Fingolimod and tumor-infiltrating lymphocytes in checkpoint-inhibitor treated cancer patients
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) are emerging as the new standard of care for treating various metastatic cancers. It is known that effective anti-tumor immune responses are associated with a stronger presence of tumor-infiltrating lymphocytes (TILs) in solid tumor tissue. Cancer patients with relapsing-remitting multiple sclerosis (RRMS) are often under continuous treatment with fingolimod, an immune-modulating drug that inhibits lymphocyte egress from secondary lymphatic organs. Little is known about the effect of fingolimod on ICI cancer therapy, as fingolimod may limit the number of TILs. Here we present three patients with RRMS, who developed various cancers during fingolimod treatment. Histology of all tumors consistently showed low numbers of TILs. A second biopsy taken from one of the tumors, a melanoma, revealed a significant increase of TILs after stopping fingolimod and starting pembrolizumab, indicating a surge in the number and re-invigoration of T cells. Our study suggests that fingolimod limits the number of TILs in solid tumors and may, thus, inhibit anti-cancer immune responses.
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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 | 0.001 |
| 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".