Abstract IA16: The Halifax Project: Can a chemical mixture be a virtual carcinogen?
Notice bibliographique
Résumé
Abstract We live in a mixture of chemicals that has literally been created during our lifetimes. The possibility that this mixture might be carcinogenic has never been assessed systematically. Concurrent with the spread of this chemical mixture, cancer has changed. For example, there is more breast cancer and it occurs earlier in life for the same mutations. This increase has been attributed to artifacts and overdiagnosis. However, the increase started long before mammograms (Doll and Peto, Oxford Mono 1981), and Anderson et al. (JCO 2010) found similar increased invasive breast cancer in men—for whom the explanations that blame women do not apply. The Halifax Project used a Hallmarks of Cancer framework to ask whether the mixture of chemicals in which we live, or some part of it, might—together as a mixture—act as a virtual carcinogen even though not one of the chemicals was considered a carcinogen in itself. Dr. Leroy Lowe recruited 12 groups of experts—not specifically cancer biologists (one for each Hallmark, one for microenviroment as a whole, one for cross validation). Each group was asked to identify one or more common chemical(s) of commerce that induced their specific Hallmark. The requirements for the chemicals were that they were considered “safe” and affected the Hallmark specifically (thus excluding global mutagens) either: 1. in a dose range measured in humans; 2. at a dose lower than usually tested; 3. at a dose lower than the LOEL for carcinogenesis; or 4. at animal blood or tissue levels similar to humans. The Project identified one or more chemicals that activated each Hallmark, including 89 chemicals (it is likely more exist that were not identified) not currently classified as carcinogens that can affect Hallmarks. Fifty of these 89 chemicals (59%) had low-dose effects. The Project thus confirmed the possibility that a mixture of “noncarcinogens” could induce all 10 Hallmarks, i.e., be a virtual carcinogen. This finding leads to follow-up questions such as, How do chemical mixtures interact when they have dissimilar structures as well as similar ones? (Corollary: Xenoestrogens are a target of convenience, but there are other chemicals.) We are unlikely to find clarity without mechanistic data on how chemicals work together, so we should avoid a rush to oversimplify and to create answers just to have an answer. What happens after a chemical exposure ends? If a mixture induces “cancer,” will it be sustained if chemicals are removed? How do known carcinogens, e.g., diethylstilbestrol, cause cancer years later? And why does cancer sometimes persist when the inciting mutation is inactivated? Might cells become addicted to being malignant? Testing the mixtures hypothesis in a meaningful way requires resources at least somewhat similar to the magnitude of funds spent on deciphering genetic carcinogenesis and developing target therapies. With over 300 million persons at risk in the US alone, even a small chance of inducing cancer creates a significant risk. Citation Format: William H. Goodson III. The Halifax Project: Can a chemical mixture be a virtual carcinogen? [abstract]. In: Proceedings of the AACR Special Conference on Environmental Carcinogenesis: Potential Pathway to Cancer Prevention; 2019 Jun 22-24; Charlotte, NC. Philadelphia (PA): AACR; Can Prev Res 2020;13(7 Suppl): Abstract nr IA16.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
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 tête enseignante, 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 ».