Project description:Never-smoker lung adenocarcinoma (NSLA) is prevalent in Asian populations, and is even more in women. EGFR mutations and ALK fusions are major alterations observed in NSLA. We have focused on NSLA without EGFR and ALK alteration (NENA) rather than NSLA with EGFR and ALK (EA). First, we selected 141 NSLA tissues, and performed proteogenomic analyses including the whole-genome sequencing (WGS), transcriptome, methylation EPIC array, total proteome and phosphoproteome. We then excluded 40 patients with EA and 7 patients with NENA microsatellite instability. Genome analysis revealed that TP53 (25%), KRAS (22%), ROS1 fusion (14%), and SETD2 (11%) were the most frequently mutated genes in NENA patients. Proteogenomic impact analysis revealed that STK11 and ERBB2 somatic mutations had broader effects on cancer-associated genes in NENA. Through DNA copy-number alteration analysis, we identified 22 prognostic proteins, influencing transcriptomic and proteomic changes. Gene set enrichment analysis revealed that the estrogen signaling emerged as the key pathway activated in NENA. A lot of proteogenomic evidence supported the increased estrogen signaling, such as copy-number deletions in chromosomes 14 and 21, STK11 mutation, and DNA hypomethylation of LLGL2 and ST14. Finally, saracatinib, an Src inhibitor, was suggested as a potential drug for targeting activated estrogen signaling in NENA, and was experimentally validated in vitro using cell line model. In this study, we enhanced our understanding of the etiology of NENA NSLA through the proteogenomic landscape, based on which we proposed saracatinib as an effective drug
Project description:Never-smoker lung adenocarcinoma (NSLA) is prevalent in Asian populations, and is even more in women. EGFR mutations and ALK fusions are major alterations observed in NSLA. We have focused on NSLA without EGFR and ALK alteration (NENA) rather than NSLA with EGFR and ALK (EA). First, we selected 141 NSLA tissues, and performed proteogenomic analyses including the whole-genome sequencing (WGS), transcriptome, methylation EPIC array, total proteome and phosphoproteome. We then excluded 40 patients with EA and 7 patients with NENA microsatellite instability. Genome analysis revealed that TP53 (25%), KRAS (22%), ROS1 fusion (14%), and SETD2 (11%) were the most frequently mutated genes in NENA patients. Proteogenomic impact analysis revealed that STK11 and ERBB2 somatic mutations had broader effects on cancer-associated genes in NENA. Through DNA copy-number alteration analysis, we identified 22 prognostic proteins, influencing transcriptomic and proteomic changes. Gene set enrichment analysis revealed that the estrogen signaling emerged as the key pathway activated in NENA. A lot of proteogenomic evidence supported the increased estrogen signaling, such as copy-number deletions in chromosomes 14 and 21, STK11 mutation, and DNA hypomethylation of LLGL2 and ST14. Finally, saracatinib, an Src inhibitor, was suggested as a potential drug for targeting activated estrogen signaling in NENA, and was experimentally validated in vitro using cell line model. In this study, we enhanced our understanding of the etiology of NENA NSLA through the proteogenomic landscape, based on which we proposed saracatinib as an effective drug
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: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 performed a pilot proteogenomic study to compare lung adenocarcinoma to lung squamous cell carcinoma using quantitative proteomics (6-plex TMT) combined with a customized Affymetrix GeneChip. Using MaxQuant software, we identified 51,001 unique peptides that mapped to 7,241 unique proteins and from these identified 6,373 genes with matching protein expression for further analysis. We found a minor correlation between gene expression and protein expression; both datasets were able to independently recapitulate known differences between the adenocarcinoma and squamous cell carcinoma subtypes. We found 565 proteins and 629 genes to be differentially expressed between adenocarcinoma and squamous cell carcinoma, with 113 of these consistently differentially expressed at both the gene and protein levels. We then compared our results to published adenocarcinoma versus squamous cell carcinoma proteomic data that we also processed with MaxQuant. We selected two proteins consistently overexpressed in squamous cell carcinoma in all studies, MCT1 (SLC16A1) and GLUT1 (SLC2A1), for further investigation. We found differential expression of these same proteins at the gene level in our study as well as in other public gene expression datasets. These findings combined with survival analysis of public datasets suggest that MCT1 and GLUT1 may be potential prognostic markers in adenocarcinoma and druggable targets in squamous cell carcinoma.
Project description:We conducted a comprehensive proteogenomic analysis comprising proteomic and phosphoproteomic profiling on 98 pre-invasive and 99 invasive lung adenocarcinomas.