Integrative analysis of pan-cancer single-cell data reveals a tumor ecosystem subtype predicting immunotherapy response.

Tumor ecosystem shapes cancer biology and potentially influence the response to immunotherapy, but there is a lack of direct clinical evidence. In this study, we utilized EcoTyper and publicly available scRNA-Seq cohorts from ICI-treated patients. We found a ecosystem subtype (ecotype) was linked to improved responses to immunotherapy. Then, a novel immunotherapy-responsive ecotype signature (IRE.Sig) was established and validated through the analysis of pan-cancer data. Utilizing IRE.Sig, machine learning models successfully predicted ICI responses in both validation and testing cohorts, achieving area under the curve (AUC) values of 0.72 and 0.71, respectively. Furthermore, using 5 CRISPR screening cohorts, we identified several potential drugs that may augment the efficacy of ICI. We also elucidated the candidate cellular biomarkers of response to the combined treatment of pembrolizumab plus eribulin in breast cancer. This signature has the potential to serve as a valuable tool for patients in selecting appropriate immunotherapy treatments.

Pan-cancer dissection of vasculogenic mimicry characteristic to provide potential therapeutic targets.

Introduction: Vasculogenic mimicry (VM) represents a novel form of tumor angiogenesis that is associated with tumor invasiveness and drug resistance. However, the VM landscape across cancer types remains poorly understood. In this study, we elucidate the characterizations of VM across cancers based on multi-omics data and provide potential targeted therapeutic strategies. Methods: Multi-omics data from The Cancer Genome Atlas was used to conduct comprehensive analyses of the characteristics of VM related genes (VRGs) across cancer types. Pan-cancer vasculogenic mimicry score was established to provide a depiction of the VM landscape across cancer types. The correlation between VM and cancer phenotypes was conducted to explore potential regulatory mechanisms of VM. We further systematically examined the relationship between VM and both tumor immunity and tumor microenvironment (TME). In addition, cell communication analysis based on single-cell transcriptome data was used to investigate the interactions between VM cells and TME. Finally, transcriptional and drug response data from the Genomics of Drug Sensitivity in Cancer database were utilized to identify potential therapeutic targets and drugs. The impact of VM on immunotherapy was also further clarified. Results: Our study revealed that VRGs were dysregulated in tumor and regulated by multiple mechanisms. Then, VM level was found to be heterogeneous among different tumors and correlated with tumor invasiveness, metastatic potential, malignancy, and prognosis. VM was found to be strongly associated with epithelial-mesenchymal transition (EMT). Further analyses revealed cancer-associated fibroblasts can promote EMT and VM formation. Furthermore, the immune-suppressive state is associated with a microenvironment characterized by high levels of VM. VM score can be used as an indicator to predict the effect of immunotherapy. Finally, seven potential drugs targeting VM were identified. Conclusion: In conclusion, we elucidate the characteristics and key regulatory mechanisms of VM across various cancer types, underscoring the pivotal role of CAFs in VM. VM was further found to be associated with the immunosuppressive TME. We also provide clues for the research of drugs targeting VM. Our study provides an initial overview and reference point for future research on VM, opening up new avenues for therapeutic intervention.

Cell-cell communications shape tumor microenvironment and predict clinical outcomes in clear cell renal carcinoma.

BACKGROUND: Cell-cell communications of various cell populations within tumor microenvironment play an essential role in primary tumor growth, metastasis evolution, and immune escape. Nevertheless, comprehensive investigation of cell-cell communications in the ccRCC (Clear cell renal carcinoma) microenvironment and how this interplay affects prognosis still remains limited. METHODS: Intercellular communications were characterized by single-cell data. Firstly, we employed "CellChat" package to characterize intercellular communications across all types of cells in microenvironment in VHL mutated and non-mutated samples from 8 patients, respectively. And pseudotime trajectory analyses were performed with monocle analyses. Finally clinical prognosis and immunotherapy efficacy with different landscapes of intercellular interplay are evaluated by TCGA-KIRC and immunotherapy cohort. RESULTS: Firstly, the VHL phenotype may be related to the intercellular communication landscape. And trajectory analysis reveals the potential relationship of cell-cell communication molecules with T cells and Myeloid cells differentiation. Furthermore, those molecules also correlate with the infiltration of T cells and Myeloid cells. A tumor cluster with highly expressed ligands was defined by quantitative analysis and transcription factor enrichment analysis, which was identified to be pivotal for intercellular communications in tumor microenvironment. Finally, bulk data indicates bulk that different clusters with different intercellular communications have significant predictive value for prognosis and distinguished immunotherapy efficiency. CONCLUSIONS: The intercellular communication landscapes of VHL wild and VHL mutant ccRCC vary. Intercellular communications within the tumor microenvironment also influence T cell and myeloid cell development and infiltration, as well as predict clinical prognosis and immunotherapy efficacy in ccRCC.

A novel classification based on non-apoptosis cell death predicts clinical outcomes and immunotherapy response of clear renal cell carcinoma.

BACKGROUND: Non-apoptosis cell death could be a secondary consequence of the immune response, which profoundly influences tumor microenvironment (TME), escaping from chemotherapy/immunotherapy-induced apoptosis resistance effects. Whereas, systemic analysis of non-apoptosis regulated cell death associated with TME and clinical outcomes remains unveiled. METHODS: Our kidney clear carcinoma (KIRC) samples from The Cancer Genome Atlas (TCGA) were stratified into three clusters based on the activity of autophagic cell death, ferroptosis, pyroptosis and necroptosis. Clinical prognosis, TME landscape, biological functions and somatic mutation frequency were compared among the clusters. Additionally, to identify a gene signature highly correlated with clinical prognosis, a risk score model was constructed, and the clinical prognosis, immune infiltration, somatic mutation and biological pathways of risk score subgroups were investigated. RESULTS: Our non-apoptosis cell death clusters are robustly predictive of immunotherapy responses. Patients in Cluster B are the most sensitive to immune checkpoint blockades-depended immunotherapy. Our risk score model was also verified as a promising biomarker for clinical prognosis and immunotherapy efficiency. Where, the High-risk score group was more sensitive to immunotherapy. CONCLUSIONS: The novel non-apoptosis cell death-based classification and risk score model could predict the outcome of immunotherapy, and highly associate with immune infiltration. These findings may provide a novel strategy to aid in identificatin of biomarkers and selecting personalized therapeutic strategies.