Dissertation Defence: Deepankar Chakroborty

    Comprehensive genetic characterization of cancer tissues was enabled with the advent of next-generation sequencing technologies. These methods helped scientists peer into the cancer genomes, and in the process, improved our understanding about the molecular players and the driver events in the life cycle of several human cancers. In addition to generating a small list of driver mutations, the large-scale sequencing efforts generated a long “tail” consisting of “variants of unknown significance” (VUS), which are cancer-associated somatic mutations with uncharacterized functional significance.

    Identification of driver mutations is an integral part of biomarker discovery in cancer research, and this thesis aimed to address this by developing a screening platform called, in vitro screen for activating mutations (iSCREAM), and a Database oRecurrent Mutations (DORM). iSCREAM is a high-throughput screen for identification of gain-of-function mutations in oncogenic kinases. The workflow was used to study EGFR and ERBB4 and identified activating mutations which were previously reported to be VUS. DORM was prepared by analyzing a public registry of somatic mutations and preparing a catalog of the mutations identified from genome-wide studies to recapitulate the “real-world” frequency of all the recurrent (n > 1) somatic mutations. DORM allows limiting the scope of search to 38 tissue types and supports advanced queries using regular expressions. The easy-to-use database and its backend are written to be very responsive and fast in comparison to contemporary public cancer databases.

    MSc Deepankar Chakroborty will present his thesis on Thursday, February 9.

    Opponent: Professor René Bernards (The Netherlands Cancer Institute, Amsterdam, Netherlands)

    Custos: Professor Klaus Elenius (Faculty of Medicine, University of Turku, Finland)

    Date and Time: February 9, 2023 at 14.15 PM

    Place: Mauno Presidentti Auditorium, Biocity, Tykistökatu 6, Turku, FI-20520.

    Link to thesis: https://urn.fi/URN:ISBN:978-951-29-9098-6