| Popis: |
Lung cancer is the most deadly form of cancer, responsible for over 1.6 milliondeaths annually, the majority of which are due to non-small cell lung cancer, of whichadenocarcinoma and squamous cell carcinoma are the major subtypes. Standardchemotherapy produces responses in a small minority of patients, and despite thetremendous growth of personalized therapies in the last decade, only a minority ofpatients benefit from these treatments in the North American setting. A greaterunderstanding of the biology of non-small cell lung cancer is desperately needed todevelop novel targeted therapies and their accompanying biomarkers.Understanding the function of cancer-associated genes requires the integrationand analysis of multiple modalities of biological data. Cancer associated genes can beactivated or repressed by DNA somatic mutations, RNA alternative splicing, epigeneticchanges, microRNA-mediated silencing, post-translational regulation, and othermechanisms. To understand how tumors form and grow, we have to be able to measureDNA, RNA, protein, metabolites, and lipids. Further, integrative and analytical methodsare necessary to leverage these data together, collectively termed integrative genomics.Here, we leverage DNA mutations and copy number measurements, RNAtranscriptomics, proteomics, and clinical data to discover regulatory relationships intumors, develop prognostic biomarkers, and identify mediators of tumor mutation burden.First, we focus on the RNA editing protein ADAR, and propose an immune-mediatedfunction in lung adenocarcinoma. Second, we develop a method to integrate RNA andprotein expression data to predict binary clinical variables, and test its ability to predicttumor recurrence in surgically resected lung adenocarcinoma samples. Finally, we definethe relationship between tumor mutation burden and genome stability protein inactivationto better understand tumor immunogenicity in non-small cell lung cancer. Takentogether, these approaches present a comprehensive methodology to utilize integrativegenomic data for clinical applications in non-small cell lung cancer. |
