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Revisiting the tumorigenesis timeline with a data-driven generative model.

ABSTRACT: Cancer is driven by the sequential accumulation of genetic and epigenetic changes in oncogenes and tumor suppressor genes. The timing of these events is not well understood. Moreover, it is currently unknown why the same driver gene change appears as an early event in some cancer types and as a later event, or not at all, in others. These questions have become even more topical with the recent progress brought by genome-wide sequencing studies of cancer. Focusing on mutational events, we provide a mathematical model of the full process of tumor evolution that includes different types of fitness advantages for driver genes and carrying-capacity considerations. The model is able to recapitulate a substantial proportion of the observed cancer incidence in several cancer types (colorectal, pancreatic, and leukemia) and inherited conditions (Lynch and familial adenomatous polyposis), by changing only 2 tissue-specific parameters: the number of stem cells in a tissue and its cell division frequency. The model sheds light on the evolutionary dynamics of cancer by suggesting a generalized early onset of tumorigenesis followed by slow mutational waves, in contrast to previous conclusions. Formulas and estimates are provided for the fitness increases induced by driver mutations, often much larger than previously described, and highly tissue dependent. Our results suggest a mechanistic explanation for why the selective fitness advantage introduced by specific driver genes is tissue dependent.

SUBMITTER: Lahouel K

PROVIDER: S-EPMC6969520 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Revisiting the tumorigenesis timeline with a data-driven generative model.

Lahouel Kamel K Younes Laurent L Danilova Ludmila L Giardiello Francis M FM Hruban Ralph H RH Groopman John J Kinzler Kenneth W KW Vogelstein Bert B Geman Donald D Tomasetti Cristian C

Proceedings of the National Academy of Sciences of the United States of America 20191227 2

Cancer is driven by the sequential accumulation of genetic and epigenetic changes in oncogenes and tumor suppressor genes. The timing of these events is not well understood. Moreover, it is currently unknown why the same driver gene change appears as an early event in some cancer types and as a later event, or not at all, in others. These questions have become even more topical with the recent progress brought by genome-wide sequencing studies of cancer. Focusing on mutational events, we provide ...[more]

PMID: 31882448

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Revisiting the tumorigenesis timeline with a data-driven generative model.

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Revisiting the tumorigenesis timeline with a data-driven generative model.

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