Predictive Oncology
Predictive oncology provides information about the effect of a specific therapeutic intervention, allowing researchers and clinicians to find an optimal strategy for personalized therapy.
Our team has two main goals in this research area:
- We aim to investigate how patient genotype information, available before treatment, can be integrated with clinical factors* and radiation dose data to improve statistical models for predicting patient outcomes. We identified a novel set of 157 specific genes that are important in predicting radiation-induced pneumonitis—the swelling, irritation and inflammation of lung tissue—in mice and plan to translate these findings to identify their human counterparts.
- We will leverage Raman spectroscopy, a powerful light scattering technique, to gain deeper insights into radiation responses in brachytherapy—a type of radiation therapy that involves placing a sealed radioactive source inside or next to a tumour. This approach will help us develop monitoring tools and predictive assays—lab tests on patient samples to measure specific substances and then predict patient responses—to improve treatment outcomes in brachytherapy.
Our discovery-driven research in predictive oncology will help move radiation therapy from a generalized, population-based approach to a more personalized treatment strategy. By integrating patients’ unique genetic and biochemical profiles, we seek to optimize dose prescriptions, ultimately enhancing treatment outcomes for individuals undergoing cancer care.
*Clinical factors include age, gender, smoking history etc.
Left: Raman spectrum and associated biochemical base constituents. Right: Radiation-induced biochemical score changes.
