KLRG1 Cell Depletion as a Novel Therapeutic Strategy in Patients with Mature T-Cell Lymphoma Subtypes
Bibliographic record
Abstract
PURPOSE: Develop a novel therapeutic strategy for patients with subtypes of mature T-cell and NK-cell neoplasms. EXPERIMENTAL DESIGN: Primary specimens, cell lines, patient-derived xenograft models, commercially available, and proprietary anti-KLRG1 antibodies were used for screening, target, and functional validation. RESULTS: Here we demonstrate that surface KLRG1 is highly expressed on tumor cells in subsets of patients with extranodal NK/T-cell lymphoma (ENKTCL), T-prolymphocytic leukemia (T-PLL), and gamma/delta T-cell lymphoma (G/D TCL). The majority of the CD8+/CD57+ or CD3-/CD56+ leukemic cells derived from patients with T- and NK-large granular lymphocytic leukemia (T-LGLL and NK-LGLL), respectively, expressed surface KLRG1. The humanized afucosylated anti-KLRG1 monoclonal antibody (mAb208) optimized for mouse in vivo use depleted KLRG1+ TCL cells by mechanisms of ADCC, ADCP, and CDC rather than apoptosis. mAb208 induced ADCC and ADCP of T-LGLL patient-derived CD8+/CD57+ cells ex vivo. mAb208 effected ADCC of subsets of healthy donor-derived KLRG1+ NK, CD4+, CD8+ Tem, and TemRA cells while sparing KLRG1- naïve and CD8+ Tcm cells. Treatment of cell line and TCL patient-derived xenografts with mAb208 or anti-CD47 mAb alone and in combination with the PI3K-δ/γ inhibitor duvelisib extended survival. The depletion of macrophages in vivo antagonized mAb208 efficacy. CONCLUSIONS: Our findings suggest the potential benefit of a broader treatment strategy combining therapeutic antibodies with PI3Ki for the treatment of patients with mature T-cell and NK-cell neoplasms. See related commentary by Varma and Diefenbach, p. 2300.
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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".