Poziotinib for EGFR exon 20-mutant NSCLC: Clinical efficacy, resistance mechanisms, and impact of insertion location on drug sensitivity.

We report a phase II study of 50 advanced non-small cell lung cancer (NSCLC) patients with point mutations or insertions in EGFR exon 20 treated with poziotinib (NCT03066206). The study achieved its primary endpoint, with confirmed objective response rates (ORRs) of 32% and 31% by investigator and blinded independent review, respectively, with a median progression-free survival of 5.5 months. Using preclinical studies, in silico modeling, and molecular dynamics simulations, we found that poziotinib sensitivity was highly dependent on the insertion location, with near-loop insertions (amino acids A767 to P772) being more sensitive than far-loop insertions, an observation confirmed clinically with ORRs of 46% and 0% observed in near versus far-loop, respectively (p = 0.0015). Putative mechanisms of acquired resistance included EGFR T790M, MET amplifications, and epithelial-to-mesenchymal transition (EMT). Our data demonstrate that poziotinib is active in EGFR exon 20-mutant NSCLC, although this activity is influenced by insertion location.

Chromosomal imbalances detected via RNA-sequencing in 28 cancers.

MOTIVATION: RNA-sequencing (RNA-seq) of tumor tissue is typically only used to measure gene expression. Here, we present a statistical approach that leverages existing RNA-seq data to also detect somatic copy number alterations (SCNAs), a pervasive phenomenon in human cancers, without a need to sequence the corresponding DNA. RESULTS: We present an analysis of 4942 participant samples from 28 cancers in The Cancer Genome Atlas (TCGA), demonstrating robust detection of SCNAs from RNA-seq. Using genotype imputation and haplotype information, our RNA-based method had a median sensitivity of 85% to detect SCNAs defined by DNA analysis, at high specificity (∼95%). As an example of translational potential, we successfully replicated SCNA features associated with breast cancer subtypes. Our results credential haplotype-based inference based on RNA-seq to detect SCNAs in clinical and population-based settings. AVAILABILITY AND IMPLEMENTATION: The analyses presented use the data publicly available from TCGA Research Network (http://cancergenome.nih.gov/). See Methods for details regarding data downloads. hapLOHseq software is freely available under The MIT license and can be downloaded from http://scheet.org/software.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.