Dr. Nurcan Tuncbag on Discovery of Latent Drivers from Double Mutations in Pan-Cancer Data Reveal their Clinical Impact

Assoc. Prof. Nurcan Tuncbag will give a seminar on “Discovery of Latent Drivers from Double Mutations in Pan-Cancer Data Reveal their Clinical Impact” on 3 January at 13:40. The abstract of the talk and a short bio is shared below.

Dr. Tuncbag is an associate professor at Koç University jointly in the Department of Chemical and Biological Engineering and School of Medicine. She received her undergraduate degree in Chemical Engineering from Istanbul Technical University, and her master and doctorate degrees from Koç University Computational Sciences and Engineering department. She did her post-doctoral research in Massachusetts Institute of Technology (MIT) Biological Engineering Department between 2010-2014. She carried out her academic studies at Middle East Technical University between 2014-2021. Her work in computational systems biology and bioinformatics has been awarded nationally and internationally by several academies, foundations and councils. She has been a member of the Global Young Academy since 2020. She develops computational approaches to serve in network medicine, network modeling, trans-omic data integration, single cell and bulk omic data analysis, and discovery of latent cancer driver mutations.

Despite massive advancements in cancer genomics, to date driver mutations whose frequencies are low, and their observable translational potential is minor have escaped identification. Yet, when paired with other mutations in cis, such ‘latent driver’ mutations can drive cancer. Additionally, the additivity of co-occurring driver mutations in different genes (in trans) can lead to powerful oncogenic signal, encoding aggressive proliferation. We applied a statistical approach to identify significantly co-occurring mutations in the pan-cancer data of mutation profiles of ~80,000 tumor sequences from the TCGA and AACR GENIE databases. Evaluation of the response of cell lines and patient-derived xenograft data to drug treatment indicate that in certain genes double mutations can increase oncogenic activity, hence obtain a better drug response (e.g., in PIK3CA), or they can promote resistance to the drugs (e.g., in EGFR). On the other hand, co-occurring double mutations on different genes can additively promote tumorigenesis through single or multiple pathways. They are mostly in primary tumors. Rare occurrences can be a signature of metastatic tumors. Interrogation of big genomic data and integration with large-scale small-molecule sensitivity data can provide deep patterns that are rare – but can prompt dramatic phenotypic alterations and serve as clinical signatures.