Rethinking the microenvironment’s role in chemical-induced malignancy
Bibliographic record
Abstract
Why and how does cancer start? Building from a Symposium at the 2025 Society of Toxicology meeting, we convened a group of international experts to answer this seemingly simple question. As experimental evidence has evolved, perspectives on cancers' origins have shifted from the accumulation of DNA mutations in single cells to complex processes involving signals from an altered tissue microenvironment which promote tumorigenesis. Carcinogen exposures impact the biology of the microenvironment in complex and tissue-specific ways. These changes can include the infiltration of inflammatory cells that produce growth factors, neo-angiogenesis, morphological changes, and immune tolerance that avoids immune-mediated elimination. In this in-depth review, we discuss the evidence linking chemical-driven microenvironmental changes in the development of a range of solid and liquid tumors. We discuss specific phenotypic alterations, such as selection pressure driving clonal expansion and cellular plasticity and reacquisition of stem cell states, linked to carcinogen-induced changes in the microenvironment. We describe assays and biomarkers which can allow us to experimentally assess links between chemical exposures, the microenvironment, and cancer phenotypes. We end by discussing how understanding the role of the microenvironment and malignancy in toxicology is essential for accurate cancer hazard evaluation, development of next-generation risk assessment frameworks, identifying new strategies for cancer prevention, and improving patient care.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".