Crosstalk Between Metabolism and Immune Activity Reveals Four Subtypes With Therapeutic Implications in Clear Cell Renal Cell Carcinoma

Abstract

Clear cell renal cell carcinoma (ccRCC) is characterized by metabolic dysregulation and distinct immunological signatures. The interplay between metabolic and immune processes in the tumor microenvironment (TME) causes the complexity and heterogeneity of immunotherapy responses observed during ccRCC treatment. Herein, we initially identified two distinct metabolic subtypes (C1 and C2 subtypes) and immune subtypes (I1 and I2 subtypes) based on the occurrence of differentially expressed metabolism-related prognostic genes and immune-related components. Notably, we observed that immune regulators with upregulated expression actively participated in multiple metabolic pathways. Therefore, we further delineated four immunometabolism-based ccRCC subtypes (M1, M2, M3, and M4 subtypes) according to the results of the above classification. Generally, we found that high metabolic activity could suppress immune infiltration. Immunometabolism subtype classification was associated with immunotherapy response, with patients possessing the immune-inflamed, metabolic-desert subtype (M3 subtype) that benefits the most from immunotherapy. Moreover, differences in the shifts in the immunometabolism subtype after immunotherapy were observed in the responder and non-responder groups, with patients from the responder group transferring to subtypes with immune-inflamed characteristics and less active metabolic activity (M3 or M4 subtype). Immunometabolism subtypes could also serve as biomarkers for predicting immunotherapy response. To decipher the genomic and epigenomic features of the four subtypes, we analyzed multiomics data, including miRNA expression, DNA methylation status, copy number variations occurrence, and somatic mutation profiles. Patients with the M2 subtype possessed the highest VHL gene mutation rates and were more likely to be sensitive to sunitinib therapy. Moreover, we developed non-invasive radiomic models to reveal the status of immune activity and metabolism. In addition, we constructed a radiomic prognostic score (PRS) for predicting ccRCC survival based on the seven radiomic features. PRS was further demonstrated to be closely linked to immunometabolism subtype classification, immune score, and tumor mutation burden. The prognostic value of the PRS and the association of the PRS with immune activity and metabolism were validated in our cohort. Overall, our study established four immunometabolism subtypes, thereby revealing the crosstalk between immune and metabolic activities and providing new insights into personal therapy selection.

The expression pattern of pyruvate dehydrogenase kinases predicts prognosis and correlates with immune exhaustion in clear cell renal cell carcinoma

“…We found an overall increase in T cell infiltration and CD8 exhausted T cells in PDK2/PDK3 positive ccRCC. This is in consistence with previous T cell landscape studies of ccRCC, which revealed higher proportion of exhausted CD8 T cell in advanced ccRCC 50 , and reinforces the importance of the crosstalk between metabolism and T cell exhaustion in ccRCC 51 , 52 . Relevant to this, lactate administration in mice was recently associated with CD8 T cell stemness and increased anti-tumor immunity 53 .…”

Small Leucine-Rich Proteoglycan PODNL1 Identified as a Potential Tumor Matrix-Mediated Biomarker for Prognosis and Immunotherapy in a Pan-Cancer Setting

“…In the present study, we found that PODNL1 was highly expressed in tumor tissues in 15 types of cancers and its high expression was significantly associated with more advanced stages and poor prognosis in multiple types of cancers. Notably, PODNL1 expressions were also enriched in more aggressive molecular subtypes in various tumors, such as the mesenchymal type of GBM [29] and OV [30], and M3 type of KIRC [31]. Combined with previous studies [14][15][16][17][18], such results indicate that PODNL1 could serve as a novel potential biomarker to guide prognosis predictions and individualized therapies in cancers.…”

Prognosis and Personalized Treatment Prediction in Different Mutation-Signature Hepatocellular Carcinoma

“…During hepatocarcinogenesis, metabolic reprogramming in the liver leading to deceleration of the tricarboxylic acid cycle and activation of fatty acid oxidation (FAO) provided sufficient ATP for cell proliferation, enhanced chemoresistance, 26 and crosstalk with the immune microenvironment. 27 Overall, CMS1 was defined by higher antigenicity (eg, SNV neoantigens, indel neoantigens, and total neoantigens), elevated co-stimulatory molecular (eg, CD28, CD40 ), and better T cell function states. However, a high rate of metabolism dysfunction in MSC1 gave rise to decreased glucose uptake and lactate accumulation in tumor killer cells, further propelling immune evasion and thereby limiting the prognosis of patients.…”