Analysis of copy number variant heterogeneity in the hierarchical NCIt cancer classification system
Zurich Seminars in Bioinformatics - Ziying Yang
- 12:15 UZH Irchel Y55-l-06/08 and ZOOM Call
Abstract Cancers are heterogeneous diseases with unifying features of abnormal and consuming cell growth, where the deregulation of normal cellular functions is caused by accumulative mutations. Due to these mutations, malignant tumors present with patterns of somatic genome variants on diverse levels of heterogeneity. Among the different mutation types, genomic copy number aberrations(CNA) have emerged as one of the most distinct classes. Cancer classification is foundational for patient care and oncology research. Traditionally, malignant diseases have been classified using domain-specific or generalized classification systems, based on histopathological features and clinical gestalt. Systems besides general “International Classification for Diseases in Oncology” (ICD-O; WHO), hierarchical terminologies such as National Cancer Institute Thesaurus (NCIt) provide large sets of cancer classification terminologies, therefore promote data interoperability and ontology- driven computational analysis. However, high heterogeneity in cellular phenotypes and dynamic plasticity of tumor microenvironments make tumor categorization a demanding and complicated task with the need to balance between categorical classifications and individual, personalized feature definitions. Therefore, we did analysis of inter-sample genomic heterogeneity at different levels of the classification hierarchies based on CNA events from our Progenetix database. The analysis uncovers the incorresponces of existing classification system with respect to homogeneous molecular groups, and provides a new sight to new cancer subtypes.