Targeting pediatric leukemia-propagating cells with anti-CD200 antibody therapy
Bibliographic record
Abstract
Treating refractory pediatric acute lymphoblastic leukemia (ALL) remains a challenge despite impressive remission rates (>90%) achieved in the last decade. The use of innovative immunotherapeutic approaches such as anti-CD19 chimeric antigen receptor T cells does not ensure durable remissions, because leukemia-propagating cells (LPCs) that lack expression of CD19 can cause relapse, which signifies the need to identify new markers of ALL. Here we investigated expression of CD58, CD97, and CD200, which were previously shown to be overexpressed in B-cell precursor ALL (BCP-ALL) in CD34+/CD19+, CD34+/CD19-, CD34-/CD19+, and CD34-/CD19- LPCs, to assess their potential as therapeutic targets. Whole-genome microarray and flow cytometric analyses showed significant overexpression of these molecules compared with normal controls. CD58 and CD97 were mainly co-expressed with CD19 and were not a prerequisite for leukemia engraftment in immune deficient mice. In contrast, expression of CD200 was essential for engraftment and serial transplantation of cells in measurable residual disease (MRD) low-risk patients. Moreover, these CD200+ LPCs could be targeted by using the monoclonal antibody TTI-CD200 in vitro and in vivo. Treating mice with established disease significantly reduced disease burden and extended survival. These findings demonstrate that CD200 could be an attractive target for treating low-risk ALL, with minimal off-tumor effects that beset current immunotherapeutic approaches.
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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".