Project description:To identify proteomic of lung adenocarcinoma, we collected five pairs of lung adenocarcinoma and normal lung tissues from the clinic for analysis
Project description:The model is based on publication:
Mathematical analysis of gefitinib resistance of lung adenocarcinoma caused by MET amplification
Abstract:
Gefitinib, one of the tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR), is effective for treating lung adenocarcinoma harboring EGFR mutation; but later, most cases acquire a resistance to gefitinib. One of the mechanisms conferring gefitinib resistance to lung adenocarcinoma is the amplification of the MET gene, which is observed in 5–22% of gefitinib-resistant tumors. A previous study suggested that MET amplification could cause gefitinib resistance by driving ErbB3-dependent activation of the PI3K pathway. In this study, we built a mathematical model of gefitinib resistance caused by MET amplification using lung adenocarcinoma HCC827-GR (gefitinib resistant) cells. The molecular reactions involved in gefitinib resistance consisted of dimerization and phosphorylation of three molecules, EGFR, ErbB3, and MET were described by a series of ordinary differential equations. To perform a computer simulation, we quantified each molecule on the cell surface using flow cytometry and estimated unknown parameters by dimensional analysis. Our simulation showed that the number of active ErbB3 molecules is around a hundred-fold smaller than that of active MET molecules. Limited contribution of ErbB3 in gefitinib resistance by MET amplification is also demonstrated using HCC827-GR cells in culture experiments. Our mathematical model provides a quantitative understanding of the molecular reactions underlying drug resistance.
Project description:We investigate tRF and tiRNA profiles in lung adenocarcinoma and adjacent tissues using a NextSeq system. Total RNA was extracted from tissues and pretreated to remove the RNA modifications. In patients with lung adenocarcinoma, 338 types of tRFs and tiRNAs were detected via sequencing, 284 of which were not previously reported. Compared with the adjacent tissues, 17 types of tRFs and tiRNAs comprising 34 subtypes were found to be abnormally expressed in lung adenocarcinoma tissues, 20 of which were upregulated and 14 of which were downregulated. Finally, we show that tRF and tiRNA profiles in lung adenocarcinoma and adjacent tissues and identify several dysregulated tRFs and tiRNAs.
Project description:To identify ubiquitinated modified proteins of lung adenocarcinoma, we collected five pairs of lung adenocarcinoma and normal lung tissues from the clinic for analysis
Project description:To characterize the etiology of lung adenocarcinoma (LUAD) in the United States, we performed deep proteogenomic profiling of 87 tumors integrating whole genome sequencing, transcriptome sequencing, proteomics and phosphoproteomics by mass spectrometry and reverse phase protein arrays. Somatic genome signature analysis revealed three subtypes including a structurally altered subtype enriched with former smokers, genomic inversions and deletions and TP53 alteration, a transition-high subtype enriched with never-smokers, and a transversion-high enriched with current smokers. We discovered that within-tumor correlations of RNA expression and protein expression were associated with tumor purity, grade, immune cell heterogeneity, and expression subtype. We detected and independently validated RNA and protein expression signatures predicting patient survival. A greater number of proteins than RNA transcripts had association with patient survival. Integrative analysis characterized three expression subtypes with divergent mutations, proteomic regulatory networks and therapeutic vulnerabilities. This proteogenomic characterization provides a new foundation for molecularly-informed medicine in LUAD.
Project description:Clinical FFPE tissue proteomic analyses were performed for early lung adenocarcinomas including adenocarcinoma in-situ (AIS), minimally invasive adenocarcinoma (MIA) and lepidic predominant invasive adenocarcinoma (LPA).
Project description:Lung cancer is the leading cause of preventable death globally and is broadly classified into adenocarcinoma and squamous cell carcinoma depending upon cell type. In this study, we carried out mass spectrometry based quantitative proteomic analysis of lung adenocarcinoma and squamous cell carcinoma primary tissue by employing the isobaric tags for relative and absolute quantitation (iTRAQ) approach. Proteomic data was analyzed using SEQUEST search algorithm which resulted in identification of 25,998 peptides corresponding to 4,342 proteins of which 610 proteins were differentially expressed (≥ 2-fold) between adenocarcinoma and squamous cell carcinoma samples. These differentially expressed proteins were further classified by gene ontology for their localizations and biological processes. Pathway analysis of differentially expressed proteins revealed distinct alterations in networks and pathways in both adenocarcinoma and squamous cell carcinoma samples. In this study, we identified a subset of proteins that shows converse expression between lung adenocarcinoma and squamous cell carcinoma samples. Such proteins may serve as signature markers to distinguish between the two subtypes.
Project description:We investigated whether the miRNA expression could distinguish lung cancers from normal tissues, examining 116 pairs of primary lung cancers with their corresponding adjacent normal lung tissues collected a minimum of 5 cm from the tumor. Our analysis identified a five microRNA classifier could distinguish malignant lung cancer lesions from adjacent normal tissues. SCLC could be distinguished from non small lung cancer by microRNAs profiling. Survival associations were examined with the SCC and adenocarcinoma subtypes. High hsa-miR-31 expression was associated with poor survival in SCC, and the association was confirmed in 20 independent SCC patients by qRT-PCR assays. Overall these findings may help advance the use of microRNA profiling in personalized diagnosis of lung cancers. Key Words: microRNA; lung cancer; microarray; diagnosis; prognosis cancer vs adjacent normal tissues
Project description:To aid in the prioritization of deubiquitinases (DUBs) as anticancer targets, we developed an approach combining activity-based protein profiling (ABPP) with mass spectrometry in both non-small cell lung cancer (NSCLC) tumor tissues and cell lines along with analysis of available RNA interference and CRISPR screens. We identified 67 DUBs in NSCLC tissues, 17 of which were overexpressed in adenocarcinoma or squamous cell histologies, and 12 scoring as affecting lung cancer cell viability in RNAi or CRISPR screens. We used the CSN5 inhibitor, which targets COPS5/ CSN5, as a tool to understand the biological significance of one of these 12 DUBs, COPS6, in lung cancer. Our study provides a powerful resource to interrogate the role of DUB signaling biology and nominates druggable targets for the treatment of lung cancer subtypes.
Project description:TMT-based quantitative mass spectrometry analysis was performed on ECM-enriched preparation from normal murine lung, bleomycin-induced fibrotic lung, primary tumors and lymph node metastasis isolated from the KrasG12D/+p53-/- mouse model of lung adenocarcinoma. Three independent samples were processed and analyzed for each condition. This study identified ECM signatures of normal and diseased lung tissue.