Establishment of a standard pipeline for large-scale cancer proteogenomic research
Abstract
There have been several notable achievements; however, cancer is an economically and socially burdensome cause of death. The exact mechanisms of tumor initiation and development remain unclear. As the amount of data provided by cost-effective high-throughput technologies increases, multi-omics data integration strategies including genomes, epigenomes, transcriptomes, proteomes, and metabolomes accelerate predictive, preventive, and personalized medicine ultimately enabling precision medicine of cancer.
In this presentation, we present the application of proteogenomics in lung cancer to understand the process and outcome of treatment. We discuss frameworks developed to identify driver genomic alterations and consider the importance of multi-omics in tumor classification, diagnosis, and prognosis. We will also discuss the importance and scientific benefits of integrating multi-omics in cancer research.
Proteogenomics for Development of Precision Oncology: Cases of Refractory Cancers
Abstract
Despite recent advances in genomics and proteomics technologies, and widespread interest in applying these technologies to achieve precision oncology, the number of new FDA-approved protein drugs and prognostic markers reported over the past decade did not meet the high expectation. The reasons for this apparent disconnect between precision oncology efforts and FDA approval are many. Some issues are technological in nature: the need for proteome measurements that are sensitive, broad, quantitative, and at the same time with sufficient throughput to analyze enough samples for statistical confidence. Some of the problems are based in cancer biology: the problems of human variability that require analysis of large numbers of samples; the need to integrate information of multiple omics data (e.g., genomics/transcriptomics and proteomics) and to link these data to clinical outcomes. We are developing and refining an advanced proteomics analysis platform that integrates cutting-edge proteome technologies and provides significantly improved sensitivity, throughput, and robustness of proteome profiling. Here we discuss our efforts to develop proteogenomics core technologies and present the results of their application to proteogenomic characterizations of refractory cancers, such as early onset gastric cancer and pancreatic ductal adenocarcinoma, to lay out foundation for precision medicine.
Longitudinal analysis of treatment-induced resistance in glioblastoma by proteogenomics
Abstract
During last decades, our understanding of caner-related molecular mechanisms and cancer diagnosis has been exponentially developed by the aid of cancer genomics. However, our knowledge based on genomic and transcriptomic understanding is still incomplete. Many clinical trials based on this information are still struggling because of lack of knowledge that can explain cancer phenotype. To overcome current lack of molecular understanding of cancer biology, we took advantage of recent progress in mass spectrometry-based protein sequencing and identification of protein modification to predict more precise action mechanism of cancer progression. Glioblastoma is one of the most highly recurrent cancer type, around 95% of cancer patients experience recurrence within 5 years. Moreover, therapeutic options for recurred patients are very limited. Therefore, it is very important to understand molecular details of GBM recurrence and find new therapeutic targets. Here we recruited 123 longitudinal pairs of GBM patient samples and analyzed whole exome, transcriptome, global proteome and phosphoproteome. Integration of multilayered data implicates dynamic regulation of cancer signaling during recurrence. Especially, subtypes that classified by gene expression are dynamically changed and oncogenic signaling pathway was diversified by proteome-based pathway analysis. These results will provide comprehensive data for tumor-stromal interaction during tumor recurrence and identify new therapeutic targets for recurrent GBM.
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