bioRxivCancer BiologyPreprint
A lottery simulation model for predicting cancer risk based on the strength and abundance of individual mutations
Abstract
Models for predicting an individuals risk of developing cancer are complicated by a lack of well-conserved mutations present in most types of disease. To address this challenge we developed a simple lottery simulation of cancer development based solely on the abundance and relative survival advantage of mutations present within individual stem or progenitor cells. In simulations requiring few mutations or that involve mutations exhibiting a strong survival advantage cancers develop in our simulation in a progressive manner amenable to oncogene-based risk prediction. In contrast, in simulations involving numerous mutations that lack a strong survival advantage, a stochastic process of neutral drift and punctuated equilibrium determines eventual tumour formation. In these situations, the development of cancer is largely attributable to the rate of mutational variability amongst progenitor cells rather than the identity and relative survival advantage of the mutations present at any single time point. These results suggest that measuring rates of stem cell mutational variability represents an important component of predicting an individuals risk of developing cancer.
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Accumulation of Oncogenic Mutations During Progression from Healthy Tissue to Cancer
- Adaptive Cancer Suppression among Tissues through Reduced Stem Cell Mutation Rates
Jack da Silva·bioRxiv·Apr 16, 2026
- Temporal order of mutations influences cancer initiation dynamics
H. Teimouri, A. B. Kolomeisky·bioRxiv·Jan 10, 2021
- A unified simulation model for understanding the diversity of cancer evolution
A. Niida, T. Hasegawa, … S. Miyano·bioRxiv·Jan 28, 2020
- Cancer models and statistical analysis of age distribution of cancers
A. N. Tetearing·bioRxiv·Mar 22, 2021
- An exactly solvable, spatial model of mutation accumulation in cancer
C. Paterson, M. A. Nowak, B. Waclaw·bioRxiv·Oct 27, 2016