Profound Functional Suppression of Tumor‐Infiltrating T‐Cells in Ovarian Cancer Patients Can Be Reversed Using PD‐1‐Blocking Antibodies or DARPin® Proteins
Bibliographic record
Abstract
PD‐1/PD‐L1 blockade has revolutionized the field of immunooncology. Despite the relative success, the response rate to anti‐PD‐1 therapy requires further improvements. Our aim was to explore the enhancement of T‐cell function by using novel PD‐1‐blocking proteins and compare with clinically approved monoclonal antibodies (mAbs). We isolated T‐cells from the ascites and tumor of 17 patients with advanced epithelial ovarian cancer (EOC) and analyzed the effects using the mAbs nivolumab and pembrolizumab and two novel engineered ankyrin repeat proteins (DARPin® proteins). PD‐1 blockade with either mAb or DARPin® molecule significantly increased the release of IFN‐ γ , granzyme B, IL‐2, and TNF‐ α , demonstrating successful reinvigoration. The monovalent DARPin ® protein was less effective compared to its bivalent equivalent, demonstrating that bivalency brings an additional benefit to PD‐1 blockade. Overall, we found a higher fold increase of lymphokine secretion in response to the PD‐1 blockade by tumor‐derived T‐cells; however, the absolute amounts were significantly lower compared to the release from ascites‐derived T‐cells. Our results demonstrate that PD‐1 blockade can only partially reinvigorate functionally suppressed T‐cells from EOC patients. This warrants further investigation preferably in combination with other therapeutics. The study provides an early pilot proof‐of‐concept for the potential use of DARPin ® proteins as eligible alternative scaffold proteins to block PD‐1.
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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 | 0.000 | 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".