Cell signaling activation and extracellular matrix remodeling underpin glioma tumor microenvironment heterogeneity and organization

Dinevska, Marija; Widodo, Samuel S; Furst, Liam; Cuzcano, Lucero; Fang, Y.; Mangiola, Stefano; Neeson, Paul J.; Darcy, Phillip K.; Ramsay, Robert G.; Hutchinson, Ryan; Mackay, Fabienne; Christie, Michael; Stylli, Stanley S.; Mantamadiotis, Theo

doi:10.1007/s13402-022-00763-9

Cited by 10 publications

(6 citation statements)

References 61 publications

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“…Tumor-instructed stromal cells have differential effects on tumor cell proliferation and migration in vitro, indicating a reciprocal crosstalk between neoplastic and non-neoplastic cells, such as EC (Bougnaud et al, 2016;Testa et al, 2022). The composition of ECM and its stored-signaling factors also influence the behavior of GBM cells and GBM stromal cells, affecting GBM angiogenesis, infiltration and aggressiveness (Alves et al, 2011a;Dinevska et al, 2023;Marino et al, 2023). Understanding the complex interplay between the ECM, GBM cells and EC in the tumor angiogenesis and aggressiveness is crucial for finding new biomarkers to improve patient outcomes and for the development of targeted therapies.…”

Section: Discussionmentioning

confidence: 99%

Identification of established and novel extracellular matrix components in glioblastoma as targets for angiogenesis and prognosis

Barbosa,

Machado,

Heringer

et al. 2024

Preprint

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Glioblastomas (GBM) are aggressive tumors, characterized by heterogeneity, high proliferation and infiltration, resistance to treatments, and abundant vasculature. Angiogenesis, the formation of new vessels from pre-existing ones, involves endothelial cells (EC) migrating and proliferating to form new tubes. Various extracellular matrix (ECM) molecules can modulate EC survival, migration, and proliferation. In this study, we cultured human brain EC (HBMEC) on GBM-derived ECM and found that GBM ECM inhibited EC proliferation and modulated the branching process (average vessel length, number of junctions, and vessel endpoints) compared to the HBMEC ECM control. Through in silico analysis, we investigated whether GBM influences the expression of ECM molecules in EC and which of these molecules directly impact the overall survival (OS) of GBM patients. We observed increased expression of collagen alpha chains (COL5A3, 6A1, 22A1, and 27A1), laminin alpha and beta chains (LAMA1 and B1), fibulins (FBLN1 and 2), metalloproteinase (MMP)-9, hyaluronan synthase (HAS)1, integrin alpha chain (ITGA)3, transforming growth factor beta-1 (TGFB1), and wingless-related integration site (Wnt)-9B in EC influenced by GBM compared to normal brain EC. Furthermore, our study demonstrated that the expression of these ECM molecules, along with neurocan (NCAN), directly influenced the OS of GBM patients. In conclusion, this study identifies both established and novel ECM molecules that can regulate GBM angiogenesis and highlights NCAN, COL22A1, and COL27A1 as potential new biomarkers for GBM prognosis.

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Section: Discussionmentioning

confidence: 99%

Identification of established and novel extracellular matrix components in glioblastoma as targets for angiogenesis and prognosis

Barbosa,

Machado,

Heringer

et al. 2024

Preprint

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“…In addition to attraction and retention of tumor-promoting myeloid cells, CAFs are known to affect antitumor immune responses indirectly by production and remodeling of ECM components, which serves as a physical barrier restricting access of immune cells to cancer cells. Indeed, a recent immunohistochemical analysis of GBM tissue showed an enrichment of T cells in ECM-rich zones (Dinevska et al 2022), suggesting that T cell trafficking to the tumor is impeded due to pathologically high deposition of ECM components by stromal cells in the perivascular niche. This has important implications for adoptive cell-based therapies, such as CAR-T and CAR-NK cell therapies.…”

Section: Discussionmentioning

confidence: 99%

“…Glioblastoma (GBM) tissue, unlike that of low-grade gliomas (LGGs), has been shown to display extensive and heterogeneous ECM deposition and tissue remodeling (Dinevska et al 2022). Thus, we began our analysis with an unbiased characterization of the ECM transcriptome in GBM (Figure 1A).…”

Section: Unsupervised Analysis Of Extracellular Matrix Expression In ...mentioning

confidence: 99%

Pan-cancer transcriptomic analysis of CNS tumor stroma identifies a population of perivascular fibroblasts that predict poor immunotherapy response in glioblastoma patients

Zarodniuk

Steele

Lü

et al. 2023

Preprint

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Excessive deposition of extracellular matrix (ECM) is a hallmark of solid tumors; however, it remains poorly understood which cellular and molecular components contribute to the formation of ECM stroma in central nervous system (CNS) tumors. Here, we undertook a pan-CNS analysis of retrospective gene expression datasets to characterize inter- and intra-tumoral heterogeneity of ECM remodeling signatures in both adult and pediatric CNS disease. We found that CNS lesions – glioblastoma in particular – can be divided into two ECM-based subtypes (ECMhi and ECMlo) that are influenced by the presence of perivascular cells resembling cancer-associated fibroblasts (CAFs). We show that perivascular fibroblasts activate chemoattractant signaling pathways to recruit tumor-associated macrophages, and promote an immune-evasive, stem-like cancer cell phenotype. Our analysis reveals that perivascular fibroblasts are correlated with unfavorable response to immune checkpoint blockade in glioblastoma and poor patient survival across a subset of CNS tumors. We provide insights into novel stroma-driven mechanisms underlying immune evasion and immunotherapy resistance in CNS tumors like glioblastoma, and discuss how targeting these perivascular fibroblasts may prove an effective approach to improving treatment response and patient survival in a variety of CNS tumors.

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“…Tumor invasion and metastasis depend on various components within the Tumor Microenvironment (TME) [ 7 , 8 ]. Recent studies have highlighted the role of neurons and axons as components of the TME in tumor invasion and metastasis, with increasing attention on the impact of nerves on other cells within the TME [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A prediction nomogram for perineural invasion in colorectal cancer patients: a retrospective study

Que,

Wu,

et al. 2024

BMC Surg

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Background Perineural invasion (PNI), as the fifth recognized pathway for the spread and metastasis of colorectal cancer (CRC), has increasingly garnered widespread attention. The preoperative identification of whether colorectal cancer (CRC) patients exhibit PNI can assist clinical practitioners in enhancing preoperative decision-making, including determining the necessity of neoadjuvant therapy and the appropriateness of surgical resection. The primary objective of this study is to construct and validate a preoperative predictive model for assessing the risk of perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). Materials and methods A total of 335 patients diagnosed with colorectal cancer (CRC) at a single medical center were subject to random allocation, with 221 individuals assigned to a training dataset and 114 to a validation dataset, maintaining a ratio of 2:1. Comprehensive preoperative clinical and pathological data were meticulously gathered for analysis. Initial exploration involved conducting univariate logistic regression analysis, with subsequent inclusion of variables demonstrating a significance level of p < 0.05 into the multivariate logistic regression analysis, aiming to ascertain independent predictive factors, all while maintaining a p-value threshold of less than 0.05. From the culmination of these factors, a nomogram was meticulously devised. Rigorous evaluation of this nomogram's precision and reliability encompassed Receiver Operating Characteristic (ROC) curve analysis, calibration curve assessment, and Decision Curve Analysis (DCA). The robustness and accuracy were further fortified through application of the bootstrap method, which entailed 1000 independent dataset samplings to perform discrimination and calibration procedures. Results The results of multivariate logistic regression analysis unveiled independent risk factors for perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). These factors included tumor histological differentiation (grade) (OR = 0.15, 95% CI = 0.03–0.74, p = 0.02), primary tumor location (OR = 2.49, 95% CI = 1.21–5.12, p = 0.013), gross tumor type (OR = 0.42, 95% CI = 0.22–0.81, p = 0.01), N staging in CT (OR = 3.44, 95% CI = 1.74–6.80, p < 0.001), carcinoembryonic antigen (CEA) level (OR = 3.13, 95% CI = 1.60–6.13, p = 0.001), and platelet-to-lymphocyte ratio (PLR) (OR = 2.07, 95% CI = 1.08–3.96, p = 0.028).These findings formed the basis for constructing a predictive nomogram, which exhibited an impressive area under the receiver operating characteristic (ROC) curve (AUC) of 0.772 (95% CI, 0.712–0.833). The Hosmer–Lemeshow test confirmed the model's excellent fit (p = 0.47), and the calibration curve demonstrated consistent performance. Furthermore, decision curve analysis (DCA) underscored a substantial net benefit across the risk range of 13% to 85%, reaffirming the nomogram's reliability through rigorous internal validation. Conclusion We have formulated a highly reliable nomogram that provides valuable assistance to clinical practitioners in preoperatively assessing the likelihood of perineural invasion (PNI) among colorectal cancer (CRC) patients. This tool holds significant potential in offering guidance for treatment strategy formulation.

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Cell signaling activation and extracellular matrix remodeling underpin glioma tumor microenvironment heterogeneity and organization

Cited by 10 publications

References 61 publications

Identification of established and novel extracellular matrix components in glioblastoma as targets for angiogenesis and prognosis

Identification of established and novel extracellular matrix components in glioblastoma as targets for angiogenesis and prognosis

Pan-cancer transcriptomic analysis of CNS tumor stroma identifies a population of perivascular fibroblasts that predict poor immunotherapy response in glioblastoma patients

A prediction nomogram for perineural invasion in colorectal cancer patients: a retrospective study

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