Immune Remodeling of the Extracellular Matrix Drives Loss of Cancer Stem Cells and Tumor Rejection

Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding of cancer and tumor heterogeneity, clonal evolution, and the complex interplay between cancer cells and tumor microenvironment. SCS provides high-resolution profiling of individual cells in genomic, transcriptomic, and epigenomic landscapes, facilitating the detection of rare mutations, the characterization of cellular diversity, and the integration of molecular data with phenotypic traits. The integration of SCS with multi-omics has provided a multidimensional view of cellular states and regulatory mechanisms in cancer, uncovering novel regulatory mechanisms and therapeutic targets. Advances in computational tools, artificial intelligence (AI), and machine learning have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and the development of predictive models for patient stratification. Furthermore, there have been emerging technologies such as spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor microenvironment organization and cellular interactions. As SCS and its related technologies continue to advance, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

CNOT6L deadenylase suppresses cardiac remodeling in heart failure through downregulation of tenascin-C mRNA

Heart failure is rapidly increasing and is a growing burden on human health and the economy in the world. The functional role of mRNA regulation in the pathogenesis of heart failure remains to be elucidated. Carbon catabolite repression 4-negative on TATA-less complex is a multisubunit protein complex that deadenylates mRNA, a process of exonuclease-mediated degradation of mRNA poly(A) tail. Here we show the cardiac protective roles of deadenylase subunit CNOT6L against cardiac stress. After 2 weeks of transverse aortic constriction (TAC)-induced pressure overload, expression of CNOT6L deadenylase subunit was significantly upregulated in the mouse hearts. When CNOT6L gene was genetically deleted, the mice exhibited marked decline of left ventricular contractility and enhancement of fibrosis at 2 weeks after TAC. Transcriptome analyses elucidated that CNOT6L targets tenascin-C mRNA, which stimulates tissue fibrosis and inflammation. CNOT6L deletion markedly upregulated tenascin-C expression in cardiac fibroblasts. Poly(A) tail length and luciferase reporter analyses revealed that CNOT6L catalyzes deadenylation of tenascin-C mRNA likely through interaction with the cis-element in its 3'-untranslated region. Double knockout of tenascin-C and CNOT6L ameliorated cardiac fibrosis and dysfunction in single CNOT6 knockout mice under TAC or chronic infusion of angiotensin II. Thus, CNOT6L deadenylase prevents the progression of heart failure through downregulation of the expression of tenascin-C in cardiac fibroblasts, implicating a potential therapeutic strategy of targeting mRNA deadenylation. SIGNIFICANCE STATEMENT: To our knowledge, this study provides the first evidence that posttranscriptional regulation of tenascin-C expression in cardiac fibroblasts, including cell-type-specific roles of CNOT6L-mediated mRNA deadenylation, is crucial to maintain heart functions against pressure overload stress or angiotensin II-induced hypertension, implicating a potential therapeutic strategy of targeting mRNA deadenylation.

Image analysis Uncovers associations between immune landscape, collagen structure, and neoadjuvant chemotherapy in high-grade serous ovarian carcinomas

Background

The changes in the tumor microenvironment of high-grade serous ovarian carcinomas following neoadjuvant chemotherapy are a complex area of study. Previous research underscores the importance of investigating the immune and collagen components within the tumor microenvironment for prognostic implications.

Methods

In this study, we utilized computational pathology techniques with Hematoxylin and Eosin-stained images to quantitatively characterize the immune and collagen architecture within the tumor microenvironment of patients with high-grade serous ovarian carcinoma.

Results

Our analysis of 12 pre- and post-neoadjuvant chemotherapy images revealed an increase in immune infiltrate, primarily within the epithelial region. Additionally, post-neoadjuvant chemotherapy images exhibited chaotic collagen architecture compared to pre-neoadjuvant chemotherapy images. Importantly, features extracted from post-neoadjuvant chemotherapy images showed associations with overall survival, potentially aiding in the selection of patients for immunotherapy trials.

Conclusions

These findings offer critical insights into the changes in the tumor microenvironment of high-grade serous ovarian carcinomas following neoadjuvant chemotherapy and their potential implications for clinical outcomes.

Glucocorticoids in lung cancer: Navigating the balance between immunosuppression and therapeutic efficacy

Glucocorticoids (GCs), a class of hormones secreted by the adrenal glands, are released into the bloodstream to maintain homeostasis and modulate responses to various stressors. These hormones function by binding to the widely expressed GC receptor (GR), thereby regulating a wide range of pathophysiological processes, especially in metabolism and immunity. The role of GCs in the tumor immune microenvironment (TIME) of lung cancer (LC) has been a focal point of research. As immunosuppressive agents, GCs exert a crucial impact on the occurrence, progression, and treatment of LC. In the TIME of LC, GCs act as a constantly swinging pendulum, simultaneously offering tumor-suppressive properties while diminishing the efficacy of immune-based therapies. The present study reviews the role and mechanisms of GCs in the TIME of LC.

Immune landscape and response to oncolytic virus-based immunotherapy

Oncolytic virus (OV)-based immunotherapy has emerged as a promising strategy for cancer treatment, offering a unique potential to selectively target malignant cells while sparing normal tissues. However, the immunosuppressive nature of tumor microenvironment (TME) poses a substantial hurdle to the development of OVs as effective immunotherapeutic agents, as it restricts the activation and recruitment of immune cells. This review elucidates the potential of OV-based immunotherapy in modulating the immune landscape within the TME to overcome immune resistance and enhance antitumor immune responses. We examine the role of OVs in targeting specific immune cell populations, including dendritic cells, T cells, natural killer cells, and macrophages, and their ability to alter the TME by inhibiting angiogenesis and reducing tumor fibrosis. Additionally, we explore strategies to optimize OV-based drug delivery and improve the efficiency of OV-mediated immunotherapy. In conclusion, this review offers a concise and comprehensive synopsis of the current status and future prospects of OV-based immunotherapy, underscoring its remarkable potential as an effective immunotherapeutic agent for cancer treatment.

Computational pathology identifies immune-mediated collagen disruption to predict clinical outcomes in gynecologic malignancies

BACKGROUND

The role of immune cells in collagen degradation within the tumor microenvironment (TME) is unclear. Immune cells, particularly tumor-infiltrating lymphocytes (TILs), are known to alter the extracellular matrix, affecting cancer progression and patient survival. However, the quantitative evaluation of the immune modulatory impact on collagen architecture within the TME remains limited.

METHODS

We introduce CollaTIL, a computational pathology method that quantitatively characterizes the immune-collagen relationship within the TME of gynecologic cancers, including high-grade serous ovarian (HGSOC), cervical squamous cell carcinoma (CSCC), and endometrial carcinomas. CollaTIL aims to investigate immune modulatory impact on collagen architecture within the TME, aiming to uncover the interplay between the immune system and tumor progression.

RESULTS

We observe that an increased immune infiltrate is associated with chaotic collagen architecture and higher entropy, while immune sparse TME exhibits ordered collagen and lower entropy. Importantly, CollaTIL-associated features that stratify disease risk are linked with gene signatures corresponding to TCA-Cycle in CSCC, and amino acid metabolism, and macrophages in HGSOC.

CONCLUSIONS

CollaTIL uncovers a relationship between immune infiltration and collagen structure in the TME of gynecologic cancers. Integrating CollaTIL with genomic analysis offers promising opportunities for future therapeutic strategies and enhanced prognostic assessments in gynecologic oncology.

A novel endoplasmic reticulum stress-related lncRNA signature for prognosis prediction and immune response evaluation in Stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) is a significant contributor to cancer-related mortality worldwide. Although previous research has identified endoplasmic reticulum stress (ERS) as a regulator of various tumor-promoting properties of cancer cells, the impact of ERS-related long non-coding RNAs (lncRNAs) on STAD prognosis has not yet been investigated. Therefore, our study aims to develop and validate an ERS-related lncRNA signature that can accurately predict the prognosis of STAD patients.

METHODS

We collected RNA expression profiles and clinical data of STAD patients from The Cancer Genome Atlas (TCGA) and identified ERS-related genes from the Molecular Signature Database (MSigDB). Co-expression analysis enabled us to identify ERS-related lncRNAs, and we applied univariate Cox, least absolute shrinkage, and selection operator (LASSO), and multivariate Cox regression analyses to construct a predictive signature comprising of 9 ERS-related lncRNAs. We assessed the prognostic accuracy of our signature using Kaplan-Meier survival analysis, and validated our predictive signature in an independent gene expression omnibus (GEO) cohort. We also performed tumor mutational burden (TMB) and tumor immune microenvironment (TIME) analyses. Enrichment analysis was used to investigate the functions and biological processes of the signature, and we identified two distinct STAD patient subgroups through consensus clustering. Finally, we performed drug sensitivity analysis and immunologic efficacy analysis to explore further insights.

RESULTS

The 9 ERS related-lncRNAs signature demonstrated satisfactory predictive performance as an independent prognostic marker and was significantly associated with STAD clinicopathological characteristics. Furthermore, patients in the high-risk group displayed a worse STAD prognosis than those in the low-risk group. Notably, gene set enrichment analysis (GSEA) revealed significant enrichment of extracellular matrix pathways in the high-risk group, indicating their involvement in STAD progression. Additionally, the high-risk group exhibited significantly lower TMB expression levels than the low-risk group. Consensus clustering revealed two distinct STAD patient subgroups, with Cluster 1 exhibiting higher immune cell infiltration and more active immune functions. Drug sensitivity analysis suggested that the low-risk group was more responsive to oxaliplatin, epirubicinl, and other drugs.

CONCLUSION

Our study highlights the crucial regulatory roles of ERS-related lncRNAs in STAD, with significant clinical implications. The 9-lncRNA signature we have constructed represents a reliable prognostic indicator that has the potential to inform more personalized treatment decisions for STAD patients. These findings shed new light on the pathogenesis of STAD and its underlying molecular mechanisms, offering opportunities for novel therapeutic strategies to be developed for STAD patients.

Skin Cancer Microenvironment: What We Can Learn from Skin Aging?

Aging is a natural intrinsic process associated with the loss of fibrous tissue, a slower cell turnover, and a reduction in immune system competence. In the skin, the continuous exposition of environmental factors superimposes extrinsic damage, mainly due to ultraviolet radiation causing photoaging. Although not usually considered a pathogenic event, photoaging affects cutaneous biology, increasing the risk of skin carcinogenesis. At the cellular level, aging is typified by the rise of senescence cells a condition characterized by reduced or absent capacity to proliferate and aberrant hyper-secretory activity. Senescence has a double-edged sword in cancer biology given that senescence prevents the uncontrolled proliferation of damaged cells and favors their clearance by paracrine secretion. Nevertheless, the cumulative insults and the poor clearance of injured cells in the elderly increase cancer incidence. However, there are not conclusive data proving that aged skin represents a permissive milieu for tumor onset. On the other hand, tumor cells are capable of activating resident fibroblasts onto a pro-tumorigenic phenotype resembling those of senescent fibroblasts suggesting that aged fibroblasts might facilitate cancer progression. This review discusses changes that occur during aging that can prime neoplasm or increase the aggressiveness of melanoma and non-melanoma skin cancer.

Tumor-Associated Fibrosis Impairs the Response to Immunotherapy

Previously, impaired responses to immunotherapy in cancer had been attributed mainly to inherent tumor characteristics (tumor cell intrinsic factors) such as low immunogenicity, (low) mutational burden, weak host immune system, etc. However, mapping the responses of immunotherapeutic regimes in clinical trials for different types of cancer has pointed towards an obvious commonality - that tumors with a rich fibrotic stroma respond poorly or not at all. This has prompted a harder look on tumor cell extrinsic factors such as the surrounding tumor microenvironment (TME), and specifically, the fibrotic stroma as a potential enabler of immunotherapy failure. Indeed, the role of cancer-associated fibrosis in impeding efficacy of immunotherapy is now well-established. In fact, recent studies reveal a complex interconnection between fibrosis and treatment efficacy. Accordingly, in this review we provide a general overview of what a tumor associated fibrotic reaction is and how it interacts with the members of immune system that are frequently seen to be modulated in a failed immunotherapeutic regime.

The Extracellular Matrix: Its Composition, Function, Remodeling, and Role in Tumorigenesis

The extracellular matrix (ECM) is a ubiquitous member of the body and is key to the maintenance of tissue and organ integrity. Initially thought to be a bystander in many cellular processes, the extracellular matrix has been shown to have diverse components that regulate and activate many cellular processes and ultimately influence cell phenotype. Importantly, the ECM's composition, architecture, and stiffness/elasticity influence cellular phenotypes. Under normal conditions and during development, the synthesized ECM constantly undergoes degradation and remodeling processes via the action of matrix proteases that maintain tissue homeostasis. In many pathological conditions including fibrosis and cancer, ECM synthesis, remodeling, and degradation is dysregulated, causing its integrity to be altered. Both physical and chemical cues from the ECM are sensed via receptors including integrins and play key roles in driving cellular proliferation and differentiation and in the progression of various diseases such as cancers. Advances in 'omics' technologies have seen an increase in studies focusing on bidirectional cell-matrix interactions, and here, we highlight the emerging knowledge on the role played by the ECM during normal development and in pathological conditions. This review summarizes current ECM-targeted therapies that can modify ECM tumors to overcome drug resistance and better cancer treatment.

Construction of anoikis-related lncRNAs risk model: Predicts prognosis and immunotherapy response for gastric adenocarcinoma patients

Background: Anoikis acts as a programmed cell death that is activated during carcinogenesis to remove undetected cells isolated from ECM. Further anoikis based risk stratification is expected to provide a deeper understanding of stomach adenocarcinoma (STAD) carcinogenesis. Methods: The information of STAD patients were acquired from TCGA dataset. Anoikis-related genes were obtained from the Molecular Signatures Database and Pearson correlation analysis was performed to identify the anoikis-related lncRNAs (ARLs). We performed machine learning algorithms, including Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (Lasso) analyses on the ARLs to build the OS-score and OS-signature. Clinical subgroup analysis, tumor mutation burden (TMB) detection, drug susceptibility analysis, immune infiltration and pathway enrichment analysis were further performed to comprehensive explore the clinical significance. Results: We established a STAD prognostic model based on five ARLs and its prognostic value was verified. Survival analysis showed that the overall survival of high-risk score patients was significantly shorter than that of low-risk score patients. The column diagrams show satisfactory discrimination and calibration. The calibration curve verifies the good agreement between the prediction of the line graph and the actual observation. TIDE analysis and drug sensitivity analysis showed significant differences between different risk groups. Conclusion: The novel prognostic model based on anoikis-related lncRNAs we identified could be used for prognosis prediction and precise therapy in gastric adenocarcinoma.

Three-dimensional Imaging Reveals Immune-driven Tumor-associated High Endothelial Venules as a Key Correlate of Tumor Rejection Following Depletion of Regulatory T Cells

High endothelial venules (HEV) are specialized post capillary venules that recruit naïve T cells and B cells into secondary lymphoid organs (SLO) such as lymph nodes (LN). Expansion of HEV networks in SLOs occurs following immune activation to support development of an effective immune response. In this study, we used a carcinogen-induced model of fibrosarcoma to examine HEV remodeling after depletion of regulatory T cells (Treg). We used light sheet fluorescence microscopy imaging to visualize entire HEV networks, subsequently applying computational tools to enable topological mapping and extraction of numerical descriptors of the networks. While these analyses revealed profound cancer- and immune-driven alterations to HEV networks within LNs, these changes did not identify successful responses to treatment. The presence of HEV networks within tumors did however clearly distinguish responders from nonresponders. Finally, we show that a successful treatment response is dependent on coupling tumor-associated HEV (TA-HEV) development to T-cell activation implying that T-cell activation acts as the trigger for development of TA-HEVs which subsequently serve to amplify the immune response by facilitating extravasation of T cells into the tumor mass.

Prognostic value of p16, p53, and pcna in sarcoma and an evaluation of immune infiltration

Background

p16, p53, and proliferating cell nuclear antigen (pcna) genes play significant roles in many chromatin modifications and have been found to be highly expressed in a variety of tumor tissues. Therefore, they have been used as target genes for some tumor therapies. However, the differential expressions of the p16, p53, and pcna genes in human sarcomas and their effects on prognosis have not been widely reported.

Methods

The Oncomine dataset was used to analyze the transcription levels of p16, p53, and pcna genes, and the gene expression profile interactive analysis (GEPIA) dataset was used to analyze the differential expressions of p16, p53, and pcna. The expression levels of p16, p53, and pcna were further analyzed by Western Blotting. GEPIA and Kaplan–Meier analyses were used to analyze the prognostic value of p16, p53, and pcna. Furthermore, p16, p53, and pcna gene mutations and their association with overall survival (OS) and disease-free survival (DFS) were analyzed using cBioPortal datasets. In addition, genes co-expressed with p16, p53, and pcna were analyzed using Oncomine. The DAVID dataset was used to analyze the functional enrichment of p16, p53, pcna, and their co-expressed genes by Gene Ontology (GO) and Metascape were used to construct a network map. Finally, the immune cell infiltration of p16, p53, and pcna in patients with sarcoma was reported by Tumor Immune Estimation Resource (TIMER).

Results

p16, p53, and pcna were up-regulated in human sarcoma tissues and almost all sarcoma cell lines. Western Blotting showed that the expression of p16, p53, and pcna was elevated in osteosarcoma cell lines. The expression of pcna was correlated with OS, the expression of p16, p53, and pcna was correlated with relapse-free survival, and the genetic mutation of p16 was negatively correlated with OS and DFS. We also found that p16, p53, and pcna genes were positively/negatively correlated with immune cell infiltration in sarcoma.

Conclusions

The results of this study showed that p16, p53, and pcna can significantly affect the survival and immune status of sarcoma patients. Therefore, p16, p53, and pcna could be used as potential biomarkers of prognosis and immune infiltration in human sarcoma and provide a possible therapeutic target for sarcoma.

Exploiting ECM remodelling to promote immune-mediated tumour destruction

Cancer immunotherapy represents a significant breakthrough in cancer treatment mainly due to the ability to harness the activities of cancer-specific T cells. Despite this, most cancers remain resistant to T cell attack. Many reasons have been proposed to explain this, ranging from a lack of antigenicity through to the immunosuppressive effects of the tumour microenvironment. In this review, we examine the relationship between the immune system and a key component of the tumour microenvironment, namely the extracellular matrix (ECM). Specifically, we explore the reciprocal effects of immune cells and the tumour ECM and how the processes underpinning this relationship act to either promote or restrain tumour progression.

Parallels between the extracellular matrix roles in developmental biology and cancer biology

Interaction of a tumor with its microenvironment is an emerging field of investigation, and the crosstalk between cancer cells and the extracellular matrix is of particular interest, since cancer patients with abundant and stiff extracellular matrices display a poorer prognosis. At the post-juvenile stage, the extracellular matrix plays predominantly a structural role by providing support to cells and tissues; however, during development, matrix proteins exert a plethora of diverse signals to guide the movement and determine the fate of pluripotent cells. Taking a closer look at the communication between the extracellular matrix and cells of a developing body may bring new insights into cancer biology and identify cancer weaknesses. This review discusses parallels between the extracellular matrix roles during development and tumor growth.

Personalized models of heterogeneous 3D epithelial tumor microenvironments: Ovarian cancer as a model

Intractable human diseases such as cancers, are context dependent, unique to both the individual patient and to the specific tumor microenvironment. However, conventional cancer treatments are often nonspecific, targeting global similarities rather than unique drivers. This limits treatment efficacy across heterogeneous patient populations and even at different tumor locations within the same patient. Ultimately, this poor efficacy can lead to adverse clinical outcomes and the development of treatment-resistant relapse. To prevent this and improve outcomes, it is necessary to be selective when choosing a patient's optimal adjuvant treatment. In this review, we posit the use of personalized, tumor-specific models (TSM) as tools to achieve this remarkable feat. First, using ovarian cancer as a model disease, we outline the heterogeneity and complexity of both the cellular and extracellular components in the tumor microenvironment. Then we examine the advantages and disadvantages of contemporary cancer models and the rationale for personalized TSM. We discuss how to generate precision 3D models through careful and detailed analysis of patient biopsies. Finally, we provide clinically relevant applications of these versatile personalized cancer models to highlight their potential impact. These models are ideal for a myriad of fundamental cancer biology and translational studies. Importantly, these approaches can be extended to other carcinomas, facilitating the discovery of new therapeutics that more effectively target the unique aspects of each individual patient's TME. STATEMENT OF SIGNIFICANCE: In this article, we have presented the case for the application of biomaterials in developing personalized models of complex diseases such as cancers. TSM could bring about breakthroughs in the promise of precision medicine. The critical components of the diverse tumor microenvironments, that lead to treatment failures, include cellular- and extracellular matrix- heterogeneity, and biophysical signals to the cells. Therefore, we have described these dynamic components of the tumor microenvironments, and have highlighted how contemporary biomaterials can be utilized to create personalized in vitro models of cancers. We have also described the application of the TSM to predict the dynamic patterns of disease progression, and predict effective therapies that can produce durable responses, limit relapses, and treat any minimal residual disease.

Endogenous and Therapeutic Estrogens: Maestro Conductors of the Microenvironment of ER+ Breast Cancers

Estrogen receptor alpha (ERα) marks heterogeneous breast cancers which display a repertoire of somatic genomic mutations and an immune environment that differs from other breast cancer subtypes. These cancers also exhibit distinct biological behaviors; despite an overall better prognosis than HER2+ or triple negative breast cancers, disseminated dormant cells can lead to disease recurrence decades after the initial diagnosis and treatment. Estrogen is the best studied driver of these cancers, and antagonism or reduction of estrogen activity is the cornerstone of therapeutic approaches. In addition to reducing proliferation of ERα+ cancer cells, these treatments also alter signals to multiple other target cells in the environment, including immune cell subpopulations, cancer-associated fibroblasts, and endothelial cells via several distinct estrogen receptors. In this review, we update progress in our understanding of the stromal cells populating the microenvironments of primary and metastatic ER+ tumors, the effects of estrogen on tumor and stromal cells to modulate immune activity and the extracellular matrix, and net outcomes in experimental and clinical studies. We highlight new approaches that will illuminate the unique biology of these cancers, provide the foundation for developing new treatment and prevention strategies, and reduce mortality of this disease.

The matrix in cancer

The extracellular matrix is a fundamental, core component of all tissues and organs, and is essential for the existence of multicellular organisms. From the earliest stages of organism development until death, it regulates and fine-tunes every cellular process in the body. In cancer, the extracellular matrix is altered at the biochemical, biomechanical, architectural and topographical levels, and recent years have seen an exponential increase in the study and recognition of the importance of the matrix in solid tumours. Coupled with the advancement of new technologies to study various elements of the matrix and cell-matrix interactions, we are also beginning to see the deployment of matrix-centric, stromal targeting cancer therapies. This Review touches on many of the facets of matrix biology in solid cancers, including breast, pancreatic and lung cancer, with the aim of highlighting some of the emerging interactions of the matrix and influences that the matrix has on tumour onset, progression and metastatic dissemination, before summarizing the ongoing work in the field aimed at developing therapies to co-target the matrix in cancer and cancer metastasis.