Project description:Patients with EGFR-mutant non-small-cell lung cancer (NSCLC) have significantly benefited from the use of EGFR tyrosine kinase inhibitors (TKIs). However, long-term efficacy of these therapies is limited due to de novo resistance (~30%) as well as acquired resistance. Epithelial-mesenchymal transition transcription factors (EMT-TFs), have been identified as drivers of EMT-mediated resistance to EGFR TKIs, however, strategies to target EMT-TFs are lacking. As the third generation EGFR TKI, osimertinib, has now been adopted in the first-line setting, the frequency of T790M mutations will significantly decrease in the acquired resistance setting. Previously less common mechanisms of acquired resistance to first generation EGFR TKIs including EMT are now being observed at an increased frequency after osimertinib. Importantly, there are no other FDA approved targeted therapies after progression on osimertinib. Here, we investigated a novel strategy to overcome EGFR TKI resistance through targeting the EMT-TF, TWIST1, in EGFR-mutant NSCLC. We demonstrated that genetic silencing of TWIST1 or treatment with the TWIST1 inhibitor, harmine, resulted in growth inhibition and apoptosis in EGFR-mutant NSCLC. TWIST1 overexpression resulted in erlotinib and osimertinib resistance in EGFR-mutant NSCLC cells. Conversely, genetic and pharmacological inhibition of TWIST1 in EGFR TKI-resistant EGFR-mutant cells increased sensitivity to EGFR TKIs. TWIST1-mediated EGFR TKI resistance was due in part to TWIST1 suppression of transcription of the pro-apoptotic BH3-only gene, BCL2L11 (BIM), by directly binding to BCL2L11 intronic regions and promoter. As such, pan-BCL2 inhibitor treatment overcame TWIST1-mediated EGFR TKI resistance and were more effective in the setting of TWIST1 overexpression. Finally, in a mouse model of autochthonous EGFR-mutant lung cancer, Twist1 overexpression resulted in erlotinib resistance and suppression of erlotinib-induced apoptosis. These studies establish TWIST1 as a driver of resistance to EGFR TKIs and provide rationale for use of TWIST1 inhibitors or BCL2 inhibitors as means to overcome EMT-mediated resistance to EGFR TKIs.

Project description:BACKGROUND: Human T-cell leukemia viruses (HTLV) tend to induce some fatal human diseases like Adult T-cell Leukemia (ATL) by targeting human T lymphocytes. To indentify the protein-protein interactions (PPI) between HTLV viruses and Homo sapiens is one of the significant approaches to reveal the underlying mechanism of HTLV infection and host defence. At present, as biological experiments are labor-intensive and expensive, the identified part of the HTLV-human PPI networks is rather small. Although recent years have witnessed much progress in computational modeling for reconstructing pathogen-host PPI networks, data scarcity and data unavailability are two major challenges to be effectively addressed. To our knowledge, no computational method for proteome-wide HTLV-human PPI networks reconstruction has been reported. RESULTS: In this work we develop Multi-instance Adaboost method to conduct homolog knowledge transfer for computationally reconstructing proteome-wide HTLV-human PPI networks. In this method, the homolog knowledge in the form of gene ontology (GO) is treated as auxiliary homolog instance to address the problems of data scarcity and data unavailability, while the potential negative knowledge transfer is automatically attenuated by AdaBoost instance reweighting. The cross validation experiments show that the homolog knowledge transfer in the form of independent homolog instances can effectively enrich the feature information and substitute for the missing GO information. Moreover, the independent tests show that the method can validate 70.3% of the recently curated interactions, significantly exceeding the 2.1% recognition rate by the HT-Y2H experiment. We have used the method to reconstruct the proteome-wide HTLV-human PPI networks and further conducted gene ontology based clustering of the predicted networks for further biomedical research. The gene ontology based clustering analysis of the predictions provides much biological insight into the pathogenesis of HTLV retroviruses. CONCLUSIONS: The Multi-instance AdaBoost method can effectively address the problems of data scarcity and data unavailability for the proteome-wide HTLV-human PPI interaction networks reconstruction. The gene ontology based clustering analysis of the predictions reveals some important signaling pathways and biological modules that HTLV retroviruses are likely to target.

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Homo sapiens

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