CD8+ T cells in the tumor microenvironment modulate the response to endocrine therapy in breast cancer
Bibliographic record
Abstract
The role of the tumor immune microenvironment (TIME) in modulating responses to antiestrogen therapy in hormone receptor-positive (HR+) breast cancers remains unclear. We analyzed pre- and on-treatment biopsies from patients with HR+ breast cancer treated with letrozole to induce estrogen deprivation (ED). Stromal tumor-infiltrating lymphocytes, assessed by H&E staining, and immune-related gene sets, including IFN-γ signaling genes, measured by RNA-Seq, were increased in ED-resistant tumors. Cyclic immunofluorescence and spatial transcriptomics revealed an abundance of CD8+ T cells and enhanced antigen processing and immune gene signatures in ED-resistant tumors. In this group, the expression of CXCL9, CXCL10, and CXCL11 - chemokine genes involved in CD8+ T cell recruitment - and the CXCR3 receptor were upregulated both before and after letrozole treatment. CXCL11 levels were higher in conditioned media from HR+ breast cancer cells cocultured with CD8+ T cells. Both recombinant CXCL11 and coculture with CD8+ T cells promoted MCF7 and T47D cell growth in estrogen-free conditions. Finally, deletion combined with silencing of the CXCL11 receptors CXCR3 and CXCR7 in MCF7 cells impaired proliferation in response to exogenous CXCL11 and to coculture with CD8+ T cells in estrogen-free conditions. These findings suggest that CD8+ T cell-associated CXCL11 in the TIME modulated the response of HR+ breast cancer cells to estrogen suppression.
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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 | 0.000 |
| 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".