Absolute lymphocyte count is associated with survival in ovarian cancer independent of tumor-infiltrating lymphocytes
Bibliographic record
Abstract
BACKGROUND: The immune system strongly influences outcome in patients with ovarian cancer. In particular, the absolute lymphocyte count in peripheral blood (ALC) and the presence of tumor-infiltrating lymphocytes (TIL) have each been associated with favourable prognosis. However, the mechanistic relationships between ALC, TIL and prognosis are poorly understood. We hypothesized that high ALC values might be associated with stronger tumor immunity as manifested by increased TIL, decreased tumor burden and longer survival. METHODS: ALC values were collected from patient records ≥ 2 years before, during or after primary treatment for high-grade serous ovarian cancer (HGSC). Lymphocyte subsets were assessed in peripheral blood by flow cytometry. CD8+ and CD20+ TIL were assessed by immunohistochemistry. RESULTS: Overall, patients had normal ALC values two or more years prior to diagnosis of HGSC. These values were not predictive of disease severity or survival upon subsequent development of HGSC. Rather, ALC declined upon development of HGSC in proportion to disease burden. This decline involved all lymphocyte subsets. ALC increased following surgery, remained stable during chemotherapy, but rarely recovered to pre-diagnostic levels. ALC values recorded at diagnosis did not correlate with CD8+ or CD20+ TIL but were associated with progression-free survival. CONCLUSIONS: Patients with high intrinsic ALC values show no clinical or survival advantage upon subsequent development of HGSC. ALC values at diagnosis are prognostic due to an association with disease burden rather than TIL. Therapeutic enhancement of ALC may be necessary but not sufficient to improve survival in HGSC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".