The Dr. Josef Steiner Cancer Research Prize 2019, goes to Serena Nik-Zainal from the Department of Medical Genetics, University of Cambridge.
Nik-Zainal won the award, originally also known as the “Nobel Prize for Cancer Research,” for her successful application to accelerate holistic cancer genome interpretation towards the clinic with collaborators Paul Calleja and Ignacio Medina.
Thanks to her research, mutations in cancer tumors can be analyzed using new bioinformatic methods, which enables new approaches to targeted therapies. The prize will be awarded on Oct. 18 at the University of Bern. Nik-Zainal will present her work under the title “Accelerating holistic cancer genome interpretation towards the clinic”.
In a statement, Nik-Zainal said:
“The rate-limiting step in cancer genomics today is not the ability to perform sequencing. It is the expertise in performing downstream analysis and making a clinically-useful interpretation, that remains the hurdle between genomic technology and the clinical context.
“Our research efforts began with showing that the totality of mutagenesis from large cohorts of whole genome sequenced tumors could reveal mutational signatures, imprints left by mutagenic DNA damage and repair processes that have occurred through cancer development. Subsequently, our team focused on experimentally validating these analytical concepts in cellular model systems. We examined mechanisms of mutagenesis related to DNA repair defects and of environmental mutagens. The powerful combination of computational analytics and experimental insights helped to drive the development of clinical computational tools to interpret whole cancer genomes more effectively.
“At the Clinical School, University of Cambridge, the Josef Steiner Award will help us enhance translation of our expertise and develop novel, clinically meaningful algorithmic tools. We seek to consolidate our current knowledge into infrastructure that is appropriate for the future. We are building a more automated foundation, that can be referred back to at any point, and that will scale with more data coming. It needs to be more user-friendly for the next generation of clinicians and scientists to explore and be suitable for advanced data analytics. We will be able to focus on asking novel biological and clinical questions of these large datasets and ultimately, accelerate making clinically-relevant progress.”
