Researcher of the Month

Researcher of the Month is a new series started in January 2023 where FCI's researchers are introduced.

September 2023

FICAN Cancer Researcher Anil Kumar has a bachelor’s degree in chemistry from Tribhuwan University, Nepal, and a master's degree in Biotechnology from Tilka Manjhi Bhagalpur University, India. His master's dissertation was focused on determining the structure of the Calcium Binding Protein-5 from Entamoeba histolytica. His studies consisted of cloning, bacterial cell culture, protein purification, and crystallization. 

After master studies, Anil became interested in the genetics of complex diseases and did his PhD project on the epigenetics of type 2 diabetes in which he studied DNA methylation differences between healthy and type 2 diabetes patients.

According to Anil, the PhD project was an exciting time, marked by intense training in statistics, computer programming, micro array experiments, and writing skills. He identified more than five novel genetic variants associated with metabolic disorders in the Indian population using genome-wide association studies. Anil obtained his PhD. in 2017 from the Academy of Scientific and Innovative Research, India with the title Role of DNA methylation in risk of Type 2 diabetes in Indian population.

From diabetes genetics to drug combination prediction 

During the PhD project, Anil got very interested in machine learning and method development and chose to do his postdoctoral research in drug response prediction. He moved to Finland in 2017 to join Professor Tero Aittokallio's group. During the following three years, he developed methods to predict safe and effective drug combinations and implemented them as free-to-use web-based tools.

Targeting tumor microenvironment interactions 

Anil’s primary interest is still to be in the front-line development of methods targeting the tumor microenvironment interactions, especially in leukemias. He believes that close collaboration between machine learning researchers, geneticists, and functional biologists can lead to the most impactful findings in this area. Indeed, from the beginning of 2020, Anil has worked as a postdoctoral researcher in Professor Mark Daly’s research group (Daly lab) at the Institute for Molecular Medicine Finland (FIMM), where he is responsible for the analysis of cancer genetics data collected in the FinnGen study.  

Currently, Anil is also involved in developing tools needed to predict drug combinations targeting tumor microenvironment interactions. He is using single cell RNA profile of more than 50 acute myeloid leukemia samples to predict the drug combinations.

Genome-wide association study of small intestinal neuroendocrine tumor

Recently, Anil and his colleagues conducted the largest genome-wide association study on small intestinal neuroendocrine tumors (SI-NETs) using 307 patients and 287 137 controls from the FinnGen study. They identified that a rare mutation (rs200138614, p.Cys712Phe) in LGR5 gene, which is 25 times enriched in the Finnish population, increases the risk for a SI-NET. Moreover, five other risk variants were identified of which three were novel and two had been reported previously. The study was published in Gastroenterology with a title Genome-Wide Association Study Identifies 4 Novel Risk Loci for Small Intestinal Neuroendocrine Tumors Including a Missense Mutation in LGR5. The discovered risk variants can be used to identify individuals with a high genetic risk for SI-NET that can be followed up for early diagnosis and better management of the disease.

Football, reading and poetry 

Outside of work, Anil draws energy particularly from different sports. Running with friends, gym, and football are his favorite activities. Additionally, Anil likes to read biographies, economics, and politics allowing him to relax and distance himself from work-related thoughts. Anil writes poetry as well.

Anil Kumar works in the Daly Lab and focuses on the analysis of cancer genetics data collected in the FinnGen study. He is also involved in developing tools needed to predict drug combinations targeting tumor microenvironment interactions.