Development and external validation of a pathological nodal staging score for patients with clear cell renal cell carcinoma

406 Background: Proteomics represents the ultimate convergence of DNA and expression alterations. We therefore sought to leverage TCGA reverse phase protein array (RPPA) data with an independent proteomic platform to identify druggable targets and pathways associated with prognosis in clear cell renal cell carcinoma (ccRCC). Methods: Unsupervised hierarchical consensus clustering was performed and differentially expressed proteins were identified for pathway analysis. Associations with clinicogenomic factors were assessed and Cox proportional hazards models were performed for disease-specific survival (DSS). Results: RPPA clustering of 324 patients from the ccRCC TCGA revealed 5 robust clusters characterized by alterations in specific pathways and divergent prognoses. Cluster 1 was characterized by poor DSS, decreased expression of receptor tyrosine kinases (RTK) and upregulation of the mTOR pathway. It was also associated with mTOR pathway genomic alterations, sarcomatoid histology and the ccb prognostic mRNA signature (all p<0.001). Cluster 2 was characterized by increased expression of RTKs and interestingly, had upregulation of the mTOR pathway with excellent DSS. After accounting for stage and grade, cluster designation remained independently associated with DSS (HR 0.23 for cluster 2, 95% CI 0.08-0.68; p=0.008). External validation was performed on a separate cohort of 189 patients with a different quantitative proteomics platform. A panel of phosphoproteins (pHER1, pHER2, pHER3, pSHC, pMEK, pAKT), highly discriminant between the most divergent RPPA clusters (1 and 2) was evaluated. Those at the highest quartile of activation in > 3 proteins were associated with improved DSS (HR 0.19, 95% CI 0.05-0.082; p=0.03). Patients with mTOR pathway activation segregated to those with coincident RTK activation (n=83) and those without (n=13). Conclusions: We have identified and validated proteomic signatures which cluster ccRCC patients into 5 prognostic groups. Furthermore, two distinct mTOR-activated clusters—one with high RTK activity and one with increased mTOR pathway genomic alterations were revealed, which may have prognostic and therapeutic implications.

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EXTERNAL VALIDATION OF IMP3 EXPRESSION AS AN INDEPENDENT PROGNOSTIC MARKER FOR METASTATIC PROGRESSION AND DEATH FOR PATIENTS WITH CLEAR CELL RENAL CELL CARCINOMA

The Journal of Urology ◽

10.1016/s0022-5347(08)60383-3 ◽

2008 ◽

Vol 179 (4S) ◽

pp. 133-134 ◽

Cited By ~ 1

Author(s):

Nathan E Hoffmann ◽

Yuri Sheinin ◽

Christine M Lohse ◽

Alexander S Parker ◽

Bradley C Leibovich ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Prognostic Marker ◽

Renal Cell ◽

Clear Cell ◽

External Validation ◽

Metastatic Progression ◽

Cell Renal Cell Carcinoma ◽

Independent Prognostic Marker

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Editorial Comment on: External Validation of the Mayo Clinic Stage, Size, Grade, and Necrosis (SSIGN) Score for Clear-Cell Renal Cell Carcinoma in a Single European Centre Applying Routine Pathology

European Urology ◽

10.1016/j.eururo.2008.11.034 ◽

2010 ◽

Vol 57 (1) ◽

pp. 109-110 ◽

Cited By ~ 1

Author(s):

Gianluca Giannarini ◽

Riccardo Autorino

Keyword(s):

Renal Cell Carcinoma ◽

Editorial Comment ◽

Cell Carcinoma ◽

Mayo Clinic ◽

Renal Cell ◽

Clear Cell ◽

External Validation ◽

Cell Renal Cell Carcinoma ◽

European Centre ◽

Size Grade

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Identification And Validation Of A Hypoxia ‑ Related Prognostic Signature In Clear Cell Renal Cell Carcinoma Patients

10.21203/rs.3.rs-48542/v1 ◽

2020 ◽

Author(s):

Zhengtian Li ◽

Lingling Jiang ◽

Rong Zhao ◽

Wenkang Yang ◽

Chan Li ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Risk Factor ◽

Cell Carcinoma ◽

Renal Cell ◽

Independent Risk Factor ◽

Clear Cell ◽

External Validation ◽

Cell Renal Cell Carcinoma ◽

Cox Regression Analysis ◽

Genetic Signatures

Abstract Background: Increasing evidence has shown that hypoxia is closely related to the development, progression and prognosis of clear cell renal cell carcinoma (ccRCC). Nevertheless, reliable prognostic signatures based on hypoxia have not been well-established. This study aimed to construct an optimized prognosis nomogram based on hypoxia-related genetic signatures for patients with ccRCC.Method: We accessed hallmark gene sets of hypoxia, including 200 genes, and an original RNA seq dataset of ccRCC cases with integrated clinical information obtained by mining the Molecular Signatures Database, the TCGA database and the ICGC database. Univariate Cox regression analysis and multivariate Cox proportional hazards regression were performed to identify prognostic hypoxia-related genetic signatures and further generate RiskScore, a new independent prognosis predictor for optimizing prognosis models. External validation of the optimized prognosis model was performed in independent cohorts from the ICGC database.Result: ANKZF1, ETS1, PLAUR, SERPINE1, FBP1 and PFKP were selected as hypoxia-related genetic signatures, and the resultant formula based on those genetic signatures and their respective coefficients helped generate RiskScore. The results of receiver operating characteristic (ROC) curve, risk plot, survival analysis and so on suggested that RiskScore based on hypoxia-related genetic signatures was an independent risk factor. A novel prognosis nomogram optimized via RiskScore showed its promising performance in both a TCGA-ccRCC cohort and an ICGC-ccRCC cohort.Conclusions: Our study reveals that the differential expressions of hypoxia-related genes are associated with the overall survival of patients with ccRCC. RiskScore based on hypoxia-related genetic signatures was an independent risk factor beyond TNM staging and grading. The novel nomogram optimized via RiskScore exhibited a promising prognostic ability. It may be able to serve as a prognostic tool for guiding clinical decisions and selecting effective individualized treatments.

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External Validation of the Leibovich Prognosis Score for Nonmetastatic Clear Cell Renal Cell Carcinoma at a Single European Center Applying Routine Pathology

The Journal of Urology ◽

10.1016/j.juro.2011.07.034 ◽

2011 ◽

Vol 186 (5) ◽

pp. 1773-1778 ◽

Cited By ~ 44

Author(s):

Martin Pichler ◽

Georg C. Hutterer ◽

Thomas F. Chromecki ◽

Johanna Jesche ◽

Karin Kampel-Kettner ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

External Validation ◽

Cell Renal Cell Carcinoma ◽

Prognosis Score

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Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma

Scientific Reports ◽

10.1038/s41598-020-80262-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Seok-Soo Byun ◽

Tak Sung Heo ◽

Jeong Myeong Choi ◽

Yeong Seok Jeong ◽

Yu Seop Kim ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Deep Learning ◽

Cell Carcinoma ◽

Renal Cell ◽

Clear Cell ◽

External Validation ◽

Training Dataset ◽

Validation Dataset ◽

Cell Renal Cell Carcinoma ◽

Test Dataset

AbstractSurvival analyses for malignancies, including renal cell carcinoma (RCC), have primarily been conducted using the Cox proportional hazards (CPH) model. We compared the random survival forest (RSF) and DeepSurv models with the CPH model to predict recurrence-free survival (RFS) and cancer-specific survival (CSS) in non-metastatic clear cell RCC (nm-cRCC) patients. Our cohort included 2139 nm-cRCC patients who underwent curative-intent surgery at six Korean institutions between 2000 and 2014. The data of two largest hospitals’ patients were assigned into the training and validation dataset, and the data of the remaining hospitals were assigned into the external validation dataset. The performance of the RSF and DeepSurv models was compared with that of CPH using Harrel’s C-index. During the follow-up, recurrence and cancer-specific deaths were recorded in 190 (12.7%) and 108 (7.0%) patients, respectively, in the training-dataset. Harrel’s C-indices for RFS in the test-dataset were 0.794, 0.789, and 0.802 for CPH, RSF, and DeepSurv, respectively. Harrel’s C-indices for CSS in the test-dataset were 0.831, 0.790, and 0.834 for CPH, RSF, and DeepSurv, respectively. In predicting RFS and CSS in nm-cRCC patients, the performance of DeepSurv was superior to that of CPH and RSF. In no distant time, deep learning-based survival predictions may be useful in RCC patients.

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Integrated Analysis to Identify a Redox-Related Prognostic Signature for Clear Cell Renal Cell Carcinoma

Oxidative Medicine and Cellular Longevity ◽

10.1155/2021/6648093 ◽

2021 ◽

Vol 2021 ◽

pp. 1-35

Author(s):

Yue Wu ◽

Xian Wei ◽

Huan Feng ◽

Bintao Hu ◽

Bo Liu ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Molecular Mechanisms ◽

Clear Cell ◽

Risk Group ◽

External Validation ◽

Integrated Analysis ◽

Prognostic Signature ◽

Cell Renal Cell Carcinoma

The imbalance of the redox system has been shown to be closely related to the occurrence and progression of many cancers. However, the biological function and clinical significance of redox-related genes (RRGs) in clear cell renal cell carcinoma (ccRCC) are unclear. In our current study, we downloaded transcriptome data from The Cancer Genome Atlas (TCGA) database of ccRCC patients and identified the differential expression of RRGs in tumor and normal kidney tissues. Then, we identified a total of 344 differentially expressed RRGs, including 234 upregulated and 110 downregulated RRGs. Fourteen prognosis-related RRGs (ADAM8, CGN, EIF4EBP1, FOXM1, G6PC, HAMP, HTR2C, ITIH4, LTB4R, MMP3, PLG, PRKCG, SAA1, and VWF) were selected out, and a prognosis-related signature was constructed based on these RRGs. Survival analysis showed that overall survival was lower in the high-risk group than in the low-risk group. The area under the receiver operating characteristic curve of the risk score signature was 0.728 at three years and 0.759 at five years in the TCGA cohort and 0.804 at three years and 0.829 at five years in the E-MTAB-1980 cohort, showing good predictive performance. In addition, we explored the regulatory relationships of these RRGs with upstream miRNA, their biological functions and molecular mechanisms, and their relationship with immune cell infiltration. We also established a nomogram based on these prognostic RRGs and performed internal and external validation in the TCGA and E-MTAB-1980 cohorts, respectively, showing an accurate prediction of ccRCC prognosis. Moreover, a stratified analysis showed a significant correlation between the prognostic signature and ccRCC progression.

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