Due to genomic and epigenetic instability of cancer cells, inter-patient and intra-patient heterogeneity in tumors creates formidable challenges in identifying optimal treatments. To address the challenges, I aim to establish comprehensive high-throughput multi-omics single-cell analysis including genome, epigenome, transcriptome, proteome, functional, and morphological methods. With large amounts of data collected from high-throughput single-cell multi-omics analysis, machine learning techniques can predict patient prognosis and suggest treatments for precision medicine. The integrated approach will change how we understand and treat cancer and ultimately improve outcomes for patients.

Chen YC, Gonzalez ME, Burman B, Zhao X, Anwar T, Tran M, Medhora N, Hiziroglu AB, Lee W, Cheng YH, Choi Y, Yoon E, Kleer CG (2019) Mesenchymal stem/stromal cell engulfment reveals metastatic advantage in breast cancer Cell Reports. 27: 3916–3926

Chen YC, Allen SG, Ingram PN, Buckanovich R, Merajver SD, Yoon E (2015) Single-Cell Migration Chip for Motility-based Microfluidic Selection of Heterogeneous Cell Populations Scientific Reports. 5: 9980 Publications