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.