Ashwini Kumar's dissertation shows the power of bioinformatics in precision medicine

MRes Ashwini Kumar’s thesis entitled "Transcriptomic data integration for precision medicine in leukemia” was examined on October 11th, 2019 with the permission of the Faculty of Biological and Environmental Sciences of the University of Helsinki. The thesis focused on utilizing gene expression information for advanced precision medicine outcomes in patients with hematological cancers.

Ashwini Kumar graduated with masters in bioinformatics from the Nottingham Trent University, UK, in 2010. When looking for a doctoral student position, he got interested in FIMM’s Individualized systems medicine in cancer (ISM) Grand Challenge programme. In 2012, he started his thesis project in Caroline Heckman’s group at FIMM. FIMM Technology Centre’s RNA sequencing expert Pirkko Mattila acted as Ashwini’s co-supervisor.

In his thesis, Ashwini has utilized different bioinformatics and machine learning approaches for analysis of genomic and transcriptomic data with the aim to identify drug sensitivity and resistance biomarkers in leukemia patients. The thesis consists of three articles, all of which have been published.

In the first study, Ashwini compared and evaluated two RNA-seq library preparation protocols, Ribo-depletion and PolyA enrichment, for detection of gene fusions, variant calling and gene expression profiling. Both methods were shown to have certain specific strengths. The PolyA protocol was more efficient in quantifying expression of leukemia marker genes and drug targets while the Ribo-depletion protocol was more suitable for detecting overall transcriptomic features.

The increased sensitivity and specificity of technologies measuring gene expression changes has started to reveal the subtle changes in transcriptomes and disease mechanisms at unprecedented precision. Therefore, it is essential to evaluate and compare each method and protocol for optimal selection of the method suitable for clinical applications.

- Ashwini Kumar

In the two other studies, Ashwini combined different types of data sources and analytical approaches and identified novel biomarkers predicting sensitivity and resistance to a promising new acute myeloid leukemia (AML) drug, BCL-2 inhibitor venetoclax.

The results highlighted both genomic IDH1/2 mutations and HOX family gene expression as potential biomarkers for venetoclax sensitivity and high expression of two S100 family genes as a promising new biomarker for venetoclax resistance in AML patients.

Despite the extensive biomarker discovery efforts, the gene expression biomarkers currently used in routine clinical practice are negligible. Our results suggest that the biomarkers identified in this thesis could predict the clinical response of venetoclax for the patients suffering from an aggressive AML.

Ashwini wants to thank the strong and multidisciplinary FIMM student community for the support, friendship and company he has received during the thesis years. He thinks that FIMM provides an excellent environment for conducting translational research in a close collaboration with the clinicians treating the patients.

Translating cancer genome and transcriptome for patients will require continued multi-disciplinary collaboration between oncologists, pathologists, basic scientists, and computational biologists. Routine molecular profiling of cancer patients for basic genomics research, tumor sequencing in the clinic, and big data sharing networks would be essential to enable precision cancer medicine in practice.

In the future, Ashwini would like to consider career options outside academia and explore his bioinformatics training in an industrial field.

The public examination of Ashwini Kumar’s doctoral dissertation took place on 11th October with the permission of the Faculty of Biological and Environmental Sciences of the University of Helsinki. Professor Inge Jonassen (University of Bergen, Norway) served as an opponent and Professor Juha Partanen as a custos. The dissertation is also available in an electronic form.