From Coursework to Innovation: A Student-Driven In Silico Framework for Personalized Cancer Vaccine Design

Abstract

Modern oncology is currently undergoing a transformative shift from broad-spectrum treatments to personalized molecular interventions, yet a significant gap remains between rapid biotechnological advancement and public understanding. This research project, emerging from a hybridized undergraduate and postgraduate Biomedical and Industrial Genomics course at the University of Houston, explores a reproducible in silico workflow (27,28) developed and put into practice by the University of Houston Sequencing Core under the guidance of Dr. Preethi Gunaratne for the development of personalized neoantigen vaccines. A case study utilizing realworld de-identified patient data consisting of an MMP14/C1QBP gene fusion associated with aggressive Non-Hodgkin Lymphoma demonstrates how raw genomic split-reads can be transformed into a tailored therapeutic roadmap. At its core, this project was a catalyst for studentled innovation, as researchers were granted full-autonomy to integrate their unique academic backgrounds into the modeling of therapeutic interventions. This collaborative environment fostered creative discovery, yielding diverse intellectual contributions ranging from specialized radiological interventions and microfluidic monitoring to the development of sophisticated algorithmic optimization programs. By navigating the transition from genomic education to hypothetical clinical synthesis, the class successfully bridged the gap between theoretical knowledge and practical application. The findings indicate that while industrial scaling for generic off-the-shelf vaccines remains a long-term challenge due to high genomic diversity, the boutique patient-specific model is a viable next-generation feasibility. This work underscores the Translational Ideal: the inherent necessity of transparent scientific communication to demystify complex genomics, replace intellectual curiosity with data-driven understanding, and ensure that the next generation of life-saving medicine is both mathematically optimized and biologically sound.