Patients with non-small cell lung cancer (NSCLC) that harbors epidermal growth factor receptor (EGFR) mutations initially respond to EGFR-tyrosine kinase inhibitors (TKI) but eventually experience relapse. Acquired resistance to EGFR-TKIs is strongly associated with patient mortality. Thus, elucidation of the mechanism of acquired resistance to EGFR-TKIs is of great importance. In this study, gefitinib-resistant cell line models were established by long-term exposure to gefitinib using the gefitinib-sensitive lung cancer cell lines, PC9 and HCC827. Expression analyses indicated that both FGFR1 and FGF2 were increased in PC9 gefitinib-resistant (PC9 GR) cells as compared with PC9 naïve (PC9 na) cells. Importantly, proliferation of gefitinib-resistant cells was dependent on the FGF2 -FGFR1 pathway. Mechanistically, inhibition of either FGF2 or FGFR1 by siRNA or FGFR inhibitor (PD173074) restored gefitinib sensitivity in PC9 GR cells. These data suggest that FGF2 -FGFR1 activation through an autocrine loop is a novel mechanism of acquired resistance to EGFR-TKIs. Mol Cancer Res; 11(7); 759-67. Ó2013 AACR.

EGFR mutated lung cancer accounts for a significant subgroup of non-small-cell lung cancer (NSCLC). Over the last decade, multiple EGFR tyrosine kinase inhibitors (EGFR-TKIs) have been developed to target mutated EGFR. However, there is little information regarding mutation specific potency of EGFR-TKIs against various types of EGFR mutations. The purpose of this study is to establish an in vitro model to determine the “therapeutic window” of EGFR-TKIs against various types of EGFR mutations, including EGFR exon 20 insertion mutations. The potency of 1st (erlotinib), 2nd (afatinib) and 3rd (osimertinib and rociletinib) generation EGFR-TKIs was compared in vitro for human lung cancer cell lines and Ba/F3 cells, which exogenously express mutated or wild type EGFR. An in vitro model of mutation specificity was created by calculating the ratio of IC50 values between mutated and wild type EGFR. The in vitro model identified a wide therapeutic window of afatinib for exon 19 deletions and L858R and of osimertinib and rociletinib for T790M positive mutations. The results obtained with our models matched well with previously reported preclinical and clinical data. Interestingly, for EGFR exon 20 insertion mutations, most of which are known to be resistant to 1st and 2nd generation EGFR-TKIS, osimertinib was potent and presented a wide therapeutic window. To our knowledge, this is the first report that has identified the therapeutic window of osimertinib for EGFR exon 20 insertion mutations. In conclusion, this model will provide a preclinical rationale for proper selection of EGFR-TKIs against clinically-relevant EGFR mutations.

EGFR-mutated lung cancers account for a significant subgroup of non-small cell lung cancers overall. Third-generation EGFR tyrosine kinase inhibitors (TKI) are mutation-selective inhibitors with minimal effects on wild-type EGFR. Acquired resistance develops to these agents, however, the mechanisms are as yet uncharacterized. In this study, we report that the Src-AKT pathway contributes to acquired resistance to these TKI. In addition, amplification of EGFR wild-type alleles but not mutant alleles was sufficient to confer acquired resistance. These findings underscore the importance of signals from wild-type EGFR alleles in acquiring resistance to mutant-selective EGFR-TKI. Our data provide evidence of wild-type allele-mediated resistance, a novel concept of acquired resistance in response to mutation-selective inhibitor therapy in cancer treatment. .

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Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge1–5. Here we conducted a genome-wide association study (GWAS) involving 2,393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3,289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target.

Abstract. Recent advances in the treatment of non-small cell lung cancer (NSCLC) with new agents require accurate histological subtyping at diagnosis to avoid the higher risk of an adverse response and to obtain the maximum therapeutic response. However, interobserver variability, tumor hetero geneity and the degree of differentiation may affect the decision concerning a pathological diagnosis of NSCLC. Therefore, the aim of this study was to identify specific microRNAs (miRNAs) as standardized biomarkers with high sensitivity and specificity in order to distinguish between squamous cell carcinoma (SCC) and adenocarcinoma (AC). Quantitative polymerase chain reaction (qPCR)-based miRNA array analysis was performed to identify microRNAs differentially expressed between SCC and AC using 86 resected NSCLC samples in addition to adjacent normal tissues. The results were confirmed by independent qRT-PCR assays with the same test samples and 88 additional validation samples, and from this we evaluated the usefulness of the identified miRNAs as biomarkers to distinguish between SCC and AC. Three miRNAs (hsa-miR-196b, hsa-miR-205 and hsa-miR-375) were identified. Discriminant analysis combining the three miRNAs appeared to distinguish SCC from AC accurately in the test and validation samples, demonstrating a sensitivity and specificity of 76 and 80%, and 85 and 83%, respectively. hsa-miR-196b, hsa-miR-205 and hsa-miR-375 were identified as biomarkers capable of distinguishing between lung SCC and lung AC. These newly identified miRNAs may prove to be highly valuable molecular markers for the classification of NSCLC histological subtypes and may contribute to the pathogenesis of each subtype of NSCLC.

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Next generation sequencing (NGS)-based tumor profiling identified an overwhelming number of uncharacterized somatic mutations, also known as variants of unknown significance (VUS). The therapeutic significance of EGFR mutations outside mutational hotspots, consisting of >50 types, in nonsmall cell lung carcinoma (NSCLC) is largely unknown. In fact, our pan-nation screening of NSCLC without hotspot EGFR mutations (n = 3,779) revealed that the majority (>90%) of cases with rare EGFR mutations, accounting for 5.5% of the cohort subjects, did not receive EGFR-tyrosine kinase inhibitors (TKIs) as a first-line treatment. To tackle this problem, we applied a molecular dynamics simulation-based model to predict the sensitivity of rare EGFR mutants to EGFR-TKIs. The model successfully predicted the diverse in vitro and in vivo sensitivities of exon 20 insertion mutants, including a singleton, to osimertinib, a third-generation EGFR-TKI (R2 = 0.72, P = 0.0037). Additionally, our model showed a higher consistency with experimentally obtained sensitivity data than other prediction approaches, indicating its robustness in analyzing complex cancer mutations. Thus, the in silico prediction model will be a powerful tool in precision medicine for NSCLC patients carrying rare EGFR mutations in the clinical setting. Here, we propose an insight to overcome mutation diversity in lung cancer.