Isolation of specific and biologically active peptides that bind cells from patients with acute myeloid leukemia (AML)
Bibliographic record
Abstract
PURPOSE: In a departure from conventional strategies to improve treatment outcome for myeloid malignancies, we report the isolation of leukemia-specific peptides using a phage display library screened with freshly obtained human myeloid leukemia cells. RESULTS: A phage display library was screened by 5 rounds of biopanning with freshly isolated human AML cells. Individual colonies were randomly picked and after purification, biologic activity (growth and differentiation) on fresh AML cells was profiled. Ten peptides were synthesized for further biological studies. Multiple peptides were found to selectively bind to acute myeloid leukemia (AML) cells. The peptides bound to leukemia cells, were internalized and could induce proliferation and/or differentiation in the target patient cells. Two of the peptides, HP-A2 and HP-G7, appeared to have a novel mechanism of inducing differentiation since they did not cause G1 arrest in cycling cells even as the expression of the differentiation marker CD11b increased. CONCLUSION: Peptide induced differentiation of leukemia cells offers a novel treatment strategy for myeloid malignancies, whereas their ability to induce proliferation could be harnessed to make cells more sensitive to chemotherapy. Conceptually, these leukemia specific peptides can also be used to refine diagnosis, document minimal residual disease, and selectively deliver toxins to malignant cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".