Unlike most other primary epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC), exon 20 insertions, comprising approximately 4% to 10% of all EGFR mutations, are generally considered to be resistant to EGFR tyrosine kinase inhibitors (TKIs). However, EGFR exon 20 insertions are structurally and pharmacologically heterogeneous, with variability in their position and size having implications for response to different EGFR TKIs. The second-generation ErbB family blocker, afatinib, is approved for the first-line treatment of EGFR mutation-positive NSCLC and has been shown to have a broad inhibitory profile against common and uncommon EGFR mutations. Here, we describe a patient with bilateral multifocal lung adenocarcinoma harboring a very rare EGFR exon 20 insertion (c.2317_2319dup3; p.H773dup), who has been receiving treatment with afatinib for 4.5 years. To our knowledge, this is the first report describing long-term benefit for a patient treated with afatinib with this rare exon 20 insertion. We are aware of two further cases with this rare EGFR mutation. One patient, also reported here, has early-stage lung adenocarcinoma and has not yet received systemic therapy for NSCLC. The other patient received afatinib in the context of a global compassionate use program and had progressive disease. Our findings may be of clinical relevance for patients carrying tumors with this rare mutation as epidemiological evidence suggests that p.H773dup may function as a driver mutation in NSCLC. Together with previous preclinical and clinical evidence for the activity of afatinib against certain EGFR exon 20 insertions, these findings warrant further investigation.

Fast and affordable benchtop sequencers are becoming more important in improving personalized medical treatment. Still, distinguishing genetic variants between healthy and diseased individuals from sequencing errors remains a challenge. Here we present VARIFI, a pipeline for finding reliable genetic variants (single nucleotide polymorphisms (SNPs) and insertions and deletions (indels)). We optimized parameters in VARIFI by analyzing more than 170 amplicon-sequenced cancer samples produced on the Personal Genome Machine (PGM). In contrast to existing pipelines, VARIFI combines different analysis methods and, based on their concordance, assigns a confidence score to each identified variant. Furthermore, VARIFI applies variant filters for biases associated with the sequencing technologies (e.g., incorrectly identified homopolymer-associated indels with Ion Torrent). VARIFI automatically extracts variant information from publicly available databases and incorporates methods for variant effect prediction. VARIFI requires little computational experience and no in-house compute power since the analyses are conducted on our server. VARIFI is a web-based tool available at varifi.cibiv.univie.ac.at.