Ask the Doctor: Bioinformatics and Cancer Treatment

285 AskDrSum2013

By Zhenqiu Liu, PhD

Director of Bioinformatics

Department of Medicine, Hematology/Oncology Division

Cedars-Sinai

 

What is bioinformatics?

Bioinformatics is the application of computer science, applied math, and statistics to the field of molecular biology. Bioinformatics is also the science of managing and analyzing vast biological data with advanced computing techniques and has an essential role in searching for unique genomic features, variations, and disease markers. The need for bioinformatics is becoming more important as next-generation sequencing (NGS) technologies continue to evolve.

 

What role does bioinformatics play in cancer research and treatment?

The merging of genomic technologies provides many opportunities for bioinformatics to bridge the gap between biological knowledge and clinical therapy. Today, efforts are focused on biomarker discovery and the early diagnosis of cancer through the application of various technologies, including genomics and other individualized DNA characteristics. Regardless of the technology being used, bioinformatics tools are required to extract the diagnostics or prognostic information from complex data. Through different technologies and novel bioinformatics tools, we attain a more complete understanding of cancer and expedite the process of biomarker discovery.

 

How does bioinformatics fit into the concept of personalized medicine?

Both the development and use of bioinformatics is essential to the future of cancer therapeutics and personalized medicine. The complexity of cancer means that most cancer treatments work only for a small group of patients and not a one-size-fits-all approach. This results in both a large portion of patients receiving ineffective treatments and a huge financial burden to healthcare systems. It is essential that we develop accurate bioinformatics tools for delivering the right treatment to the right patient, based on the individual makeup of their specific tumor.

 

Are there specific research developments or advances in the field related to cancer treatment that are especially exciting right now?

One exciting development is system biology, which studies biological systems by integrating data into a dynamic network. This approach can identify where a network should be perturbed to achieve a desired effect, provide insights into drug function, and evaluate a drug’s likely efficiency and toxicity. System biology analysis requires sophisticated bioinformatics software to find and analyze patterns in diverse data sources, producing an integrated view of a specific cancer.

In addition, we are entering a time for the convergence and unity of all sciences to fight cancer. The merging of engineering, physics, mathematics, technology, and medicine will be one of history’s most fruitful unions. Bioinformatics will certainly be an important part of it.  _

 

Zhenqiu Liu, PhD, is the director of bioinformatics in the Department of Medicine’s Hematology and Oncology Division at Cedars-Sinai. Prior to his current position, Liu was an associate professor of bioinformatics at the Department of Epidemiology, Public Health and the Greenebaum Cancer Center at the University of Maryland School of Medicine. Dr. Liu’s research is in the broad area of bioinformatics, computational biology, and big data mining. His research is concentrated on survivorship prediction, biomarker identification, and pathway and network constructions. His extensive research experience in laboratory investigations, biomarker evaluation, genomics, and clinical and epidemiological researches has earned him funding by both the National Cancer Institute and the National Science Foundation. Dr. Liu earned a PhD in Operations Research with a concentration in data mining, as well as a master’s degree in Computer Science, both from the University of Tennessee at Knoxville. His postdoctoral training was at the Bioinformatics Cell, Telemedicine, and Advanced Technology Research Center in Fort Detrick, Maryland, and at the Department of Statistics at Ohio State University.