MétaCan
Menu
Back to cohort

Abstract IA16: The Halifax Project: Can a chemical mixture be a virtual carcinogen?

2020· article· en· W3158712382 on OpenAlexaboutno aff
William H. Goodson

Bibliographic record

VenueCancer Prevention Research · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsnot available
Fundersnot available
KeywordsCarcinogenCancerBreast cancerOverdiagnosisCarcinogenesisMedicinePhysiologyToxicologyInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.126
GPT teacher head0.404
Teacher spread0.277 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCancer Prevention ResearchSame topicHealth, Environment, Cognitive AgingFrench-language works237,207