More Efficiently Diagnosing Cancer

Steven Gore, doctoral candidate in biological sciences, is developing a broader cancer diagnosis model.

More Efficiently Diagnosing Cancer

Steven Gore, doctoral candidate in biological sciences, is developing a broader cancer diagnosis model.

UNT Diving Eagle
April 15, 2020

By Brittney Dear

Steven Gore, doctoral candidate in biological sciences, has been interested in biology for most of his life.

“Biology is one of those things where you look at the world and ask, ‘Why did this happen?’ and moreover, ‘How could we manipulate an organism to do certain things? Could we make something to help combat climate change or engineer an organism to help us with the creation of carbon nanotubes?’”

When Gore found out that his aunt had been diagnosed with late-stage cancer, his computational biology research took a turn. He was curious as to why her cancer wasn’t caught earlier, and he wondered how many people have missed cancer diagnoses.

“I started thinking about the problem while watching The Daily Show,” Gore says. “A computer won a game against human players, which was supposed to be impossible, and one of the correspondents said, ‘This is great and all, but can we do this for cancer?’ I think we can.”

Gore spoke to his mentor, Rajeev Azad, associate professor of bioinformatics, about using data science to address the problem of more effective cancer diagnoses, and together they went down the path of getting Gore into the lab. 

“Computational advances, coupled with emerging high throughput technologies, have spawned new opportunities in disease diagnostics and therapeutics,” Azad says. “And cancers, in particular, are being examined or reexamined through different sets of lenses.” 

As of now, Gore is working alongside Azad on a broader cancer diagnosis model, which will initially classify cancers across 18 tissues with plans to expand from there, rather than focusing on specific types. He aims to create a model with Pan-Cancer Analysis, which can differentiate and compare cancers that could be found across diverse tumor types.

“The idea is that there’s a lot of people who can’t get a cancer diagnosis because there’s no robust early diagnostic tool for it,” Gore says. “We want to expand access on early diagnostics so we can improve patient outcomes.” 

To Gore, mentorship is very important and is one of the reasons he’s been able to conduct cancer research and get to where he is now. 

“I’ve been very blessed at UNT to have two great mentors, Dr. Shah and Dr. Azad, and a very good committee,” Gore says. “They’ve helped prepare me for the writing and grant applications. Mentorship at this level is really key.”

“Steven’s passion to contribute to this field by utilizing deep learning techniques to cancer diagnosis, and importantly, as my mentee, his perseverance through the process, has come to fruition with the development of a pan-cancer diagnostic tool, planned to be launched by the summer of this year,” Azad says. “We are excited by this success and hope our efforts will make an impact in mitigating the sufferings caused by cancer.”

Gore recently attended the annual conference of the Academy of Medicine, Engineering and Science of Texas where he presented his research. At the conference, he met with potential collaborators and saw a lot of interest in the project. 

“People were especially interested in artificial intelligence in biology and what that could mean for the future of research in biology and medicine,” Gore says.

Gore plans to complete his Ph.D. by December 2020, and the next area of research he’d like to explore is pediatric cancer. He plans on conducting research as a postdoctoral researcher and then start a company to continue developing his system. He would like to apply for funding from the National Science Foundation and the National Institutes for Health to help his project reach patients.

“I think what we have is really special,” Gore says. “We have data from people who knew they had cancer, and what we need to do now for the future is see how this all works in the setting of the clinic.”