Predicting Disease Recurrence, Early Progression, and Overall Survival Following Surgical Resection for High-risk Localized and Locally Advanced Renal Cell Carcinoma.

BACKGROUND: Risk stratification for localized renal cell carcinoma (RCC) relies heavily on retrospective models, limiting their generalizability to contemporary cohorts. OBJECTIVE: To introduce a contemporary RCC prognostic model, developed using prospective, highly annotated data from a phase III adjuvant trial. DESIGN, SETTING, AND PARTICIPANTS: The model utilizes outcome data from the ECOG-ACRIN 2805 (ASSURE) RCC trial. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcome for the model is disease-free survival (DFS), with overall survival (OS) and early disease progression (EDP) as secondary outcomes. Model performance was assessed using discrimination and calibration tests. RESULTS AND LIMITATIONS: A total of 1735 patients were included in the analysis, with 887 DFS events occurring over a median follow-up of 9.6 yr. Five common tumor variables (histology, size, grade, tumor necrosis, and nodal involvement) were included in each model. Tumor histology was the single most powerful predictor for each model outcome. The C-statistics at 1 yr were 78.4% and 81.9% for DFS and OS, respectively. Degradation of the DFS, DFS validation set, and OS model's discriminatory ability was seen over time, with a global c-index of 68.0% (95% confidence interval or CI [65.5, 70.4]), 68.6% [65.1%, 72.2%], and 69.4% (95% CI [66.9%, 71.9%], respectively. The EDP model had a c-index of 75.1% (95% CI [71.3, 79.0]). CONCLUSIONS: We introduce a contemporary RCC recurrence model built and internally validated using prospective and highly annotated data from a clinical trial. Performance characteristics of the current model exceed available prognostic models with the added benefit of being histology inclusive and TNM agnostic. PATIENT SUMMARY: Important decisions, including treatment protocols, clinical trial eligibility, and life planning, rest on our ability to predict cancer outcomes accurately. Here, we introduce a contemporary renal cell carcinoma prognostic model leveraging high-quality data from a clinical trial. The current model predicts three outcome measures commonly utilized in clinical practice and exceeds the predictive ability of available prognostic models.

Association between age and sex and mortality after adjuvant therapy for renal cancer.

BACKGROUND: In phase 3 trials of patients with resected high-risk renal cell carcinoma, adjuvant sunitinib has demonstrated no overall survival (OS) benefit, an uncertain disease-free survival (DFS) benefit, and increased toxicity versus placebo. To identify patients who may derive benefit or harm from adjuvant therapy, the authors assessed the effects of age and sex on treatment outcomes in the phase 3 Adjuvant Sorafenib or Sunitinib for Unfavorable Renal Cancer (ASSURE) trial. METHODS: The authors conducted a post hoc subgroup analysis of age and sex among patients in the ASSURE trial. Adjusted hazard ratios (HRs) for OS and DFS were evaluated with sunitinib or sorafenib versus placebo in 4 patient subgroups defined by sex and median age at the time of the study. RESULTS: Sunitinib treatment was associated with decreased OS (HR, 2.21; 95% confidence interval, 1.29-3.80) among women aged >56 years, but not in women aged ≤56 years or men of any age. Similar associations with age and sex were observed for DFS, but these were not statistically significant (women aged >56 years: HR, 1.41 [95% confidence interval, 0.94-2.10]). No such association was found for sorafenib. The interaction by age and sex on mortality was found to be statistically significant for sunitinib (P = .01), but not sorafenib (P = .10). CONCLUSIONS: Adjuvant sunitinib may increase mortality among older women with renal cell carcinoma. Given the recent approval of adjuvant sunitinib for patients with high-risk resected renal cell carcinoma, additional studies are needed to confirm these findings.

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

Background: Prognostic scores have been developed to estimate the risk of recurrence and the probability of survival after nephrectomy for renal cell carcinoma (RCC). The use of these tools, despite being helpful to plan a customized schedule of follow-up, to the patient's tailored counselling and to select individuals who could potentially benefit from adjuvant treatment, currently is not routine, due to their relative complexity and to the lack of histological data (i.e. necrosis). Patients and methods: We developed a simple score called GRade, Age, Nodes and Tumor (GRANT) based on four easily obtained parameters: Fuhrman grade, age, pathological nodal status and pathological tumor size. Patients with 0 or 1 factor are classified as favorable risk, whereas patients with two or more risk factors as unfavorable risk. The large population of RCC patients from the ASSURE adjuvant trial was used as independent dataset for this external validation, to investigate the prognostic value of the new score in terms of disease-free survival and overall survival and to evaluate its possible application as predictive tool. Statistical analyses were carried out by the Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute (Boston, USA) for the ASSURE trial patients' population. Results: The performance of the new model is similar to that of the already validated score systems, but its strength, compared with the others already available, is the ease and clarity of its calculation, with great speed of use during the clinical practice. Limitations are the use of the Fuhrman nuclear grade, not valid for rare histologies, and the TNM classification modifications over time. Conclusion: The GRANT score demonstrated its potential usefulness for clinical practice. ClinicalTrials.gov Identifier for the ASSURE trial: NCT00326898.