Predicting Progression
Doctoral student Rituparna Khan鈥檚 new tool gives health care providers an edge in diagnosing, treating early-stage cancer
To many people, the never-ending stream of letters, numbers, and symbols on Rituparna Khan鈥檚 computer monitor are nonsensical cyan, magenta, yellow, and white characters floating on a plain black background. To her, however, the screen represents years of hard work and a breakthrough in cancer research.
Khan鈥檚 area of expertise is bioinformatics, an interdisciplinary field developing methods and software tools to understand biological data, especially large and complex data sets.
The sixth-year doctoral student in the is combining her passion for medicine with computer programming and coding to better treat early-stage cancer. The software Khan developed characterizes and predicts cellular mutations that lead to cancer, and it is two-and-a-half times more accurate than the only other similar tool in existence.
鈥淢y software tool, ScLongTree, shows promise for the improved care of early-stage cancer patients because when it is successfully implemented, medical professionals can use it to predict how a particular patient鈥檚 cancer will likely progress,鈥 Khan said. 鈥淎ccurately predicting the cancer鈥檚 progression leads to better-informed treatment plans.鈥
Cancers spread by acquiring new mutations at different stages of advancement. Knowing the specific moment in time when a mutation is set to happen is critical for determining accurate diagnoses, delivering realistic prognoses, and developing personalized treatment plans that can minimize side effects and improve patient outcomes.
Single-cell DNA sequencing makes it possible to determine genetic differences within a single tumor and provides a clearer understanding of how a particular patient鈥檚 cancer has developed. As the cost of single-cell DNA sequencing has decreased in recent years, it has become possible to check in on patients every few years, which has generated data allowing researchers to sample across longer timelines. Based on a particular patient鈥檚 collected data from their existing mutated cells, Khan鈥檚 tool can predict their future data, or their cancer鈥檚 progression.
鈥淭he beauty of computer science is that it has such a wide variety of applications,鈥 said Khan, who had originally planned to go to medical school before pivoting to her current area of research. 鈥淐oding and programming have the ability to solve incredibly difficult and complex issues in nearly any field.鈥
The only other tool like Khan鈥檚 is the Longitudinal Analysis of Cancer Evolution, better known as LACE, which was first published in the Journal of Computational Science in 2022. Compared to ScLongTree, LACE is limited in terms of scalability, meaning it cannot compute more than four timepoints. ScLongTree is also more accurate 鈥 in a triple negative breast cancer dataset with three sequencing time points, ScLongTree was able to recover the evolutionary history of the cancer and detect a novel mutation at the second sequencing time point, which was not flagged by LACE. In this particular scenario, ScLongTree鈥檚 insights could help doctors determine if the patient needs a mastectomy or only chemotherapy and radiation.
Prior to coming to FSU in 2018, Khan earned an undergraduate degree in computer science at the Future Institute of Engineering and Management in Kolkata, India, and worked as a senior project engineer for Wipro, an IT consulting company headquartered in Bengaluru.
鈥淚 decided to pursue my doctorate after nearly four years at Wipro because I wanted to dive even deeper into the field,鈥 Khan said. 鈥淣ow, applying my computer science skills in cancer research feels like a full-circle moment with my dream of working in medicine coming true.鈥
After more than a year and two revisions, Khan鈥檚 paper on her tool is awaiting approval for presentation at the Research in Computational Molecular Biology鈥檚 2024 conference in Boston, Massachusetts. Having her research published is the next step towards reaching her primary goal: Wide implementation of and access to her software tool.
鈥淩itu has an incredible talent for presenting her research, and this skill will take her far whether she goes into academia or industry,鈥 said Xian Mallory, assistant professor of computer science and Khan鈥檚 doctoral major professor. 鈥淪he is also easy-going and selfless, and she is currently mentoring an undergraduate student in my stead.鈥
Upon her anticipated graduation this fall, Khan aspires to enter industry and sees herself working for pharmaceutical companies or national laboratories.
鈥淗aving the opportunity to advance cancer research through my work is a blessing,鈥 Khan said. 鈥淲hile cancer research is absolutely challenging, I enjoy working through the challenge, and knowing that my research can help so many people in the future drives me to do my best. I am happy that my research is helping the computer science community solve larger issues.鈥