Validation of a new prognostic model to easily predict outcome in renal cell carcinoma: the GRANT score applied to the ASSURE trial population

Buti, Sebastiano; Puligandla, Mäneka; Bersanelli, Melissa; DiPaola, Robert S.; Manola, Judith; Taguchi, Satoru; Haas, Naomi B.

doi:10.1093/annonc/mdx492

Cited by 54 publications

(52 citation statements)

References 23 publications

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“…It was then validated in the large prospective population of the ASSURE adjuvant trial 15 , using a two-risk group stratification. Its prognostic value in terms of disease-free survival (DFS) and OS 13 was demonstrated in this setting, as evidenced by the respective accuracy values of 0.589 and 0.613, with a better performance compared to that of the UISS model in the same patient population 15 .…”

Section: Introductionmentioning

confidence: 84%

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Validation of the GRade, Age, Nodes and Tumor (GRANT) score within the Surveillance Epidemiology and End Results (SEER) database: A new tool to predict survival in surgically treated renal cell carcinoma patients

Buti

Karakiewicz

Bersanelli

et al. 2019

Sci Rep

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The purpose of the present study was to validate the new GRade, Age, Nodes and Tumor (GRANT) score for renal cell carcinoma (RCC) prognostication within a large population of patients. Within the Surveillance, Epidemiology, and End Results database, we identified patients with either clear-cell or papillary RCC, who underwent nephrectomy between 2001 and 2015. Harrell’s C-Index, calibration plot and decision curve analysis were used to validate the GRANT model using a five-risk group stratification (0 vs. 1 vs. 2 vs. 3 vs. 4 risk factors). The primary endpoint was overall survival (OS) at 60 months. The analyses were repeated according to the histologic subgroup. The overall population included 73217 cases; 60900 with clear-cell RCC and 12317 with papillary histology, respectively. According to a five-risk group stratification, 23985 patients (32.8%) had no risk factor (0), 35019 (47.8%) had only one risk factor (1), 13275 (18.1%) had risk score 2, 854 (1.2%) had 3 risk factors and 84 (0.1%) of cases had a GRANT score of 4, respectively. At 60 months, OS rates as determined by the GRANT score were respectively 94% (score 0) vs. 86% (score 1) vs. 76% (score 2) vs. 46% (score 3) vs. 16% (score 4). In both histologic subtypes, the GRANT score yielded good calibration and high net benefit. OS C-Index values were 0.677 and 0.650 for clear-cell and papillary RCC at 60 months after surgery, respectively. In conclusion, the GRANT score was validated with a five-risk group stratification in a huge population from the SEER database, offering a further demonstration of its reliability for prognostication in RCC.

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Section: Introductionmentioning

confidence: 84%

“…An easier nomogram, recently created and validated by our group, is the GRANT score 13 . The name is an acronym of the four risk factors included in the score calculation: Grade (Fuhrman grading of the tumor), Age (patient’s age), Nodes (pathological nodal status, pN) and Tumor (pathological tumor size, pT).…”

Section: Introductionmentioning

confidence: 99%

Validation of the GRade, Age, Nodes and Tumor (GRANT) score within the Surveillance Epidemiology and End Results (SEER) database: A new tool to predict survival in surgically treated renal cell carcinoma patients

Buti

Karakiewicz

Bersanelli

et al. 2019

Sci Rep

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“…On multivariable analysis, histological subtype was a significant factor for thoracic recurrences (HR: 4.6, CI [1.4-14.3]; HR: 3.6, CI [1. [1][2][3][4][5][6][7][8][9][10][11][12]). Regarding the number of sites, 319 patients had a single-site recurrence, with the lung being the most common site (119 patients), followed by local recurrence and contralateral kidney recurrence in 51 and 40 patients, respectively.…”

Section: Results Of Primary Analysis For the Entire Cohortmentioning

confidence: 99%

“…However, until recent years, these risk groups were established for ccRCC only [7,8] or for all RCC subtypes considered collectively [9][10][11], ignoring the inherent association between individual histological subtypes and the significant survival differences in patients after radical surgery. Over the years, further prognostic models were developed for the non-ccRCC population, including the "GRade, Age, Nodes and Tumour" (GRANT) score for both ccRCC and non-ccRCC [12] and the VENUSS score for nonmetastatic pRCC [12,13]. A recent study by Leibovich et al [14] has generated specific prognostic models for all major histological subtypes.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Histological Subtype on the Incidence, Timing, and Patterns of Recurrence in Patients with Renal Cell Carcinoma After Surgery—Results from RECUR Consortium

Abu-Ghanem

Powles

Capitanio

et al. 2021

European Urology Oncology

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Background: Current follow-up strategies for patients with renal cell carcinoma (RCC) after curative surgery rely mainly on risk models and the treatment delivered, regardless of the histological subtype. Objective: To determine the impact of RCC histological subtype on recurrence and to examine the incidence, pattern, and timing of recurrences to improve follow-up recommendations. Design, setting, and participants: This study included consecutive patients treated surgically with curative intention (ie, radical and partial nephrectomy) for nonmetastatic RCC (cT1-4, M0) between January 2006 and December 2011 across 15 centres from 10 countries, as part of the euRopEan association of urology renal cell carcinoma guidelines panel Collaborative multicenter consortium for the studies of follow-Up and recurrence patterns in Radically treated renal cell carcinoma patients (RECUR) database project. Outcome measurements and statistical analysis: The impact of histological subtype (ie, clear cell RCC [ccRCC], papillary RCC [pRCC], and chromophobe RCC [chRCC]) on recurrence-free survival (RFS) was assessed via univariate and multivariate analyses, adjusting for potential interactions with important variables

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“…In 2004, Kim et al first introduced molecular markers into prognostic model of ccRCC [10], which based on a combination of clinical and molecular predictors included metastasis status, T stage, Eastern Cooperative Oncology Group performance status, as well as immunohistochemically staining of p53, CA9, and vimentin. There was also another prognostic model, GRANT score, reported by Buti et al recently [17]. In 2015, Rini et al reported A 16-gene assay to predict recurrence after surgery in localized ccRCC patients [18], which did provide a more accurate and individualized risk assessment than previous clinicopathologic characteristics.…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of epithelial-mesenchymal transition markers in clear cell renal cell carcinoma

Ren

et al. 2020

Aging

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Epithelial-to-mesenchymal transition (EMT) is important in tumor invasiveness and metastasis. We aimed to determine prognostic value of six key EMT markers (CDH1, CDH2, SNAI1, SNAI2, VIM, TWIST1) in clear cell renal cell carcinoma (ccRCC). A total of 533 ccRCC patients with RNASeq data from The Cancer Genome Atlas (TCGA) cohort were included for analysis. Gene expression of these EMT markers was compared between tumor and normal tissues based on Oncomine database and TCGA cohort. Their correlations with progression-free survival (PFS) and overall survival (OS) were also examined in both TCGA cohort and FUSCC (Fudan University Shanghai Cancer Center) cohort. Cox proportional hazards regression model and Kaplan-Meier plot were used to assess the relative factors. Functional enrichment analyses were utilized to describe biologic function annotations and significantly involved hallmarks pathways of each gene. We found that Epithelial marker, CDH1 expression was lower, while mesenchymal markers (CDH2, SNAI1, VIM, TWIST1) expression was higher in ccRCC primary tumors. In the TCGA cohort, we found that patients with higher expression of VIM, TWIST1 or lower expression of CDH1 had worse prognosis. Further, in the FUSCC cohort, we confirmed the predictive ability of mesenchymal markers and epithelial marker expression in PFS and OS of ccRCC patients. After generating Cox regression models, EMT markers (CDH1, SNAI1, VIM, and TWIST1) were independent prognostic factors of both PFS and OS in ccRCC patients. Our preliminary EMT prediction model can facilitate further screening of EMT biomarkers and cast a better understanding of EMT gene function in ccRCC. We further compared the mRNA expression of CDH1, CDH2, SNAI1, SNAI2, VIM, TWIST1 between ccRCC tumor samples and adjacent normal tissues respectively based on RNA-sequence data from TCGA database. Consistent with Oncomine data, CDH1 expression was lower in ccRCC primary tumors in comparison with adjacent normal tissues (Figure 1B). However, expression of mesenchymal markers, CDH2, SNAI1, VIM, TWIST1, were significantly higher in ccRCC primary tumors (Figure 1C-1G). Neighbor genes and hierarchical partitioning of hub genes from TCGA As was shown in Supplementary Figure 1, network of CDH1, CDH2, SNAI1, SNAI2, VIM, TWIST1 and their 49 frequently genetic altered neighbor genes was integrated and constructed using cBioPortal.

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Validation of a new prognostic model to easily predict outcome in renal cell carcinoma: the GRANT score applied to the ASSURE trial population

Abstract: NCT00326898.

Cited by 54 publications

References 23 publications

Validation of the GRade, Age, Nodes and Tumor (GRANT) score within the Surveillance Epidemiology and End Results (SEER) database: A new tool to predict survival in surgically treated renal cell carcinoma patients

Validation of the GRade, Age, Nodes and Tumor (GRANT) score within the Surveillance Epidemiology and End Results (SEER) database: A new tool to predict survival in surgically treated renal cell carcinoma patients

The Impact of Histological Subtype on the Incidence, Timing, and Patterns of Recurrence in Patients with Renal Cell Carcinoma After Surgery—Results from RECUR Consortium

Prognostic value of epithelial-mesenchymal transition markers in clear cell renal cell carcinoma

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