CD123 target validation and preclinical evaluation of ADCC activity of anti-CD123 antibody CSL362 in combination with NKs from AML patients in remission
Bibliographic record
Abstract
Abstract Despite the heterogeneity of acute myeloid leukemia (AML), overexpression of the interleukin-3 receptor-α (CD123) on both the more differentiated leukemic blast and leukemic stem cells (LSCs) provides a therapeutic target for antibody treatment. Here we present data on the potential clinical activity of the monoclonal antibody CSL362, which binds to CD123 with high affinity. We first validated the expression of CD123 by 100% (52/52) of patient samples and the correlation of NPM1 and FLT3-ITD mutations with the high frequency of CD123 in AML. In vitro studies demonstrated that CSL362 potently induced antibody-dependent cell cytotoxicity (ADCC) of AML blasts including CD34+CD38−CD123+ LSCs by natural killer cells (NKs). Importantly, compared with healthy donor (HD) NKs, NKs drawn from AML patients in remission had a comparable ADCC activity against leukemic cells; of note, during remission, immature NKs were five times higher in AML patients than that in HDs. Significantly, we report a case where leukemic cells were resistant to autologous ADCC; however, the blasts were effectively lysed by CSL362 together with donor-derived NKs after allogeneic hematopoietic stem cell transplantation. These studies highlight CSL362 as a promising therapeutic option following chemotherapy and transplant so as to improve the outcome of AML patients.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".