Tumor‐Infiltrating Lymphocytes in Breast and Female Genital Tract Cancers: Overlooked Potential and Unexplored Frontiers
Bibliographic record
Abstract
BACKGROUND: The growing success of cancer immunotherapies has led to significant advances in oncology. However, despite these promising developments, cancer-related mortality remains high for common cancer types such as breast and lower female genital tract cancers. METHOD: Here, we synthesize recent findings on the prognostic relevance of tumor-infiltrating lymphocytes (TILs) in breast, endometrial, tubo-ovarian, and vulvar cancer. Our analysis covers the relationship between TIL counts and density, immune cell subtype combinations, immunotherapy approaches, and patient outcomes. RESULTS: T-cells, generally correlates with improved outcomes such as in endometrial cancer (especially the POLE-ultramutated subgroup), invasive breast cancer, and ovarian epithelial tumors. However, in ductal carcinoma in situ (DCIS) of the breast, elevated TIL counts are linked to a worse prognosis. Ethnicity, the tumor microenvironment (TME), and molecular profiles further complicate the prognostic utility of TILs. CONCLUSIONS: TIL-based therapies have shown potential in personalized immunotherapy, particularly in recurrent, refractory ovarian cancer. Limited research on rarer gynecologic tumors hinders broader clinical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".