Tumor-infiltrating lymphocyte composition, organization and PD-1/ PD-L1 expression are linked in breast cancer
Bibliographic record
Abstract
The clinical relevance of tumor-infiltrating lymphocytes (TIL) in breast cancer (BC) has been clearly established by their demonstrated correlation with long-term positive outcomes. Nevertheless, the relationship between protective immunity, observed in some patients, and critical features of the infiltrate remains unresolved. This study examined TIL density, composition and organization together with PD-1 and PD-L1 expression in freshly collected and paraffin-embedded tissues from 125 patients with invasive primary BC. Tumor and normal breast tissues were analyzed using both flow cytometry and immunohistochemistry. TIL density distribution is a continuum with 25% of tumors identified as TIL-negative at a TIL density equivalent to normal breast tissues. TIL-positive tumors (75%) were equally divided into TIL-intermediate and TIL-high. Tumors had higher mean frequencies of CD4+ T cells and CD19+ B cells and a lower mean frequency of CD8+ T cells compare with normal tissues, increasing the CD4+/CD8+ T-cell ratio. Tertiary lymphoid structures (TLS), principally located in the peri-tumoral stroma, were detected in 60% of tumors and correlated with higher TIL infiltration. PD-1 and PD-L1 expression were also associated with higher TIL densities and TLS. TIL density, TLS and PD-L1 expression were correlated with more aggressive tumor characteristics, including higher proliferation and hormone receptor negativity. Our findings reveal an important relationship between PD-1/PD-L1 expression, increased CD4+ T and B-cell infiltration, TIL density and TLS, suggesting that evaluating not only the extent but also the nature and location of the immune infiltrate should be considered when evaluating antitumor immunity and the potential for benefit from 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 | 0.001 | 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".