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.

Zhou M, Ma Y, Chiang CC, Rock EC, Luker KE, Luker GD, Chen YC (2022) High-throughput cellular heterogeneity analysis in cell migration at the single-cell level Small. 2206754:

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 Publications 

 

Grants

Project Title	Proj Start Date	Proj End Date	Funding Source
Deciphering Cellular Heterogeneity and Inheritability in Migration	2023	2028	NIH R35