Project description:We obtained small cell lung cancer specimens and normal lung specimens from patients who died of drug-resistant SCLC. The small lung cancer specimens include primary lesions and metastatic lesions. Next generation sequencing was performed to assess the expression of miRNA in drug-resistant small cell lung cancer.
Project description:Non-small cell lung cancer (NSCLC) death rates exceed the next 3 prevalent cancers combined; however, most NSCLC tumors lack actionable mutations. Recent studies of NSCLC and other cancers revealed profound proteome remodelling with prognostic impact that is not fully predicted by DNA or RNA analyses. These revelations portend proteome-based cancer classification and treatment. This will require model systems that recapitulate tumor proteomes and phenotypes. A subset (~35%) of the most aggressive NSCLC can form a patient-derived xenograft (PDX). We generated 137 PDX models of aggressive NSCLC, which represent the histological, genome, transcriptome, and DNA methylation features and proteome remodelling of primary NSCLC. The models indicate 3 lung adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, candidate targets, and in adenocarcinoma, distinct stromal immune features. The PDX resource will foster proteome-directed stratification and development of new treatments for aggressive NSCLC.
Project description:Non-small cell lung cancer (NSCLC) death rates exceed the next 3 prevalent cancers combined; however, most NSCLC tumors lack actionable mutations. Recent studies of NSCLC and other cancers revealed profound proteome remodelling with prognostic impact that is not fully predicted by DNA or RNA analyses. These revelations portend proteome-based cancer classification and treatment. This will require model systems that recapitulate tumor proteomes and phenotypes. A subset (~35%) of the most aggressive NSCLC can form a patient-derived xenograft (PDX). We generated 137 PDX models of aggressive NSCLC, which represent the histological, genome, transcriptome, and DNA methylation features and proteome remodelling of primary NSCLC. The models indicate 3 lung adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, candidate targets, and in adenocarcinoma, distinct stromal immune features. The PDX resource will foster proteome-directed stratification and development of new treatments for aggressive NSCLC.
Project description:Lung cancer is the leading cause of cancer-related death worldwide, and non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancers. Lymphatic metastasis serves as a predominant NSCLC metastatic route and an essential predictor of patient prognosis. Recently, circular RNA (circRNA) has emerged as critical mediator in various tumor initiation and progression. To identify essential circRNA that involves in the lymphatic metastasis of NSCLC, Next generation sequencing (NSG) was performed in 6 paired NSCLC tissues and normal adjacent tissues (NAT).
Project description:In addition to the generation and analysis of metabolomics data on cell lines, samples of normal lung tissue, adenocarcinoma lung tissue and small cell lung carcinoma tissue (seven samples/group) were processed and evaluated metabolite profile differences under the scope of the pilot and feasibility study. These data can be correlated to the metabolite profiles defined in the SCLC and NSCLC cell lines and integrated with the ABPP-determined metabolic kinases to identify distinct metabolic signatures or biomarkers (?oncometabolites?) that distinguish small cell lung cancer from non-small cell lung cancer.
Project description:The expression profiles of miRNAs in drug-resistant non-small cell lung cancer (NSCLC) cell lines were identified via next generation sequencing and the common dysregulated miRNAs in drug-resistant NSCLC cell lines were picked up for further analysis.
Project description:Immunotherapy has shown great therapeutic potential for cancers with high tumor mutational burden (TMB), but much less promise for cancers with low TMB. One primary approach for adoptive lymphocyte transfer-based immunotherapy is to target the somatic mutated peptide neoantigens and cancer testis (CT) antigens recognized by cytotoxic T cells. Here, we employed mass spectrometry (MS)-based proteogenomic large-scale profiling to identify potential immunogenic human leukocyte antigen (HLA) Class ǀ- associated peptides in both melanoma, a “hot tumor”, and EGFR mutant lung adenocarcinoma, a “cold tumor”. We uncovered 19 common driver oncogene-derived peptides and more than 1000 post-translationally modified peptides (PTM) representing 58 different PTMs. We constructed a CT antigen database with 286 antigens by compiling reputed CT antigen resources and “in-house” genomic data and used this to identify 45 CT antigen-derived peptides from the identified HLA peptidome. Using integrated next generation sequencing data, we discovered 12 neopeptides in EGFR mutant lung cancer cell lines. Finally, we report a novel approach for non-canonical peptide discovery, whereby we leveraged a deep learning-based de novo search and a high confidence annotated long noncoding RNA (LncRNA) database to identify 44 lncRNA-derived peptides. Findings of this study, for the first time, provide evidence for a large pool of actionable cancer antigen-derived peptides for use in mutant EGFR lung cancer immunotherapy.
Project description:Through multidimensional genomic/protein multiomics analysis and clinical information integration of cancer tissue samples, a prognostic method for lung cancer, including non-small cell lung cancer (NSCLC), is developed and applied to precision medical care after discovering new drug targets.
