Bhagwan Yadav graduated from KTH Royal Institute of Technology in Stockholm in 2012, having computational and systems biology as his major subject. He started his PhD project in the newly established group of FIMM-EMBL Group Leader Tero Aittokallio in 2011, after finishing his master’s thesis project in Germany.
Bhagwan’s thesis project is part of FIMM’s “Individualised systems medicine in cancer” Grand Challenge programme. Individualised drug screening data is an essential part of this Grand Challenge research. The high-throughput drug sensitivity and resistance testing approach developed at FIMM provides the means for profiling of patient-derived cancer cells in terms of drug responses to hundreds of oncology compounds and also some of their combinations. However, these vast amounts of data produced need to be effectively processed and analysed before key information for the clinical decision making can be extracted.
In his thesis study, Bhagwan concentrated on developing new computational models for data analysis to facilitate the individualised treatment of cancer patients. His thesis consists of four publications, all of which have already been published. In these publications, he describes three novel models for the analysis and integration of drug sensitivity data and demonstrates their robustness and better performance compared to the existing ones.
The most important computational pipeline developed in the thesis is based on a novel metric to quantify drug response, called the drug sensitivity score (DSS).
When I started the thesis project, high-throughput drug screening data was a relatively new concept. The conventional IC50 as a measures of drug response were not suitable for high-throughput screening settings and their produced too much noise for clinical decision making. My aim was to develop a method that would be both computationally effective and sensitive and DSS fulfilled both these requirements.
In addition to the DSS method, which is currently widely applied also outside FIMM, Bhagwan developed two other novel methods. Of these, the target addiction score (TAS) model was developed for predicting the target signal networks behind individual drug response profiles while the zero-interaction potency (ZIP) model can be used as reference model for scoring drug combinations.
As part of the thesis work, Bhagwan applied the DSS, TAS and ZIP methods to datasets from patients with either acute myeloid leukeamia or ovarian cancer. These case studies showed the general added value of the computational pipeline developed in this thesis for high-throughput drug screening for personalized oncology.
Bhagwan emphasises motivation, efficiency and his hardworking nature as keys to his successful PhD thesis project. In his free time, he enjoys different sport activities, especially badminton and table tennis.
I have always considered that I’m working for myself, not for my supervisor or somebody else and thus don’t mind about the long hours.
The computational modelling skills are very valued in the scientific community and Bhagwan holds an impressive track record of collaborative projects and has more than 20 co-authored publications.
Bhagwan will continue working to advance personalised cancer medicine also in the future. He has already started his post-doctoral project concentrating on cancer immunology and autoimmune disorders in the group of Professor Satu Mustjoki, one of the long-term collaborators of FIMM.
The public examination of Bhaqwan Yadav’s doctoral dissertation will take place on 24 March at 12 o'clock noon in Lecture hall 2 at Biomedicum Helsinki 1, Haartmaninkatu 8. The thesis has been supervised by FIMM-EMBL Group Leaders, Professor Tero Aittokallio (FIMM, University of Helsinki) and Dr. Krister Wennerberg (FIMM, University of Helsinki). Professor Mats Gustafsson (Uppsala University) will serve as the opponent and Professor Olli Kallioniemi as the custos.
The dissertation is also available in an electronic form.