Author : Michael F. Sharpnack
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (17 download)
Book Synopsis Integrative Genomics Methods for Personalized Treatment of Non-small-cell Lung Cancer by : Michael F. Sharpnack
Download or read book Integrative Genomics Methods for Personalized Treatment of Non-small-cell Lung Cancer written by Michael F. Sharpnack and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Lung cancer is the most deadly form of cancer, responsible for over 1.6 million deaths annually, the majority of which are due to non-small cell lung cancer, of which adenocarcinoma and squamous cell carcinoma are the major subtypes. Standard chemotherapy produces responses in a small minority of patients, and despite the tremendous growth of personalized therapies in the last decade, only a minority of patients benefit from these treatments in the North American setting. A greater understanding of the biology of non-small cell lung cancer is desperately needed to develop novel targeted therapies and their accompanying biomarkers. Understanding the function of cancer-associated genes requires the integration and analysis of multiple modalities of biological data. Cancer associated genes can be activated or repressed by DNA somatic mutations, RNA alternative splicing, epigenetic changes, microRNA-mediated silencing, post-translational regulation, and other mechanisms. To understand how tumors form and grow, we have to be able to measure DNA, RNA, protein, metabolites, and lipids. Further, integrative and analytical methods are necessary to leverage these data together, collectively termed integrative genomics. Here, we leverage DNA mutations and copy number measurements, RNA transcriptomics, proteomics, and clinical data to discover regulatory relationships in tumors, develop prognostic biomarkers, and identify mediators of tumor mutation burden. First, we focus on the RNA editing protein ADAR, and propose an immune-mediated function in lung adenocarcinoma. Second, we develop a method to integrate RNA and protein expression data to predict binary clinical variables, and test its ability to predict tumor recurrence in surgically resected lung adenocarcinoma samples. Finally, we define the relationship between tumor mutation burden and genome stability protein inactivation to better understand tumor immunogenicity in non-small cell lung cancer. Taken together, these approaches present a comprehensive methodology to utilize integrative genomic data for clinical applications in non-small cell lung cancer.
