RT Journal Article
SR Electronic
T1 Transcriptome dynamics describe and predict state transition from health to leukemia
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 238923
DO 10.1101/238923
A1 Rockne, Russell C.
A1 Branciamore, Sergio
A1 Qi, Jing
A1 Cook, Guerry J.
A1 Hua, Wei-Kai
A1 Carnahan, Emily
A1 Marom, Ayelet
A1 Wu, Herman
A1 Maestrini, Davide
A1 Wu, Xiwei
A1 Guo, Chao
A1 Oâ€™Meally, Denis
A1 Yuan, Yate-Ching
A1 Liu, Zheng
A1 Carlesso, Nadia
A1 Wang, Leo D.
A1 Forman, Stephen J.
A1 Kuo, Ya-Huei
A1 Marcucci, Guido
YR 2017
UL http://biorxiv.org/content/early/2017/12/22/238923.abstract
AB Time series gene expression (transcriptome) data provide information on global changes in expression patterns occurring through the course of cancer progression. The premise of this work is that cancer can be viewed as a state transition of the transcriptome, and that the transcriptome behaves as a particle in a force field obeying basic physical principles of motion. The implication of this concept is that cancer progression may be understood, and predicted, with mathematical models of motion of the transcriptome in a low-dimensional projection that can be constructed to retain a maximal amount of relevant information in the system. We use a genetic mouse model of acute myeloid leukemia (AML) to demonstrate the concepts of our mathematical model. We show that the transition of the transcriptome from a health state to a leukemia state can be understood in terms of mathematically-derived inflection points which characterize the dynamic probability of leukemia development.