Heterogeneity In Tumor Microenvironment Research Articles

Integrative analyses of bulk and single-cell transcriptomics reveals the infiltration and crosstalk of cancer-associated fibroblasts as a novel predictor for prognosis and microenvironment remodeling in intrahepatic cholangiocarcinoma

BackgroundIntrahepatic cholangiocarcinoma (ICC) is a highly malignant neoplasm and characterized by desmoplastic matrix. The heterogeneity and crosstalk of tumor microenvironment remain incompletely understood.MethodsTo address this gap, we performed Weighted Gene Co-expression Network Analysis (WGCNA) to identify and construct a cancer associated fibroblasts (CAFs) infiltration biomarker. We also depicted the intercellular communication network and important receptor-ligand complexes using the single-cell transcriptomics analysis of tumor and Adjacent normal tissue.ResultsThrough the intersection of TCGA DEGs and WGCNA module genes, 784 differential genes related to CAFs infiltration were obtained. After a series of regression analyses, the CAFs score was generated by integrating the expressions of EVA1A, APBA2, LRRTM4, GOLGA8M, BPIFB2, and their corresponding coefficients. In the TCGA-CHOL, GSE89748, and 107,943 cohorts, the high CAFs score group showed unfavorable survival prognosis (p < 0.001, p = 0.0074, p = 0.028, respectively). Additionally, a series of drugs have been predicted to be more sensitive to the high-risk group (p < 0.05). Subsequent to dimension reduction and clustering, thirteen clusters were identified to construct the single-cell atlas. Cell-cell interaction analysis unveiled significant enhancement of signal transduction in tumor tissues, particularly from fibroblasts to malignant cells via diverse pathways. Moreover, SCENIC analysis indicated that HOXA5, WT1, and LHX2 are fibroblast specific motifs.ConclusionsThis study reveals the key role of fibroblasts - oncocytes interaction in the remodeling of the immunosuppressive microenvironment in intrahepatic cholangiocarcinoma. Subsequently, it may trigger cascade activation of downstream signaling pathways such as PI3K-AKT and Notch in tumor, thus initiating tumorigenesis. Targeted drugs aimed at disrupting fibroblasts-tumor cell interaction, along with associated enrichment pathways, show potential in mitigating the immunosuppressive microenvironment that facilitates tumor progression.

Abstract PO4-26-03: Collagen deposition in the breast cancer microenvironment correlates with ER-positive, Histological Grade I and low proliferation tumors

Abstract Breast cancer is a highly heterogeneous neoplasm both at the molecular and histological levels. Breast cancer cells present extremely high levels of genetic heterogeneity concerning mutational burden, copy number variations (CNVs) and epigenetic signatures. While this level of heterogeneity is well established, other layers of heterogeneity, such as tumor microenvironment (TME) heterogeneity, remains to be elucidated. TME in breast cancer is an intricate network encompassing tumor cells, “normal” stroma cells such as fibroblasts, lymphocytes, and a plethora of extracellular matrix (ECM) proteins produced either by cancer cells or stromal cells. These three interacting modules (cancer cells, stromal cells and the ECM) can affect each other in a reciprocal way with profound effects on patient prognosis and treatment outcome. Major protein components of the TME are ECM-related collagens, that are produced by breast cancer cells or/and cancer associated fibroblasts. The ECM collagen in breast cancer, that is mainly composed of collagen types I, III and V, has multiple roles modulating tumor growth, cancer cell invasiveness and metastasis. Here by Masson Trichrome and Immunohistochemistry/Aniline Blue co-stains for collagen fibers and proliferation index (Ki-67) in breast cancer FFPE tissue samples, we identified a statistically significant correlation between collagen deposition and Estrogen Receptor (ER)) status (p&amp;lt; 0.05, proliferation index (p&amp;lt; 0.01) and histological Grade (p=0.01). Further, in order to expand and support our findings, we analyzed the METABRIC study through the cBioPortal hub in relevance to collagen genes and multiple clinicopathological parameters. We were able to identify a coregulated subset of collagen genes that presented a positive correlation with ER-positive (p&amp;lt; 10−10, q&amp;lt; 10−10), low proliferating (p&amp;lt; 10−10, q&amp;lt; 10−10), Grade I (p=2.35e−9, q&amp;lt; 1.07e−8) group of breast cancer cases, supporting our findings. In addition, analyses of collagen-high patients from the METABRIC study indicated that most cases presented with PIK3CA, CBFB and MAP3K1 mutations, while through the g.Profiler software we identified collagen-high relevant gene signatures that encompassed gene ontology terms related to tissue development, extracellular matrix organization and cell migration. Collectively, our preliminary results indicate that ECM collagen deposition in breast cancer is characteristic of low-grade tumors with a better differentiation and possibly with a favorable prognosis. Citation Format: Angelos Koutras, Chaido Sirinian, Olga Bogri, Martha Nifora, Maria Theakou, Soren Degn, Ioannis Mouratidis, Dimitrios Chaniotis, Stavros Peroukidis, Ilias Georgakopoulos-Soares, Anastasios Papanastasiou. Collagen deposition in the breast cancer microenvironment correlates with ER-positive, Histological Grade I and low proliferation tumors [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 PO4-26-03.

Abstract PO1-15-03: Spatially resolved analysis of tumor microenvironment in invasive lobular carcinoma

Abstract Background: Invasive lobular carcinoma (ILC) is the second most common histological breast cancer subtype; however, little is known about its tumor microenvironment (TME). Here, we aimed to study ILC TME using spatial transcriptomics (ST). Methods: We performed ST (Visium 10x Genomics) on frozen tumor samples from 43 primary hormone receptor positive (HR+), HER2-negative (HER2-) ILCs. Of note, 9 samples were coming from patients who experienced disease relapse. Relative hematoxylin/eosin (H&amp;E) slides were morphologically annotated (QuPath software). xCell was used to perform cell type enrichment analysis on sample pseudo-bulks. After cross-samples integration, ST spots were clustered using hierarchical clustering (STutility R package). intNMF algorithm was used to perform clustering at the patient-level by combining: RNA sequencing information (relative percentage of spot-level clusters in each sample), morphology and level of colocalization between cancer cells and the other cell types of the tumor microenvironment. METABRIC (HR+, HER2- ILC samples, n = 122) was used as external validation cohort. Survival analyses (univariable and multivariable adjusting for clinicopathological features) were performed using Cox proportional hazard models. Results: The patient-level classification revealed four groups showing different biological characteristics. Differences in terms of morphology (annotation) and pathway enrichment analysis based on marker genes (GSEA) were observed between groups. This information allowed us to annotate our groups as: proliferative (P, n = 12, enriched in tumor cells and proliferation-related pathways), normal-stroma enriched (NSE, n = 10, enriched in fibroblasts and carcinoma in situ), metabolic (M, n = 9, enriched in metabolic-related pathways) and metabolic-immune enriched (MIE, n = 10, enriched in adipose tissue, metabolic and immune-related pathways). Interestingly, a significantly higher presence of macrophages M2 was found in MIE group. Using group-specific gene signatures, we were able to reproduce the same 4 groups in the METABRIC. Of note, we observed significant differences in relapse-free survival (RFS) in METABRIC (p = 0.03), with NSE presenting better outcome, while P, M and MIE presented worse prognosis. Analysing the area of contact between adipocytes and cancer cells typical of MIE group, we noticed an enrichment in metabolic and M2 macrophages-related pathways. In doing so, we derived a 28 gene signature relative to the tumor-adipocytes contact area. Importantly, our signature was highly expressed in M and MIE groups, and it was not correlated with proliferation-related signatures. Interestingly, the adipocytes-related signature was significantly associated with shorter RFS in ILC patients in METABRIC (univariable: HR 1.4, p = 0.023; multivariable: HR 1.6, p = 0.005). Since both proliferation and metabolism showed to be key processes in defining prognosis in ILC, we built a prognostic index by integrating our adipocytes-related signature with genomic grade index (GGI, a proliferation-related signature). Our index outperformed other existing prognostic signatures (e.g., Oncotype DX, MammaPrint, EndoPredict, LobSig) in assessing prognosis (RFS) in ILC in METABRIC (univariable: HR 1.7, p &amp;lt; 0.001; multivariable: HR 1.7, p = 0.002). Conclusions: We identified 4 biologically driven HR+, HER2- ILC groups describing tumor microenvironment heterogeneity. Of note, two of the three groups associated to worse disease outcome were related to metabolism, highlighting the importance of such process in ILC biology and in the future development of new treatment strategies. Moreover, the prognostic power of our index has the potential to refine the assessment of the risk of relapse in ILC. Further validation is warranted. Citation Format: Matteo Serra, Mattia Rediti, Laetitia Collet, Frédéric Lifrange, David Venet, Nicola Occelli, Xiaoxiao Wang, Delphine Vincent, Ghizlane Rouas, Ligia Craciun, Denis Larsimont, Laurence Buisseret, Miikka Vikkula, François Duhoux, Françoise Rothé, Christos Sotiriou. Spatially resolved analysis of tumor microenvironment in 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 PO1-15-03.

Single-cell analyzing of tumor microenvironment and cell adhesion between early and late-stage lung cancer

Lung adenocarcinoma (LUAD) is a highly heterogeneous disease that threaten human life with serious incidence and high mortality. High heterogeneity of tumor microenvironment (TME) was reported in multiple studies. However, the factor of controlling the tumor migration progression between eary and late-stage LUAD is still not fully understood. In this study, we conducted a comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data of LUAD obtained from the GEO database. The identification of cell clusters revealed significant expansion of epithelial cells in late-stage patients. Interpretation of the cell-cell communication results between early-stage and late-stage patient samples indicated that early tumor cells may interact with epithelial cells through the TGF-β pathway to promote tumor progression. The cell cycle analysis demonstrated a significant increase in the number of cells in the G2 and M phases in late-stage lung cancer. Further analysis using Non-negative Matrix Factorization (NMF) revealed early-stage cell-specific gene features involved in cell adhesion-related biological processes. Among these, the Tensin (TNS) gene family, particularly TNS1, showed high expression in epithelial cells and fibroblasts of early-stage samples, specifically associated with cell adhesion. Survival analysis using TCGA database for LUAD demonstrated that patients with high expression of TNS1 exhibited significantly higher overall survival rates compared to those with low expression. Immunofluorescence experiments have demonstrated co-expression of TNS1 with fibroblast and tumor cell markers (α-SMA and EPCAM). Immunohistochemistry experiments further validated the significantly higher expression levels of TNS1 in early-stage LUAD tissues compared to late-stage lung cancer tissues (P<0.05). Pathway experiments have shown that early-stage tumor patients with high expression of TNS1 exhibit stronger phosphorylation levels of Akt and mTOR, indicating a more potent activation of the Akt/mTOR signaling pathway. In conclusion, the results of this study demonstrate that TNS1 is an adhesive molecule in the immune microenvironment of early-stage tumor cells, and it may serve as a novel prognostic marker for lug cancer.

Establishment of Human Pituitary Neuroendocrine Tumor Derived Organoid and Its Pilot Application for Drug Screening.

Precision medicine for pituitary neuroendocrine tumors (PitNETs) is limited by the lack of reliable research models. To generate patient-derived organoids (PDOs), which could serve as a platform for personalized drug screening for PitNET patients. From July 2019 to May 2022, a total of 32 human PitNET specimens were collected for the establishment of organoids with an optimized culture protocol. This study was conducted at Sun Yat-Sen University Cancer Center. PitNET patients who were pathologically confirmed were enrolled in this study. Histological staining and whole-exome sequencing were utilized to confirm the pathologic and genomic features of PDOs. A drug response assay on PDOs was also performed. PDOs retained key genetic and morphological features of their parental tumors. PDOs were successfully established from various types of PitNET samples with an overall success rate of 87.5%. Clinical nonfunctioning PitNETs-derived organoids (22/23, 95.7%) showed a higher likelihood of successful generation compared to those from functioning PitNETs (6/9, 66.7%). Preservation of cellular structure, subtype-specific neuroendocrine profiles, mutational features, and tumor microenvironment heterogeneity from parental tumors was observed. A distinctive response profile in drug tests was observed among the organoids from patients with different subtypes of PitNETs. With the validation of key characteristics from parental tumors in histological, genomic, and microenvironment heterogeneity consistency assays, we demonstrated the predictive value of the PDOs in testing individual drugs. The established PDOs, retaining typical features of parental tumors, indicate a translational significance in innovating personalized treatment for refractory PitNETs.

Immune-tumor interaction dictates spatially directed evolution of esophageal squamous cell carcinoma

Abstract Esophageal squamous cell carcinoma (ESCC) is a poor-prognostic cancer type with extensive intra- and inter-patient heterogeneity in both genomic variations and tumor microenvironment (TME). However, the patterns and drivers of spatial genomic and microenvironmental heterogeneity of ESCC remain largely unknown. Here, we generated a spatial multi-omic atlas by whole-exome, transcriptome, and methylome sequencing of 507 tumor samples from 103 patients. We identified a novel tumor suppressor PREX2, accounting for 22% ESCCs with frequent somatic mutations or hyper-methylation, which promoted migration and invasion of ESCC cells in vitro. Analysis of the tumor microenvironment and quantification of subclonal expansion indicated that ESCCs undergo spatially directed evolution, where subclones mostly originated from the tumor center but had a biased clonal expansion to the upper direction of the esophagus. Interestingly, we found upper regions of ESCCs often underwent stronger immunoediting with increased selective fitness, suggesting more stringent immune selection. In addition, distinct TMEs were associated with variable genomic and clinical outcomes. Of which, hot TME was associated with high immune evasion and subclonal heterogeneity. We also found that immunoediting, instead of CD8+ T cell abundance, acts as an independent prognostic factor of ESCCs. Importantly, we found significant heterogeneity in previously considered potential therapeutic targets, as well as BRCAness characteristics in a subset of patients, emphasizing the importance of focusing on heterogeneity in ESCC targeted therapy. Collectively, these findings provide novel insights into the mechanisms of the spatial evolution of ESCC and inform precision therapeutic strategies.

A patient-specific lung cancer assembloid model with heterogeneous tumor microenvironments.

Cancer models play critical roles in basic cancer research and precision medicine. However, current in vitro cancer models are limited by their inability to mimic the three-dimensional architecture and heterogeneous tumor microenvironments (TME) of in vivo tumors. Here, we develop an innovative patient-specific lung cancer assembloid (LCA) model by using droplet microfluidic technology based on a microinjection strategy. This method enables precise manipulation of clinical microsamples and rapid generation of LCAs with good intra-batch consistency in size and cell composition by evenly encapsulating patient tumor-derived TME cells and lung cancer organoids inside microgels. LCAs recapitulate the inter- and intratumoral heterogeneity, TME cellular diversity, and genomic and transcriptomic landscape of their parental tumors. LCA model could reconstruct the functional heterogeneity of cancer-associated fibroblasts and reflect the influence of TME on drug responses compared to cancer organoids. Notably, LCAs accurately replicate the clinical outcomes of patients, suggesting the potential of the LCA model to predict personalized treatments. Collectively, our studies provide a valuable method for precisely fabricating cancer assembloids and a promising LCA model for cancer research and personalized medicine.

Abstract P20: Unveiling the Influence of Tumor Microenvironment and Spatial Heterogeneity on Temozolomide Resistance in Glioblastoma using an Advanced Human in vitro Model of the Blood-brain Barrier and Glioblastoma

Abstract Glioblastoma (GBM) is the most common primary malignant brain cancer in adults, with a dismal prognosis of 15 months. This is despite decades of research and improvements in surgery and radiotherapy. Temozolomide (TMZ) is the only first-in-line chemotherapeutic; however, resistance is frequent and multifactorial. While many molecular and genetic factors have been linked to TMZ resistance, the role of the solid tumor morphology and the tumor microenvironment (TME), particularly the blood-brain barrier (BBB), is unknown. Complex 3D in vitro models are able to recapitulate aspects of clinical disease such as the tumor microenvironment, such that research into the role of the TME and how it affects tumor biology and therapy resistance can be studied more easily and accurately. We have developed a complex 3D in vitro model for GBM and its surrounding BBB, that recapitulates important clinical features such as a dense tumor core with tumor cells that invade along the perivascular space; and a perfusable BBB with a physiological permeability with morphology that is altered in the presence of a tumor spheroid. We demonstrate that TMZ sensitivity decreases with increasing cancer cell spatial organization, and that the BBB can contribute to TMZ resistance. Proteomic analysis of cultured microtissues using low volume sample workflows revealed proteins linked to aggressive tumors and TMZ resistance. Notably, proteomic analysis revealed a potential role for cell cycle proteins in TMZ resistance. Preliminary data with tumor spheroids formed by patient derived GBM progenitor cells revealed a similar trend of an increase in proteins associated with tumor aggressiveness upon co-culture with a BBB. The data demonstrate the utility of combining complex physiological in vitro models and next generation mass spectrometry for interrogating the tumour microenvironment, tumor biology and accurate in vitro therapy validation. Citation Format: Maxine SY Lam, Joey JY Aw, Damien Tan, Ragavi Vijayakumar, Hui Yi Grace Lim, Swathi Yada, Qing You Pang, Nick Barker, Carol Tang, Beng Ti Ang, Radoslaw M. Sobota, Andrea Pavesi. Unveiling the Influence of Tumor Microenvironment and Spatial Heterogeneity on Temozolomide Resistance in Glioblastoma using an Advanced Human in vitro Model of the Blood-brain Barrier and Glioblastoma [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P20.

IFI35 regulates non-canonical NF-κB signaling to maintain glioblastoma stem cells and recruit tumor-associated macrophages.

Glioblastoma (GBM) is the most aggressive malignant primary brain tumor characterized by a highly heterogeneous and immunosuppressive tumor microenvironment (TME). The symbiotic interactions between glioblastoma stem cells (GSCs) and tumor-associated macrophages (TAM) in the TME are critical for tumor progression. Here, we identified that IFI35, a transcriptional regulatory factor, plays both cell-intrinsic and cell-extrinsic roles in maintaining GSCs and the immunosuppressive TME. IFI35 induced non-canonical NF-kB signaling through proteasomal processing of p105 to the DNA-binding transcription factor p50, which heterodimerizes with RELB (RELB/p50), and activated cell chemotaxis in a cell-autonomous manner. Further, IFI35 induced recruitment and maintenance of M2-like TAMs in TME in a paracrine manner. Targeting IFI35 effectively suppressed in vivo tumor growth and prolonged survival of orthotopic xenograft-bearing mice. Collectively, these findings reveal the tumor-promoting functions of IFI35 and suggest that targeting IFI35 or its downstream effectors may provide effective approaches to improve GBM treatment.

Abstract LB120: Stem cell-derived ACUA CAR-NKT allogeneic cell therapies harness orthogonal mechanisms of action for the treatment of solid tumors

Abstract Despite the remarkable success of chimeric antigen receptor T cell (CAR-T) therapies in treating hematological malignancies, their use in solid tumor indications has been met with distinct challenges. The immunosuppressive tumor microenvironment (TME) and tumor antigen heterogeneity are two significant factors that may adversely impact the efficacy of these ground-breaking therapies. Engineered natural killer T (NKT) cells harbor biological features distinct from CAR-T cell or CAR-NK cell therapies and thus represent a novel therapeutic approach to deploy “off the shelf” to patients. Appia Bio’s ACUA platform is capable of generating large quantities of highly pure CAR-NKT cells from cord blood CD34+ hematopoietic stem and progenitor cells (HSPCs) for clinical use. We have demonstrated that multiple distinct mechanisms of anti-tumor activity are operative in CAR-NKT cells, including CAR-mediated killing, NK receptor-mediated killing, adjuvant effects on antigen-specific T cells and immature dendritic cells, as well as the ability to deplete of pro-tumorigenic macrophages.When transduced with a CAR, ACUA-derived CAR-NKT cells show potent CAR-mediated tumor cell killing, similar to CAR-T cells. In contrast to conventional T cells, CAR-NKT cells also demonstrate potent CAR antigen-independent tumor cell killing, mediated partially through NKG2D and DNAM-1 activating NK receptors. CAR-NKT cells, but not conventional CAR-T cells, enhanced the function of NY-ESO specific T cells in controlling multiple rounds of tumor rechallenge, through the secretion of soluble components. In regard to other adjuvant effects which may provide additional benefit in vivo, ACUA-derived NKT cells enhanced the activation of peripheral blood monocyte-derived dendritic cells, which can regulate beneficial Th1 type immune responses. To explore how ACUA-derived NKT cells could reshape the TME, we generated immunosuppressive M2-polarized macrophages and observed that ACUA-derived NKT cells can either kill or reprogram these cells to a pro-inflammatory phenotype following co-culture. Notably, these immune cell adjuvant mechanisms are specific to ACUA-derived NKT cells and are not observed with conventional CAR-T cells. In summary, these mechanistic studies underscore the advantages of ACUA-derived NKT cells over conventional CAR-T cells and highlight their potential as a promising differentiated allogeneic therapy for the treatment of solid tumors. Citation Format: Xi Wang, Andrew J. Hou, Michael A. Christopher, Yannick Bulliard, Jason S. Damiano. Stem cell-derived ACUA CAR-NKT allogeneic cell therapies harness orthogonal mechanisms of action for the treatment of solid tumors [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 LB120.

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.

Ultrasound-Activated Piezoelectric Nanoparticles Trigger Microglia Activity Against Glioblastoma Cells.

Glioblastoma multiforme (GBM) is the most aggressive brain cancer, characterized by a rapid and drug-resistant progression. GBM "builds" around its primary core a genetically heterogeneous tumor-microenvironment (TME), recruiting surrounding healthy brain cells by releasing various intercellular signals. Glioma-associated microglia (GAM) represent the largest population of collaborating cells, which, in the TME, usually exhibit the anti-inflammatory M2 phenotype, thus promoting an immunosuppressing environment that helps tumor growth. Conversely, "classically activated" M1 microglia could provide proinflammatory and antitumorigenic activity, expected to exert a beneficial effect in defeating glioblastoma. In this work, an immunotherapy approach based on proinflammatory modulation of the GAM phenotype is proposed, through a controlled and localized electrical stimulation. The developed strategy relies on the wireless ultrasonic excitation of polymeric piezoelectric nanoparticles coated with GBM cell membrane extracts, to exploit homotypic targeting in antiglioma applications. Such camouflaged nanotransducers locally generate electrical cues on GAM membranes, activating their M1 phenotype and ultimately triggering a promising anticancer activity. Collected findings open new perspectives in the modulation of immune cell activities through "smart" nanomaterials and, more specifically, provide an innovative auspicious tool in glioma immunotherapy.

M6A-mediated molecular patterns and tumor microenvironment infiltration characterization in nasopharyngeal carcinoma

ABSTRACT N6-methyladenosine (m6A) is the most predominant RNA epigenetic regulation in eukaryotic cells. Numerous evidence revealed that m6A modification exerts a crucial role in the regulation of tumor microenvironment (TME) cell infiltration in several tumors. Nevertheless, the potential role and mechanism of m6A modification in nasopharyngeal carcinoma (NPC) remains unknown. mRNA expression data and clinical information from GSE102349, and GSE53819 datasets obtained from Gene Expression Omnibus (GEO) was used for differential gene expression and subsequent analysis. Consensus clustering was used to identify m6A-related molecular patterns of 88 NPC samples based on prognostic m6A regulators using Univariate Cox analysis. The TME cell-infiltrating characteristics of each m6A-related subclass were explored using single-sample gene set enrichment (ssGSEA) algorithm and CIBERSORT algotithm. DEGs between two m6A-related subclasses were screened using edgeR package. The prognostic signature and predicated nomogram were constructed based on the m6A-related DEGs. The cell infiltration and expression of prognostic signature in NPC was determined using immunohistochemistry (IHC) analysis. Chi-square test was used to analysis the significance of difference of the categorical variables. And survival analysis was performed using Kaplan–Meier plots and log-rank tests. The NPC samples were divided into two m6A-related subclasses. The TME cell-infiltrating characteristics analyses indicated that cluster 1 is characterized by immune-related and metabolism pathways activation, better response to anit-PD1 and anti-CTLA4 treatment and chemotherapy. And cluster 2 is characterized by stromal activation, low expression of HLA family and immune checkpoints, and a worse response to anti-PD1 and anti-CTLA4 treatment and chemotherapy. Furthermore, we identified 1558 DEGs between two m6A-related subclasses and constructed prognostic signatures to predicate the progression-free survival (PFS) for NPC patients. Compared to non-tumor samples, REEP2, TMSB15A, DSEL, and ID4 were upregulated in NPC samples. High expression of REEP2 and TMSB15A showed poor survival in NPC patients. The interaction between REEP2, TMSB15A, DSEL, ID4, and m6A regulators was detected. Our finding indicated that m6A modification plays an important role in the regulation of TME heterogeneity and complexity.

Abstract 5066: Integrating spatial and single cell transcriptomics to identify and characterise biologically driven subgroups in invasive lobular carcinoma

Abstract Background: Invasive lobular carcinoma (ILC) is the second most common histological breast cancer subtype; however, little is known about its tumor microenvironment (TME). Here, we aimed to identify and to characterise ILC subgroups based on TME heterogeneity by combining spatial transcriptomics (ST) and single cell RNA-sequencing (scRNA-seq). Methods: We performed ST (Visium 10x Genomics) on frozen tumor samples from 41 primary hormone receptor positive (HR+), HER2-negative (HER2-) ILCs. Information coming from the morphological annotation of ST slides and the gene expression-based clustering of ST spots was integrated and used as input for a patient level classification (using intNMF algorithm). Subgroups of patients were annotated using morphology, gene set enrichment analysis and ST deconvolution based on scRNA-seq data (CARD software). SCAN-B bulk RNA-seq dataset was used as validation set (HR+, HER2- ILC samples, n = 853). Results: Four subgroups of patients were identified: proliferative (P, n = 12, enriched in tumor cells and proliferation-related pathways), normal-stroma enriched (NSE, n = 10, enriched in fibroblasts, stroma-related pathways and showing an higher level of colocalization between invasive and in situ carcinoma), metabolic (M, n = 9, enriched in metabolic related-pathways) and metabolic-immune enriched (MIE, n = 10, enriched in adipocytes, metabolic and immune-related pathways). Deconvolution of ST spots with single cell data revealed matching results with morphology (in terms of cell types). We observed an enrichment of myofibroblasts in NSE, an enrichment of cancer epithelial in P and, interestingly, an enrichment of endothelial cells in M subgroup (fdr &amp;lt; 0.25). MIE was characterized by perivascular like-endothelial cells and myeloid cells (monocytes and macrophages, fdr &amp;lt; 0.25). Of note, at the level of the ST slide, myeloid cells in MIE subgroup were more present in the adipose tissue compartment (fdr &amp;lt; 0.25) compared to the rest of the slide. NSE subgroup showed a trend for association with better prognosis. Gene signature-based assignment identified the same four subgroups in SCAN-B: GSEA analysis and comparisons among the four subgroups showed matching results between our dataset and SCAN-B. Of note, in SCAN-B, we observed differences in prognosis among the subgroups, with NSE showing better, M and MIE intermediate and P worse prognosis for overall survival and relapse free interval (p &amp;lt; 0.01). Conclusions: Four subgroups of ILC based on TME heterogeneity and showing differences in prognosis were identified and validated in external cohort. Of note, two of these groups were related to metabolism, highlighting the value of such process in ILC biology. Importantly, myeloid cells were the predominant immune cell types in ILC, and they showed a specific compartmentalization inside the TME. Further validation is needed. Citation Format: Matteo Serra, Andreas Papagiannis, Mattia Rediti, Frederic Lifrange, Nicola Occelli, Laetitia Collet, Delphine Vincent, Ghizlane Rouas, Danai Fimereli, Ligia Craciun, Denis Larsimont, David Venet, Miikka Vikkula, Francois P. Duhoux, Françoise Rothé, Christos Sotiriou. Integrating spatial and single cell transcriptomics to identify and characterise biologically driven subgroups in invasive lobular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5066.

Abstract 2329: Cancer region definition using spatial gene expression patterns by super resolution reconstruction algorithm for spatial transcriptomics data

Abstract Background: Spatially resolved transcriptomics (ST) has enabled a variety of cancer research on the heterogeneity of tumor microenvironment. However, when identifying cancer boundaries based on ST, the limited resolution of ST such as Visium hinders the accurate identification of cancerous regions. To address these issues, we have developed a novel approach for accurately classifying regions of the tumor by using deep image prior (DIP) algorithm to convert Visium data into high-resolution images. Methods: We proposed SuperST, which utilizes deep image prior to transform low-resolution ST data into high-resolution images, providing more accurate spatial marker expression patterns. This method was used to define cancer regions by combining with a pre-trained deep learning algorithm that recognizes spatial patterns. ST data of hepatocellular carcinoma (HCC) patients were used to apply SuperST. Firstly, 400 spatially variable genes were identified. SuperST leverages the deep image prior (DIP) algorithm and a U-Net neural network to transform ST data into high-resolution ones without needing ground-truth high-resolution data. The final high-resolution images of gene expression data were used to extract 512-dimensional feature vectors for each gene via a pre-trained convolutional neural network model (VGG16). Subsequently, we apply K-means clustering to these features to identify transcriptionally distinct tissue regions considering spatial information and compared with the human-labeled cancer regions. Results: The cancer region detection using spatial gene expression data and our algorithm yielded results that substantially align with the human annotations on the H&amp;E images. Furthermore, compared to the cancer definition obtained using the CopyKat and SPACET algorithms, our results were more spatially consistent for cancerous regions as well as providing high-resolution image-level definition of cancer region margin. This indicates that our algorithm better utilizes spatial information and more effectively overcomes the low resolution of ST data. Conclusion: This research demonstrates the effective application of our SuperST algorithm to the challenging problem of cancer region detection only using spatial gene expression data. It suggests that our algorithm could be utilized for a variety of problems in spatial biology in cancer research. Citation Format: Jeongbin Park, Seungho Cook, Dongjoo Lee, Jinyeong Choi, Seongjin Yoo, Hyung-Jun Im, Daeseung Lee, Hongyoon Choi. Cancer region definition using spatial gene expression patterns by super resolution reconstruction algorithm for spatial transcriptomics data [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 2329.

Abstract 1161: Deep spatial proteomics: A new approach for obtaining insight into the glioblastoma microenvironment

Abstract Glioblastoma is the most frequent and malignant brain tumor, with a median survival of approximately 14 months. The tumor microenvironment is composed of different cell types and is known to support the glioblastoma cells and contribute to poor patient prognosis. Since accumulating studies report discordance between mRNA and protein abundances both on population and single-cell levels, and since proteins are the effector molecules of the cells and the actual target molecules of most drugs, our aim is to dissect the tumor microenvironment by spatial LC-MS based proteomics. Glioblastoma formalin-fixed paraffin-embedded (FFPE) tissue was sectioned and placed on membrane glass slides suitable for laser capture microdissection (LCM), followed by precise isolation of single cells from the heterogeneous tumor microenvironment. We used the Zeiss Palm MicroBeam laser microdissection instrument, combined with an Orbitrap Eclipse Tribrid Mass Spectrometer running our in-house sensitivity tailored Data Independent Acquisition method (WISH-DIA) to analyze and quantify the proteomes. We obtained up to 3000 proteins out of only 100 cells and were able to demonstrate the feasibility of collecting and analyzing various cell types at the single-cell level. The proteins identified belonged to many different classes/families of proteins and were able to distinguish the different cell types. In conclusion, this on-going study demonstrates our ability to isolate single cells from the highly heterogeneous tumor microenvironment of glioblastoma with ultra-high sensitivity, thereby allowing the characterization of the proteome of the glioblastoma microenvironment followed by identification of potential therapeutic and/or diagnostic targets. Citation Format: Kartikey Saxena, Rune Daucke, Vilde Pedersen, Pia Helene Klausen, Erwin Schoof, Bjarne Winther Kristensen. Deep spatial proteomics: A new approach for obtaining insight into the glioblastoma microenvironment [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 1161.

Abstract 1621: Integrated analysis of genetic, transcriptional and TME evolution of ccRCC: TRACERx Renal

Abstract Background: Genetic evolution of clear cell renal cell carcinoma (ccRCC) follows distinct trajectories, with varying levels of intratumor heterogeneity (ITH) and chromosomal complexity (WGII). While these patterns associate with clinical outcomes, it remains unknown whether they fully reconcile tumor behavior and how genetic and transcriptional features co-evolve in relation to the tumor microenvironment (TME). Methods: To analyze the patterns of transcriptional and TME heterogeneity, we performed bulk whole-transcriptome sequencing on 244 samples, including 22 metastatic and 12 tumor-adjacent normal samples, from 79 ccRCC patients recruited to the TRACERx Renal study. We integrated transcriptional data with previously published genetic, phylogenetic, spatial and clinical information. Results: Transcriptional distances between paired samples from the same primary tumor mirrored but were not fully determined by genetic distance (p-value &amp;lt; 0.001); and increased from primary-primary to primary-metastasis and primary-normal pairs. Within primary-metastasis pairs, metastasis-seeding primary tumor regions were transcriptionally closest to their matched metastasis (p-value &amp;lt; 0.001), suggesting that an important fraction of metastatic transcriptional traits were acquired in the primary tumor. Regarding the tumor clonal structure, transcriptional evolution followed a conserved path through increasing cell proliferation and oxidative phosphorylation and downregulating DNA repair from earlier to later clones. Further, within tumors with increasing WGII we observed upregulation and downregulation of repressors and downstream effectors, respectively, of the canonical cGAS-STING pathway. Combining the presence of this transcriptional pattern with WGII predicted shorter PFS in TRACERx Renal (p-value &amp;lt; 0.001) and in TCGA-KIRC (p-value &amp;lt; 0.001). Clonal evolution was also linked to changes in TME, with each of the previously defined genetic evolutionary trajectories associated to a specific TME (p-value &amp;lt; 0.001). For example, ccRCCs on a PBRM1-SETD2 trajectory demonstrated increased infiltration of cytotoxic immune cells. TME ITH was pervasive and associated with shorter PFS (p-value = 0.03). A recurrent trend from earlier to later clones was progressive T cell depletion (p-value &amp;lt; 0.001). The evolution of the TCR repertoire mirrored the tumor clonal structure (p-value = 0.002), suggesting the thus far elusive antigenic source in ccRCC is heritable. Accordingly, the TCR repertoire in metastasis-seeding primary tumor regions resembled the closest the TCR repertoire of matched metastasis (p-value = 0.06). Conclusion: Integrated analysis of genetic and transcriptional data in TRACERx Renal showed i) transcriptional and TME ITH not fully recapitulated by genetic ITH, ii) conserved paths of transcriptional and TME evolution and iii) a heritable nature of part of the ccRCC antigen source. Citation Format: Ángel Fernández Sanromán, Lewis Au, Benjy Jek Yang Tan, Charlotte Spencer, Anne-Laure Catin, Irene Lobon, Husayn Pallikonda, Kevin Litchfield, Fiona Byrne, James Larkin, Annika Fendler, Samra Turajlic. Integrated analysis of genetic, transcriptional and TME evolution of ccRCC: TRACERx Renal [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 1621.

Abstract 3423: Spatial-resolved single-cell analysis of the tumor microenvironment in Alaska Native colorectal cancer patients

Abstract Tribal Health Organizations recognize the high rates of colorectal cancer (CRC) among Alaska Native peoples and are undertaking initiatives to address it. The tumor microenvironment (TME) is a complex ecosystem including tumor, stromal, and immune cells. Understanding the cell composition and spatial organization of the TME among Alaska Native patients with CRC will provide new insights into disease progression. We performed spatial profiling on 3 tissue microarrays (TMA) from 37 patients using the Akoya Biosciences’ s PhenoCycler system. Patients with CRC were selected from a nested case-control study which includes 16 patients who died of CRC and 21 patients who lived as long as patients with lethal CRC and matched on age at diagnosis, sex, tumor site and tumor stage. We designed a 40-antibody panel that captured tumor, epithelium, stromal, vascular, and immune cells, as well as cell functional states (e.g., PD1). Initial images were processed using QuPath for image stitching, artifact removal, background subtraction, cell segmentation, and measurement of average intensity of each marker per cell. Cell quality control (QC) was performed to filter out low quality cells based on cell size and log10-transformed sum of intensity. After quantile normalization and arcsinh transformation, cells that passed QC were clustered using the R package ‘Seurat’ and manually annotated based on the cell type and function marker intensities for each cluster. We identified 1.17 million cells and 15 cell types. Those cell types included 3 stromal and vascular cells, 1 epithelium, 1 mixed immune cluster, and 10 different immune cells. We quantified each cell type as a fraction of total cells on a per-patient basis. Among the subset of immune cells, the proportions of macrophages, CD4+T cells, and CD8+T cells were high (21%, 11.2%, and 14.5%, respectively), and the proportion of B cells was low (5.1%). We also identified regulatory T cells, cytotoxic CD8+T cells, and monocytes at 1-3%. We observed differences in the composition of cell clusters by CRC-specific death. The frequency of epithelium was higher among patients with lethal CRC, and the frequency of CD4+T cells and CD8+T cells were higher among patients without lethal CRC. In summary, the overall composition of tumor and immune cells varies between patients with and without CRC-specific death, indicating TME heterogeneity. Further investigation of spatial domains and relationships with clinical molecular features may facilitate discovery of novel predictors of CRC-specific death among Alaska Native peoples. We are clustering cell types into different cellular neighborhoods using spatial information and conducting statistical analysis integrating RNA sequencing data from the same patients. Further, we are generating spatial profiling data for an additional 5 TMAs comprising 60 additional patients and will present results of the combined data analyses. Citation Format: Hang Yin, Diana Redwood, Kimberly Smythe, Daniel Jones, McGarry Houghton, Kevin C. Barry, Amanda L. Koehne, Elizabeth Donato, Cecilia Yeung, Mingang Lin, James J. Tiesinga, Tabitha A. Harrison, Sushma S. Thomas, Li Hsu, Jane C. Figueiredo, Li Li, Timothy K. Thomas, Christopher Li, Ulrike Peters, Jeroen R. Huyghe. Spatial-resolved single-cell analysis of the tumor microenvironment in Alaska Native colorectal cancer patients [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 3423.

Abstract 5481: Spatial multiplex proteins profiling of tumor microenvironment to decipher de novo metastasis in nasopharyngeal carcinoma

Abstract Background: Nasopharyngeal carcinoma (NPC) has a high incidence rate in Southeast Asia, where more than 90% of NPC cases are associated with Epstein-Barr virus (EBV). Although NPC patients are generally sensitive to radiotherapy and chemotherapy, about 20-30% of patients have treatment failure due to the development of distant metastasis, i.e. de novo metastasis. In this study, we utilized GeoMx Digital Spatial Profiler (DSP), a multiplex and high-throughput sequencing platform designed for spatially- and morphologically resolved protein expression profiling to investigate the intra-tumoral and inter-tumoral heterogeneity of tumor microenvironment (TME) and identify the mechanisms underlying distant failure in NPC. Methods: We utilized GeoMx Protein Assays consisting of 147 proteins from 15 functional modules to compare the difference between NPC cases developing distant failure (DF) (n=21) and stage-, age-, gender- and treatment-matched NPC cases without developing distant failure (noDF) (n=15) within five years of treatment. Two fluorescent morphological markers including PanCK and CD45, were used for delineating the structural architecture of the tissues, identifying cell types, and selecting regions of interest (ROIs), followed by sequencing for protein expression quantification. NanoString GeoMx DSP Analysis Suite was used for quality control and data normalization. The NPC TME heterogeneity was further investigated by the 10X Visum platform for spatial transcriptome. The public single-cell RNA sequencing (scRNA-Seq) and bulk RNA sequencing datasets were obtained for the downstream analyses. Results: By analyzing protein expression profiles, the study revealed the intratumoral and intertumoral heterogeneity associated with EBV latent infection in the TME of NPC. Regions with high EBV latent gene expression exhibited a noticeable reduction in T cell infiltration, indicating an "immune cold" phenotype, whereas regions with low EBV latent gene expression displayed heavy T cell infiltration, indicating an "immune hot" phenotype. Moreover, the study identified a panel of autophagic and anti-apoptotic proteins that were significantly upregulated in the cancer cells of NPC cases developing distant failure after treatment. This observation, supported by the single-cell RNA sequencing datasets, further implicated EBV latent infection for the upregulation of autophagic and anti-apoptotic genes. Notably, the study also demonstrated that the genes upregulated in the immune cells in NPC cases with distant failure were associated with poor progression-free survival in an independent NPC cohort. Conclusions: These findings shed light on the crucial role of EBV latent infection in TME heterogeneity and highlight the potential involvement of autophagy and anti-apoptosis in de novo metastasis in NPC. Citation Format: Larry Ka-Yue Chow, Ying Wang, Lanqi Gong, Siqi Xu, Zhaozheng Hou, Xinyuan Guan, Dora Lai-Wan Kwong, Victor Ho-Fun Lee, Wai To ng, Anne Wing-Mui Lee, Zhonghua Liu, Yunfei Xia, Wei Dai. Spatial multiplex proteins profiling of tumor microenvironment to decipher de novo metastasis in nasopharyngeal carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5481.

Abstract 2777: A unique sub-population of PDGFRB+ glioma-associated mesenchymal stem cells constitute the perivascular niche in murine glioblastoma

Abstract Microvascular proliferation (MVP) and spatial heterogeneity are defining histological features of glioblastoma (GBM). Using ex vivo culture method to analyze human GBMs we have previously identified mesenchymal stem cell (MSC)-like cells, called glioma-associated-MSCs (GA-MSCs) in the heterogenous tumor-microenvironment (TME) of GBMs. Like pericytes, fibroblastic cells and smooth muscle cells (SMCs), these GA-MSCs reside in the perivascular niche (PVN) within GBM, and their presence correlates with MVP, decrease in immune cell counts, and poor prognosis. However, the impact of GA-MSCs on the TME remains correlative, and to date, a causative role of GA-MSCs in MVP and immune evasion has not been established in vivo. We hypothesize that recruited GA-MSCs promote MVP and enhance immune evasion in the GBM-TME. To begin to address this hypothesis, we first identified and characterized GA-MSCs at single-cell resolution in an immunocompetent murine GBM model. We sorted tumor, immune, and non-tumor-non-immune (NTNI) cells from GFP-labelled GSC005 tumors in C57Bl/6 mice and performed scRNA-seq. Tumor cells (GFP+ cells) clustered together into 13 distinct cell states. Of the immune cells (CD45+), the most abundant cell types were microglia (33.9%), macrophages (31.9%), T cells (10.3%), dendritic cells (7.5%) and B cells (1.4%), mimicking human GBM. In the NTNI compartment, we identified 17 clusters, which mapped to endothelial cells, oligodendrocytes, astrocytes and other cell types. Interestingly, there were 4 distinct cell clusters that exclusively expressed platelet-derived growth factor receptor beta (PDGFRB). Three of these clusters mapped to pericytes, SMCs and fibroblastic cells. The fourth cluster expressed both adipose tissue and bone marrow MSC markers, identifying for the first time, GA-MSCs in murine GBM. Next, to define the spatial organization of GA-MSCs and their relationship to immune cells in the GBM TME, we conducted spatial proteomics. GA-MSC counts were significantly higher in the PVN compared with the avascular niche (AN) (p= 0.009) and GA-MSCs were absent in contralateral non-tumor regions. Interestingly, GA-MSCs co-localized with SMCs, fibroblastic cells and pericytes as well as pro-tumorigenic macrophages/microglia (Iba1+/CD163+) in the PVN indicating potential interactions between these cell populations. In conclusion, we have identified for the first time, MSC-like cells in the PVN of murine GBM. Our results suggest that these GA-MSCs are recruited to the TME where they may promote MVP, a hallmark of GBM, and interact with immunosuppressive immune cells. Building on these observations, future experiments will investigate the extent to which GA-MSCs drive MVP formation and immune evasion in GBM. Citation Format: Mohammad F. Zaman, Sanjay K. Singh, Anwar Hossain, Lynette M. Phillips, Joy Gumin, Daniel Ledbetter, Bojana Milutinovic. A unique sub-population of PDGFRB+ glioma-associated mesenchymal stem cells constitute the perivascular niche in murine glioblastoma [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 2777.