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Interpreting overall survival results when progression-free survival benefits exist in today's oncology landscape: a metastatic renal cell carcinoma case study.

ABSTRACT: BACKGROUND: The debate surrounding the acceptance of progression-free survival (PFS) as an intermediate endpoint to overall survival (OS) has grown in recent years, due to the challenges in demonstrating an OS benefit within clinical trials today. PFS is generally a good predictor of OS for cases where survival post-progression (SPP) is short, and less so when SPP is long. SPP depends on multiple factors, including residual effect from experimental treatment and effect from crossover or other subsequent therapies, posing unique challenges into the translation of PFS benefit into OS. METHODS: The objective of this analysis was to conduct simulations investigating how increasing SPP impacts PFS translation to OS, utilizing data from the AXIS (axitinib versus sorafenib in advanced metastatic renal cell carcinoma) trial. The underlying assumption was a treatment benefit in PFS (the PFS distribution parameters were chosen to be equal to median PFS in the AXIS trial) but no treatment effect on SPP, implying that PFS improvement is directly reflected in OS improvement. RESULTS: The probability of a statistically significant difference between arms for OS decreased from 54.7% to 6.1% when median SPP was increased from one to 20 months. The probability of the hazard ratio of OS being ≥0.9 was similarly increased from 24.3% to 72.6%, even though the hazard ratio for PFS was 0.69. CONCLUSION: The present study shows that when simulated SPP is added to trial PFS data, the existing PFS benefit is diluted. Knowing that the AXIS treatment arms are well balanced with respect to post-trial treatments, we conclude that the PFS to OS benefit translation is primarily obscured by random variability largely unrelated to the true outcomes. The implications for drug development are not insignificant, as there would be a need to include more patients in studies or utilize a longer follow-up time to overcome the SPP variability issue.

SUBMITTER: Tang Y

PROVIDER: S-EPMC4179830 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Project description:BackgroundUveal melanoma (UM) represents the most common primary intra-ocular malignancy in adults. Up to 50% of the patients develop distant metastases within 10 years from diagnosis, with the liver as the most common site. Upon metastatization, life expectancy strongly reduces and immune checkpoint inhibitors that prove effective in cutaneous melanoma do not modify clinical outcome. To date, few studies have focused on deciphering the immunomodulatory features of metastatic UM microenvironment, and there are no prognostic models for clinical use. This highlights the urgent need to understand the delicate interplay between tumor and immune cells acting at the site of metastasis.MethodsWe collected a patient cohort comprising 21 metastatic UM patients. Hepatic and extra-hepatic UM metastasis samples were studied by multiplex immunofluorescence to assess the tumor immune cell composition. Quantitative analyses were performed to correlate immune cell densities with treatment response, metastasis site and patient survival.ResultsCompared to patients with progressive disease, those with controlled disease had a higher intra-tumoral/peritumoral ratio of CD8 + Granzyme B+ cells, higher density of intra-tumoral CD8+ cytotoxic T lymphocytes (CTL) and an increased percentage of UM cells in close proximity to T lymphocytes, reflecting a role of tumor-killing T cells in the disease. In liver metastases (LM), the intra-tumoral densities of CD163+ tumor-associated macrophages (TAM) and of total CD8+ T cells were higher than in extra-hepatic UM metastases, but the percentage of Granzyme B+ CTL was lower. Moreover, LM displayed more UM cells adjacent to both CTL and TAM, and also more T cells in proximity to TAM, all signs of an impaired immune response. The percentage of activated CTL within the tumor represented a prognostic indicator, as patients with a higher intra-tumoral percentage of CD8 + Granzyme B+ cells had the better outcome. A temptative Immunoscore was generated and proved capable to stratify patients with improved survival. Finally, CD4 + FoxP3+ T cells appeared a crucial population for response to immunotherapy.ConclusionThe results of this study underly the clinical relevance and functional importance of composition and localization of antitumor effector cells for the progression of UM metastasis.

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.

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Interpreting overall survival results when progression-free survival benefits exist in today's oncology landscape: a metastatic renal cell carcinoma case study.

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