ABSTRACT: 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.
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.
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.
Project description:Never-smoker lung adenocarcinoma (NSLA) is prevalent in Asian populations and even more in women. Since epidermal growth factor receptor (EGFR) mutations or anaplastic lymphoma kinase (ALK) fusions are major alterations found in NSLA, studies have focused on NSLA with EGFR and ALK alteration (EA), but not for NSLA without EGFR and ALK alteration (NENA). To reveal the proteogenomic landscape of NENA, we selected 101 NSLA tissues without EGFR and ALK by targeted sequencing of 1597 FFPE samples, and performed multiomics analyses including whole genome, transcriptome, methylation EPIC array, total proteome, and phosphoproteome. Genome analysis revealed that TP53 (25%), KRAS (22%), ROS1 fusion (13%), SETD2 (11%), and ERRB2 (9%) were the most frequently mutated genes in NENA. Proteogenomic impact analysis found that STK11 and ERBB2 somatic mutations had more profound effects on cancer-associated genes in NENA. From DNA copy number alteration analysis, we identified 22 prognostic proteins whose expression was controlled through transcriptome from copy number alterations Intriguingly, from gene set enrichment analysis, estrogen signaling emerged as the key pathway activated in NENA compared with EA. Evidence from multiomics analysis including copy number gains in chromosomes 14 and 21, STK11 mutation, and DNA hypomethylation of LLGL2 and ST14, also supported the increased estrogen signaling. Finally, the saracatinib, an Src inhibitor, was suggested as a potential drug for targeting activated estrogen signaling in NENA. Taken together, the proteogenomic landscape for NENA from this study will enhance our understanding of the etiology of NSLA.
Project description:Through multidimensional genomic/protein multiomics analysis and integration of clinical information on tissue samples collected and stored over many years in a biobank, we analyze cancer tissue samples to provide appropriate criteria for selecting samples for such analysis.
Project description:Through multidimensional genomic/protein multiomics analysis and integration of clinical information on tissue samples collected and stored over many years in a biobank, we analyze cancer tissue samples to provide appropriate criteria for selecting samples for such analysis.
Project description:Through multidimensional genomic/protein multiomics analysis and integration of clinical information on tissue samples collected and stored over many years in a biobank, we analyze cancer tissue samples to provide appropriate criteria for selecting samples for such analysis.
Project description:We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of NSCLC. A prognostic signature of 17 genes showed the best association with post-surgery survival time. The performance of the signatures was validated using a patient cohort of similar size A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples
Project description:The analytical validation of a 15 gene prognostic signature for early-stage, completely resected, non-small-cell lung carcinoma that distinguishes between patients with good and poor prognoses.
Project description:Molecular networks are key to understanding the complexity of biological systems. Despite the advances in network construction and analysis techniques, challenges remain in incorporating multidimensional interactions into a hybrid network model and reducing the complexity of large networks into functional modules or influential nodes. Here, we introduce CREAM, a user-friendly application that empowers biologists to construct multilayer networks by integrating gene expression data with various known interactions predicted by experimental and computational techniques, and to identify functional modules and prognostic biomarkers within these networks. When applied to multiple cancer types, CREAM outperforms state-of-the-art module detection methods in identifying biologically relevant modules. Further investigation of colon adenocarcinoma revealed numerous well-known cancer regulators and a promising new therapeutic target, miR-8485. Our in vitro and in vivo experiments demonstrated its inhibitory effects on colon cancer cell proliferation and migration. Overall, CREAM is expected to significantly advance network-based precision medicine research.