RELAY, Ramucirumab Plus Erlotinib (RAM+ERL) in Untreated Metastatic EGFR-Mutant NSCLC (EGFR+ NSCLC): Association Between TP53 Status and Clinical Outcome.

BACKGROUND: Ramucirumab plus erlotinib (RAM+ERL) demonstrated superior progression-free survival (PFS) in RELAY, a randomised Phase III trial in patients with untreated, metastatic, EGFR-mutated, non-small-cell lung cancer (EGFR+ NSCLC). Here, we present the relationship between TP53 status and outcomes in RELAY. MATERIALS AND METHODS: Patients received oral ERL plus intravenous RAM (10 mg/kg IV) or placebo (PBO+ERL) every 2 weeks. Plasma was assessed by Guardant 360 next-generation sequencing and patients with any gene alteration detected at baseline were included in this exploratory analysis. Endpoints included PFS, overall response rate (ORR), disease control rate (DCR), DoR, overall survival (OS), safety, and biomarker analysis. The association between TP53 status and outcomes was evaluated. RESULTS: Mutated TP53 was detected in 165 (42.7%; 74 RAM+ERL, 91 PBO+ERL) patients, wild-type TP53 in 221 (57.3%; 118 RAM+ERL, 103 PBO+ERL) patients. Patient and disease characteristics and concurrent gene alterations were comparable between those with mutant and wildtype TP53. Independent of treatment, TP53 mutations, most notably on exon 8, were associated with worse clinical outcomes. In all patients, RAM+ERL improved PFS. While ORR and DCR were comparable across all patients, DoR was superior with RAM+ERL. There were no clinically meaningful differences in the safety profiles between those with baseline TP53 mutation and wild-type. CONCLUSION: This analysis indicates that while TP53 mutations are a negative prognostic marker in EGFR+ NSCLC, the addition of a VEGF inhibitor improves outcomes in those with mutant TP53. RAM+ERL is an efficacious first-line treatment option for patients with EGFR+ NSCLC, independent of TP53 status.

Incorporating Concomitant Medications into Genome-Wide Analyses for the Study of Complex Disease and Drug Response.

Given the high costs of conducting a drug-response trial, researchers are now aiming to use retrospective analyses to conduct genome-wide association studies (GWAS) to identify underlying genetic contributions to drug-response variation. To prevent confounding results from a GWAS to investigate drug response, it is necessary to account for concomitant medications, defined as any medication taken concurrently with the primary medication being investigated. We use data from the Action to Control Cardiovascular Disease (ACCORD) trial in order to implement a novel scoring procedure for incorporating concomitant medication information into a linear regression model in preparation for GWAS. In order to accomplish this, two primary medications were selected: thiazolidinediones and metformin because of the wide-spread use of these medications and large sample sizes available within the ACCORD trial. A third medication, fenofibrate, along with a known confounding medication, statin, were chosen as a proof-of-principle for the scoring procedure. Previous studies have identified SNP rs7412 as being associated with statin response. Here we hypothesize that including the score for statin as a covariate in the GWAS model will correct for confounding of statin and yield a change in association at rs7412. The response of the confounded signal was successfully diminished from p = 3.19 × 10-7 to p = 1.76 × 10-5, by accounting for statin using the scoring procedure presented here. This approach provides the ability for researchers to account for concomitant medications in complex trial designs where monotherapy treatment regimens are not available.