ArticleProceedings of the National Academy of Sciences of the United States of America2020
Revisiting the tumorigenesis timeline with a data-driven generative model.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 57 citations in OpenAlex.
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- Estimating the carcinogenesis timelines in early-onset versus late-onset cancers and changes across birth cohorts.medRxiv : the preprint server for health sciences · 2025Article
- Trans-Scale Insights into Variability in Radiation Cancer Risk Across Tissues, Individuals, and Species.Biology · 2025Review
- OncoImmune machine-learning model predicts immune response and prognosis in leiomyosarcoma.Biomolecules & biomedicine · 2025Article
- DNA repair-dependent immunogenic liabilities in colorectal cancer: opportunities from errors.British journal of cancer · 2024Review
- Early-onset cancers: Biological bases and clinical implications.Cell reports. Medicine · 2024Review
- Fecundity and Nutrient Deficiency Following Obesity Treatment: Implications for Young-Onset Cancer Risk in Offspring.Cancers · 2024Article
- Perinatal Famine Exposure and Young-Onset Cancer-Lessons from China Health and Nutrition Survey.Cancers · 2024Article
- Sequential mutations in exponentially growing populations.PLoS computational biology · 2023Article
- Adherence to the World Cancer Research Fund/American Institute for Cancer Research cancer prevention recommendations throughout the life course and risk of colorectal cancer precursors.British journal of cancer · 2023Article
- Understanding human aging and the fundamental cell signaling link in age-related diseases: the middle-aging hypovascularity hypoxia hypothesis.Frontiers in aging · 2023Article
- Evaluating cancer etiology and risk with a mathematical model of tumor evolution.Nature communications · 2022Article
- Inferring parameters of cancer evolution in chronic lymphocytic leukemia.PLoS computational biology · 2022Article
- Optimal pathways control fixation of multiple mutations during cancer initiation.Biophysical journal · 2022Article
- Trade-off between reducing mutational accumulation and increasing commitment to differentiation determines tissue organization.Nature communications · 2022Article
- Article
- Gastrointestinal Adenocarcinoma Incidence and Survival Trends in South Australia, 1990-2017.Cancers · 2022Article
- Beneficial autoimmunity and maladaptive inflammation shape epidemiological links between cancer and immune-inflammatory diseases.Oncoimmunology · 2022Article
- Prognostic value and immune infiltration of novel signatures in colon cancer microenvironment.Cancer cell international · 2021Article
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10 authors at 2 institutions in 1 country.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.