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The majority of lung cancer patients are diagnosed with metastatic disease. This study identified a set of 73 microRNAs (miRNAs) that classified lung cancer tumors from normal lung tissues with an overall accuracy of 96.3% in the training patient cohort (n = 109) and 91.7% in unsupervised classification and 92.3% in supervised classification in the validation set (n = 375). Based on association with patient survival (n = 1016), 10 miRNAs were identified as potential tumor suppressors (hsa-miR-144, hsa-miR-195, hsa-miR-223, hsa-miR-30a, hsa-miR-30b, hsa-miR-30d, hsa-miR-335, hsa-miR-363, hsa-miR-451, and hsa-miR-99a), and 4 were identified as potential oncogenes (hsa-miR-21, hsa-miR-31, hsa-miR-411, and hsa-miR-494) in lung cancer. Experimentally confirmed target genes were identified for the 73 diagnostic miRNAs, from which proliferation genes were selected from CRISPR-Cas9/RNA interference (RNAi) screening assays. Pansensitive and panresistant genes to 21 NCCN-recommended drugs with concordant mRNA and protein expression were identified. DGKE and WDR47 were found with significant associations with responses to both systemic therapies and radiotherapy in lung cancer. Based on our identified miRNA-regulated molecular machinery, an inhibitor of PDK1/Akt BX-912, an anthracycline antibiotic daunorubicin, and a multi-targeted protein kinase inhibitor midostaurin were discovered as potential repositioning drugs for treating lung cancer. These findings have implications for improving lung cancer diagnosis, optimizing treatment selection, and discovering new drug options for better patient outcomes.
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Quantitative Structure–Mutation–Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction
Abstract Background Protein kinases are a large family of druggable proteins that are genomically and proteomically altered in many human cancers. Kinase-targeted drugs are emerging as promising avenues for personalized medicine because of the differential response shown by altered kinases to drug treatment in patients and cell-based assays. However, an incomplete understanding of the relationships connecting genome, proteome and drug sensitivity profiles present a major bottleneck in targeting kinases for personalized medicine. Results In this study, we propose a multi-component Quantitative Structure–Mutation–Activity Relationship Tests (QSMART) model and neural networks framework for providing explainable models of protein kinase inhibition and drug response ( $$\hbox {IC}_{50}$$ IC 50 ) profiles in cell lines. Using non-small cell lung cancer as a case study, we show that interaction terms that capture associations between drugs, pathways, and mutant kinases quantitatively contribute to the response of two EGFR inhibitors (afatinib and lapatinib). In particular, protein–protein interactions associated with the JNK apoptotic pathway, associations between lung development and axon extension, and interaction terms connecting drug substructures and the volume/charge of mutant residues at specific structural locations contribute significantly to the observed $$\hbox {IC}_{50}$$ IC 50 values in cell-based assays. Conclusions By integrating multi-omics data in the QSMART model, we not only predict drug responses in cancer cell lines with high accuracy but also identify features and explainable interaction terms contributing to the accuracy. Although we have tested our multi-component explainable framework on protein kinase inhibitors, it can be extended across the proteome to investigate the complex relationships connecting genotypes and drug sensitivity profiles.
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- PAR ID:
- 10328568
- Date Published:
- Journal Name:
- BMC Bioinformatics
- Volume:
- 21
- Issue:
- 1
- ISSN:
- 1471-2105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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91 Background: Colorectal cancer (CRC) is the third leading type of cancer worldwide, with ~150,000 new cases in the US annually and a grim 14% 5-year survival for patients diagnosed at a late stage. A lack of treatment options leads to persistently poor prognosis for patients with advanced stage disease. KRAS mutations are well known drivers of CRC and other GI cancers. Multiple KRAS mutations occur in CRC, including G12D (34%), G12V (21%), G13D (20%), G12C (8%), and others (18%). Existing KRAS-targeted therapies have limited use in CRC, underscoring the need for pan-RAS inhibitors in treating CRC and other RAS driven cancers. Objective: Assess activity of ADT-007, our pan-RAS inhibitor, on wild-type (WT) and KRAS-mutant 3D bioprinted organoid tumor (BOT) tissue using our high-throughput ex vivo platform. Methods: Using previously established bioprinting protocols, WT and mutant BOTs were printed with HT29 and HCT116 cells, respectively. HT29 is an established human WT CRC cell line with known sensitivity to proteosome and survivin inhibitors. HCT116 is a KRASG13Dmutant human CRC cell line. 3 sets of BOTs were generated and acclimated for 24h. One set was treated for 72h with proteosome inhibitor Bortezomib, another with survivin inhibitor YM155, and the third with our novel pan-RAS inhibitor ADT-007. Dose response curves were generated from both conventional ATP luminescence readouts and high-content imaging. Results: BOT tissue microarchitecture was validated and >200 µm diffusion in BOTs was confirmed using high-content imaging. Differential response was quantified using Cell TiterGlo endpoint assay as well as advanced image processing of high-content live/dead nuclear stained images captured at multiple z-plains. ADT-007 IC50was found to be substantially lower for mutant HCT116 compared to that for WT HT29 cell line BOTs, which was consistent with separately conducted in vitro and in vivo studies. Conclusions: A pan-RAS inhibitor, such as ADT-007 with high selectivity for cancer cells with activated RAS that is not limited to a specific KRAS mutant allele or RAS isozyme, could have broader use for CRC and other RAS-driven cancers. Further, due to their potential to replicate biophysical characteristics of a tumor and its microenvironment, BOT based precision and personalized medicine platforms can provide more accurate drug efficacy readout compared to in vitro cancer models.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1186/s12859-020-03842-6
