Inferring Relative Fitness of Tumor Cells from Reconstructed Genealogical Trees

Zicheng Wang is an assistant professor at the School of Data Science of The Chinese University of Hong Kong, Shenzhen. He received his Ph.D. degree from the University of Minnesota, Twin Cities. His research interests include applied probability and its application in mathematical modeling of cancer and operations management. His research work has been published in Management Science, Manufacturing & Service Operations Management, Nature Communications, Stochastic Systems, Stochastic Processes and their Applications, Journal of Mathematical Biology, etc.

Subclones within a patient tumor can differ in their growth rates within the tumor microenvironment. Characterizing this intra-tumor-heterogeneity (ITH) in growth rates offer insights into the tumor’s evolutionary trajectories and the specific aberrations that underpin adaptive success. Recent advancement in single cell sequencing technique provides enhanced resolution in genomic architecture, especially for tumors with the chromosomal instability phenotype. We developed ith.Fitness, a mathematical framework that leverages the topology of reconstructed single cell phylogenetic trees to infer the relative fitness of individual cells at the leaves and quantify growth-rate variation among subclones.