Neoadjuvant immune checkpoint blockade triggers persistent and systemic T <sub>reg</sub> activation which blunts therapeutic efficacy against metastatic spread of breast tumors
Bibliographic record
Abstract
The clinical successes of immune checkpoint blockade (ICB) in advanced cancer patients have recently spurred the clinical implementation of ICB in the neoadjuvant and perioperative setting. However, how neoadjuvant ICB therapy affects the systemic immune landscape and metastatic spread remains to be established. Tumors promote both local and systemic expansion of regulatory T cells (Tregs), which are key orchestrators of tumor-induced immunosuppression, contributing to immune evasion, tumor progression and metastasis. Tregs express inhibitory immune checkpoint molecules and thus may be unintended targets for ICB therapy counteracting its efficacy. Using ICB-refractory models of spontaneous primary and metastatic breast cancer that recapitulate the poor ICB response of breast cancer patients, we observed that combined anti-PD-1 and anti-CTLA-4 therapy inadvertently promotes proliferation and activation of Tregs in the tumor, tumor-draining lymph node and circulation. Also in breast cancer patients, Treg levels were elevated upon ICB. Depletion of Tregs during neoadjuvant ICB in tumor-bearing mice not only reshaped the intratumoral immune landscape into a state favorable for ICB response but also induced profound and persistent alterations in systemic immunity, characterized by elevated CD8+ T cells and NK cells and durable T cell activation that was maintained after treatment cessation. While depletion of Tregs in combination with neoadjuvant ICB did not inhibit primary tumor growth, it prolonged metastasis-related survival driven predominantly by CD8+ T cells. This study demonstrates that neoadjuvant ICB therapy of breast cancer can be empowered by simultaneous targeting of Tregs, extending metastasis-related survival, independent of a primary tumor response.
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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".