Engineering Professor Uses Computers to Unravel Molecular Mysteries of Prostate Cancer – [email protected]

Suzan Arslanturk is an assistant professor of computer science and industrial and systems engineering at Wayne State College of Engineering. His research focuses on data mining and predictive modeling, particularly with applications in healthcare. Some of his research aims to identify molecular biomarkers to better understand how prostate cancer develops, progresses and responds to various therapeutic treatments. She recently spoke about her upbringing, schooling, and love for research.

Where did you grow up? where did you go to high school?

Suzan Arslanturk talks about her education, her schooling and her love for research.

I grew up in a small town in Turkey near Istanbul, the biggest city in the country. I finished my secondary education there. I then moved to Ankara, the capital, to pursue my B.Sc. degree in Computer Engineering.

What did your parents do?

My father, now retired, is a doctor specializing in general surgery and my mother, also retired now, is a preschool teacher.

Where did you get your undergraduate and graduate degrees and in what fields?

I got my undergraduate degree in Computer Engineering from Baskent University in Ankara. Later, I moved to Rochester, Michigan to pursue my masters and doctoral studies. degrees – both in computer science – from Oakland University.

How did you get to Wayne State?

Given my interest in research and teaching, I was interested in an academic position. At WSU, there was an open tenure-track position that turned out to be a great fit.

How did you learn that research was something that really interested you?

I enjoyed participating in small research projects throughout my undergraduate and graduate studies, so I decided to pursue a PhD. with a research orientation in health informatics. I am driven by solving challenging theoretical and applied research problems that enable the development of innovative machine learning and clinical solutions to improve individual and population health outcomes, improve patient care and optimize operational performance health care delivery systems. In other words, the analysis of big data, common in cancer research, which can potentially help doctors in their decision-making process was an interesting area of ​​research that I wanted to pursue.

How did you become interested in research around cancer biomarkers, and more particularly those involved in prostate cancer?

My collaborations and discussions with oncologists from the Karmanos Cancer Institute made me understand the importance of better understanding the biology of deadly prostate cancers through big data analysis and computational approaches.

In simple terms, can you describe the essence of your research and what are your aspirations for its real-world applications?

The majority of prostate cancer tumors grow slowly and are never life threatening. Scientific studies have yet to conclude whether screening for prostate cancer reduces the risk of death from prostate cancer. There are several reasons for this. One of the main reasons is that screening tests do not tell doctors whether the cancer detected is really dangerous (and requires treatment) or harmless.

Ideally, dangerous cancers should be treated aggressively and others should be spared the harmful effects of such treatment. Therefore, doctors need new indicators in the form of biological markers (“biomarkers”) associated with deadly and harmless prostate cancers for better treatment planning.
Since tumors are caused by the molecular abnormalities of the cell, biomarkers based on the molecular properties of tumors provide earlier and more accurate prediction capability. Due to the limited availability of molecular data and the rarity of fatal prostate cancer (compared to the harmless type), scientists have failed to discover clinically useful molecular biomarkers of prostate cancer that can detect with accurately lethal prostate cancer and predict the outcome of its treatments.

Oncologists have found remarkable biological similarities between breast, ovarian and prostate cancers, suggesting biomarker similarity between these cancers. This discovery presents unique opportunities in synthesizing knowledge of other cancers with prostate cancer.

The progress of computer scientists in the exploitation of information and the transfer of knowledge in various fields makes it possible to discover biomarkers of prostate cancer. These cancer biomarkers can also be used to measure the effect on biological processes in cells for drug redirection (investigation of existing drugs to be used in the treatment of new conditions). My research focuses on the discovery of clinically useful biomarkers of prostate cancer by synthesizing knowledge on breast and ovarian cancers and repositioning existing drugs, using the biological mechanisms impacted by these biomarkers.

What is the next step, in terms of research?

I would like to focus on identifying jointly important biomarkers and treatment options for biologically similar cancers for patients who are unresponsive to organ-specific treatments. This way, patients may be able to be treated with a drug based on a common biomarker, instead of the organ of origin of the tumour. There are several studies that have already shown the effectiveness of such treatments.

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