Suzan Arslanturk is an assistant professor of computer science and industrial and systems engineering in the Wayne State College of Engineering. Her research focuses on data mining and predictive modeling, particularly with applications in health care. Some of her research seeks to identify molecular biomarkers to better understand how prostate cancer develops, progresses and responds to various therapeutic treatments. She recently talked about her upbringing, schooling and love for research.
Where did you grow up? Where did you go to high school?
I grew up in a small town in Turkey close to Istanbul, the country’s largest city. I completed my high school education there. I then moved to Ankara, the capital, to pursue my B.Sc. degree in computer engineering.
What did your parents do?
My dad, now retired, is an M.D., specialized in general surgery and mom, also retired now, is a preschool teacher.
Where did you receive your undergrad and graduate degrees and in what fields?
I received my undergraduate degree in computer engineering from Baskent University, Ankara. Later, I moved to Rochester, Michigan, to pursue my M.S. and Ph.D. degrees — both in computer science — from Oakland University.
How did you make your way 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 ended up being a great fit.
How did you come to know that research was something you were really interested in?
I enjoyed participating in small research projects throughout my undergrad and graduate studies, so I decided to pursue a Ph.D. with a research focus in health informatics. I am motivated by tackling challenging theoretical and applied research problems that enable developing innovative machine learning and clinical solutions to enhance individual and population health outcomes, improve patient care, and optimize the operational performance of health care delivery systems. In other words, analysis of big data, common in cancer research, that can potentially help physicians in their decision-making process was an interesting area of research that I wanted to pursue.
How did you get interested in research around cancer bio markers, and especially those involved with prostate cancer?
My collaborations and discussions with oncologists at the Karmanos Cancer Institute resulted in my understanding of a significant need to attain a better understanding of the biology of lethal prostate cancers through big data analysis and computational approaches.
In layperson’s terms, can you describe the essence of your research and what your aspirations are for its real-world applications?
The majority of prostate cancer tumors grow slowly and never become life threatening. Scientific studies are yet to conclude whether screening for prostate cancer lowers the risk of death from prostate cancer. There are several reasons for this. One major reason is that screening tests do not tell the physicians whether the detected cancer is truly dangerous (and needs treatment) or harmless.
Ideally, dangerous cancers should be aggressively treated, and others be spared the harmful effects of such treatment. Hence, physicians need new indicators in the form of biological markers (“biomarkers”) associated with lethal and harmless prostate cancers for better treatment planning.
Given that tumors are caused by the molecular anomalies in the cell, biomarkers based on molecular properties of tumors offer earlier and more accurate prediction capability. Due to limited availability of molecular data and the infrequency of lethal prostate cancer (compared to harmless type), scientists have not been successful in discovering clinically useful molecular biomarkers of prostate cancer that can accurately detect lethal prostate cancer and predict the outcomes of its treatments.
Oncologists have discovered remarkable biological similarities between breast, ovarian and prostate cancers, which suggests biomarker commonality across these cancers. This discovery presents unique opportunities in synthesizing knowledge from other cancers with prostate cancer.
Computer scientists’ progress in harnessing information and transfer of knowledge across different fields makes it possible to discover prostate cancer biomarkers. These cancer biomarkers can be also used to measure the effect on cells’ biological processes for drug repurposing (investigating existing drugs for use in treating new conditions). My research focus is on discovering clinically useful prostate cancer biomarkers by synthesizing knowledge from breast and ovarian cancers and repositioning existing drugs, using biological mechanisms impacted by these biomarkers.
What’s next, in terms of your research?
I would like to focus on identifying jointly important biomarkers and treatment options across biologically similar cancers for patients who are not responding to organ-specific treatments. That way, patients may be able to get treated with a drug based on a common biomarker, instead of the organ in which the tumor originated. There are several studies that have already shown the effectiveness of such treatments.