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.