Accumulation of driver and passenger mutations during tumor progression

By I. Bozic, T. Antal, H. Ohtsuki, Hannah Carter, Dewey Kim, Sining Chen, Rachel Karchin, Kenneth W. Kinzler, Bert Vogelstein, Martin A. Nowak. Proceedings of the National Academy of Sciences of the United States of America, 2009

798 citations recorded by the source corpus.

Major efforts to sequence cancer genomes are now occurring throughout the world. Though the emerging data from these studies are illuminating, their reconciliation with epidemiologic and clinical observations poses a major challenge. In the current study, we provide a mathematical model that begins to address this challenge. We model tumors as a discrete time branching process that starts with a single driver mutation and proceeds as each new driver mutation leads to a slightly increased rate of clonal expansion. Using the model, we observe tremendous variation in the rate of tumor development—providing an understanding of the heterogeneity in tumor sizes and development times that have been observed by epidemiologists and clinicians. Furthermore, the model provides a simple formula for the number of driver mutations as a function of the total number of mutations in the tumor. Finally, when applied to recent experimental data, the model allows us to calculate the actual selective advantage provided by typical somatic mutations in human tumors in situ. This selective advantage is surprisingly small—0.004 ± 0.0004—and has major implications for experimental cancer research.

Subfields: Cancer Research, Genetics, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/9206d31c196456161a336d04a1650462c899deb9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Bert Vogelstein
2. Niko Beerenwinkel
3. Moritz Gerstung

In Genetics

1. Niko Beerenwinkel
2. Moritz Gerstung
3. Florian Markowetz
4. Roland F. Schwarz
5. Bert Vogelstein
6. Martin A. Nowak
7. David G. Rand
8. E. Smith

Citations in the corpus

• A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity (2015, background): “[8] for exponentially growing tumors without any spatial structure, where the number of drivers increases exponentially in time.”
• Cancer Evolution: Mathematical Models and Computational Inference (2014, background): “Using a branching process, Bozic et al. (2010) showed that in a growing population, the acquisition of subsequent driver mutations becomes increasingly faster.”
• Cancer heterogeneity—a multifaceted view (2013, neutral): “Indeed, few studies have attempted to quantify the evolutionary advantage that mutations confer to a clone [18].”
• Mutations driving CLL and their evolution in progression and relapse (2015, neutral): “3B ) in which del(11q) precedes ATM mutations, reflecting the higher likelihood of a focal copy number loss compared with a deleterious point mutation 35,36 .”
• CLONAL EVOLUTION IN CANCER (2012, neutral): “Bozic et al.21 (using a non-spatial population genetics model of sequential, exponential clonal expansion) derived a formula for the proportion of expected neutral passenger mutations versus the proportion of selectively advantageous driver mutations as a function of the selective advantage of the d”