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. 2022 Sep 5:13:971142.
doi: 10.3389/fimmu.2022.971142. eCollection 2022.

The cuproptosis-associated 13 gene signature as a robust predictor for outcome and response to immune- and targeted-therapies in clear cell renal cell carcinoma

Huiyang Yuan  1 , Xin Qin  1 , Jing Wang  2 , Qingya Yang  1 , Yidong Fan  1 , Dawei Xu  3
Affiliations

The cuproptosis-associated 13 gene signature as a robust predictor for outcome and response to immune- and targeted-therapies in clear cell renal cell carcinoma

Huiyang Yuan et al. Front Immunol. .

Abstract

Cuproptosis, the newly identified form of regulatory cell death (RCD), results from mitochondrial proteotoxic stress mediated by copper and FDX1. Little is known about significances of cuproptosis in oncogenesis. Here we determined clinical implications of cuproptosis in clear cell renal cell carcinoma (ccRCC). Based on the correlation and survival analyses of cuproptosis-correlated genes in TCGA ccRCC cohort, we constructed a cuproptosis-associated 13 gene signature (CuAGS-13) score system. In both TCGA training and two validation cohorts, when patients were categorized into high- and low-risk groups according to a median score as the cutoff, the CuAGS-13 high-risk group was significantly associated with shorter overall survival (OS) and/or progression-free survival (PFS) independently (P<0. 001 for all). The CuAGS-13 score assessment could also predict recurrence and recurrence-free survival of patients at stage I - III with a high accuracy, which outperformed the ccAccB/ClearCode34 model, a well-established molecular predictor for ccRCC prognosis. Moreover, patients treated with immune checkpoint inhibitors (ICIs) acquired complete/partial remissions up to 3-time higher coupled with significantly longer PFS in the CuAGS-13 low- than high-risk groups in both training and validation cohorts of ccRCCs (7. 2 - 14. 1 vs. 2. 1 - 3. 0 months, P<0. 001). The combination of ICI with anti-angiogenic agent Bevacizumab doubled remission rates in CuAGS-13 high-risk patients while did not improve the efficacy in the low-risk group. Further analyses showed a positive correlation between CuAGS-13 and TIDE scores VSports手机版. We also observed that the CuAGS-13 score assessment accurately predicted patient response to Sunitinib, and higher remission rates in the low-risk group led to longer PFS (Low- vs. high-risk, 13. 9 vs. 5. 8 months, P = 5. 0e-12). Taken together, the CuAGS-13 score assessment serves as a robust predictor for survival, recurrence, and response to ICIs, ICI plus anti-angiogenic drugs and Sunitinib in ccRCC patients, which significantly improves patient stratifications for precision medicine of ccRCC. .

Keywords: ccRCC; cuproptosis; immune checkpoint inhibitors; immunotherapy; prognosis; targeted therapy V体育安卓版. .

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The Cuproptosis pathway and study workflow. (A) Left panel: Ten factors involved in cuproptosis. Right panel: The cuproptosis signaling pathway. Extracellular copper Cu++ enters cells by binding to copper chelators and elesclomol serves as the most efficient Cu++ transporter. The reductase FDX1 reduces Cu++ to Cu+, a more toxic form, while lipoyl synthase (LIAS) catalyzes lipoylation of the pyruvate dehydrogenase (PDH) complex proteins including dihydrolipoamide S-acetyltransferase (DLAT) and others. Cu+ and lipoylation promote the protein aggregation. DLAT is one of the key enzymes participating in the tricarboxylic acid cycle, and its aggregation results in mitochondrial proteotoxic stress and subsequent cuproptotic cell death. Moreover, FDX1 and Cu+ induce the destabilization of Fe–S cluster proteins, further facilitating cuproptosis. Additionally, SLC31A1 and ATP7B function as the Cu+ importer and exporter, respectively, and regulate cuproptosis by controlling intracellular Cu+ concentrations. (B) The schematic workflow of the present study.
Figure 2
Figure 2
The construction of the cuproptosis-associated 13 gene signature (CuAGS-13) for ccRCC prognosis. (A) Left panel: Gene modules correlated with cuproptosis factors as determined using Weighted gene co-expression network analysis (WGCNA) and Pearson’s co-efficiency analysis. (B) Scatter plot of module eigengenes in the MEBROWN (left) and MEMANGE (right) modules from (A). The genes in the upper right are selected for further analyses. (C) Construction of the cuproptosis-associated 13 gene signature (CuAGS-13) for progression-free survival (PFS) prediction in ccRCC. Top panel: LASSO coefficient profiles of the CuAGS associated with PFS. Bottom panel: Plots of the cross-validation error rates. Each red dot represents a lambda value with its error bar (the confidence interval for the cross-validated error rate). The analysis identified 13 cuproptosis-associated genes most relevant to PFS. (D) Differences in the CuAGS-13 expression between ccRCC tumors and their non-tumorous adjacent renal tissues in the TCGA cohort. (E) Kaplan–Meier survival analysis showing the impact of each gene contained in CuAGS-13 on PFS in the TCGA ccRCC cohort. Patients are divided into high and low groups based on the expression of each gene in tumors using a median value as the cutoff. ****p < 0.0001.
Figure 3
Figure 3
The cuproptosis-associated 13 gene signature (CuAGS-13) model for ccRCC survival prediction. (A) Kaplan–Meier survival analysis showing the significant association of the CuGAS-13 score with OS and PFS in the TCGA ccRCC cohort. Patients were classified into high- and low-risk groups based on the CuGAS-13 score using a median value as the cutoff. (B) The ROC curve showing a high accuracy in predicting 1-, 3- and 5-year OS and PFS using the CuGAS-13 model. (C) and (D) Univariate and multivariate Cox regression analyses of OS and PFS in ccRCC, respectively. (E) and (F) The nomogram composed of CuAGS-13 model, age, grade and stage for predicting 1-, 3- and 5-year OS and PFS, respectively. (G) The validation of the CuGAS-13 model for the prediction of OS in the EMBA-1980 cohort of ccRCC. (H) The validation of the CuGAS-13 model for the prediction of OS in the ICGC-RECA-EU cohort of ccRCC.
Figure 4
Figure 4
Comparison of predictive powers for recurrence and recurrence-free survival (RFS) between the CuAGS-13 and ccAccB/Clearcode34 models. (A) The ROC curve showing accuracy in predicting 1-, 3- and 5-year RFS for patients at stage I – III using CuAGS-13 (Left) and Clearcode34 (Middle) models. Right: The Sankey diagram showing different patient groups classified the CuAGS-13 and ccAccB/Clearcode34 models. (B) Left: The ROC curve showing accuracy in predicting 1-, 3- and 5-year RFS for patients at stage II – III using CuAGS-13 (Left) and ccAccB/Clearcode34 (Right) models. (C): C-index analysis showing higher sensitivity of CuAGS-13 than Clearcode34 models for predicting recurrence in all patients at stage I – III. (D) C-index analysis showing no significant differences by CuAGS-13 and Clearcode34 models for predicting recurrence in patients at stage I (E) C-index analysis showing higher sensitivities of the CuAGS-13 than Clearcode34 models for predicting recurrence in stage II-III patients. (F, G) Kaplan–Meier survival analysis showing RFS predictive powers of CuAGS-13 (F) and Clearcode34 (G) models in patients at stage I and stage II – III, respectively. (H) The CuAGS-13 model-based nomogram for predicting 1-, 3- and 5-year RFS in TCGA ccRCC patients (stage I – IV). (I) The ccAccB/Clearcode34 model-based nomogram for predicting 1-, 3- and 5-year RFS in TCGA ccRCC patients (stage I – IV).
Figure 5
Figure 5
The association between genomic alterations and CuAGS-13 score in ccRCC. (A) The overview of the somatic mutations and relation to the CuAGS-13 score and clinical-pathological variables in the TCGA ccRCCs. (B) The positive correlation between CuAGS-13 score and tumor mutation burden (TMB) in the TCGA ccRCCs. (C) ccRCC tumors harboring BAP1 and SETD2 mutations exhibit significantly higher CuAGS-13 scores. (D) Positive correlation between the CuAGS-13 score and aneuploidy in ccRCC tumors. (E) Positive correlation between the CuAGS-13 score and homologous recombination deficiency (HRD) in ccRCC tumors. (F) Positive correlation between the CuAGS-13 score and intratumor heterogeneity in ccRCC tumors.
Figure 6
Figure 6
The CuAGS-13 score prediction of patient response to immune checkpoint inhibitors (ICIs) and combination with Bevacizumab in ccRCC. (A–C) The CuAGS-13 score prediction of patient response to Atezolizumab alone or Atezolizumab plus Bevacizumab in IMmotion150 trial. Differences in response rates and PFS between the CuAGS-13 high- and low-risk group patients treated with Atezolizumab alone (A), Atezolizumab plus Bevacizumab (B) and all together (C). (D) TIDE score analyses showing differences between the CuAGS-13 high- and low-risk group patients in IMmotion150 trial. (E) Differences in response rates and survival (OS and PFS) between the CuAGS-13 high- and low-risk group patients treated with Nivolumab in CheMate025 trial. (F) TIDE score analyses showing differences between the CuAGS-13 high- and low-risk group patients in CheMate025 trial. (G) TIDE score analyses showing differences between the CuAGS-13 high- and low-risk group patients in the TCGA ccRCC cohort.
Figure 7
Figure 7
The CuAGS-13 score prediction of patient response to Sunitinib in ccRCC. (A) Differences in response rates between the CuAGS-13 high- and low-risk group patients treated with Sunitinib in IMmotion150 trial. (B) Differences in response rates between the CuAGS-13 high- and low-risk group patients treated with Sunitinib in IMmotion151 trial. (C) Significant association between shorter PFS and the CuAGS-13 high-risk group patients treated with Sunitinib in IMmotion150 trial (left) and IMmotion151 trial (right). (D) The lower cuproptosis score in Sunitinib-resistant PDX tumors. Microarray data in five untreated and four Sunitinib-resistant PDX tumors were analyzed for their cuproptosis score. Left panel: Heatmap showing expression of 10 cuproptosis factors. Right panel: The cuproptosis score in untreated and Sunitinib-resistant PDX tumors. A cuproptosis score was calculated using ssGSEA.
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