Tumor is a complex system involving adaptive cellular response to heterogeneous microenvironment, and nonlinear interactions among various types of cells. The fundamental question I addressed in my thesis is how cancer cells ultimately acquire chemotherapy resistance using microfluidics, game theory, and genomic approaches.
Within a tumor, a complex fitness landscape of cells is established by non-uniform distributions of nutrient, oxygen, and drug (during chemotherapeutic treatment). Therefore, we designed various chemotherapy gradient devices to mimic a tumor microecology for the assessment of multi-day spatio-temporal dynamics of mammalian cancer cells. Cancer resistance emerged within 2 weeks in such microecology. We performed RNA sequencing analyses of the rapidly emerged resistant cancer against non-resistant cancer, demonstrating that mutations and expressions play exclusive but equally important roles in elevated chemotherapy resistance.
Furthermore, we assessed the population dynamics of cancer versus non-cancer cells in such engineered microecology. Considering the migration of the cells, fitness affected by chemotherapy gradient, and fitness affected by population ratio, we successfully predict future densities of cancer and non-cancer cells using a spatially coupled model inspired by evolutionary game theory.
In order to study the complexity of cancer and therapy resistance, my approaches include microfluidics experiments, next-generation sequencing analyses, and theoretical modeling. This work contributes to the field of cancer evolution and rapid drug screening by focusing on reconstruction of heterogeneous tumor microenvironment to study cancer genomes and interactions among various cells. In the last part of my talk, I will discuss the potential of probing the dynamics of genetic material exchange among cancer cells in such engineered microecology and its impact on cancer progression.