ASSESSMENT OF EXPOSURE TO PERSISTENT ORGANOCHLORINE COMPOUNDS IN EPIDEMIOLOGICAL STUDIES ON BREAST CANCER: A LITERATURE REVIEW AND PERSPECTIVES FOR THE CECILE STUDY
Bibliographic record
Abstract
Breast cancer is the most frequent neoplastic disease in women representing 50,000 new cases each year in France. The well-established risk factors, as those related to the reproductive history, cannot account for all cases of breast cancer. Other environmental or lifestyle factors need to be explored in depth. Persistent organochlorine compounds (OCs) have attracted attention because of their endocrine disrupting properties that make them possible risk factors for breast cancer, but most epidemiological studies did not report an association between OC concentrations in blood or adipose tissue and breast cancer risk. In these studies, OC levels were measured in biological samples obtained at the time of cancer diagnosis or only a few years before.In this paper, we review the studies on dichlorodiphenyltrichloroethane (DDT) and polychlorobiphenyl (PCB) exposures in relation to breast cancer. We discuss the relevance of OC biological measurements as lifelong exposure indicators, and we describe a new method for assessing exposure to OCs in epidemiological studies.Most studies were carried out recently and reported OC concentrations that were substantially lower than those reported during the 1960s and 1970s. We make the assumption that these OC levels were not reliable indicators, as they were not measured during etiologically relevant periods in a woman’s lifetime, i.e. during the prenatal period, the puberty or the period before a first full-term pregnancy, which are regarded as key periods of vulnerability of mammary gland cells to carcinogens.This may have resulted in non differential exposure misclassification and hence in the absence of an observed association between OC levels and breast cancer in most epidemiological studies.Physiologically-based pharmacokinetic (PBPK) models allow estimating persistent organic pollutant lifetime toxicokinetics profiles retrospectively in women, by taking into account individual differences in metabolism and key events that affect OC kinetics such as lactation and weight variations. PBPK models will be applied to the participants of a large French population-based case-control study including 1080 cases and 1055 controls.Exposure misclassification could have prevented from observing an association between exposure to OCs and breast cancer risk. PBPK models could be used as a novel way of assessing exposure to OCs and to investigate the impact of internal exposure at different time windows on breast cancer incidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".