Cancer-associated fibroblasts at the crossroads of tumor progression and therapy resistance: from heterogeneity to precision reprogramming
Bibliographic record
Abstract
Cancer-associated fibroblasts (CAFs) are pivotal regulators of the tumor microenvironment (TME), driving malignancy through extracellular matrix remodeling, paracrine and metabolic crosstalk, angiogenesis, fibrosis, and immune suppression. Emerging single-cell and spatial multi-omics have revealed CAF heterogeneity and plasticity, with subtypes such as myofibroblastic, inflammatory, antigen-presenting, and metabolic CAFs exerting context-dependent functions that can either promote or restrain tumor growth. This duality cautions against indiscriminate stromal ablation and highlights the need for precision strategies. CAFs also mediate resistance to chemotherapy, radiotherapy, targeted agents, and immunotherapy by creating physical and biochemical barriers and fostering immune exclusion. Therapeutic approaches span depletion strategies, pathway inhibitors, and stromal reprogramming using vitamin D receptor agonists, retinoids, and epigenetic modulators, often in combination with immunotherapies. However, CAF plasticity and the lack of exclusive markers remain major challenges. This review positions CAFs as dynamic regulators of cancer hallmarks and argues for a paradigm shift toward precision stromal oncology, where the trajectory from CAF depletion to CAF reprogramming and CAF-guided combinatorial therapies reshapes cancer treatment itself.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".