Personalized medicines and personalized treatments for cancers are the latest trends in today’s healthcare. In addition to the various tools available to understand a specific patient’s needs, A*STAR researchers have developed a cutting-edge system called OncoIMPACT which combines cancer omics data and models learned from numerous patients to help look through genetic mutations and pick the potential ones.
A*STAR’s Genome Institute of Singapore (GIS) has developed this tool which has the ability to take into account the unique genetic makeup of each patient to predict treatment targets. Dr Niranjan Nagarajan, Associate Director of Computational and Systems Biology at the GIS said, “OncoIMPACT allows us to crunch massive cancer genome data sets in an integrative and model-driven fashion to distill them down to the few key driver mutations.”
The development of OncoIMPACT was published in the high-impact journal Nucleic Acids Research.
Assistant Professor Johannes Schumacher from the Institute of Human Genetics at the University of Bonn, added: “The integration of different ‘omics’ datasets for the identification of cancer driver genes is a challenge. OncoIMPACT fills a gap in integrative analyses and provides the opportunity to revisit large complex datasets for the identification of disease driving genes.”
The team of researchers at A*STAR have applied OncoIMPACT to more than a thousand cancers such as melanomas, glioblastomas, prostate, bladder and ovarian cancers, and are in the process of building a complete map of driver mutations across cancers. They also demonstrated a proof-of-concept in this study for using driver mutation signatures to predict clinical outcomes for cancer patients. This is an exciting alternative to currently available tests based on RNA and protein levels as DNA can be more reliably assayed, and the team plans to develop this work further.
OncoIMPACT is the latest in the series of expert systems from the GIS and follows the recent publication of Phen-Gen – the first such system to cross-reference patient’s symptoms with genome sequence to detect causal genes for rare diseases. Both methods fall in the emerging area of integrative omics, where complex, multi-dimensional datasets are jointly analysed with sophisticated algorithms to reveal novel biological and medical insights.
This article is on materials provided by A*STAR.