Rong Li, PhD

Topic: Multimodal network-based cancer heterogeneity analysis

Project Summary: In the past decade, the maturity of profiling techniques has led to the discovery that previously defined cancer types/subtypes, which is based on pathological images, can be further classified into sub-subtypes. This refined classification has different omics landscapes and clinical paths and demand different treatment strategies. Accordingly, the first guiding principle of this study is that effectively integrating multimodal data, in particular pathological imaging and multi-omics data, can lead to more refined cancer heterogeneity structures. In heterogeneity analysis, incorporating the interconnections among variables can future reveal more subtle cancer heterogeneity structures. As such, the second guiding principle is that utilizing cutting-edge methods to incorporate interconnections can further improve cancer heterogeneity analysis. Our overarching goal is to develop more effective statistical learning methods for cancer heterogeneity analysis, which can deepen our understanding of cancer biology and facilitate more personalized treatment.