Adjuvant Tyrosine Kinase Inhibitors in Renal Cell Carcinoma: A Concluded Living Systematic Review and Meta-Analysis.

PURPOSE: Multiple large clinical trials have investigated adjuvant tyrosine kinase inhibitors (TKIs) to reduce the risk of cancer recurrence and progression to metastasis in high-risk renal cell carcinoma. We sought to maintain living and interactive evidence on this topic, until a high level of certainty is reached for key clinical outcomes such that further updates become unnecessary and unlikely to change clinical practice. METHODS: We created a living interactive evidence synthesis platform to maintain a continuously updated meta-analysis on TKI monotherapy in adjuvant renal cell carcinoma. We implemented an automated search strategy with weekly updates to identify randomized phase 2 and 3 clinical trials. Study selection, appraisal, and data extraction were done in duplicate. Cumulative meta-analysis was performed using Analyzer Module in Living Interactive Evidence platform. For each outcome (overall survival [OS], disease-free survival [DFS], and all-cause and treatment-related adverse events), we assessed certainty of evidence using GRADE approach and conducted trial sequential analysis. RESULTS: This final update includes five randomized trials including recently updated data from PROTECT trial. Meta-analysis shows that adjuvant TKI monotherapy offers no benefit in OS (hazard ratio, 1.01; 95% CI, 0.91 to 1.12, high certainty) or DFS (hazard ratio, 0.92; 95% CI, 0.86 to 1.00, high certainty) and significantly increases adverse event risk. Lack of benefit was consistent across subgroups including highest-risk patients (test for subgroup differences: P = .32). Optimal information size criteria were met, and there was high certainty of evidence for lack of DFS and OS benefit for adjuvant TKIs. CONCLUSION: There is no guidance on when to stop maintaining a living review. In this example, we used trial sequential analysis and high certainty of evidence (future clinical trials unlikely to change current conclusions) as a benchmark to conclude a living review in view of convincing evidence.

Discovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing.

PURPOSE: Drug development is becoming increasingly expensive and time consuming. Drug repurposing is one potential solution to accelerate drug discovery. However, limited research exists on the use of electronic health record (EHR) data for drug repurposing, and most published studies have been conducted in a hypothesis-driven manner that requires a predefined hypothesis about drugs and new indications. Whether EHRs can be used to detect drug repurposing signals is not clear. We want to demonstrate the feasibility of mining large, longitudinal EHRs for drug repurposing by detecting candidate noncancer drugs that can potentially be used for the treatment of cancer. PATIENTS AND METHODS: By linking cancer registry data to EHRs, we identified 43,310 patients with cancer treated at Vanderbilt University Medical Center (VUMC) and 98,366 treated at the Mayo Clinic. We assessed the effect of 146 noncancer drugs on cancer survival using VUMC EHR data and sought to replicate significant associations (false discovery rate < .1) using the identical approach with Mayo Clinic EHR data. To evaluate replicated signals further, we reviewed the biomedical literature and clinical trials on cancers for corroborating evidence. RESULTS: We identified 22 drugs from six drug classes (statins, proton pump inhibitors, angiotensin-converting enzyme inhibitors, ß-blockers, nonsteroidal anti-inflammatory drugs, and α-1 blockers) associated with improved overall cancer survival (false discovery rate < .1) from VUMC; nine of the 22 drug associations were replicated at the Mayo Clinic. Literature and cancer clinical trial evaluations also showed very strong evidence to support the repurposing signals from EHRs. CONCLUSION: Mining of EHRs for drug exposure-mediated survival signals is feasible and identifies potential candidates for antineoplastic repurposing. This study sets up a new model of mining EHRs for drug repurposing signals.