Cancer Stem Cells in the Development of Novel Therapeutics for Refractory Pediatric Leukemia
Bibliographic record
Abstract
Although treatment strategies for pediatric leukemia have improved overall survival rates in the recent past, relapse rates in certain subgroups such as infant leukemia remain unacceptably high. Despite undergoing extensive chemotherapy designed to target the rapidly proliferating leukemia cells, many of these children experience relapse. In refractory leukemia, the existence of cell populations with stemness characteristics, termed leukemia stem cells (LSCs), which remain quiescent and subsequently replenish the blast population, has been described. A significant body of evidence exists, derived largely from xenograft models of adult acute myeloid leukemia, to support the idea that LSCs may play a fundamental role in refractory disease. In addition, clinical studies have also linked LSCs with increased minimal residual disease, higher relapse rate, and decreased survival rates in these patients. Recently, a number of reports have addressed effective ways to utilize new-generation genomic sequencing and transcriptomic analyses to identify targeted therapeutic agents aimed at LSCs, while sparing normal hematopoietic stem cells. These data underscore the value of timely translation of knowledge from adult studies to the unique molecular and physiological characteristics seen in pediatric leukemia. We aim to summarize this article in the rapidly expanding field of stem cell biology in hematopoietic malignancies, focusing particularly on relevant preclinical models and novel targeted therapeutics, and their applicability to childhood leukemia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".