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Purpose: In EGFR-mutated metastatic non–small cell lung cancer (NSCLC), outcomes from EGFR tyrosine kinase inhibitors have differed historically by mutation type present, with lower benefit reported in patients with ex21L858R versus ex19del mutations. We investigated if EGFR-activating mutation subtypes impact treatment outcomes in the phase III RELAY study. Associations between EGFR mutation type and preexisting co-occurring and treatment-emergent genetic alterations were also explored. Patients and Methods: Patients with metastatic NSCLC, an EGFR ex19del or ex21L858R mutation, and no central nervous system metastases were randomized (1:1) to erlotinib (150 mg/day) with either ramucirumab (10 mg/kg; RAM+ERL) or placebo (PBO+ERL), every 2 weeks, until RECIST v1.1–defined progression or unacceptable toxicity. The primary endpoint was progression-free survival (PFS). Secondary and exploratory endpoints included overall response rate (ORR), duration of response (DOR), PFS2, time-to-chemotherapy (TTCT), safety, and next-generation sequencing analyses. Results: Patients with ex19del and ex21L858R mutations had similar clinical characteristics and comutational profiles. One-year PFS rates for ex19del patients were 74% for RAM+ERL versus 54% for PBO+ERL; for ex21L858R rates were 70% (RAM+ERL) versus 47% (PBO+ERL). Similar treatment benefits (ORR, DOR, PFS2, and TTCT) were observed in RAM+ERL–treated patients with ex19del and ex21L858R. Baseline TP53 comutation was associated with superior outcomes for RAM+ERL in both ex19del and ex21L858R subgroups. EGFR T790M mutation rate at progression was similar between treatment arms and by mutation type. Conclusions: RAM+ERL provided significant clinical benefit for both EGFR ex19del and ex21L858R NSCLC, supporting this regimen as suitable for patients with either of these EGFR mutation types.

Purpose: Lung cancer remains as the leading cause of cancer-related death worldwide, mainly due to late diagnosis. Cytology is the gold-standard method for lung cancer diagnosis in minimally invasive respiratory samples, despite its low sensitivity. We aimed to identify epigenetic biomarkers with clinical utility for cancer diagnosis in minimally/noninvasive specimens to improve accuracy of current technologies. Experimental Design: The identification of novel epigenetic biomarkers in stage I lung tumors was accomplished using an integrative genome-wide restrictive analysis of two different large public databases. DNA methylation levels for the selected biomarkers were validated by pyrosequencing in paraffin-embedded tissues and minimally invasive and noninvasive respiratory samples in independent cohorts. Results: We identified nine cancer-specific hypermethylated genes in early-stage lung primary tumors. Four of these genes presented consistent CpG island hypermethylation compared with nonmalignant lung and were associated with transcriptional silencing. A diagnostic signature was built using multivariate logistic regression model based on the combination of four genes: BCAT1, CDO1, TRIM58, and ZNF177. Clinical diagnostic value was also validated in multiple independent cohorts and yielded a remarkable diagnostic accuracy in all cohorts tested. Calibrated and cross-validated epigenetic model predicts with high accuracy the probability to detect cancer in minimally and noninvasive samples. We demonstrated that this epigenetic signature achieved higher diagnostic efficacy in bronchial fluids as compared with conventional cytology for lung cancer diagnosis. Conclusions: Minimally invasive epigenetic biomarkers have emerged as promising tools for cancer diagnosis. The herein obtained epigenetic model in combination with current diagnostic protocols may improve early diagnosis and outcome of lung cancer patients. Clin Cancer Res; 22(13); 3361–71. ©2016 AACR.