The Immune Landscape of Cancer

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis

Background

Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis.

Methods

We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm.

Findings

The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line.

Conclusion

In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.

High expression of SLC27A2 predicts unfavorable prognosis and promotes inhibitory immune infiltration in acute lymphoblastic leukemia

Solute carrier family 27 member 2 (SLC27A2) is involved in fatty acid metabolism in tumors and represents a prospective target for cancer therapy. However, the role and mechanism of action of SLC27A2 in acute lymphoblastic leukemia (ALL) remain unclear. In this study, we aimed to explore the intrinsic associations between SLC27A2 and ALL and evaluate the prognostic significance, biological functions, and correlation with immune infiltration. We used the transcriptome and clinical data from the TARGET dataset. Differentially expressed genes (DEGs) in the SLC27A2 low- and high-expression groups were analyzed for prognostic implications and functional enrichment. Furthermore, we analyzed the relationship between SLC27A2 gene expression and immune cell infiltration using the ESTIMATE method, which was evaluated using the TIGER platform. Finally, we knocked down SLC27A2 in the Jurkat ALL cell line and conducted cell proliferation, western blotting, flow cytometry, and CCK-8 assays to elucidate the biological function of SLC27A2 in ALL. Patients with ALL who have higher expression levels of SLC27A2 have poorer overall survival and event-free survival. According to gene set enrichment analysis, the DEGs were primarily enriched with immune system processes and the PI3K-Akt signaling pathway. There was an inverse relationship between SLC27A2 expression and immune cell invasion, suggesting involvement of the former in tumor immune evasion. In vitro experiments showed that knockdown of SLC27A2 inhibited cell proliferation and protein expression and altered the Akt pathway, with a reduced proportion of B cells. In conclusion, SLC27A2 plays a vital role in the development of ALL.

Tumor microenvironment in thyroid cancer: Immune cells, patterns, and novel treatments

The tumor immune microenvironment of thyroid cancer is the heterogeneous histological space in which tumor cells coexist with host cells. Published data from this review were identified by search and selection database of Pubmed, Elsevier, and Science Direct. Searching was made in two steps using different keywords. In thyroid pathology, the inflammatory response is very important, and might have a key role finding new diagnostic and therapeutic methods, particularly in thyroid cancer. Different immune cells may be more or less present in different types of thyroid cancer and may even have different functions, hence the importance of knowing their presence in different thyroid tumor pathologies. Cancer-related inflammation could be a useful target for new diagnostic and therapeutic strategies by analyzing peritumoral and intratumoral immune cells in different types of thyroid tumors. Moreover, novel strategies for thyroid cancer treatments, such as monoclonal antibodies targeting checkpoint inhibitors, are emerging as promising alternatives.

Mitochondrial RNA modification-based signature to predict prognosis of lower grade glioma: a multi-omics exploration and verification study

Mitochondrial RNA modification (MRM) plays a crucial role in regulating the expression of key mitochondrial genes and promoting tumor metastasis. Despite its significance, comprehensive studies on MRM in lower grade gliomas (LGGs) remain unknown. Single-cell RNA-seq data (GSE89567) was used to evaluate the distribution functional status, and correlation of MRM-related genes in different cell types of LGG microenvironment. We developed an MRM scoring system by selecting potential MRM-related genes using LASSO regression analysis and the Random Survival Forest algorithm, based on multiple bulk RNA-seq datasets from TCGA, CGGA, GSE16011, and E-MTAB-3892. Analysis was performed on prognostic and immunological features, signaling pathways, metabolism, somatic mutations and copy number variations (CNVs), treatment responses, and forecasting of potential small-molecule agents. A total of 35 MRM-related genes were selected from the literature. Differential expression analysis of 1120 normal brain tissues and 529 LGGs revealed that 22 and 10 genes were upregulated and downregulated, respectively. Most genes were associated with prognosis of LGG. METLL8, METLL2A, TRMT112, and METTL2B were extensively expressed in all cell types and different cell cycle of each cell type. Almost all cell types had clusters related to mitochondrial RNA processing, ribosome biogenesis, or oxidative phosphorylation. Cell-cell communication and Pearson correlation analyses indicated that MRM may promoting the development of microenvironment beneficial to malignant progression via modulating NCMA signaling pathway and ICP expression. A total of 11 and 9 MRM-related genes were observed by LASSO and the RSF algorithm, respectively, and finally 6 MRM-related genes were used to establish MRM scoring system (TRMT2B, TRMT11, METTL6, METTL8, TRMT6, and TRUB2). The six MRM-related genes were then validated by qPCR in glioma and normal tissues. MRM score can predict the malignant clinical characteristics, abundance of immune infiltration, gene variation, clinical outcome, the enrichment of signaling pathways and metabolism. In vitro experiments demonstrated that silencing METTL8 significantly curbs glioma cell proliferation and enhances apoptosis. Patients with a high MRM score showed a better response to immunotherapies and small-molecule agents such as arachidonyl trifluoromethyl ketone, MS.275, AH.6809, tacrolimus, and TTNPB. These novel insights into the biological impacts of MRM within the glioma microenvironment underscore its potential as a target for developing precise therapies, including immunotherapeutic approaches.

Somatic USP8 alteration affects the immune landscape of corticotroph pituitary adenomas- a pilot study

INTRODUCTION

Somatic mutations in ubiquitin-specific protease-8 (USP8), encoding a deubiquinating protein, are found in approximately 30% of corticotroph-derived pituitary adenomas (CPAs). Stratifin, a protein encoded by SFN, inhibits USP8 catalytic activity. USP8 has immunomodulating properties that have been demonstrated in non-tumoral diseases.

METHODS

We assessed the influence of USP8 on the immune landscape of CPA and validated this effect and its dependency on stratifin in large cohorts of non-pituitary tumors. We analyzed data of CPA samples (n = 20) and additional non-pituitary tumors from the TCGA database, using transcriptome signature-recognition algorithms. Immune tumor microenvironment (iTME) was compared both by USP8 and SFN expression levels (n = 843) and by USP8 mutation status and SFN expression (n = 12,389).

RESULTS

CPA with activating USP8 mutations was associated with "cold" iTME compared with wild-type USP8 CPA, as reflected by lower fractions of immune cells, including B cells, CD4, regulatory and gamma/delta T cells, natural killer cells, M0 and M1 macrophages, dendritic cells, and eosinophils (p < 0.05 for all comparisons). Pathways altered by the presence of USP8 mutation, based on the most differentially expressed genes (3061 genes), included microglia pathogen phagocytosis and multiple toll-like receptor signaling pathways (p < 0.0001). In a validation analysis based on large cohorts of non-pituitary tumors, high expression of USP8 was associated with a suppressed iTME effect that was augmented by a low SFN expression.

CONCLUSIONS

Our data demonstrate for the first time, to our knowledge, a distinct immune landscape of tumors based on USP8 status and expression and the dependency of this immunological effect on SFN expression.

Paracrine Ovarian Cancer Cell-Derived CSF1 Signaling Regulates Macrophage Migration Dynamics in a 3D Microfluidic Model that Recapitulates In Vivo Infiltration Patterns in Patient-Derived Xenografts

A high density of macrophages in the ovarian cancer microenvironment is associated with disease progression and poor outcomes. Understanding cancer-macrophage interaction mechanisms that establish this pro-tumorigenic microenvironment is critical for developing macrophage-targeted therapies. Here, 3D microfluidic assays and patient-derived xenografts are utilized to define the role of cancer-derived colony stimulating factor 1 (CSF1) on macrophage infiltration dynamics toward ovarian cancer cells. It is demonstrated that multiple ovarian cancer models promote the infiltration of macrophages into a 3D extracellular matrix in vitro in a cell density-dependent manner. Macrophages exhibit directional migration and increased migration speed under both direct interactions with cancer cells embedded within the matrix and paracrine crosstalk with cancer cells seeded in an independent microchannel. It is also found that platinum-based chemotherapy increases macrophage recruitment and the levels of cancer cell-derived CSF1. Targeting CSF1 signaling under baseline or chemotherapy-treatment conditions reduces the number of infiltrated macrophages. It is further shown that results obtained with the 3D microfluidic model reflect the recruitment profiles of macrophages in patient-derived xenografts in vivo. These findings highlight the role of CSF1 signaling in establishing macrophage-rich ovarian cancer microenvironments, as well as the utility of microfluidic models in recapitulating 3D tumor ecosystems and dissecting cancer-macrophage signaling.

Identification of SLC12A8 as a valuable prognostic biomarker and immunotherapeutic target by comprehensive pan-cancer analysis

Solute carrier family 12 member 8 (SLC12A8) is a nicotinamide mononucleotide transporter. Despite emerging evidence supporting its potential involvement in oncogenesis, a systematic pan-cancer analysis of SLC12A8 has not been performed. Thus, this research aimed to explore the prognostic implications of SLC12A8 and assess its possible immune-related functions across 33 different tumor types. And multiple datasets were retrieved from the databases of TCGA, GTEx, Broad Institute CCLE, TISCH, HPA, and GDSC2. After this data acquisition, bioinformatics analyses were conducted to assess the potential involvement of SLC12A8 in cancer pathogenesis. These analyses focused on examining the relationship between SLC12A8 and prognosis, drug sensitivity, chemotherapy response, immune checkpoints (ICPs), immune cell infiltration, and immunotherapy efficacy across various tumor types. Furthermore, experimental methods such as EdU assay, wound healing assay, and transwell assay were conducted to evaluate the cell proliferative and invasive abilities. Finally, the data analysis demonstrated that SLC12A8 was differentially expressed and predicted unfavorable survival outcomes in the majority of the tumor types in the TCGA dataset. Furthermore, a notable upregulation in the expression of SLC12A8 mRNA and protein was observed in cancer tissues compared to normal tissues. Additionally, the SLC12A8 levels demonstrated a strong association with ICPs, chemokines, immune-activating genes, immune-suppressive genes, chemokine receptors, chemotherapy response, and immunotherapy efficacy. In vitro experiments substantiated that knockdown of SLC12A8 restricted the malignant phenotypes of MDA-MB-231 and BT-549 cells. So SLC12A8 holds promise as a cancer biomarker with the capacity to interact with other ICPs to synergistically regulate the immune microenvironment. Thus, the identification of SLC12A8 contributes to the development of novel therapeutic strategies for enhancing the efficacy of immunotherapy.

Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data

BACKGROUND

Colorectal cancer is a malignant tumor of the digestive system originating from abnormal cell proliferation in the colon or rectum, often leading to gastrointestinal symptoms and severe health issues. Nucleotide metabolism, which encompasses the synthesis of DNA and RNA, is a pivotal cellular biochemical process that significantly impacts both the progression and therapeutic strategies of colorectal cancer METHODS: For single-cell RNA sequencing (scRNA-seq), five functions were employed to calculate scores related to nucleotide metabolism. Cell developmental trajectory analysis and intercellular interaction analysis were utilized to explore the metabolic characteristics and communication patterns of different epithelial cells. These findings were further validated using spatial transcriptome RNA sequencing (stRNA-seq). A risk model was constructed using expression profile data from TCGA and GEO cohorts to optimize clinical decision-making. Key nucleotide metabolism-related genes (NMRGs) were functionally validated by further in vitro experiments.

RESULTS

In both scRNA-seq and stRNA-seq, colorectal cancer (CRC) exhibited unique cellular heterogeneity, with myeloid cells and epithelial cells in tumor samples displaying higher nucleotide metabolism scores. Analysis of intercellular communication revealed enhanced signaling pathways and ligand-receptor interactions between epithelial cells with high nucleotide metabolism and fibroblasts. Spatial transcriptome sequencing confirmed elevated nucleotide metabolism states in the core region of tumor tissue. After identifying differentially expressed NMRGs in epithelial cells, a risk prognostic model based on four genes effectively predicted overall survival and immunotherapy outcomes in patients. High-risk group patients exhibited an immunosuppressive microenvironment and relatively poorer prognosis and responses to chemotherapy and immunotherapy. Finally, based on data analysis and a series of cellular functional experiments, ACOX1 and CPT2 were identified as novel therapeutic targets for CRC.

CONCLUSION

In this study, a comprehensive analysis of NMRGs in CRC was conducted using a combination of single-cell sequencing, spatial transcriptome sequencing, and high-throughput data. The prognostic model constructed with NMRGs shows potential as a standalone prognostic marker for colorectal cancer patients and may significantly influence the development of personalized treatment approaches for CRC.

GRIN2A mutation is a novel indicator of stratifying beneficiaries of immune checkpoint inhibitors in multiple cancers

Glutamate-NMDAR receptors (GRINs) have been reported to influence cancer immunogenicity; however, the relationship between GRIN alterations and the response to immune checkpoint inhibitors (ICIs) has not been determined. This study combined clinical characteristics and mutational profiles from multiple cohorts to form a discovery cohort (n = 901). The aim of this study was to investigate the correlation between the mutation status of the GRIN gene and the response to ICI therapy. Additionally, an independent ICI-treated cohort from the Memorial Sloan Kettering Cancer Center (MSKCC, N = 1513) was used for validation. Furthermore, this study explored the associations between GRIN2A mutations and intrinsic and extrinsic immunity using multiomics analysis. In the discovery cohort, patients with GRIN2A-MUTs had improved clinical outcomes, as indicated by a higher objective response rate (ORR: 36.8% vs 25.8%, P = 0.020), durable clinical benefit (DCB: 55.2% vs 38.7%, P = 0.005), prolonged progression-free survival (PFS: HR = 0.65; 95% CI 0.49 to 0.87; P = 0.003), and increased overall survival (OS: HR = 0.67; 95% CI 0.50 to 0.89; P = 0.006). Similar results were observed in the validation cohort, in which GRIN2A-MUT patients exhibited a significant improvement in overall survival (HR = 0.66; 95% CI = 0.49 to 0.88; P = 0.005; adjusted P = 0.045). Moreover, patients with GRIN2A-MUTs exhibited an increase in tumor mutational burden, high expression of costimulatory molecules, increased activity of antigen-processing machinery, and infiltration of various immune cells. Additionally, gene sets associated with cell cycle regulation and the interferon response were enriched in GRIN2A-mutated tumors. In conclusion, GRIN2A mutation is a novel biomarker associated with a favorable response to ICIs in multiple cancers.

Advances in Engineered Macrophages: A New Frontier in Cancer Immunotherapy

Macrophages, as pivotal cells within the tumour microenvironment, significantly influence the impact of and reactions to treatments for solid tumours. The rapid evolution of bioengineering technology has revealed the vast potential of engineered macrophages in immunotherapy, disease diagnosis, and tissue engineering. Given this landscape, the goal of harnessing and innovating macrophages as a novel strategy for solid tumour immunotherapy cannot be overstated. The diverse strategies for engineered macrophages in the realm of cancer immunotherapy, encompassing macrophage drug delivery systems, chimeric antigen receptor macrophage therapy, and synergistic treatment approaches involving bacterial outer membrane vesicles and macrophages, are meticulously examined in this review. These methodologies are designed to enhance the therapeutic efficacy of macrophages against solid tumours, particularly those that are drug-resistant and metastatic. Collectively, these immunotherapies are poised to supplement and refine current solid tumour treatment paradigms, thus heralding a new frontier in the fight against malignant tumours.

Rothia in Nonsmall Cell Lung Cancer is Associated With Worse Survival

INTRODUCTION

The microbiome is known to play a significant role in cancer biology; however, few studies have elucidated its relationship with Nonsmall Cell Lung Cancer (NSCLC) patient outcomes. We hypothesized that there are specific microorganisms that are closely related with NSCLC patient survival.

METHODS

Total of 647 NSCLC (Adenocarcinoma and Squamous Cell Carcinoma combined) patients in The Cancer Genome Atlas (TCGA) were analyzed using the R software.

RESULTS

A Volcano Plot was analyzed with the patients divided into Short and Long Survivors by overall survival of 0.9 years, and we found that a bacterium Rothia was significantly abundant in Short Survivors, and Blastococcus, Leptospira, and Haematobacter in Long Survivors, but presence of Rothia alone was associated with overall survival. The age, race, subtype, and sex were not significantly different by the presence of Rothia in NSCLC. Unexpectedly, Rothia-positive NSCLC was associated with less cell proliferation by gene set enrichment analysis, Mki67 expression, proliferation score, with less fraction altered and homologous recombination deficiency, and with high infiltration of stromal cells, indicating favorable oncological characteristics. Further, Rothia-positive tumors were associated with significantly higher infiltration of CD8 T cells, CD4 T cells, Monocytes, and NK cells, and high interferon-gamma response, T-cell receptor richness, cytolytic activity, indicating favorable tumor immune microenvironment.

CONCLUSIONS

NSCLC with Rothia was associated with worse survival but also with favorable oncological characteristics such as less cell proliferation and favorable tumor immune microenvironment. We cannot help but speculate that Rothia in NSCLC is associated with mortality unrelated to oncological characteristics.

Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response

Tertiary lymphoid structures (TLSs) have been associated with favorable immunotherapy responses and prognosis in various cancers. Despite their significance, their quantification using multiplex immunohistochemistry (mIHC) staining of T and B lymphocytes remains labor-intensive, limiting its clinical utility. To address this challenge, we curated a dataset from matched mIHC and H&E whole-slide images (WSIs) and developed a deep learning model for automated segmentation of TLSs. The model achieved Dice coefficients of 0.91 on the internal test set and 0.866 on the external validation set, along with intersection over union (IoU) scores of 0.819 and 0.787, respectively. The TLS ratio, defined as the segmented TLS area over the total tissue area, correlated with B lymphocyte levels and the expression of CXCL13, a chemokine associated with TLS formation, in 6140 patients spanning 16 tumor types from The Cancer Genome Atlas (TCGA). The prognostic models for overall survival indicated that the inclusion of the TLS ratio with TNM staging significantly enhanced the models' discriminative ability, outperforming the traditional models that solely incorporated TNM staging, in 10 out of 15 TCGA tumor types. Furthermore, when applied to biopsied treatment-naïve tumor samples, higher TLS ratios predicted a positive immunotherapy response across multiple cohorts, including specific therapies for esophageal squamous cell carcinoma, non-small cell lung cancer, and stomach adenocarcinoma. In conclusion, our deep learning-based approach offers an automated and reproducible method for TLS segmentation and quantification, highlighting its potential in predicting immunotherapy response and informing cancer prognosis.

Interleukins and interferons in mesenchymal stromal stem cell-based gene therapy of cancer

The tumor microenvironment is importantly shaped by various cytokines, where interleukins (ILs) and interferons (IFNs) shape the balance of immune activity within tumor niche and associated lymphoid organs. Their importance in activation and tuning of both innate and adaptive immune responses prompted their use in several clinical trials, albeit with limited therapeutic efficacy and risk of toxicity due to systemic administration. Increasing preclinical evidence suggests that local delivery of ILs and IFNs could significantly increase their effectiveness, while simultaneously attenuate the known side effects and issues related to their biological activity. A prominent way to achieve this is to use cell-based delivery vehicles. For this purpose, mesenchymal stromal stem cells (MSCs) are considered an almost ideal candidate. Namely, MSCs can be obtained in large quantities and from obtainable sources (e.g. umbilical cord or adipose tissue), their ex vivo expansion is relatively straightforward compared to other cell types and they possess very low immunogenicity making them suitable for allogeneic use. Importantly, MSCs have shown an intrinsic capacity to respond to tumor-directed chemotaxis. This review provides a focused and detailed discussion on MSC-based gene therapy using ILs and IFNs, engineering techniques and insights on potential future advancements.

EPRIM: An approach of identifying cancer immune-related epigenetic regulators

Epigenetic regulation contributes to the dysregulation of gene expression involved in cancer biology. Nevertheless, the roles of epigenetic regulators (ERs) in tumor immunity and immune response remain basically unclear. Here, we developed the epigenetic regulator in immunology (EPRIM) approach to identify immune-related ERs and comprehensively dissected the ER regulation in tumor immune response across 33 cancers. The identified immune-related ERs were related to immune infiltration and could stratify cancer patients into two risk groups in multiple independent datasets. These patient groups were characterized by distinct immune functions, immune infiltrates, driver gene mutations, and prognoses. Furthermore, we constructed an immune ER-based signature and highlighted its potential utility in predicting clinical benefit from immunotherapy and selecting therapeutic agents. Taken together, our identification and evaluation of immune-related ERs highlight the usefulness of EPRIM for the understanding of ERs in immune regulation and the clinical relevance in evaluation of cancer patient prognosis and response to immune checkpoint blockade therapy.

A Human Adenovirus C Infection-Related Gene Panel for Predicting Survival and Treatment Responsiveness in Glioma Patients

BACKGROUND

Viruses are critical for the regulation of cancer development and for therapy. Human adenovirus C (HadVC) has been detected in central nervous system and glioma tissue. The objective of the present study was the development of a robust prognostic model based on HadVC infection (HadVCi)-relevant genes.

METHODS

The genome, transcriptome, and virome were systemically analyzed using The Cancer Genome Atlas dataset for training and 2 cohorts from the Chinese Glioma Genome Atlas and an immunotherapy trial cohort with 17 patients receiving anti-PD-1 treatment for validation. HadVCi-relevant gene selection from differentially expressed genes between HadVC-infected and non-HadVC-infected glioma patients using least absolute shrinkage and selection operator regression was followed by Cox regression modeling to establish a prognostic HadVCi score. Kaplan-Meier and receiver operating characteristic curve analyses were performed to estimate the predictive capacity of the HadVCi score. The χ2, Spearman, and Mann-Whitney U tests were used to identify the correlation with the clinicopathological parameters, treatment responsiveness, and immune landscape. Temozolomide-resistant glioma cells were established and analyzed at the transcriptional level using RNA sequencing data.

RESULTS

The HadVCi score was (-0.2526673∗TRPC6) + (-0.2244276∗RNF207) + (-0.0894468∗SEC31B) + (-0.0190214∗ZCRB1) + (-0.017122∗DNPH1) + (0.0495818∗CCDC34) + (0.1196349∗PURG) + (0.1778997∗LILRA5). The score possesses a strong ability to predict overall survival. Further analysis revealed a higher HadVCi score correlated with a malignant phenotype and poorer treatment responsiveness to temozolomide-based chemotherapy and combined therapies. Additionally, transcriptomic analysis showed malignancy-, stemness-, and radioresistant-related gene activation in the HadVCi group, which characterized the poor outcomes and limited sensitivity to standard therapy.

CONCLUSIONS

The HadVCi score could be an effective tool for survival prediction and treatment guidance for patients with glioma.

Galectin-7 Induction by EHMT2 Inhibition Enhances Immunity in Microsatellite Stability Colorectal Cancer

BACKGROUND & AIMS

Although immunotherapy shows substantial advancement in colorectal cancer (CRC) with microsatellite instability high, it has limited efficacy for CRC with microsatellite stability (MSS). Identifying combinations that reverse immune suppression and prime MSS tumors for current immunotherapy approaches remains an urgent need.

METHODS

An in vitro CRISPR screen was performed using coculture models of primary tumor cells and autologous immune cells from MSS CRC patients to identify epigenetic targets that could enhance immunotherapy efficacy in MSS tumors.

RESULTS

We revealed EHMT2, a histone methyltransferase, as a potential target for MSS CRC. EHMT2 inhibition transformed the immunosuppressive microenvironment of MSS tumors into an immunomodulatory one by altering cytokine expression, leading to T-cell-mediated cytotoxicity activation and improved responsiveness to anti-PD1 treatment. We observed galectin-7 up-regulation upon EHMT2 inhibition, which converted a "cold" MSS tumor environment into a T-cell-inflamed one. Mechanistically, CHD4 repressed galectin-7 expression by recruiting EHMT2 to form a cotranscriptional silencing complex. Galectin-7 administration enhanced anti-PD1 efficacy in MSS CRC, serving as a potent adjunct cytokine therapy.

CONCLUSIONS

Our findings suggest that targeting the EHMT2/galectin-7 axis could provide a novel combination strategy for immunotherapy in MSS CRC.

B cell clonality in cancer

Carcinogenesis in the process of long-term co-evolution of tumor cells and immune environment essentially becomes possible due to incorrect decisions made, remembered, and reproduced by the immune system at the level of clonal populations of antigen-specific T- and B-lymphocytes. Tumor-immunity interaction determines the nature of such errors and, consequently, delineates the possible ways of successful immunotherapeutic intervention. It is generally recognized that tumor-infiltrating B cells (TIL-B) can play both pro-tumor and anti-tumor roles. However, the exact mechanisms that determine the contribution of clonal B cell lineages with different specificities and functions remain largely unclear. This is due to the variability of cancer types, the molecular heterogeneity of tumor cells, and, to a large extent, the individual pattern of each immune response. Further progress requires detailed investigation of the functional properties and phenotypes of clonally heterogeneous B cells in relation to their antigenic specificities, which determine the functionality of both effector B lymphocytes and immunoglobulins produced in the tumor environment. Based on a real understanding of the role of clonal antigen-specific populations of B lymphocytes in the tumor microenvironment, we need to learn how to develop new methods of targeted immunotherapy, as well as adapt existing treatment options to the specific needs of different patients and patient subgroups. In this review, we will cover B cells functional diversity and their multifaceted roles in the tumor environment.

Translating p53-based therapies for cancer into the clinic

Inactivation of the most important tumour suppressor gene TP53 occurs in most, if not all, human cancers. Loss of functional wild-type p53 is achieved via two main mechanisms: mutation of the gene leading to an absence of tumour suppressor activity and, in some cases, gain-of-oncogenic function; or inhibition of the wild-type p53 protein mediated by overexpression of its negative regulators MDM2 and MDMX. Because of its high potency as a tumour suppressor and the dependence of at least some established tumours on its inactivation, p53 appears to be a highly attractive target for the development of new anticancer drugs. However, p53 is a transcription factor and therefore has long been considered undruggable. Nevertheless, several innovative strategies have been pursued for targeting dysfunctional p53 for cancer treatment. In mutant p53-expressing tumours, the predominant strategy is to restore tumour suppressor function with compounds acting either in a generic manner or otherwise selective for one or a few specific p53 mutations. In addition, approaches to deplete mutant p53 or to target vulnerabilities created by mutant p53 expression are currently under development. In wild-type p53 tumours, the major approach is to protect p53 from the actions of MDM2 and MDMX by targeting these negative regulators with inhibitors. Although the results of at least some clinical trials of MDM2 inhibitors and mutant p53-restoring compounds are promising, none of the agents has yet been approved by the FDA. Alternative strategies, based on a better understanding of p53 biology, the mechanisms of action of compounds and treatment regimens as well as the development of new technologies are gaining interest, such as proteolysis-targeting chimeras for MDM2 degradation. Other approaches are taking advantage of the progress made in immune-based therapies for cancer. In this Review, we present these ongoing clinical trials and emerging approaches to re-evaluate the current state of knowledge of p53-based therapies for cancer.

An integrative pan-cancer bioinformatics analysis of MSRB1 and its association with tumor immune microenvironment, prognosis, and immunotherapy

Methionine sulfoxide reductase B1 (MSRB1) is involved in the development and immune regulation of multiple tumors. However, the role of MSRB1 in the tumor microenvironment and its potential as a therapeutic target remain largely unknown. In this study, MSRB1 expression patterns were evaluated using pan-cancer RNA sequencing data from multiple cell lines, tissues, and single cells. The pan-cancer prognostic role of MSRB1 was assessed and the association between MSRB1 expression and certain cancer characteristics was analyzed. We showed that MSRB1 expression levels were increased in several types of cancer (P < 0.05) and in certain cell types (macrophages, dendritic cells, and malignant tumor cells). The upregulation of MSRB1 expression was due to DNA copy number amplification. Furthermore, MSRB1 was significantly associated with the activation of immune pathways (P < 0.05, NES > 0), immune cell infiltration, and expression of immune checkpoint molecules. In addition, high expression of MSRB1 was found in a series of in vivo and in vitro immunotherapy response models (P < 0.05), and showed resistance to most targeted drugs. Our results indicated that MSRB1 may regulate the tumor immune microenvironment through an immunoresponse and potentially influence cancer development. This could make it a promising predictive biomarker and therapeutic target for precise tumor immunotherapy.

Identification and preliminary analysis of hub genes associated with bladder cancer progression by comprehensive bioinformatics analysis

Bladder cancer (BC) is a crisis to human health. It is necessary to understand the molecular mechanisms of the development and progression of BC to determine treatment options. Publicly available expression data were obtained from TCGA and GEO databases to spot differentially expressed genes (DEGs) between cancer and normal bladder tissues. Weighted co-expression networks were constructed, and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Associations in hub genes, immune infiltration, and immune therapy were evaluated separately. Protein-protein interaction (PPI) networks for the genes identified in the normal and tumor groups were launched. 3461 DEGs in the TCGA dataset and 1069 DEGs in the GSE dataset were identified, including 87 overlapping genes between cancer and normal bladder groups. Hub genes in the tumor group were mainly enriched for cell proliferation, while hub genes in the normal group were related to the synthesis and secretion of neurotransmitters. Based on survival analysis, CDH19, RELN, PLP1, and TRIB3 were considerably associated with prognosis (P < 0.05). CDH19, RELN, PLP1, and TRIB3 may play important roles in the development of BC and are potential biomarkers in therapy and prognosis.

Association of Baseline Tumor-Specific Neoantigens and CD8+ T-Cell Infiltration With Immune-Related Adverse Events Secondary to Immune Checkpoint Inhibitors

PURPOSE

Recent evidence has shown that higher tumor mutational burden strongly correlates with an increased risk of immune-related adverse events (irAEs). By using an integrated multiomics approach, we further studied the association between relevant tumor immune microenvironment (TIME) features and irAEs.

METHODS

Leveraging the US Food and Drug Administration Adverse Event Reporting System, we extracted cases of suspected irAEs to calculate the reporting odds ratios (RORs) of irAEs for cancers treated with immune checkpoint inhibitors (ICIs). TIME features for 32 cancer types were calculated on the basis of the cancer genomic atlas cohorts and indirectly correlated with each cancer's ROR for irAEs. A separate ICI-treated cohort of non-small-cell lung cancer (NSCLC) was used to evaluate the correlation between tissue-based immune markers (CD8+, PD-1/L1+, FOXP3+, tumor-infiltrating lymphocytes [TILs]) and irAE occurrence.

RESULTS

The analysis of 32 cancers and 33 TIME features demonstrated a significant association between irAE RORs and the median number of base insertions and deletions (INDEL), neoantigens (r = 0.72), single-nucleotide variant neoantigens (r = 0.67), and CD8+ T-cell fraction (r = 0.51). A bivariate model using the median number of INDEL neoantigens and CD8 T-cell fraction had the highest accuracy in predicting RORs (adjusted r2 = 0.52, P = .002). Immunoprofile assessment of 156 patients with NSCLC revealed a strong trend for higher baseline median CD8+ T cells within patients' tumors who experienced any grade irAEs. Using machine learning, an expanded ICI-treated NSCLC cohort (n = 378) further showed a treatment duration-independent association of an increased proportion of high TIL (>median) in patients with irAEs (59.7% v 44%, P = .005). This was confirmed by using the Fine-Gray competing risk approach, demonstrating higher baseline TIL density (>median) associated with a higher cumulative incidence of irAEs (P = .028).

CONCLUSION

Our findings highlight a potential role for TIME features, specifically INDEL neoantigens and baseline-immune infiltration, in enabling optimal irAE risk stratification of patients.

Molecular map of cGAS-STING pathway-related genes in bladder cancer: the perspective toward immune microenvironment and prognosis

BACKGROUND

The cGAS-STING pathway emerges as a pivotal innate immune pathway with the potential to profoundly influence all facets of tumor initiation and progression. The prognostic significance and immunological role of cGAS-STING pathway-related genes (CRGs) in individuals diagnosed with bladder cancer (BLCA) have not yet been fully elucidated.

METHODS

Performed unsupervised cluster analysis to identify distinct clusters. Utilizing LASSO and multivariate Cox regression analysis to construct a prognostic risk model. The IMvigor210, GSE13507 and GSE78220 cohorts were utilized to explore the potential value of risk score in immune therapy response and survival prediction.

RESULTS

A risk model was developed utilizing four CRGs in order to forecast the overall survival (OS) of BLCA patients. The risk score to be a standalone risk factor, which was further corroborated by the external validation set obtained from the GEO database (GSE13507). We established an integrated nomogram that combined risk scoring and clinical information, exhibiting commendable clinical practicality in predicting the overall survival period of BLCA patients. It is noteworthy that risk score could differentiate tumor microenvironments among different risk groups and individuals who were more responsive to immunotherapy in IMvigor210 and GSE13507 cohorts. In vitro experiments, we noted an up-regulation of IRF3 and IKBKB upon the activation of the cGAS-STING pathway. Conversely, the activation of the cGAS-STING pathway resulted in a down-regulation of POLR3G and CTNNB1.

CONCLUSIONS

CRG risk model shows promise as a potential stratification approach for bladder cancer patients.

Historical perspective and future directions: computational science in immuno-oncology

Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.

Image-Based Subtype Classification for Glioblastoma Using Deep Learning: Prognostic Significance and Biologic Relevance

PURPOSE

To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of the glioblastoma tumor microenvironment.

MATERIALS AND METHODS

Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features. Biclustering was used to identify subtypes and image feature modules. Prognostic value of image subtypes was assessed via Cox regression on survival outcomes and validated with 189 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set. Morphological, molecular, and immune characteristics of glioblastoma image subtypes were analyzed.

RESULTS

Four distinct subtypes and modules (imClust1-4) were identified for the TCGA patients with glioblastoma on the basis of the image feature data. The glioblastoma image subtypes were significantly associated with overall survival (OS; P = .028) and progression-free survival (P = .003). Apparent association was also observed for disease-specific survival (P = .096). imClust2 had the best prognosis for all three survival end points (eg, after 25 months, imClust2 had >7% surviving patients than the other subtypes). Examination of OS in the external validation using the unseen CPTAC data set showed consistent patterns. Multivariable Cox analyses confirmed that the image subtypes carry unique prognostic information independent of known clinical and molecular predictors. Molecular and immune profiling revealed distinct immune compositions of the tumor microenvironment in different image subtypes and may provide biologic explanations for the patterns in patients' outcomes.

CONCLUSION

Our image-based subtype classification on the basis of deep learning models is a novel tool to refine risk stratification in cancers. The image subtypes detected for glioblastoma represent a promising prognostic biomarker with distinct molecular and immune characteristics and may facilitate developing novel, individualized immunotherapies for glioblastoma.

Loss of Human Leukocyte Antigen and Immune Escape in Head and Neck Cancer

OBJECTIVES/HYPOTHESIS

Cancer cells evade recognition by the immune system to survive. Head and neck squamous cell carcinoma (HNSCC) is characterized by high levels of immune infiltration and mutation-associated neoantigens; therefore, immune evasion is likely to be an important mechanism in HNSCC tumorigenesis and progression. A commonly employed mechanism of immune evasion is downregulation of human leukocyte antigen (HLA) or loss of heterozygosity (LOH) in tumor cells. The objective of this study was to integrate multi-dimensional genomic and transcriptomic data from HNSCC tumors to better understand the clinical and immunologic implications of HLA LOH.

STUDY TYPE/DESIGN

Cross-sectional integrated clinical and genomic analysis.

METHODS

Whole-exome sequencing and RNA-sequencing data from 522 tumors profiled in The Cancer Genome Atlas HNSCC cohort were analyzed and integrated with secondary analyses including immune cell deconvolution data. Associations were analyzed with categorical hypothesis testing and multivariable logistic and Cox regression.

RESULTS

HLA LOH was a prevalent event that was identified in 53% of HNSCC tumors; in many cases, more than one class I HLA gene was targeted for LOH. HLA LOH was more common in advanced-stage tumors. Tumors with somatic HLA LOH had tumor microenvironments defined by decreased lymphocyte and T cell infiltration.

CONCLUSIONS

HLA LOH is one of the most prevalent genetic alterations in HNSCC, and is associated with a cold immune microenvironment, suggesting that HLA LOH is a means of immune evasion. It may have value as a predictive biomarker or potential as a cancer cell-specific therapeutic target.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:160-165, 2024.

A Five-gene Signature based on MicroRNA for Predicting Prognosis and Immunotherapy in Stomach Adenocarcinoma

AIMS

We aimed to classify molecular subtypes and establish a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in Stomach adenocarcinoma (STAD).

BACKGROUND

STAD is a common diagnosed gastrointestinal malignancy and its heterogeneity is a big challenge that influences prognosis and precision therapies. Present study was designed to classify molecular subtypes and construct a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in STAD.

OBJECTIVE

The objective of this study is to investigate the molecular subtypes and prognostic model for STAD.

METHODS

A STAD specific miRNA-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network was generated using the RNA-Seq and miRNA expression profiles from The Cancer Genome Atlas (TCGA) database, in which miRNA-related mRNAs were screened. Molecular subtypes were then determined using miRNA-related genes. Through univariate Cox analysis and multivariate regression analysis, a prognostic model was established in GSE84437 Train dataset and validated in GSE84437 Test, TCGA, GSE84437 and GSE66229 datasets. Immunotherapy datasets were employed for assessing the performance of the risk model. Finally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was applied to validate the expression of hub genes used for the risk score signature.

RESULTS

We constructed a ceRNA network containing 84 miRNAs and 907 mRNAs and determined two molecular subtypes based on 26 genes from the intersection of TCGASTAD and GSE84437 datasets. Subtype S2 had poor prognosis, lower tumor mutational burden, higher immune score and lower response to immunotherapy. Subtype S1 was more sensitive to Sorafenib, Pyrimethamine, Salubrinal, Gemcitabine, Vinorelbine and AKT inhibitor VIII. Next, a five-gene signature was generated and its robustness was validated in Test and external datasets. This risk model also had a good prediction performance in immunotherapy datasets.

CONCLUSION

This study promotes the underlying mechanisms of miRNA-based genes in STAD and offers directions for classification. A five-gene signature accurately predicts the prognosis and helps therapeutic options.

Analysis of multiple programmed cell death-related prognostic genes and functional validations of necroptosis-associated genes in oesophageal squamous cell carcinoma

BACKGROUND

Oesophageal squamous cell carcinoma (ESCC) is a lethal malignancy. Immune checkpoint inhibitors (ICIs) showed great clinical benefits for patients with ESCC. We aimed to construct a model predicting prognosis and response to ICIs by integrating diverse programmed cell death (PCD) forms.

METHODS

Genes related to 14 PCDs were collected to generate multi-gene signatures, including apoptosis, necroptosis, pyroptosis, ferroptosis, and cuproptosis. Bulk and single-cell RNA transcriptome datasets were used to develop and validate the model. We assessed the functions of two necroptosis-related genes in ESCC cells by Western blot, co-immunoprecipitation (Co-IP), LDH release assay, CCK-8, and migration assay, followed by immunohistochemistry (IHC) staining on samples of patients with ESCC (n = 67).

FINDINGS

We built and validated a 16-gene prognostic combined cell death index (CCDI) by combining immunogenic cell death (ICD) and necroptosis signatures. The CCDI could also predict response to ICIs in cancer, as shown by Tumour Immune Dysfunction and Exclusion (TIDE) analysis, confirmed in four independent ICI clinical trials. Trajectory analysis revealed that HOOK1 and CUL4A might affect ESCC cell fate. We found that HOOK1 induced necroptosis and inhibited the proliferation and migration of ESCC cells, while CUL4A exhibited the opposite effects. Co-IP assay confirmed that HOOK1 and CUL4A promoted and reduced necrosome formation in ESCC cells. Data from patients with ESCC further supported that HOOK1 and CUL4A might be a tumour suppressor and oncogene, respectively.

INTERPRETATION

We constructed a CCDI model with potential in predicting prognosis and response to ICIs in cancer. HOOK1 and CUL4A in the CCDI model are crucial prognostic biomarkers in ESCC.

FUNDING

The Natural Science Foundation of China [82172786], The National Cancer Center Climbing Fund of China [NCC201908B06], The Natural Science Foundation of Heilongjiang Province [LH2021H077].

p53 Gain-of-Function Mutation Induces Metastasis via BRD4-Dependent CSF-1 Expression

TP53 mutations are frequent in esophageal squamous cell carcinoma (ESCC) and other SCCs and are associated with a proclivity for metastasis. Here, we report that colony-stimulating factor-1 (CSF-1) expression is upregulated significantly in a p53-R172H-dependent manner in metastatic lung lesions of ESCC. The p53-R172H-dependent CSF-1 signaling, through its cognate receptor CSF-1R, increases tumor cell invasion and lung metastasis, which in turn is mediated in part through Stat3 phosphorylation and epithelial-to-mesenchymal transition (EMT). In Trp53R172H tumor cells, p53 occupies the Csf-1 promoter. The Csf-1 locus is enriched with histone 3 lysine 27 acetylation (H3K27ac), which is likely permissive for fostering an interaction between bromodomain-containing domain 4 (BRD4) and p53-R172H to regulate Csf-1 transcription. Inhibition of BRD4 not only reduces tumor invasion and lung metastasis but also reduces circulating CSF-1 levels. Overall, our results establish a novel p53-R172H-dependent BRD4-CSF-1 axis that promotes ESCC lung metastasis and suggest avenues for therapeutic strategies for this difficult-to-treat disease.

SIGNIFICANCE

The invasion-metastasis cascade is a recalcitrant barrier to effective cancer therapy. We establish that the p53-R172H-dependent BRD4-CSF-1 axis is a mediator of prometastatic properties, correlates with patient survival and tumor stages, and its inhibition significantly reduces tumor cell invasion and lung metastasis. This axis can be exploited for therapeutic advantage. This article is featured in Selected Articles from This Issue, p. 2489.

Phenotypic signatures of circulating neoantigen-reactive CD8+ T cells in patients with metastatic cancers

Circulating T cells from peripheral blood (PBL) can provide a rich and noninvasive source for antitumor T cells. By single-cell transcriptomic profiling of 36 neoantigen-specific T cell clones from 6 metastatic cancer patients, we report the transcriptional and cell surface signatures of antitumor PBL-derived CD8+ T cells (NeoTCRPBL). Comparison of tumor-infiltrating lymphocyte (TIL)- and PBL-neoantigen-specific T cells revealed that NeoTCRPBL T cells are low in frequency and display less-dysfunctional memory phenotypes relative to their TIL counterparts. Analysis of 100 antitumor TCR clonotypes indicates that most NeoTCRPBL populations target the same neoantigens as TILs. However, NeoTCRPBL TCR repertoire is only partially shared with TIL. Prediction and testing of NeoTCRPBL signature-derived TCRs from PBL of 6 prospective patients demonstrate high enrichment of clonotypes targeting tumor mutations, a viral oncogene, and patient-derived tumor. Thus, the NeoTCRPBL signature provides an alternative source for identifying antitumor T cells from PBL of cancer patients, enabling immune monitoring and immunotherapies.

Oncolytic adenoviruses expressing checkpoint inhibitors for cancer therapy

Despite the remarkable success of immune checkpoint inhibitors (ICIs), primary resistance to ICIs causes only subsets of patients to achieve durable responses due to the complex tumor microenvironment (TME). Oncolytic viruses (OVs) can overcome the immunosuppressive TME and promote systemic antitumor immunity in hosts. Engineered OVs armed with ICIs would likely have improved effectiveness as a cancer therapy. According to the diverse immune cell landscapes among different types of tumors, we rationally and precisely generated three recombinant oncolytic adenoviruses (OAds): OAd-SIRPα-Fc, OAd-Siglec10-Fc and OAd-TIGIT-Fc. These viruses were designed to locally deliver SIRPα-Fc, Siglec10-Fc or TIGIT-Fc fusion proteins recognizing CD47, CD24 or CD155, respectively, in the TME to achieve enhanced antitumor effects. Our results suggested that OAd-SIRPα-Fc and OAd-Siglec10-Fc both showed outstanding efficacy in tumor suppression of macrophage-dominated tumors, while OAd-TIGIT-Fc showed the best antitumor immunity in CD8+ T-cell-dominated tumors. Importantly, the recombinant OAds activated an inflammatory immune response and generated long-term antitumor memory. In addition, the combination of OAd-Siglec10-Fc with anti-PD-1 significantly enhanced the antitumor effect in a 4T1 tumor model by remodeling the TME. In summary, rationally designed OAds expressing ICIs tailored to the immune cell landscape in the TME can precisely achieve tumor-specific immunotherapy of cancer.

Crosstalk between copper homeostasis and cuproptosis reveals a lncRNA signature to prognosis prediction, immunotherapy personalization, and agent selection for patients with lung adenocarcinoma

BACKGROUND

Copper homeostasis and cuproptosis play critical roles in various biological processes of cancer; however, whether they can impact the prognosis of lung adenocarcinoma (LUAD) remain to be fully elucidated. We aimed to adopt these concepts to create and validate a lncRNA signature for LUAD prognostic prediction.

METHODS

For this study, the TCGA-LUAD dataset was used as the training cohort, and multiple datasets from the GEO database were pooled as the validation cohort. Copper homeostasis and cuproptosis regulated genes were obtained from published studies, and various statistical methods, including Kaplan-Meier (KM), Cox, and LASSO, were used to train our gene signature CoCuLncSig. We utilized KM analysis, COX analysis, receiver operating characteristic analysis, time-dependent AUC analysis, principal component analysis, and nomogram predictor analysis in our validation process. We also compared CoCuLncSig with previous studies. We performed analyses using R software to evaluate CoCuLncSig's immunotherapeutic ability, focusing on eight immune algorithms, TMB, and TIDE. Additionally, we investigated potential drugs that could be effective in treating patients with high-risk scores. Additionally qRT-PCR examined the expression patterns of CoCuLncSig lncRNAs, and the ability of CoCuLncSig in pan-cancer was also assessed.

RESULTS

CoCuLncSig containing eight lncRNAs was trained and showed strong predictive ability in the validation cohort. Compared with previous similar studies, CoCuLncSig had more prognostic ability advantages. CoCuLncSig was closely related to the immune status of LUAD, and its tight relationship with checkpoints IL10, IL2, CD40LG, SELP, BTLA, and CD28 may be the key to its potential immunotherapeutic ability. For the high CoCuLncSig score population, we found 16 drug candidates, among which epothilone-b and gemcitabine may have the most potential. The pan-cancer analysis found that CoCuLncSig was a risk factor in multiple cancers. Additionally, we discovered that some of the CoCuLncSig lncRNAs could play crucial roles in specific cancer types.

CONCLUSION

The current study established a powerful prognostic CoCuLncSig signature for LUAD that was also valid for most pan-cancers. This signature could serve as a potential target for immunotherapy and might help the more efficient application of drugs to specific populations.

Mutant RB1 enhances therapeutic efficacy of PARPis in lung adenocarcinoma by triggering the cGAS/STING pathway

Poly (ADP-ribose) polymerase inhibitors (PARPis) are approved for cancer therapy according to their synthetic lethal interactions, and clinical trials have been applied in non-small cell lung cancer. However, the therapeutic efficacy of PARPis in lung adenocarcinoma (LUAD) is still unknown. We explored the effect of a mutated retinoblastoma gene (RB1) on PARPi sensitivity in LUAD. Bioinformatic screening was performed to identify PARPi-sensitive biomarkers. Here, we showed that viability of LUAD cell lines with mutated RB1 was significantly decreased by PARPis (niraparib, rucaparib, and olaparib). RB1 deficiency induced genomic instability, prompted cytosolic double-stranded DNA (dsDNA) formation, activated the cGAS/STING pathway, and upregulated downstream chemokines CCL5 and CXCL10, triggering immune cell infiltration. Xenograft experiments indicated that PARPi treatment reduced tumorigenesis in RB1-KO mice. Additionally, single-cell RNA sequencing analysis showed that malignant cells with downregulated expression of RB1 had more communications with other cell types, exhibiting activation of specific signaling such as GAS, IFN response, and antigen-presenting and cytokine activities. Our findings suggest that RB1 mutation mediates the sensitivity to PARPis through a synthetic lethal effect by triggering the cGAS/STING pathway and upregulation of immune infiltration in LUAD, which may be a potential therapeutic strategy.

Innovative molecular subtypes of multiple signaling pathways in colon cancer and validation of FMOD as a prognostic-related marker

PURPOSE

Colon cancer is highly heterogeneous in terms of the immune and stromal microenvironment, genomic integrity, and oncogenic properties; therefore, molecular subtypes of the four characteristic dimensions are expected to provide novel clues for immunotherapy of colon cancer.

METHODS

According to the enrichment of four dimensions, we performed consensus cluster analysis and identified three robust molecular subtypes for colon cancer, namely immune enriched, immune deficiency, and stroma enriched. We characterized and validated the immune infiltration, gene mutations, copy number variants, methylation, protein expression, and clinical features in different datasets. Finally, we developed an 8-gene risk prognostic model and proposed the innovative RiskScore. In addition, a nomogram model was constructed combining clinical characteristics and RiskScore to validate its excellent clinical predictive power.

RESULTS

Combining clinical patient tissue samples and histochemical microarray data, we found that high FMOD expression in tumor epithelial cells was associated with poorer patient prognosis, but FMOD expression in the mesenchyme was not associated with prognosis. In pan-cancer, RiskScore, a prognostic model constructed based on characteristic pathway scores, was a poor prognostic factor for malignancy and was negatively associated with immunotherapy response.

CONCLUSION

The identification of molecular subtypes could provide innovative ideas for immunotherapy of colon cancer.

Pan-cancer landscape of immunology PIWI-interacting RNAs

PIWI-interacting RNAs (piRNAs), an emergent type of non-coding RNAs during oncogenesis, play critical roles in regulating tumor microenvironment. Systematic analysis of piRNAs' roles in modulating immune pathways is important for tumor immunotherapy. In this study, in-depth analysis of piRNAs was performed to develop an integrated computational algorithm, the immunology piRNA (ImmPI) pipeline, for uncovering the global expression landscape of piRNAs and identifying their regulatory roles in immune pathways. The immunology piRNAs show a tendency towards overexpression patterns in immune cells, causing perturbations in tumors, being significantly associated with infiltration of immune cells, and having prognostic value. The ImmPI score can contribute to prioritizing tumor-related piRNAs and distinguish two subtypes of SKCM (immune-cold and hot phenotypes), as characterized by different prognoses, immunogenicity and antitumor immunity. Finally, we developed an interactive web resource (ImmPI portal: http://www.hbpding.com/ImmPi) for the biomedical research community, with several useful modules to browse, visualize, and download the results of immunology piRNAs analysis. Overall, our work provides a comprehensive landscape of piRNAs across multiple cancer types and sheds light on their regulatory and functional roles in tumor immunity. These findings pave the way for future research and development of piRNA-based immunotherapies for cancer treatment.

The tumor multi-omic landscape of endometrial cancers developed on a germline genetic background of adiposity

High body mass index (BMI) is a causal risk factor for endometrial cancer but the tumor molecular mechanisms affected by adiposity and their therapeutic relevance remain poorly understood. Here we characterize the tumor multi-omic landscape of endometrial cancers that have developed on a background of lifelong germline genetic exposure to elevated BMI. We built a polygenic score (PGS) for BMI in women using data on independent, genome-wide significant variants associated with adult BMI in 434,794 women. We performed germline (blood) genotype quality control and imputation on data from 354 endometrial cancer cases from The Cancer Genome Atlas (TCGA). We assigned each case in this TCGA cohort their genetically predicted life-course BMI based on the BMI PGS. Multivariable generalized linear models adjusted for age, stage, microsatellite status and genetic principal components were used to test for associations between the BMI germline PGS and endometrial cancer tumor genome-wide genomic, transcriptomic, proteomic, epigenomic and immune traits in TCGA. High BMI germline PGS was associated with (i) upregulated tumor gene expression in the IL6-JAK-STAT3 pathway (FDR=4.2×10-7); (ii) increased estimated intra-tumor activated mast cell infiltration (FDR=0.008); (iii) increased single base substitution (SBS) mutational signatures 1 (FDR=0.03) and 5 (FDR=0.09) and decreased SBS13 (FDR=0.09), implicating age-related and APOBEC mutagenesis, respectively; and (iv) decreased tumor EGFR protein expression (FDR=0.07). Alterations in IL6-JAK-STAT3 signaling gene and EGFR protein expression were, in turn, significantly associated with both overall survival and progression-free interval. Thus, we integrated germline and somatic data using a novel study design to identify associations between genetically predicted lifelong exposure to higher BMI and potentially actionable endometrial cancer tumor molecular features. These associations inform our understanding of how high BMI may influence the development and progression of this cancer, impacting endometrial tumor biology and clinical outcomes.

Construction of Immune Infiltration-Related LncRNA Signatures Based on Machine Learning for the Prognosis in Colon Cancer

Colon cancer is one of the malignant tumors with high morbidity, lethality, and prevalence across global human health. Molecular biomarkers play key roles in its prognosis. In particular, immune-related lncRNAs (IRL) have attracted enormous interest in diagnosis and treatment, but less is known about their potential functions. We aimed to investigate dysfunctional IRL and construct a risk model for improving the outcomes of patients. Nineteen immune cell types were collected for identifying house-keeping lncRNAs (HKLncRNA). GSE39582 and TCGA-COAD were treated as the discovery and validation datasets, respectively. Four machine learning algorithms (LASSO, Random Forest, Boruta, and Xgboost) and a Gaussian mixture model were utilized to mine the optimal combination of lncRNAs. Univariate and multivariate Cox regression was utilized to construct the risk score model. We distinguished the functional difference in an immune perspective between low- and high-risk cohorts calculated by this scoring system. Finally, we provided a nomogram. By leveraging the microarray, sequencing, and clinical data for immune cells and colon cancer patients, we identified the 221 HKLncRNAs with a low cell type-specificity index. Eighty-seven lncRNAs were up-regulated in the immune compared to cancer cells. Twelve lncRNAs were beneficial in improving performance. A risk score model with three lncRNAs (CYB561D2, LINC00638, and DANCR) was proposed with robust ROC performance on an independent dataset. According to immune-related analysis, the risk score is strongly associated with the tumor immune microenvironment. Our results emphasized IRL has the potential to be a powerful and effective therapy for enhancing the prognostic of colon cancer.

Pan-cancer analysis of NFE2L2 mutations identifies a subset of lung cancers with distinct genomic and improved immunotherapy outcomes

BACKGROUND

Mutations in the KEAP1-NFE2L2 signaling pathway were linked to increased tumorigenesis and aggressiveness. Interestingly, not all hotspot mutations on NFE2L2 were damaging; some even were activating. However, there was conflicting evidence about the association between NFE2L2 mutation and Nrf2-activating mutation and responsiveness to immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) and other multiple cancers.

METHODS

The study with the largest sample size (n = 49,533) explored the landscape of NFE2L2 mutations and their impact response/resistance to ICIs using public cohorts. In addition, the in-house WXPH cohort was used to validate the efficacy of immunotherapy in the NFE2L2 mutated patients with NSCLC.

RESULTS

In two pan-cancer cohorts, Nrf2-activating mutation was associated with higher TMB value compared to wild-type. We identified a significant association between Nrf2-activating mutation and shorter overall survival in pan-cancer patients and NSCLC patients but not in those undergoing ICIs treatment. Similar findings were obtained in cancer patients carrying the NFE2L2 mutation. Furthermore, in NSCLC and other cancer cohorts, patients with NFE2L2 mutation demonstrated more objective responses to ICIs than patients with wild type. Our in-house WXPH cohort further confirmed the efficacy of immunotherapy in the NFE2L2 mutated patients with NSCLC. Lastly, decreased inflammatory signaling pathways and immune-depleted immunological microenvironments were enriched in Nrf2-activating mutation patients with NSCLC.

CONCLUSIONS

Our study found that patients with Nrf2-activating mutation had improved immunotherapy outcomes than patients with wild type in NSCLC and other tumor cohorts, implying that Nrf2-activating mutation defined a distinct subset of pan-cancers and might have implications as a biomarker for guiding ICI treatment, especially NSCLC.

The Role and Therapeutic Targeting of CCR5 in Breast Cancer

The G-protein-coupled receptor C-C chemokine receptor 5 (CCR5) functions as a co-receptor for the entry of HIV into immune cells. CCR5 binds promiscuously to a diverse array of ligands initiating cell signaling that includes guided migration. Although well known to be expressed on immune cells, recent studies have shown the induction of CCR5 on the surface of breast cancer epithelial cells. The function of CCR5 on breast cancer epithelial cells includes the induction of aberrant cell survival signaling and tropism towards chemo attractants. As CCR5 is not expressed on normal epithelium, the receptor provides a potential useful target for therapy. Inhibitors of CCR5 (CCR5i), either small molecules (maraviroc, vicriviroc) or humanized monoclonal antibodies (leronlimab) have shown anti-tumor and anti-metastatic properties in preclinical studies. In early clinical studies, reviewed herein, CCR5i have shown promising results and evidence for effects on both the tumor and the anti-tumor immune response. Current clinical studies have therefore included combination therapy approaches with checkpoint inhibitors.

Non-spatial and spatial heterogeneity revealed a suppressive immune feature of Siglec-15 in lung adenocarcinomas

BACKGROUND

Sialic acid-binding immunoglobulin-like lectin-15 (Siglec-15) has emerged as a novel immunotherapy candidate, which deserves a comprehensive investigation in lung adenocarcinoma (LUAD).

METHODS

Multiplex fluorescence-based immunohistochemistry was conducted to assess Siglec-15 expression and tumor-infiltrating immune cells in LUAD from Tianjin cohort, with validation cohorts Xinchao 04 and 07.

RESULTS

This study revealed that Siglec-15 was positively correlated with CD8+ T cells and tumor-associated macrophages (TAMs) infiltration, but CD8+ T cells were mostly infiltrated in the stroma area, not in the tumor area. Spatially, fewer CD8+ T cells surrounded Siglec-15+ tumor cells in PD-L1- cells, and more TAMs surrounded Siglec-15+ tumor cells in PD-L1-/+ cells. Siglec-15+ TAMs infiltrated with more CD8+ T cells, and were closer to CD8+ T cells than Siglec-15- TAMs and Siglec-15+ tumor cells. Siglec-15+ TAMs infiltrated with more Tregs and were closer to Tregs than Siglec-15+ tumor cells. Siglec-15+ tumor cells or TAMs reversed CD8+ T cells prognosis value, and enhanced the prognosis value of Tregs and TAMs. The immunotyping based on Siglec-15 and CD8A / CD8+ T cells revealed that patients with high CD8A and Siglec-15 expression exhibited immune activation. Patients with low CD8A expression / CD8+ T cells infiltration and Siglec-15 overexpression were related to the activation of immunosuppressive signature and metabolism-related pathway, and infiltrated with more TAMs.

CONCLUSIONS

We revealed the distinct characteristics between Siglec-15+ tumor cells and TAMs in relation to CD8+ T cells, and a unique relationship between Siglec-15 and immunosuppressive TIME in LUAD, which may provide potential value for anti-Siglec-15 therapy.

SHP2 is involved in the occurrence, development and prognosis of cancer

Src homology-2 domain-containing protein tyrosine phosphatase (SHP2), encoded by protein tyrosine phosphatase non-receptor type 11 (PTPN11), is widely expressed in several human tissue types, and plays an important role in a variety of diseases. The present study assessed the impact of SHP2 on the occurrence, development and prognosis of solid tumors. The transcriptome sequencing data of 33 cancer types were downloaded from The Cancer Genome Atlas database. Clinical information of the corresponding patients, tumor mutational burden and information pertinent to microsatellite instability were also downloaded. The log-rank test and univariate Cox's regression test were used to evaluate patient survival. The 'ESTIMATE' method was used to assess the tumor microenvironment, and the 'CIBERSORT' algorithm was used to evaluate tumor immune cell infiltration. Spearman's correlation analysis was used to evaluate the correlation between SHP2 expression and the targets identified. ELISA was used to assess the SHP2 expression levels in peripheral blood samples of patients with breast, ovarian, endometrial and cervical cancer. The data indicated that the expression levels of SHP2 were increased in a variety of tumor tissues, and were associated with tumor progression and prognosis. In peripheral blood, the positive rates of SHP2 expression in breast cancer (71.43%) and ovarian cancer (58.82%) were significantly higher than those in the corresponding control groups. However, the positive rates of SHP2 expression in patients with endometrial cancer (31.03%) and cervical cancer (41.30%) were significantly lower than those in the corresponding control groups. Increased SHP2 expression improved overall survival (OS) and disease free survival (DFS) time in patients with kidney renal clear cell carcinoma. However, increased SHP2 expression reduced OS and DFS in patients with urothelial carcinoma, and cervical and endocervical cancer types. Moreover, the elevated expression of SHP2 could also reduce the OS of patients with breast invasive carcinoma, mesothelioma and liver hepatocellular carcinoma. PTPN11 expression was associated with the tumor microenvironment of various tumor types. The tumor mutational burden of various tumor types was associated with microsatellite instability. PTPN11 inhibited T-cell activation and promoted M2 macrophage activation in several tumors. Therefore, SHP2 may be used in the evaluation of tumor progression and prognosis, and it may be an optimal potential biological target for cancer therapy.

Prognostic utility of TME-associated genes in pancreatic cancer

Background: Pancreatic cancer (PC) is a deadly disease. The tumor microenvironment (TME) participates in PC oncogenesis. This study focuses on the assessment of the prognostic and treatment utility of TME-associated genes in PC. Methods: After obtaining the differentially expressed TME-related genes, univariate and multivariate Cox analyses and least absolute shrinkage and selection operator (LASSO) were performed to identify genes related to prognosis, and a risk model was established to evaluate risk scores, based on The Cancer Genome Atlas (TCGA) data set, and it was validated by external data sets from the Gene Expression Omnibus (GEO) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Multiomics analyses were adopted to explore the potential mechanisms, discover novel treatment targets, and assess the sensitivities of immunotherapy and chemotherapy. Results: Five TME-associated genes, namely, FERMT1, CARD9, IL20RB, MET, and MMP3, were identified and a risk score formula constructed. Next, their mRNA expressions were verified in cancer and normal pancreatic cells. Multiple algorithms confirmed that the risk model displayed a reliable ability of prognosis prediction and was an independent prognostic factor, indicating that high-risk patients had poor outcomes. Immunocyte infiltration, gene set enrichment analysis (GSEA), and single-cell analysis all showed a strong relationship between immune mechanism and low-risk samples. The risk score could predict the sensitivity of immunotherapy and some chemotherapy regimens, which included oxaliplatin and irinotecan. Various latent treatment targets (LAG3, TIGIT, and ARID1A) were addressed by mutation landscape based on the risk model. Conclusion: The risk model based on TME-related genes can reflect the prognosis of PC patients and functions as a novel set of biomarkers for PC therapy.

Combining single-cell sequencing and spatial transcriptome sequencing to identify exosome-related features of glioblastoma and constructing a prognostic model to identify BARD1 as a potential therapeutic target for GBM patients

Background

Glioblastoma (GBM) is a malignant primary brain tumor. This study focused on exploring the exosome-related features of glioblastoma to better understand its cellular composition and molecular characteristics.

Methods

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptome RNA sequencing (stRNA-seq) were used to analyze the heterogeneity of glioblastomas. After data integration, cell clustering, and annotation, five algorithms were used to calculate scores for exosome-related genes(ERGs). Cell trajectory analysis and intercellular communication analysis were performed to explore exosome-related communication patterns. Spatial transcriptome sequencing data were analyzed to validate the findings. To further utilize exosome-related features to aid in clinical decision-making, a prognostic model was constructed using GBM's bulk RNA-seq.

Results

Different cell subpopulations were observed in GBM, with Monocytes/macrophages and malignant cells in tumor samples showing higher exosome-related scores. After identifying differentially expressed ERGs in malignant cells, pseudotime analysis revealed the cellular status of malignant cells during development. Intercellular communication analysis highlighted signaling pathways and ligand-receptor interactions. Spatial transcriptome sequencing confirmed the high expression of exosome-related gene features in the tumor core region. A prognostic model based on six ERGs was shown to be predictive of overall survival and immunotherapy outcome in GBM patients. Finally, based on the results of scRNA-seq and prognostic modeling as well as a series of cell function experiments, BARD1 was identified as a novel target for the treatment of GBM.

Conclusion

This study provides a comprehensive understanding of the exosome-related features of GBM in both scRNA-seq and stRNA-seq, with malignant cells with higher exosome-related scores exhibiting stronger communication with Monocytes/macrophages. In terms of spatial data, highly scored malignant cells were also concentrated in the tumor core region. In bulk RNA-seq, patients with a high exosome-related index exhibited an immunosuppressive microenvironment, which was accompanied by a worse prognosis as well as immunotherapy outcomes. Prognostic models constructed using ERGs are expected to be independent prognostic indicators for GBM patients, with potential implications for personalized treatment strategies for GBM. Knockdown of BARD1 in GBM cell lines reduces the invasive and value-added capacity of tumor cells, and thus BARD1-positively expressing malignant cells are a risk factor for GBM patients.

A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers

Background: The tumor-associated endothelial cell (TAE) component plays a vital role in tumor immunity. However, systematic tumor-associated endothelial-related gene assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers have not been explored. Herein, we investigated a TAE gene risk model to predict CIT responses and patient survival in a pan-cancer analysis. Methods: We analyzed publicly available datasets of tumor samples with gene expression and clinical information, including gastric cancer, metastatic urothelial cancer, metastatic melanoma, non-small cell lung cancer, primary bladder cancer, and renal cell carcinoma. We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Results: The model demonstrated a high predictive accuracy in both training and validation cohorts. The response rate of the high score group to immunotherapy in the training cohort was significantly higher than that of the low score group, with CIT response rates of 51% and 27%, respectively. The survival analysis showed that the prognosis of the high score group was significantly better than that of the low score group (all p < 0·001). Tumor-associated endothelial gene signature scores positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may benefit patients in the high score group. The analysis of TAE scores across 33 human cancers revealed that the TAE model could reflect immune cell infiltration and predict the survival of cancer patients. Conclusion: The TAE signature model could represent a CIT response prediction model with a prognostic value in multiple cancer types.

Molecular Evolutionary Landscape of the Immune Microenvironment of Head and Neck Cancer

Head and neck cancer is a highly heterogeneous malignant tumor. Numerous studies have shown that the immune microenvironment of head and neck cancer has a significant impact on its occurrence and development, as well as its prognosis. However, there have been fewer studies related to the accurate immunophenotyping of head and neck cancer. In this study, we used gene expression profile information and clinical information from the TCGA-HNSC cohort (502 samples) and the GSE655858 cohort (270 samples) to identify and independently validate three immune subtypes (Cluster1-Cluster3) with different immune-related molecular profiles and clinical outcomes. Cluster2, which is mainly dominated by B-lymphocyte infiltration, was found to have the best prognosis. In addition, a support vector machine (SVM)-based classifier was constructed, which could accurately classify HNSC based on 19 genes. Furthermore, the results of the prognostic analysis showed activation of antibody-secreting B-lymphocyte function, which showed a good prognostic effect in all three immune subtypes of HNSC. Finally, the immune evolutionary landscape of HNSC was constructed in an attempt to explain the evolutionary pattern of the immune subtypes of HNSC. In summary, we provide a conceptual framework for understanding the tumor immune microenvironment in HNSC and demonstrate the importance of immune infiltration of B lymphocytes in HNSC. Further research is needed to assess the importance of these immunophenotypes in combination drug therapy and to provide a basis for screening appropriate patients for immunotherapy.

High infiltration of CD209+ dendritic cells and CD163+ macrophages in the peritumor area of prostate cancer is predictive of late adverse outcomes

Introduction

Prostate cancer (PCa) shows considerable variation in clinical outcomes between individuals with similar diseases. The initial host-tumor interaction as assessed by detailed analysis of tumor infiltrating immune cells within the primary tumor may dictate tumor evolution and late clinical outcomes. In this study, we assessed the association between clinical outcomes and dendritic cell (DC) or macrophage (MΦ) tumor infiltration as well as with expression of genes related to their functions.

Methods

Infiltration and localization of immature DC, mature DC, total MΦ and M2-type MΦ was analyzed by immunohistochemistry in 99 radical prostatectomy specimens from patients with 15.5 years median clinical follow-up using antibodies against CD209, CD83, CD68 and CD163, respectively. The density of positive cells for each marker in various tumor areas was determined. In addition, expression of immune genes associated with DC and MΦ was tested in a series of 50 radical prostatectomy specimens by Taqman Low-Density Array with similarly long follow-up. Gene expression was classified as low and high after unsupervised hierarchical clustering. Numbers and ratio of positive cells and levels of gene expression were correlated with endpoints such as biochemical recurrence (BCR), need for definitive androgen deprivation therapy (ADT) or lethal PCa using Cox regression analyses and/or Kaplan-Meier curves.

Results

Positive immune cells were observed in tumor, tumor margin, and normal-like adjacent epithelium areas. CD209⁺ and CD163⁺ cells were more abundant at the tumor margin. Higher CD209⁺/CD83⁺ cell density ratio at the tumor margin was associated with higher risk of ADT and lethal PCa while higher density of CD163⁺ cells in the normal-like adjacent epithelium was associated with a higher risk of lethal PCa. A combination of 5 genes expressed at high levels correlated with a shorter survival without ADT and lethal PCa. Among these five genes, expression of IL12A and CD163 was correlated to each other and was associated with shorter survival without BCR and ADT/lethal PCa, respectively.

Conclusion

A higher level of infiltration of CD209⁺ immature DC and CD163⁺ M2-type MΦ in the peritumor area was associated with late adverse clinical outcomes.

An integrated bioinformatic investigation of focal adhesion-related genes in glioma followed by preliminary validation of COL1A2 in tumorigenesis

Focal adhesions (FAs) allow cells to contact the extracellular matrix, helping to maintain tension and enabling signal transmission in cell migration, differentiation, and apoptosis. In addition, FAs are associated with changes in the tumor microenvironment (TME) that lead to malignant progression and drug resistance in tumors. However, there are still few studies on the comprehensive analysis of focal adhesion-related genes (FARGs) in glioma. Expression data and clinical information of glioma samples were downloaded from public databases. Two distinct molecular subtypes were identified based on FARGs using an unsupervised consensus clustering algorithm. A scoring system consisting of nine FARGs was constructed using integrated LASSO regression and multivariate Cox regression. It not only has outstanding prognostic value but also can guide immunotherapy of glioma patients, which was verified in TCGA, CGGA, GSE16011, and IMvigor210 cohorts. The results of bioinformatics analysis, immunohistochemistry staining, and western blotting all revealed that the expression of COL1A2 was up-regulated in glioblastoma and related to poor prognosis outcomes in patients from public datasets. COL1A2 promotes the proliferation, migration, and invasion of glioblastoma cells. A positive correlation between COL1A2 and CD8 was determined in GBM specimens from eight patients. Moreover, the results of cell co-cultured assay showed that COL1A2 participated in the killing of GBM cells by Jurkat cells. Our study indicates that the FARGs have prominent application value in the identification of molecular subtypes and prediction of survival outcomes in glioma patients. Bioinformatics analysis and experimental verification provide a direction for further research on FARGs.

Pan-cancer analyses of clinical prognosis, immune infiltration, and immunotherapy efficacy for TRPV family using multi-omics data

Background

Transient receptor potential cation channel subfamily V (TRPV) play an essential in cancer initiation, progression, and treatment. TRPV expression alteration are shown relate to multiple cancers prognosis and treatment of cancers but are less-studied in pan-cancer. In this study, we characterize the clinical prediction value of TRPV at pan-cancer level.

Methods

Several databases were used to examine the transcript expression difference in tumor vs. normal tissue, copy-number variant (CNV) and single nucleotide polymorphisms (SNP) mutation of each TRPV members in pan-cancer, including The Cancer Genome Atlas (TCGA) and cBioPortal. We performed K-M survival curve and univariate Cox regression analyses to identify survival and prognosis value of TRPV. CellMiner were selected to explore drug sensitivity. We also analyzed association between tumor mutation burden (TMB), microsatellite instability (MSI), tumor immune microenvironment and TRPV family genes expression. Moreover, we investigated the relationship between TRPVs expression and effectiveness of immunotherapy in multiple cohorts, including one melanoma (GSE78220), one renal cell carcinoma (GSE67501), and three bladder cancer cohorts (GSE111636, IMvigor210, GSE176307 and our own sequencing dataset (TRUCE-01)), and further analyzed the changes of TRPVs expression before and after treatment (tislelizumab combined with nab-paclitaxel) of bladder cancer. Next, we made a special effort to investigate and study biological functions of TRPV in bladder cancer using gene set enrichment analysis (GSEA), and conducted immune infiltration analysis with TRPVs family genes expression, copy number or somatic mutations of bladder cancer by TIMER 2.0. Finally, real-time PCR and protein expression validation of TRPVs within 10 paired cancer and para-carcinoma tissue samples, were also performed in bladder cancer.

Results

Only TRPV2 expression was lower in most cancer types among TRPV family genes. All TRPVs were correlated with survival changes. Amplification was the significant gene alternation in all TRPVs. Next, analysis between TRPVs and clinical traits showed that TRPVs were related to pathologic stage, TNM stage and first course treatment outcome. Moreover, TRPV expression was highly correlated with MSI and TMB. Immunotherapy is a research hotspot at present, our result showed the significant association between TRPVs expression and immune infiltration indicated that TRPV expression alternation could be used to guide prognosis. In addition, we also discovered that the expression level of TRPV1/2/3/4/6 was positively or negatively correlated with objective responses to anti-PD-1/PD-L1 across multiple immunotherapy cohort. Further analysis of drug sensitivity showed the value to treatment. Based on the above analysis, we next focused on TRPV family in bladder cancer. The result demonstrated TRPV also played an important role in bladder cancer. Finally, qPCR assay verified our analysis in bladder cancer.

Conclusion

Our study firstly revealed expression and genome alternation of TRPV in pan-cancer. TRPV could be used to predict prognosis or instructing treatment of human cancers, especially bladder cancer.

Lactate metabolism-related genes to predict the clinical outcome and molecular characteristics of endometrial cancer

BACKGROUND

Metabolic reprogramming is one of hallmarks of cancer progression and is of great importance for the tumor microenvironment (TME). As an abundant metabolite, lactate has been found to play a critical role in cancer development and immunosuppression of TME. However, the potential role of lactate metabolism-related genes in endometrial cancer (EC) remains obscure.

METHODS

RNA sequencing data and clinical information of EC were obtained from The Cancer Genome Atlas (TCGA) database. Lactate metabolism-related genes (LMRGs) WERE from Molecular Signature Database v7.4 and then compared the candidate genes from TCGA to obtain final genes. Univariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were performed to screen prognostic genes. A lactate metabolism-related risk profile was constructed using multivariate Cox regression analysis. The signature was validated by time-dependent ROC curve analysis and Kaplan-Meier analysis. The relationship between the risk score and age, grade, stage, tumor microenvironmental characteristics, and drug sensitivity was as well explored by correlation analyses. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional analysis between the high and low-risk groups were performed. CCK8, EdU, and clone formation assays were applied to detect the proliferation ability of EC cells, Transwell assay was performed to detect the migration ability of EC cells, and intracellular lactate and glucose content was used to asses lactate metabolism.

RESULTS

We constructed a risk signature based on 18 LMRGs. Kaplan-Meier curves confirmed that the high-risk group had poorer prognosis compared to the low-risk group. A nomogram was then constructed to predict the probability of EC survival. We also performed GO enrichment analysis and KEGG pathway functional analysis between the high and low-risk groups, and the outcome revealed that the features were significantly associated with energy metabolism. There was a significant correspondence between LMRGs and tumor mutational load, checkpoints and immune cell infiltration. C1, C2, and C4 were the most infiltrated in the high-risk group. The high-risk group showed increased dendritic cell activation, while the low-risk group showed increased plasma cells and Treg cells. Drug sensitivity analysis showed LMRGs risk was more resistant to Scr kinase inhibitors. We further proved that one of the lactate metabolism related genes, TIMM50 could promote EC cell proliferation, migration and lactate metabolism.

CONCLUSION

In conclusion, we have established an effective prognostic signature based on LMRG expression patterns, which may greatly facilitate the assessment of prognosis, molecular features and treatment modalities in EC patients and may be useful in the future translation to clinical applications. TIMM50 was identified as a novel molecule that mediates lactate metabolism in vitro and in vivo, maybe a promising target for EC prognosis.

Implications of CRNDE in prognosis, tumor immunity, and therapeutic sensitivity in low grade glioma patients

BACKGROUND

Colorectal tumor differentially expressed (CRNDE) is specifically expressed in human brains and is the most highly expressed lncRNA in gliomas. Nevertheless, its implications in low grade glioma (LGG) are still indistinct. This study presented systematic analyses of CRNDE in LGG biology.

METHODS

We retrospectively retrieved TCGA, CGGC and GSE16011 LGG cohorts. Survival analysis was conducted for evaluating the prognostic significance of CRNDE in LGG. A CRNDE-based nomogram was established, and its predictive performance was verified. Signaling pathways underlying CRNDE were analyzed through ssGSEA and GSEA approaches. The abundance of immune cells and activity of cancer-immunity cycle were estimated with ssGSEA approach. Immune checkpoints, HLAs, chemokines, and immunotherapeutic response indicators (TIDE, and TMB) was quantified. U251 and SW1088 cells were transfected with specific shRNAs of CRNDE, and flow cytometry (apoptosis) and western blot (β-catenin and Wnt5a) assays were conducted.

RESULTS

Up-regulated CRNDE was found in LGG, and was linked to unfavorable clinical outcomes. The CRNDE-based nomogram enabled to accurately predict patients' prognosis. High CRNDE expression was linked to more genomic variations, activity of tumorigenic pathways, tumor immunity (increase in infiltration of immune cells, expression of immune checkpoints, HLAs and chemokines, and cancer-immunity cycle), and therapeutic sensitivity. CRNDE knockdown mitigated malignant phenotypes of LGG cells.

CONCLUSIONS

Our study determined CRNDE as a novel predictor for patient prognosis, tumor immunity and therapeutic response in LGG. Assessment of CRNDE expression is a promising approach for predicting the therapeutic benefits of LGG patients.

Mast cell marker gene signature: prognosis and immunotherapy response prediction in lung adenocarcinoma through integrated scRNA-seq and bulk RNA-seq

Background

Mast cells, comprising a crucial component of the tumor immune milieu, modulate neoplastic progression by secreting an array of pro- and antitumorigenic factors. Numerous extant studies have produced conflicting conclusions regarding the impact of mast cells on the prognosis of patients afflicted with lung adenocarcinoma (LUAD).

Methods

Employing single-cell RNA sequencing (scRNA-seq) analysis, mast cell-specific marker genes in LUAD were ascertained. Subsequently, a mast cell-related genes (MRGs) signature was devised to stratify LUAD patients into high- and low-risk cohorts based on the median risk value. Further investigations were conducted to assess the influence of distinct risk categories on the tumor microenvironment. The prognostic import and capacity to prognosticate immunotherapy benefits of the MRGs signature were corroborated using four external cohorts. Ultimately, the functional roles of SYAP1 were validated through in vitro experimentation.

Results

After scRNA-seq and bulk RNA-seq data analysis, we established a prognostic signature consisting of nine MRGs. This profile effectively distinguished favorable survival outcomes in both the training and validation cohorts. In addition, we identified the low-risk group as a population more effective for immunotherapy. In cellular experiments, we found that silencing SYAP1 significantly reduced the proliferation, invasion and migratory capacity of LUAD cells while increasing apoptosis.

Conclusion

Our MRGs signature offers valuable insights into the involvement of mast cells in determining the prognosis of LUAD and may prove instrumental as a navigational aid for immunotherapy selection, as well as a predictor of immunotherapy response in LUAD patients.