Adoptive cell therapy with tumor-infiltrating lymphocytes in patients with metastatic ovarian cancer: a pilot study
Bibliographic record
Abstract
Objective:Ovarian cancer (OC) is often diagnosed at an advanced stage with two thirds of patients experiencing recurrent disease with a poor prognosis. Adoptive cell therapy (ACT) with tumor-infiltrating lymphocytes (TIL) has shown curative potential in malignant melanoma, but has only been investigated scarcely in other cancers. In this pilot study, we tested TIL based ACT in patients with metastatic OC.Methods:Six patients with progressive platinum-resistant metastatic OC were treated with an infusion of TIL preceded by standard lymphodepleting chemotherapy and followed by decrescendo intravenous interleukin-2 (IL-2). Primarily, the feasibility and tolerability of the treatment was assessed. Secondarily, disease control rate was described and immune responses against tumor cells were monitored.Results:Treatment was well tolerated with manageable toxicities. Four patients had stable disease for three months and two patients for five months with five patients having a decrease in target lesions. Progression was primarily due to new lesions while target lesions in general remained stable or in regression. Antitumor reactivity was observed in TIL infusion products from five patients but no antitumor reactivity was detectable in peripheral blood lymphocytes collected after treatment. High numbers of infused TIL expressed exhaustion markers including LAG3 and PD-1, and immunostaining of tumor tissue demonstrated substantial MHCII and PD-L1 expression.Conclusions:ACT with TIL in combination with decrescendo IL-2 is feasible in patients with metastatic OC. Early indications of clinical activity were found. However, TIL ACT efficacy was incomplete with possible involvement of the inhibitory immune checkpoint pathways LAG3/MHCII and PD1/PD-L1.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".