Abstract SY36-01: Somatic evolution, cancer, and our inevitable decline with age: Inextricably linked
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».