and co-cultured with T cells in the presence of T cell receptor triggering. The proliferation and cytotoxicity of CD8+ T cells were alleviated by co-culture with IL-10R2+, IL-22R1+, or IL-10R2+IL-22R1+ myeloid cells, compared to that with IL-10R2-IL-22R1- cells. In addition, the population of regulatory T cells was increased when they were co-cultured with IL-22R1+ myeloid cells. Furthermore, the proliferation of human T cells in vitro was also inhibited by co-culture with IL-10R2+/IL-22R1+ myeloid cells from human peripheral blood, indicating the immunosuppressive function of IL-10R2+/IL-22R1+ cell population. Therefore, IL-10R2+/IL-22R1+ myeloid cell population could be a possible promising early detection marker for PDAC, contributing to the non-immunogenic features of tumor microenvironment during PDAC progression. Citation Format: Yoolim Sung, Ji-Eun Kim, Hyo-Jin Min, Soo-Hyun Chung, Seong Who Kim, Eun-Ju Chang. Immunosuppressive effect of tumor-infiltrating IL-22 receptor-expressing myeloid cells in early-stage pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB313.

Pathway-based stratification of gliomas uncovers four subtypes with different TME characteristics and prognosis.

Increasing evidences have found that the interactions between hypoxia, immune response and metabolism status in tumour microenvironment (TME) have clinical importance of predicting clinical outcomes and therapeutic efficacy. This study aimed to develop a reliable molecular stratification based on these key components of TME. The TCGA data set (training cohort) and two independent cohorts from CGGA database (validation cohort) were enrolled in this study. First, the enrichment score of 277 TME-related signalling pathways was calculated by gene set variation analysis (GSVA). Then, consensus clustering identified four stable and reproducible subtypes (AFM, CSS, HIS and GLU) based on TME-related signalling pathways, which were characterized by differences in hypoxia and immune responses, metabolism status, somatic alterations and clinical outcomes. Among the four subtypes, HIS subtype had features of immunosuppression, oxygen deprivation and active energy metabolism, resulting in a worst prognosis. Thus, for better clinical application of this acquired stratification, we constructed a risk signature by using the LASSO regression model to identify patients in HIS subtype accurately. We found that the risk signature could accurately screen out the patients in HIS subtype and had important reference value for individualized treatment of glioma patients. In brief, the definition of the TME-related subtypes was a valuable tool for risk stratification in gliomas. It might serve as a reliable prognostic classifier and provide rational design of individualized treatment, and follow-up scheduling for patients with gliomas.

Inflammatory and subtype-dependent serum protein signatures predict survival beyond the ctDNA in aggressive B cell lymphomas.

Biological heterogeneity of large B cell lymphomas (LBCLs) is poorly captured by current prognostic tools, hampering optimal treatment decisions. We dissected the levels of 1,463 serum proteins in a uniformly treated trial cohort of 109 patients with high-risk primary LBCL (ClinicalTrials.gov: NCT01325194) and correlated the profiles with molecular data from tumor tissue and circulating tumor DNA (ctDNA) together with clinical data. We discovered clinically and biologically relevant associations beyond established clinical estimates and ctDNA. We identified an inflamed serum protein profile, which reflected host response to lymphoma, associated with inflamed and exhausted tumor microenvironment features and high ctDNA burden, and translated to poor outcome. We composed an inflammation score based on the identified inflammatory proteins and used the score to predict survival in an independent LBCL trial cohort (ClinicalTrials.gov: NCT03293173). Furthermore, joint analyses with ctDNA uncovered multiple serum proteins that correlate with tumor burden. We found that SERPINA9, TACI, and TARC complement minimally invasive subtype profiling and that TACI and TARC can be used to evaluate treatment response in a subtype-dependent manner in the liquid biopsy. Altogether, we discovered distinct serum protein landscapes that dissect the heterogeneity of LBCLs and provide agile, minimally invasive tools for precision oncology. This research was funded by grants from the Research Council of Finland, Finnish Cancer Organizations, Sigrid Juselius Foundation, University of Helsinki, iCAN Digital Precision Cancer Medicine Flagship, Orion Research Foundation sr, and Helsinki University Hospital.

Comprehensive molecular classification predicted microenvironment profiles and therapy response for hepatocellular carcinoma.

Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for hepatocellular carcinoma (HCC) patients. The clinical applications of documented molecular subtypes are constrained by several issues. We integrated three single-cell datasets to describe the TME landscape and identified six prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes. The predictive value of these molecular subtypes for prognosis and treatment response was explored in multiple external HCC cohorts and the Xiangya HCC cohort. TME features were estimated using single-cell immune repertoire sequencing, mass cytometry and multiplex immunofluorescence. The prognosis-related score (PRS) was constructed based on machine learning algorithm. Comprehensive single-cell analysis described TME heterogeneity in HCC. The five transcriptomic subtypes possessed different clinical prognoses, stemness characteristics, immune landscapes and therapeutic responses. Class 1 exhibited an inflamed phenotype with better clinical outcomes, while Classes 2 and 4 were characterized by a lack of T cell infiltration. Classes 5 and 3 indicated an inhibitory tumor immune microenvironment. Analysis of multiple therapeutic cohorts suggested that Classes 5 and 3 were sensitive to ICB and targeted therapy, whereas Classes 1 and 2 were more responsive to transcatheter arterial chemoembolization treatment. Class 4 displayed resistant to all conventional HCC therapies. Three potential therapeutic agents and four targets were further identified for high-PRS HCC patients. Our study generated a clinically valid molecular classification to guide precision medicine in patients with HCC.

Abstract 1182: PDC*lung01: An innovative therapeutic cancer vaccine induces specific immune responses in combination with anti- PD-1 treatment in patients with non-small cell lung cancer

Abstract We have developed an innovative cancer vaccine for the treatment of advanced NSCLC patients in combination with checkpoint inhibitors. This PDC*lung01 product based on an irradiated plasmacytoid dendritic cell line loaded with HLA-A*02:01 restricted peptides (NY-ESO-1, MAGE-A3, MAGE-A4, Multi-MAGE-A, MUC1, Survivin and Melan-A) is able to prime and expand peptide-specific CD8+ T cells in vitro and in vivo, and is synergistic with anti-Programmed Death (PD)-1 (Plumas et al, 2022; Hannani et al, 2023, NCT03970746). In study PDC-LUNG-101 with EudraCT number 2018-002382-19, PDC*lung01 is injected intravenously and subcutaneously for 6 times at two dose levels, 14 million, or 140 million cells in stage II/IIIA (cohort A1 & A2, monotherapy) or stage IV with TPS≥50% (cohort B1 & B2; in combination with pembrolizumab) patients. We report here the analysis of immune response of 3 cohorts (A1, A2, B1) of patients and the first 19 patients out of 42 (cohort B2) treated with high dose of PDC*lung01 in combination with Pembrolizumab at baseline and 3 timepoints post injection. Several circulating immune parameters have been evaluated at different times before and after vaccination using different assays we have developed: leukocyte count and determination of circulation peptide specific CD8+ T cells by flow cytometry using multimer tools without prior in vitro culture. The assay made on purified CD8+ T-cells allowed us to define a limit of quantification (LOQ) to accurately assess the fold changes of the cell expansion. In addition, tumor microenvironment (TME) was analyzed by multiplex fluorescence immunochemistry. The data of 42 patients for who an immune response has been evaluated (cohorts A and B) will be presented. No major changes in circulating main lymphocyte frequencies (B cells, NK cells, CD4+, CD8+, or Treg T cells) were observed during treatment. In addition, no major cell activation (CD25+, CD54, or DR+) was noted. In contrast, in a significant number of patients, PDC*lung01 induced circulating peptide-specific CD8+ T cell expansion in all 4 cohorts at different levels and time points against at least one out of seven peptides used. The percentages of patients for who an expansion of antigen-specific T-cell were observed were 50%, 64%, 66.7% and 68.4% in A1, A2, B1 and B2 cohorts respectively. The intensity of the immune response was proportional to the dose of PDC*lung01 and to the combination with pembrolizumab. When possible, total and peptide-specific CD8+ T cells were sorted for analysis of TCR repertoire, illustrating the modelling and dynamics of the immune response. The TME features will also be illustrated. To conclude, treatment with PDC*lung01 induces an anti-tumor immune response in a significant number of patients which appears to be enhanced by the combination with pembrolizumab. Citation Format: Joel Plumas, Anne Sibille, Ingel Demedts, Elvire Pons-Tostivint, Charlotte Van de Kerkhove, Sofie Derijcke, Willemijn Theelen, Bonne Biesma, Frank Borm, Els Wauters, Mike Collodoro, Kays Al Badawy, Camille Duchayne, Channa Debruyne, Beatrice Devos, Benoit Colinet, Denis Moro-Sibilot, Maurice Perol, Eva-Lotte Buchmeier, Marcin Skrzypski, Kristof Cuppens, Johan Vansteenkiste. PDC*lung01: An innovative therapeutic cancer vaccine induces specific immune responses in combination with anti- PD-1 treatment in patients with 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 1182.

Abstract 7644: Prognostication of genomic characteristics of residual breast cancer after neoadjuvant chemotherapy

Abstract Residual cancer burden (RCB) based on pathologic characteristics is the most powerful prognostic factor in breast cancer (BC) which received neoadjuvant chemotherapy (NAC) followed by surgery. According to RCB classification, class III predicted short survival duration regardless of BC subtypes according to hormone receptor (HR) and human epidermal growth factor receptor-2 (HER-2) state. However, up to 20-40% of BCs with RCB class III did not experience BC recurrence and had long term survival without disease recurrence. In our study, we evaluated genomic characteristics of BC which had high RCB class. We collected 96 fresh frozen BC specimens which had undergone surgery after NAC between 2010 and 2018 from Samsung Medical Center Biobank. We performed whole genome or exome sequencing, RNA sequencing and high-plex single-cell spatial transcriptomics. Of 96 BCs, 65 BCs had experienced BC recurrence and 31 BCs have not been BC recurrence. Of 65 BCs with dismal prognosis, 12 were HR+HER2-, four in HR+HER2+, 39 in triple negative breast cancer (TNBC) and 10 in HR-HER2+ BC. Of 31 BCs without recurrence, 11 were HR+HER2-, two in HR+HER2+, 14 in TNBC and four in HR-HER2+ BC. In HR+HER2- BC, ten of 12 BCs with dismal prognosis were categorized into basal like intrinsic subtype and two were into luminal B subtype compared that six of luminal A and five of luminal B subtype categorization in good prognosis BC group. Age associated mutation signature were enriched in HR+HER2- BC with good prognosis (p=0.027). Tumor microenvironment (TME) analysis using Kassandra suggested that high monocytes were associated with BC with dismal prognosis (p=0.00031). In single-cell spatial transcriptome analysis, T-cell abundance in the tumoral niche is associated with dismal prognosis in HR+HER2- BC (p=0.039). In TNBC, almost BCs (88.7%) were categorized into basal-like intrinsic subtype regardless of their prognosis. TME analysis presented that immune cells including macrophages and lymphocytes with CD4 and CD8 T cells were significantly enriched in TNBC with good prognosis (ps <0.05, respectively). In HER2+ BC, intrinsic subtypes were heterogeneous in both groups. Focal amplification in HER2+BC patient harbored DNA segments from different chromosomes more frequently than in those with good prognosis (p=0.00091), which was prominent in oncogenic extrachromosomal DNA amplifications compared to other oncogenic amplifications. In TME analysis, there were no specific cell enrichments according to BC prognosis but sing-cell spatial transcriptome analysis revealed that higher T-cell abundance in the tumoral niche was associated with HR+HER2+ BC with good prognosis (p=0.047). In conclusion, each BC subtype had their own genomic characteristics to determine their prognosis. Our study also suggested that TME characteristics was common prognostic factor for survival outcome of BC with residual tumor after NAC. Citation Format: Ji-Yeon Kim, Sabin Park, Sepil Ahn, Eun Seop Seo, Soyeon Kim, Mark Gregory, Emily Killingbeck, Eun Yoon Cho, Jeong Eon Lee, Jin Seok Ahn, Yeon Hee Park, Woong-Yang Park, Hoon Kim, Semin Lee, Young-Hyuck Im. Prognostication of genomic characteristics of residual breast cancer after neoadjuvant chemotherapy [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 7644.

Abstract 773: Overexpression of phosphoserine phosphatase (PSPH) in prostate cancer and in tumor microenvironment promotes tumor cell proliferation

Abstract Reprogrammed cellular metabolism is one of the key features of cancer and tumor microenvironment. L-3-phosphoserine phosphatase (PSPH) is one of the five rate-limiting enzymes in the biosynthesis of serine from glucose that supports cell proliferation. Here, we aim to study the role of PSPH in prostate cancer (PCa) and its tumor microenvironment. We find that PSPH not only is overexpressed in prostate cancer cell lines compared to normal prostate epithelial cells (PrEC) and benign prostate epithelial cells (BPH-1), but also exhibits a higher level in patient-derived carcinoma-associated fibroblasts (CAFs) compared to the matched benign associated fibroblasts (BAFs), which are derived from fresh radical prostatectomy specimens of African American (AA) and European American (EA) men. Notably, MDAPCa2b cells derived from an AA prostate cancer patient and AA CAFs have even higher expression of PSPH protein. The mRNA levels of PSPH are also significantly elevated in prostate cancer tissues in AA men compared to EA men and predict poor survival in PCa patients. Knock-down of PSPH expression significantly inhibits the proliferation and colony formation of prostate cancer cells. Growth of xenograft tumors derived from PSPH knockdown MDAPCa2b cells has also been suppressed markedly. Gene expression profiling revealed that suppression of PSPH expression by shRNAs in LNCaP and MDAPCa2b cells is associated with the regulation of gene expression related to metabolism, immunity, extracellular matrix remodeling, and Ion channels. Similarly, analysis of publicly available PCa RNA seq databases also demonstrates that PSPH amplification or over-expression is associated with alterations of gene expression related to metabolism and immunity. Our results suggest that PSPH is upregulated in prostate cancer cells and their surrounding fibroblasts to support tumor growth via reprogramming metabolic pathways and transcriptomes. Citation Format: Liankun Song, Noriko Yokoyama, Jun Xie, Yunjie Hu, Zhenyu (Arthur) Jia, Beverly Wang, Dan Mercola, Edward Uchio, Thomas Ahlering, Michael Lilly, Xiaolin Zi. Overexpression of phosphoserine phosphatase (PSPH) in prostate cancer and in tumor microenvironment promotes tumor cell proliferation [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 773.

Abstract 6462: Somatic mutational profile in MUTYH- associated colorectal cancers (MUTYH-CRCs)

Abstract Background: Biallelic inactivation of MUTYH leads to hereditary predisposition to colorectal polyposis and CRC. Herein we evaluated co-alterations in somatic next-generation sequencing (NGS) in patients with MUTYH-CRC, mainly KRAS G12C, high tumor mutational burden and microsatellite instability (MSI-H)/mismatch repair deficiency (dMMR). Methods: Data was collected on patients with MUTYH-CRC in the period between 2015 and 2023 at Moffitt Cancer Center (Tampa, Florida, United States). All patients with MUTYH-CRC were included regardless disease stage. Different germline panels were used to detect MUTYH and Foundation One, Caris and Moffitt STAR (in house NGS) were used to detect somatic co-alterations. Results: A total of 36 patients were identified with MUTYH- CRC, the median age at diagnosis was 44-year-old, 55% were men. All patients had a somatic NGS available, 18 patients (50%) had KRAS mutation, and specifically KRAS G12C was detected in 9 patients (24%), 6 patients (17%) had MSI-H/dMMR, 5 patients (14%) had BRAF mutation and 4 patients (11%) with BRCA2 mutation. Conclusion: MUTYH-CRC is associated with a high incidence of targetable alterations including KRAS G12C and BRAF. Considering unique characteristics in MUTYH-CRC tumor microenvironment, including T cell infiltrate, trials combining KRAS G12C/BRAF inhibitors and immune checkpoint inhibitors are warranted. Citation Format: David Kaldas, Tiago Biachi de Castria. Somatic mutational profile in MUTYH- associated colorectal cancers (MUTYH-CRCs) [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 6462.

Abstract 7609: Potential gene expression meta-signatures predict neoadjuvant chemotherapy response in invasive breast cancer

Abstract Early prediction of Neoadjuvant Chemotherapy (NAC) response is crucial for women with locally advanced breast cancer (BC), given their low response rates. This study aims to identify gene expression patterns and tumor microenvironment (TME) characteristics associated with resistance to NAC, which could significantly impact tumor progression and treatment outcomes. The study encompassed differential gene expression comparing non-responders and responders, enrichment analysis, tumor microenvironment estimation, and cytokines identification from a female patient's cohort with distinct molecular subtypes and candidates to NAC at the Colombian National Cancer Institute. Candidate differentially expressed genes validated with RT-PCR in a larger validation cohort and corroborated in several publicly available databases, including associated treatment drugs. Distinct gene expression profiles were observed between responders and non-responders, emphasizing immune system-related pathways such as IL-17 signaling, B cell receptor signaling, complement and coagulation cascades, natural killer cell-mediated cytotoxicity, and NF-kB signaling. Immune-related genes (AGTR1, APOD, CCL19, CGA, ECEL1, NPY1R, NTNG1, PCSK1, PFDN2, S100B, TPRG1, WDR5B, ZNF385B) were identified as biomarkers correlated with non-response and worse prognosis. Responders showed increased B cell populations, CD4+ and CD8+ effector memory T-cells in lymphoid groups, while non-responders displayed elevated levels of CD8+ naive T-cells. Additionally, responders revealed upregulated myeloid cell populations like macrophages and dendritic cells. Several cytokines like IL1B, IL-2, IL-4, IL-12, IL-21, IL-22, IL-36, and TWEAK showed elevated expression in non-responders. Dexamethasone, Vinblastine, and Streptozocin were candidate drugs that interacted with AGTR1, CGA, and S100B, respectively and are associated with progression and metastasis. Logistic regression revealed that overexpression of CGA showed a higher probability (OR:1.83, p=0.03) while ECEL1 had a lower probability (OR=0.44, p=0.04) in non-responders compared to responders who lacked expression of these genes. Immune-related genes and various subtypes of immune cells within the TME exhibit correlations with response to NAC, and several cell fractions significantly changed before and after NAC. These results suggest meta-signatures that warrant further evaluation as concordance assessment between immunohistochemistry (IHC) and gene expression profiling, in addition to an extensive validation to triage patients before NAC and target personalized interventions. Detection and evaluation of TME components are essential to predict NAC efficacy and avoid disease relapse. Finally, these findings emphasize the importance of assessing admixed populations to facilitate novel findings and alleviate disparities. Citation Format: Hedda Michelle Guevara-Nieto, Rafael S. Parra-Medina, Jovanny Zabaleta, Liliana Lopez-Kleine, Alba L. Combita. Potential gene expression meta-signatures predict neoadjuvant chemotherapy response in invasive breast 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 7609.

Abstract 6771: Tumor-microenvironment-on-chip: an ex vivo drug screening platform enabling real-time observation of regional tumor responses during drug development and clinical treatments

Abstract Background: Current tumor models face challenges in accurately replicating the intricate and dynamic conditions of the tumor microenvironment (TME), limiting predictions of drug efficacy in drug development and personalized treatment. Furthermore, the continuous and real-time observation of drug responses variations resulting from tumor heterogeneity poses challenges in existing models, including patient-derived organoids and animal models. To address these limitations, our study introduces a novel approach through a tumor-microenvironment-on-chip (TMoC) that combines 3D tissue cultivation and a circulation system, incorporating physiological gradients of oxygen and nutrients. Serving as a tool to faithfully replicate the intricate TME, TMoC facilitates highly accurate drug screening for enhanced therapeutic precision. Method: Based on organ-on-chip and microfluidic systems, our platform reconstructs the cellular heterogeneity of the TME using tumor tissue-dissociated cells in collagen I or Matrigel within a strip-shaped 3D cultivation space, enabling simple analysis of regional responses. A circulation system, powered by a peristaltic pump, aids medication and immune cell entry for combination immunotherapy assessment. Real-time apoptosis analysis via fluorescence microscopy and sample retrieval for in-depth analysis are enabled. In drug screening, TMoC results are compared with mouse model outcomes and medication records for various cancers and treatments. Result: TMoC reconstructed physiological-like gradients, including oxygen levels, and facilitated the infiltration of cytotoxic CD8+ T cells, providing a distinctive model for assessing the synergistic impact of immune checkpoint inhibitors and chemotherapy drugs. Additionally, tissues cultured in TMoC retained their original cellular composition. TMoC demonstrated efficacy in drug screening across various cancer models, including breast, pancreatic, and colorectal cancers. In clinical studies, tumor tissue-derived cells from patients were cultivated on the TMoC, and the treating results are subsequently compared with clinical outcomes. Whether in animal models or clinical cases, the drug screening outcomes of TMoC consistently aligned with in vivo drug responses. Conclusion: The TMoC offering a platform that modeling some complex characteristics of TME. With applications in drug screening and clinical studies, it shows promising correlations between TMoC and in vivo responses. Citation Format: Chiao-Min Lin, Hsaun-Yu Mu, Li-An Chu, Ya-Hui Lin, Ji Li, Chao-Yu Liu, Hsi-Chien Huang, Sheng-Liang Cheng, Tsung-Ying Lee, Hsin Mei Lee, Hsin-Min Chen, Yun-Jen Tsai, Tzu-Hung Hsiao, Kee-Ming Man, Yunching Chen, Jen-Huang Huang. Tumor-microenvironment-on-chip: an ex vivo drug screening platform enabling real-time observation of regional tumor responses during drug development and clinical treatments [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 6771.

Abstract 1593: Intercellular network in immune suppressive microenvironment of pancreatic cancer

Abstract Despite constant efforts to improve treatment, the prognosis remains poor, with an overall survival rate of 6% (ranges from 2% to 9%) due to lack of efficient chemotherapy, radiotherapy, and targeted therapy. Immune checkpoint inhibitors (ICIs) has been reported to be effective in various solid cancers, such as melanoma, lung cancer, and renal cell carcinoma. However, pancreatic ductal adenocarcinoma (PDAC) is resistant against ICIs. The responsiveness of immune therapy is mainly determined by immune tumor microenvironment (TME) composed of tumor-infiltrating lymphocytes (TIL) and stromal cells. PDAC possesses unique TME, which is abundant of fibrosis, termed of desmoplasia, and the role of fibrosis in constructing immune suppressive TME remains unclear. First, in this study, to examine the immunological characteristics of TME, we constructed an integrated data base of TIL profiling, RNA-seq and whole exome seq using 31 fresh resected tissues of PDAC. An unsupervised clustering of based on numbers and percentages of 12 TIL types analyzed by flow cytometry showed that PDAC was divided into 2 types of cluster (myeloid cell-, T cell-dominant type), and the prognosis of myeloid type was significantly poorer than T cell-type. Myeloid type contained the high frequency of myeloid cell lineage including monocytes, macrophages and myeloid-derived suppressor cells (MDSC). Only MDSCs were significantly associated with poor prognosis among various immune cell types. Furthermore, gene enrichment analysis identified activated and suppressed pathways in each immune type, and in myeloid cell-dominant type, the immune-related pathways were significantly down-regulated. Then, to understand the characteristics of MDSCs infiltrated into pancreatic cancer tissues, single cell RNA-seq with surgical specimens was performed, and the analysis showed that a specific subtype of MDSCs infiltrated into cancer tissues. In addition, activation status of cancer-associated fibroblasts (CAFs) was related with MDSC recruitment and activation, indicating that the interaction between MDSCs and CAFs is crucial to create MDSC-rich TME. Inhibition of interaction between MDSCs and CAFs is expected to overcome immunosuppressive TME can be a therapy in PDAC. Citation Format: Kazunori Aoki, Hironori Fukuda, Yukihiro Mizoguchi, Kosuke Arai. Intercellular network in immune suppressive microenvironment of pancreatic 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 1593.

Abstract 1488: Spatial characterization of the tumor microenvironment of metastatic triple-negative breast cancer

Abstract Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer associated with high rates of recurrence and metastasis, which contribute to poor overall survival. The challenges in treating TNBC stem from its high intratumor heterogeneity and lack of expression of the estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 druggable targets. The tumor microenvironment (TME) plays a crucial role in disease progression. Within the TME, complex spatial interactions among tumor cells, immune cells, stroma, angiogenesis- and hypoxia-inducing factors collectively contribute to the advancement and spread of TNBC. In-depth understanding of the TME and interactions of the various components within can elucidate newer therapeutic targets, enhancing the prognosis of TNBC patients. Imaging mass cytometry (IMC) enables detailed analysis of cell-cell and cell-matrix interactions within the TME at the single-cell level. In this study, we aim to characterize the spatial relationships among various cell types within the TNBC TME using IMC, while also investigating differences in the TME of TNBC patients who developed distant metastasis (DM) compared to those who remained non-metastatic (NM). IMC was performed on formalin-fixed paraffin-embedded TNBC primary resection tissues (n= 16; DM= 7, NM= 9) from Stavanger University Hospital, Norway, using a panel of 25 metal-conjugated antibodies. Regions of interest were marked by the pathologist and the slides were laser ablated using Hyperion Imaging System. DeepCell was used to segment single cells based on surface markers. Mean intensity values of all markers for each cell were extracted and consolidated in R scripts for unsupervised clustering. Cell clusters were annotated based on phenotypic markers. Normalized expression of markers was compared for each cell type across DM and NM patients, including markers for cell proliferation, angiogenesis, hypoxia, and cell migration. Neighborhood analysis examined spatial associations between different cell types, highlighting differences in DM and NM patients. The IMC panel successfully provided spatial insights into the TME, including the characterization of tumor cells, immune cells (B cells, T cells, macrophages), endothelial cells, and proliferating cells. We identified 18 unique phenotypic cell clusters that were present at different frequencies in DM and NM patients. These clusters demonstrated different expression profiles of functional markers. t-SNE plots revealed distinct patterns of cell type distribution and unique protein expression profiles, highlighting significant differences between the two groups. Our data suggest a distinct spatial organization of the TME in TNBC patients who develop metastasis. This warrants further investigations to determine the role and interplay of TME features in predicting the risk of distant metastasis in TNBC. Citation Format: Mahak Bhargava, Jaspreet Kaur, Geetanjali Saini, Junjun Zheng, Yitian Xu, Shu-Hsia Chen, Ping-Ying Pan, Emma Rewcastle, Umay Kiraz, Emiel Adrianus Janssen, Ritu Aneja. Spatial characterization of the tumor microenvironment of metastatic triple-negative breast 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 1488.

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 625: Remodeling of the tumor microenvironment in esophageal squamous cell carcinoma by neoadjuvant immunochemotherapy and its impact on therapeutic efficacy

Abstract Background: Patients with local advanced esophageal squamous cell carcinoma (ESCC) benefited from neoadjuvant immunochemotherapy (NAI) with 22%-55% of complete pathological responses (pCR). While unpredicted therapeutic resistance hampered prognosis of these patients. Thus, this study aimed to investigate tumor microenvironment (TME) remodeling pre- and post-NAI identifying predictive biomarkers and potential targets for patients with resistance to NAI. Methods: 65 patients with locally advanced ESCC, who were treated with neoadjuvant anti-PD-1 antibody and platinum-based chemotherapy at the First Affiliated Hospital of Zhejiang University School of Medicine between November 2019 and June 2020, were included in this study. Genomic DNA and RNA from paired pre-and post-treatment FFPE samples were harvested and subjected to targeted DNA and RNA sequencing. Pre- and post- therapeutic microenvironment and immune cell infiltration were dynamically assessed based on RNA sequencing data. Results: 37 of 65 patients responded to NAI(TRG0-1), while 28 of 65 patients displayed unfavored response (TRG2-3). Comparative analysis of TME characteristics before and after NAI revealed that, in the TRG2-3 group, LRRC15+ CAFs, and fibroblasts were significantly upregulated, while CD4 T cells and endothelium cells exhibited downregulation. Especially, TIGIT tended to be upregulated after NAI in the TRG 2-3 group. In the TRG0-1 group, significant upregulation of activated B cells, T-cells, myeloid dendritic cells, together with notable downregulations of immune suppressive features like Type 2 T helper cells, Tregs and LRRC15+ CAFs were observed in post-treatment samples. Conclusions: Our study indicates the dynamic remodeling of TME during-NAI treatment might influence therapeutic efficacy and disclose potentially targetable resistant mechanisms. These preliminary findings pave the way for further validation through prospective clinical studies. Citation Format: Rusidanmu Aizemaiti, Huifang Zhang, Changbin Zhu, Kun Zhou, Tao Hu, Shuang Lin, Xiao Teng, Jin Wang, Xing Li, Peng Ye. Remodeling of the tumor microenvironment in esophageal squamous cell carcinoma by neoadjuvant immunochemotherapy and its impact on therapeutic efficacy [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 625.

Abstract 1142: Spatial decomposition of lung adenocarcinoma expression subtypes reveals tumor microenvironment characteristics

Abstract The expression subtypes of lung adenocarcinoma (LUAD) capture tumors with distinct pathway activity, mutations and histopathology and also differentiate clinical outcomes. The microenvironments of these subtypes, proximal-inflammatory (PI), proximal-proliferative (PP), and terminal respiratory unit (TRU), have been described generally as immune hot, immune moderate and immune cold, respectively, but otherwise have not been analyzed at high resolution. Here, we aimed to characterize and compare tumor microenvironments between LUAD subtypes. Using spatially barcoded arrays and cDNA libraries (10x Genomics), we sequenced the spatial transcriptomes of a 6.5mm2 plane of 14 LUAD tumors from the Applied Proteogenomics and Organizational Learning Outcomes (APOLLO) program. Spatial transcriptomes had a median of 3,560 spots and a median of 4,026 genes detected per spot. First, collapsing the spatial array to a bulk measurement per sample, we applied our published expression subtype predictor classifying 4 PI, 5 PP, and 5 TRU cases. We then decomposed each tumor’s spatial expression profile by unsupervised clustering, followed by signature scoring and collapsing into tumor, immune, and stroma tumor microenvironment (TME) components. Twelve of the fourteen tumors harbored multiple components while two tumors had one component. The region areas of TME components showed trends among the subtypes, with PI having the greatest immune area and PP having the greatest tumor area. Within each tumor, we calculated differentially-expressed genes between each TME component. Comparing TME genes to the subtype predictor genes, we found significant overlap (chi-square p << 0.001). This indicates that genes that are variable among bulk tumors also have variability within tumors. We then predicted expression subtype for decomposed compartments. Five tumors had the same expression subtype across their TME components, which we refer to as single subtype tumors. However, six tumors had more than one expression subtype prediction among the tumor’s TME components, which we call ‘multi-subtype tumors’. Multi-subtype tumors had lower bulk subtype prediction scores than single-subtype tumors (p < 0.01), indicating that the TME diversity among tumors affects the bulk expression subtype. Interestingly, the six multi-subtype tumors were in the PP and PI subtypes, suggesting greater TME component diversity than TRU. Calculating the spatial compactness of the tumors through continuity indices, we found that PI subtype trended with greater intermixing of TME components. In summary, the bulk LUAD expression subtypes capture differences between tumors and within tumors related to the 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: Shaoqiu He, Camille Alba, Savannah Kounelis-Wuillaume, Teri J. Franks, Martin L. Doughty, Robert F. Browning, Craig D. Shriver, Clifton L. Dalgard, APOLLO Research Network, Matthew D. Wilkerson. Spatial decomposition of lung adenocarcinoma expression subtypes reveals tumor microenvironment characteristics [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 1142.

Spatial relationships in the urothelial and head and neck tumor microenvironment predict response to combination immune checkpoint inhibitors

Immune checkpoint inhibitors (ICI) can achieve remarkable responses in urothelial cancer (UC), which may depend on tumor microenvironment (TME) characteristics. However, the relationship between the TME, usually characterized by immune cell density, and response to ICI is unclear. Here, we quantify the TME immune cell densities and spatial relationships (SRs) of 24 baseline UC samples, obtained before pre-operative combination ICI treatment, using multiplex immunofluorescence. We describe SRs by approximating the first nearest-neighbor distance distribution with a Weibull distribution and evaluate the association between TME metrics and ipilimumab+nivolumab response. Immune cell density does not discriminate between response groups. However, the Weibull SR metrics of CD8+ T cells or macrophages to their closest cancer cell positively associate with response. CD8+ T cells close to B cells are characteristic of non-response. We validate our SR response associations in a combination ICI cohort of head and neck tumors. Our data confirm that SRs, in contrast to density metrics, are strong biomarkers of response to pre-operative combination ICIs.

Machine learning links T-cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer.

Tumor molecular datasets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning (ML) to analyze a single-cell, spatial, and highly multiplexed proteomic dataset from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcome. We designed a multiplex immunohistochemistry antibody panel to compare T-cell functionality and spatial localization in resected tumors from treatment-naive patients with localized pancreatic ductal adenocarcinoma (PDAC) with resected tumors from a second cohort of patients treated with neoadjuvant agonistic CD40 (anti-CD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both cohorts were assayed, and over 1,000 tumor microenvironment (TME) features were quantified. We then trained ML models to accurately predict anti-CD40 treatment status and disease-free survival (DFS) following anti-CD40 therapy based upon TME features. Through downstream interpretation of the ML models' predictions, we found anti-CD40 therapy reduced canonical aspects of T-cell exhaustion within the TME, as compared to treatment-naive TMEs. Using automated clustering approaches, we found improved DFS following anti-CD40 therapy correlated with an increased presence of CD44+CD4+ Th1 cells located specifically within cellular neighborhoods characterized by increased T-cell proliferation, antigen-experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of ML in molecular cancer immunology applications, highlight the impact of anti-CD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for anti-CD40-treated patients with PDAC.

Glutamine metabolism prognostic index predicts tumour microenvironment characteristics and therapeutic efficacy in ovarian cancer.

Mounting evidence has highlighted the multifunctional characteristics of glutamine metabolism (GM) in cancer initiation, progression and therapeutic regimens. However, the overall role of GM in the tumour microenvironment (TME), clinical stratification and therapeutic efficacy in patients with ovarian cancer (OC) has not been fully elucidated. Here, three distinct GM clusters were identified and exhibited different prognostic values, biological functions and immune infiltration in TME. Subsequently, glutamine metabolism prognostic index (GMPI) was constructed as a new scoring model to quantify the GM subtypes and was verified as an independent predictor of OC. Patients with low-GMPI exhibited favourable survival outcomes, lower enrichment of several oncogenic pathways, less immunosuppressive cell infiltration and better immunotherapy responses. Single-cell sequencing analysis revealed a unique evolutionary trajectory of OC cells from high-GMPI to low-GMPI, and OC cells with different GMPI might communicate with distinct cell populations through ligand-receptor interactions. Critically, the therapeutic efficacy of several drug candidates was validated based on patient-derived organoids (PDOs). The proposed GMPI could serve as a reliable signature for predicting patient prognosis and contribute to optimising therapeutic strategies for OC.