RT Journal Article
SR Electronic
T1 Inferring parameters of cancer evolution from sequencing and clinical data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.11.18.387837
DO 10.1101/2020.11.18.387837
A1 Lee, Nathan
A1 Bozic, Ivana
YR 2022
UL http://biorxiv.org/content/early/2022/01/09/2020.11.18.387837.abstract
AB As a cancer develops, its cells accrue new mutations, resulting in a heterogeneous, complex genomic profile. We make use of this heterogeneity to derive simple, analytic estimates of parameters driving carcinogenesis and reconstruct the timeline of selective events following initiation of an individual cancer. Using stochastic computer simulations of cancer growth, we show that we can accurately estimate mutation rate, time before and after a driver event occurred, and growth rates of both initiated cancer cells and subsequently appearing subclones. We demonstrate that in order to obtain accurate estimates of mutation rate and timing of events, observed mutation counts should be corrected to account for clonal mutations that occurred after the founding of the tumor, as well as sequencing coverage. We apply our methodology to reconstruct the individual evolutionary histories of chronic lymphocytic leukemia patients, finding that the parental leukemic clone typically appears within the first fifteen years of life.Competing Interest StatementThe authors have declared no competing interest.