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Abstract SY36-01: Somatic evolution, cancer, and our inevitable decline with age: Inextricably linked

2024· article· en· W4393983127 on OpenAlexaff
Edward J. Evans, Emilia L. Lim, Fabio Marongiu, Shi Biao Chia, William Hill, Clare E. Weeden, Oriol Pich, Andrew Rowan, Faiz Jabbar, Cristina Naceur‐Lombardelli, Raju Veeriah, Moumita Ghosh, Daniel T. Merrick, York E. Miller, Robert L. Keith, Mariam Jamal‐Hanjani, Charles Swanton, James DeGregori

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicScience, Research, and Medicine
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSomatic cellCancerGerontologyDemographyBiologyMedicineGeneticsSociology

Abstract

fetched live from OpenAlex

Abstract Why do we get cancer? Why is cancer highly associated with old age? Aging is associated with the accumulation of more mutations; some of which can contribute to cancer phenotypes. However, we now understand that carcinogenesis is much more complex than originally appreciated. In particular, there are tissue environmental forces that both impede and promote cancer evolution. Just as organismal evolution is known to be driven by environmental changes, cellular (somatic) evolution in our bodies is similarly driven by changes in tissue environments, whether caused by the normal process of aging, by lifestyle choices, or by extrinsic exposures. Environmental change promotes selection for new phenotypes that are adaptive to the new context. In our tissues, aging or insult-driven alterations in tissues drives selection for adaptive mutations, and some of these mutations can confer malignant phenotypes. To better understand the evolutionary forces that control somatic cell evolution, we used mouse models of cancer initiation, mathematical models of cellular evolution, and analyses of human tissue samples. We have shown that aging- and inflammation-dependent changes in stem cells and their tissue environments dramatically dictate whether cancer-causing mutations are advantageous to stem cells in our tissues, starting the cells down the path to cancer and deepening our insight into cancer risk. Recent studies from many labs have shown how as we age our tissues become dominated by clones bearing mutations in known cancer-associated genes. I will present studies from our labs exploring how cigarette smoking and aging can alter mutational landscapes in the non-malignant lung. To observe somatic mutations in lung biopsies from individuals with different smoking histories, we used a rare-mutation detection technique called Duplex Sequencing to analyze somatic variants from over 200 lung samples, including from subjects from the BDRE (Colorado), PEACE, and TRACERx cohorts. Using this method, we have identified large numbers of mutations in each brushing or biopsy, many of which are cataloged in the Catalogue Of Somatic Mutations In Cancer (COSMIC), and most of these are predicted to disrupt protein function. We also observe pervasive positive selection acting on mutations in many but not all of the cancer-associated genes, with different patterns of selection in the lungs of ever-smokers and never-smokers. Ongoing studies are using primary human and mouse lung epithelial cultures and mouse models to study how these mutations interact with smoking and age to alter associated clonal expansions, tissue integrity, and malignant progression. We propose a model whereby aging and microenvironmental changes induced through lifestyle choices like smoking can promote selection for cells with adaptive mutations, which can contribute to not only cancer risk but also tissue aging. Thus, while young tissues impede selection for such adaptive mutations, old age is associated with a feed-forward loop of aging tissue-mediated selection for mutant clones that then increase tissue aging. Thus, understanding the forces controlling clonal selection as we age and due to lifetime exposures could be critical for controlling multiple diseases of old age. [EJE, EL and FM contributed equally.These studies were led by CS and JD.] Citation Format: Edward J. Evans, Emilia Lim, Fabio Marongiu, Shi Biao Chia, William Hill, Clare Weeden, Oriol Pich, Andrew Rowan, Faiz Jabbar, Cristina Naceur-Lombardelli, Raju Veeriah, Moumita Ghosh, Daniel Merrick, York E. Miller, Robert Keith, Mariam Jamal-Hanjani, Charles Swanton, James V. DeGregori. Somatic evolution, cancer, and our inevitable decline with age: Inextricably linked [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY36-01.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.116
GPT teacher head0.467
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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