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A simple prognostic model for overall survival in metastatic renal cell carcinoma.

ABSTRACT: INTRODUCTION:The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. METHODS:We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. RESULTS:There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. CONCLUSIONS:In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis.

SUBMITTER: Assi HI

PROVIDER: S-EPMC4839992 | biostudies-other | 2016 Mar-Apr

REPOSITORIES: biostudies-other

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A simple prognostic model for overall survival in metastatic renal cell carcinoma.

Assi Hazem I HI Patenaude Francois F Toumishey Ethan E Ross Laura L Abdelsalam Mahmoud M Reiman Tony T

Canadian Urological Association journal = Journal de l'Association des urologues du Canada 20160301 3-4

<h4>Introduction</h4>The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival.<h4>Methods</h4>We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regressi ...[more]

PMID: 27217858

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Project description:Renal-cell carcinoma (RCC) affects over 330,000 new patients every year, of whom 1/3 present with metastatic RCC (mRCC) at diagnosis. Most mRCC patients treated with a first-line agent relapse within 1 year and need second-line therapy. The present study aims to compare overall survival (OS) between nivolumab and cabozantinib from two recent pivotal studies comparing, respectively, each one of the two emerging treatments against everolimus in patients who relapse following first-line treatment. Comparison is traditionally carried out using the Bucher method, which assumes proportional hazard. Since OS curves intersected in one of the pivotal studies, models not assuming proportional hazards were also considered to refine the comparison. Four Bayesian parametric survival network meta-analysis models were implemented on overall survival (OS) data digitized from the Kaplan-Meier curves reported in the studies. Three models allowing hazard ratios (HR) to vary over time were assessed against a fixed-HR model. The Bucher method favored cabozantinib, with a fixed HR for OS vs. nivolumab of 1.09 (95% confidence interval: [0.77, 1.54]). However, all models with time-varying HR showed better fits than the fixed-HR model. The log-logistic model fitted the data best, exhibiting a HR for OS initially favoring cabozantinib, the trend inverting to favor nivolumab after month 5 (95% credible interval <1 from 10 months). The initial probability of cabozantinib conferring superior OS was 54%, falling to 41.5% by month 24. Numerical differences in study-adjusted OS estimates between the two treatments remained small. This study evidences that HR for OS of nivolumab vs. cabozantinib varies over time, favoring cabozantinib in the first months of treatment but nivolumab afterwards, a possible indication that patients with poor prognosis benefit more from cabozantinib in terms of survival, nivolumab benefiting patients with better prognosis. More evidence, including real-world observational data, is needed to compare effectiveness between cabozantinib and nivolumab.

Project description:Background and objectivesInvestigations of the prognosis are vital for better patient management and decision-making in patients with advanced metastatic renal cell carcinoma (mRCC). The purpose of this study is to evaluate the capacity of emerging Artificial Intelligence (AI) technologies to predict three- and five-year overall survival (OS) for mRCC patients starting their first-line of systemic treatment.Patients and methodsThe retrospective study included 322 Italian patients with mRCC who underwent systemic treatment between 2004 and 2019. Statistical analysis included the univariate and multivariate Cox proportional-hazard model and the Kaplan-Meier analysis for the prognostic factors' investigation. The patients were split into a training cohort to establish the predictive models and a hold-out cohort to validate the results. The models were evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We assessed the clinical benefit of the models using decision curve analysis (DCA). Then, the proposed AI models were compared with well-known pre-existing prognostic systems.ResultsThe median age of patients in the study was 56.7 years at RCC diagnosis and 78% of participants were male. The median survival time from the start of systemic treatment was 29.2 months; 95% of the patients died during the follow-up that finished by the end of 2019. The proposed predictive model, which was constructed as an ensemble of three individual predictive models, outperformed all well-known prognostic models to which it was compared. It also demonstrated better usability in supporting clinical decisions for 3- and 5-year OS. The model achieved (0.786 and 0.771) AUC and (0.675 and 0.558) specificity at sensitivity 0.90 for 3 and 5 years, respectively. We also applied explainability methods to identify the important clinical features that were found to be partially matched with the prognostic factors identified in the Kaplan-Meier and Cox analyses.ConclusionsOur AI models provide best predictive accuracy and clinical net benefits over well-known prognostic models. As a result, they can potentially be used in clinical practice for providing better management for mRCC patients starting their first-line of systemic treatment. Larger studies would be needed to validate the developed model.

Project description:IntroductionGene-expression signatures for prognosis have been reported in localized renal cell carcinoma (RCC). The aim of this study was to test the predictive power of two different signatures, ClearCode34, a 34-gene signature model [Eur Urol 2014;66:77-84], and an 8-gene signature model [Eur Urol 2015;67:17-20], in the setting of systemic therapy for metastatic disease.Materials and methodsMetastatic RCC (mRCC) patients from five institutions who were part of TCGA were identified and clinical data were retrieved. We trained and implemented each gene model as described by the original study. The latter was demonstrated by faithful regeneration of a figure and results from the original study. mRCC patients were dichotomized to good or poor prognostic risk groups using each gene model. Cox proportional hazard regression and concordance index (C-Index) analysis were used to investigate an association between each prognostic risk model and overall survival (OS) from first-line therapy.ResultsOverall, 54 patients were included in the final analysis. The primary endpoint was OS. Applying the ClearCode34 model, median survival for the low-risk-ccA (n = 17)-and the high-risk-ccB (n = 37)-subtypes were 27.6 and 22.3 months (hazard ratio (HR): 2.33; p = .039), respectively. ClearCode34 ccA/ccB and International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) classifications appear to represent distinct risk criteria in mRCC, and we observed no significant overlap in classification (p > .05, chi-square test). On multivariable analyses and adjusting for IMDC groups, ccB remained independently associated with a worse OS (p = .044); the joint model of ccA/ccB and IMDC was significantly more accurate in predicting OS than a model with IMDC alone (p = .045, F-test). This was also observed in C-Index analysis; a model with both ccA and ccB subtypes had higher accuracy (C-Index 0.63, 95% confidence interval [CI] = 0.51-0.75) and 95% CIs of the C-Index that did not include the null value of 0.5 in contrast to a model with IMDC alone (0.60, CI = 0.47-0.72). The 8-gene signature molecular subtype model was a weak but insignificant predictor of survival in this cohort (p = .13). A model that included both the 8-gene signature and IMDC (C-Index 0.62, CI = 0.49-0.76) was more prognostic than IMDC alone but did not reach significance, as the 95% CI included the null value of 0.5. These two genomic signatures share no genes in common and are enriched in different biological pathways. The ClearCode34 included genes ARNT and EPAS1 (also known as HIF2a), which are involved in regulation of gene expression by hypoxia-inducible factor.ConclusionThe ClearCode34 but not the 8-gene molecular model improved the prognostic predictive power of the IMDC model in this cohort of 54 patients with metastatic clear cell RCC. The Oncologist 2017;22:286-292 IMPLICATIONS FOR PRACTICE: The clinical and laboratory factors included in the International Metastatic Renal Cell Carcinoma Database Consortium model provide prognostic information in metastatic renal cell carcinoma (mRCC). The present study shows that genomic signatures, originally validated in localized RCC, may add further complementary prognostic information in the metastatic setting. This study may provide new insights into the molecular basis of certain mRCC subgroups. The integration of clinical and molecular data has the potential to redefine mRCC classification, enhance the understanding of mRCC biology, and potentially predict response to treatment in the future.

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A simple prognostic model for overall survival in metastatic renal cell carcinoma.

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A simple prognostic model for overall survival in metastatic renal cell carcinoma.

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