TY - JOUR
T1 - Modeling the subclonal evolution of cancer cell populations
AU - Chowell, Diego
AU - Napier, James
AU - Gupta, Rohan
AU - Anderson, Karen
AU - Maley, Carlo
AU - Wilson Sayres, Melissa
N1 - Funding Information:
We thank Jay Taylor, Sergey Kryazhimskiy, and Andriy Marusyk for their helpful comments and insightful discussions. We also thank ASU Research Computing for providing computational resources, and Lisa Faiss who provided technical expertise that assisted the research. This research was partially supported through startup funds from the ASU School of Life Sciences and the Biodesign Institute to M.A. Wilson Sayres. Grant-based support includes the Flinn Foundation to M.A. Wilson Sayres and C.C. Maley. This work was also supported in part by the Breast Cancer Research Foundation to K.S. Anderson and NIH grants R21 CA196460 to K.S. Anderson, NIH grants P01 CA91955, R01 CA149566, R01 CA170595, R01 CA185138, and R01 CA140657 as well as CDMRP Breast Cancer Research Program Award BC132057 to C.C. Maley.
Publisher Copyright:
© 2017 American Association for Cancer Research.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - Increasing evidence shows that tumor clonal architectures are often the consequence of a complex branching process, yet little is known about the expected dynamics and extent to which these divergent subclonal expansions occur. Here, we develop and implement more than 88,000 instances of a stochastic evolutionary model simulating genetic drift and neoplastic progression. Under different combinations of population genetic parameter values, including those estimated for colorectal cancer and glioblastoma multiforme, the distribution of sizes of subclones carrying driver mutations had a heavy right tail at the time of tumor detection, with only 1 to 4 dominant clones present at 10% frequency. In contrast, the vast majority of subclones were present at <10% frequency, many of which had higher fitness than currently dominant clones. The number of dominant clones (10% frequency) in a tumor correlated strongly with the number of subclones (<10% of the tumor). Overall, these subclones were frequently below current standard detection thresholds, frequently harbored treatment-resistant mutations, and were more common in slow-growing tumors. Significance: The model presented in this paper addresses tumor heterogeneity by framing expectations for the number of resistant subclones in a tumor, with implications for future studies of the evolution of therapeutic resistance.
AB - Increasing evidence shows that tumor clonal architectures are often the consequence of a complex branching process, yet little is known about the expected dynamics and extent to which these divergent subclonal expansions occur. Here, we develop and implement more than 88,000 instances of a stochastic evolutionary model simulating genetic drift and neoplastic progression. Under different combinations of population genetic parameter values, including those estimated for colorectal cancer and glioblastoma multiforme, the distribution of sizes of subclones carrying driver mutations had a heavy right tail at the time of tumor detection, with only 1 to 4 dominant clones present at 10% frequency. In contrast, the vast majority of subclones were present at <10% frequency, many of which had higher fitness than currently dominant clones. The number of dominant clones (10% frequency) in a tumor correlated strongly with the number of subclones (<10% of the tumor). Overall, these subclones were frequently below current standard detection thresholds, frequently harbored treatment-resistant mutations, and were more common in slow-growing tumors. Significance: The model presented in this paper addresses tumor heterogeneity by framing expectations for the number of resistant subclones in a tumor, with implications for future studies of the evolution of therapeutic resistance.
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U2 - 10.1158/0008-5472.CAN-17-1229
DO - 10.1158/0008-5472.CAN-17-1229
M3 - Article
C2 - 29187407
AN - SCOPUS:85041486880
SN - 0008-5472
VL - 78
SP - 830
EP - 839
JO - Cancer Research
JF - Cancer Research
IS - 3
ER -