Preoperative neoadjuvant targeted therapy remodels intra-tumoral heterogeneity of clear-cell renal cell carcinoma and ferroptosis inhibition induces resistance progression.

Neoadjuvant tyrosine kinase inhibitor (TKI) therapy is an important treatment option for advanced renal cell carcinoma (RCC). Many RCC patients may fail to respond or be resistant to TKI therapy. We aimed to explore the key mechanisms of neoadjuvant therapy résistance. We obtained tumor samples from matched pre-treatment biopsy and post-treatment surgical samples and performed single-cell RNA sequencing. Sunitinib-resistant ccRCC cell lines were established. Ferroptosis was detected by ferrous ion and lipid peroxidation levels. Tumor growth and resistance to Sunitinib was validated in vitro and vivo. Immunohistochemistry was used to validate the levels key genes and lipid peroxidation. Multi-center cohorts were included, including TCGA, ICGC, Checkmate-025 and IMmotion151 clinical trial. Survival analysis was performed to identify the associated clinical and genomic variables. Intratumoral heterogeneity was first described in the whole neoadjuvant management. The signature of endothelial cells was correlated with drug sensitivity and progression-free survival. Ferroptosis was shown to be the key biological program in malignant cell resistance. We observed tissue lipid peroxidation was negatively correlated with IL6 and tumor response. TKI-resistant cell line was established. SLC7A11 knockdown promoted cell growth and lipid peroxidation, increased the ferroptosis level, and suppressed the growth of tumor xenografts significantly (P < 0.01). IL6 could reverse the ferroptosis and malignant behavior caused by SLC7A11 (-) via JAK2/STAT3 pathway, which was rescued by the ferroptosis inducer Erastin. Our data indicate that ferroptosis is a novel strategy for advanced RCC treatment, which activated by IL6, providing a new idea for resistance to TKIs.

Immunogenic Material Vaccine for Cancer Immunotherapy by Structure-Dependent Immune Cell Trafficking and Modulation.

Inherently immunogenic materials offer enormous prospects in enhancing vaccine efficacy. However, the understanding and improving material adjuvanticity remain elusive. Herein how the structural presentation of immunopotentiators in a material governs the dynamic dialogue between innate and adaptive immunity for enhanced cancer vaccination is reported. The immunopotentiator manganese into six differing structures that resemble the architectures of two types of pathogens (spherical viruses or rod-like bacteria) is precisely manipulated. The results reveal that innate immune cells accurately sense and respond to the architectures, of which two outperformed material candidates (151 nm hollow spheres and hollow microrods with an aspect ratio of 4.5) show higher competence in creating local proinflammatory environment with promoted innate immune cell influx and stimulation on dendritic cells (DCs). In combination with viral peptides, model proteins, or cell lysate antigens, the outperformed microrod material remarkably primes antigen-specific CD8 cytolytic T cells. In prophylactic and therapeutic regimens, the microrod adjuvanted vaccines display optimal aptitude in tumor suppression in four aggressive murine tumor models, by promoting the infiltration of heterogeneous cytolytic effector cells while decreasing suppressive immunoregulatory populations in tumors. This study demonstrates that a rationally selected architecture of immunogenic materials potentially advances the clinical reality of cancer vaccination.

Glycolysis-related biomarker TCIRG1 participates in regulation of renal cell carcinoma progression and tumor immune microenvironment by affecting aerobic glycolysis and AKT/mTOR signaling pathway.

BACKGROUND: Renal cell carcinoma (RCC) is a hypermetabolic disease. Abnormal up-regulation of glycolytic signaling promotes tumor growth, and glycolytic metabolism is closely related to immunotherapy of renal cancer. The aim of the present study was to determine whether and how the glycolysis-related biomarker TCIRG1 affects aerobic glycolysis, the tumor microenvironment (TME) and malignant progression of clear cell renal cell carcinoma (ccRCC). METHODS: Based on The Cancer Genome Atlas (TCGA, n = 533) and the glycolysis-related gene set from MSigDB, we identified the glycolysis-related gene TCIRG1 by bioinformatics analysis, analyzed its immunological properties in ccRCC and observed how it affected the biological function and glycolytic metabolism using online databases such as TIMER 2.0, UALCAN, LinkedOmics and in vitro experiments. RESULTS: It was found that the expression of TCIRG1, was significantly increased in ccRCC tissue, and that high TCIRG1 expression was associated with poor overall survival (OS) and short progression-free interval (PFI). In addition, TCIRG1 expression was highly correlated with the infiltration immune cells, especially CD4+T cell Th1, CD8+T cell, NK cell, and M1 macrophage, and positively correlated with PDCD1, CTLA4 and other immunoinhibitors, CCL5, CXCR3 and other chemokines and chemokine receptors. More importantly, TCIRG1 may regulate aerobic glycolysis in ccRCC via the AKT/mTOR signaling pathway, thereby affecting the malignant progression of ccRCC cell lines. CONCLUSIONS: Our results demonstrate that the glycolysis-related biomarker TCIRG1 is a tumor-promoting factor by affecting aerobic glycolysis and tumor immune microenvironment in ccRCC, and this finding may provide a new idea for the treatment of ccRCC by combination of metabolic intervention and immunotherapy.

Construction and validation of a prognostic model for predicting clear cell renal cell carcinoma based on complement-related genes.

Background: Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous tumor and is the most common subtype of renal cell carcinoma (RCC). Surgery is used to cure most early ccRCC, but the 5-year overall survival (OS) of ccRCC patients is far from satisfactory. Thus, new prognostic features and therapeutic targets for ccRCC need to be identified. Since complement factors can influence tumor development, we aimed to develop a model to predict the prognosis of ccRCC through complement-related genes. Methods: Differentially expressed genes were screened from an International Cancer Genome Consortium (ICGC) data set, and the genes associated with prognosis were screened by univariate regression and least absolute shrinkage and selection operator-Cox regression, and column line plots were generated using the rms R package to predict OS. The C-index was used to show the accuracy of the survival prediction and the prediction effects were verified using a data set from The Cancer Genome Atlas (TCGA). An immuno-infiltration analysis was performed with CIBERSORT analysis, and a drug sensitivity analysis was performed using the Gene Set Cancer Analysis (GSCA) (http://bioinfo.life.hust.edu.cn/GSCA/#/) database. Results: We identified 5 complement-related genes (i.e., A2M, APOBEC3G, COL4A2, DOCK4, and NOTCH4) for risk-score modeling to predict OS at 1, 2, 3, and 5 years, and the C-index of the prediction mode was 0.795. In addition, the model was successfully validated in TCGA data set. The CIBERSORT analysis showed that M1 macrophages were downregulated in the high-risk group. The GSCA database analysis showed that DOCK4, COL4A2, and A2M were positively correlated with the half maximal inhibitory concentration (IC50) of 10 drugs and small molecules, and COL4A2, NOTCH4, A2M, and APOBEC3G were negatively correlated with the IC50 of dozens of different drugs and small molecules. Conclusions: We developed and validated a survival prognostic model based on 5 complement-related genes for ccRCC. We also elucidated the relationship with tumor immune status and developed a new predictive tool for clinical purposes. In addition, our results showed that A2M, APOBEC3G, COL4A2, DOCK4, and NOTCH4 may be potential targets for the treatment of ccRCC in the future.