Data for: Loss of TET2 affects proliferation and drug sensitivity through altered dynamics of cell-state transitions
Data accompanying the paper "Loss of TET2 affects proliferation and drug sensitivity through altered dynamics of cell-state transitions". The dataset includes: * DNA methylation beta values and log2FC (Illumina Infinium) * RNA transcript counts and log2FC (Lexogen Quantseq) * Flow cytometry (Aria IIu) * qPCR * fit parameter values * code to generate figures Please refer to the manuscript for more detailed methods on how the data were generated. A persistent puzzle in cancer biology is how mutations, which neither alter growth signaling pathways nor directly interfere with drug mechanism, can still recur and persist in tumors. One example is the mutation of the DNA demethylase TET2 in acute myeloid leukemias (AMLs) that frequently persists from diagnosis through remission and relapse, but whose fitness advantage in chemotherapy is unclear. Here we use isogenic human AML cell lines to show that TET2 loss-of-function alters the dynamics of transitions between differentiated and stem-like states. A conceptual mathematical model and experimental validation suggest these altered cell-state dynamics can benefit the cell population by slowing population decay during drug treatment and lowering the number of survivor cells needed to re-establish the initial population. These studies shed light on the functional and phenotypic effects of a TET2 mutation in AML and illustrate how a single gene mutation can alter a cells’ phenotypic plasticity.
Steps to reproduce
Generating the figures assumes the file "make_figures.Rmd" and the folder "data" are in the same root directory. MATLAB code to generate parameter fits or run the Gillespie algorithm is available on Github (https://github.com/AltschulerWu-Lab/tet2-dynamics).