Dormant tumors circumvent tumor-specific adaptive immunity by establishing a Treg-dominated niche via DKK3
Bibliographic record
Abstract
Approximately 30% of breast cancer survivors deemed free of disease will experience locoregional or metastatic recurrence even up to 30 years after initial diagnosis, yet how residual/dormant tumor cells escape immunity elicited by the primary tumor remains unclear. We demonstrate that intrinsically dormant tumor cells are indeed recognized and lysed by antigen-specific T cells in vitro and elicit robust immune responses in vivo. However, despite close proximity to CD8+ killer T cells, dormant tumor cells themselves support early accumulation of protective FoxP3+ T regulatory cells (Tregs), which can be targeted to reduce tumor burden. These intrinsically dormant tumor cells maintain a hybrid epithelial/mesenchymal state that is associated with immune dysfunction, and we find that the tumor-derived, stem cell/basal cell protein Dickkopf WNT signaling pathway inhibitor 3 (DKK3) is critical for Treg inhibition of CD8+ T cells. We also demonstrate that DKK3 promotes immune-mediated progression of proliferative tumors and is significantly associated with poor survival and immunosuppression in human breast cancers. Together, these findings reveal that latent tumors can use fundamental mechanisms of tolerance to alter the T cell microenvironment and subvert immune detection. Thus, targeting these pathways, such as DKK3, may help render dormant tumors susceptible to immunotherapies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".