Citations

PURPOSEDrug 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 METHODSBy 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.RESULTSWe identified 22 drugs from six drug classes (statins, proton pump inhibitors, angiotensin-converting enzyme inhibitors, β-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.CONCLUSIONMining 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.

Purpose According to the lung cancer staging project, T1a (≤ 2 cm) non–small-cell lung cancer (NSCLC) should be additionally classified into ≤ 1 cm and > 1 to 2 cm groups. This study aimed to investigate the surgical procedure for NSCLC ≤ 1 cm and > 1 to 2 cm. Methods We identified 15,760 patients with T1aN0M0 NSCLC after surgery from the Surveillance, Epidemiology, and End Results database. Overall survival (OS) and lung cancer–specific survival (LCSS) were compared among patients after lobectomy, segmentectomy, or wedge resection. The proportional hazards model was applied to evaluate multiple prognostic factors. Results OS and LCSS favored lobectomy compared with segmentectomy or wedge resection in patients with NSCLC ≤ 1 cm and > 1 to 2 cm. Multivariable analysis showed that segmentectomy and wedge resection were independently associated with poorer OS and LCSS than lobectomy for NSCLC ≤ 1 cm and > 1 to 2 cm. With sublobar resection, lower OS and LCSS emerged for NSCLC > 1 to 2 cm after wedge resection, whereas similar survivals were observed for NSCLC ≤ 1 cm. Multivariable analyses showed that wedge resection is an independent risk factor of survival for NSCLC > 1 to 2 cm but not for NSCLC ≤ 1 cm. Conclusion Lobectomy showed better survival than sublobar resection for patients with NSCLC ≤ 1 cm and > 1 to 2 cm. For patients in whom lobectomy is unsuitable, segmentectomy should be recommended for NSCLC > 1 to 2 cm, whereas surgeons could rely on surgical skills and the patient profile to decide between segmentectomy and wedge resection for NSCLC ≤ 1 cm.