Targeting Leukemia-Initiating Cells in Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
The concept that different leukemias are developmentally distinct and, like in normal hematopoiesis, generated by restricted populations of cells named leukemia-initiating cells (LIC), is becoming more established. These cancer stem-like cells have been assumed to have unique properties, including the capability of self-renewing and giving rise to "differentiated" or non-LICs that make up the whole tumor. Cell populations enriched with LIC activity have been characterized in different hematopoietic malignancies, including human acute lymphoblastic leukemia (ALL). Related studies have also demonstrated that LICs are functionally distinct from bulk cells and modulated by distinct molecular signaling pathways and epigenetic mechanisms. Here we review several biological and clinical aspects related to LICs in ALL, including (i) immunophenotypic characterization of LIC-enriched subsets in human and mouse models of ALL, (ii) emerging therapeutics against regulatory signaling pathways involved in LIC progression and maintenance in T- and B-cell leukemias, (iii) novel epigenetic and age-related mechanisms of LIC propagation, and (iv) ongoing efforts in immunotherapy to eradicate LIC-enriched cell subsets in relapsed and refractory ALL cases. Current conventional treatments do not efficiently eliminate LICs. Therefore, innovative therapeutics that exclusively target LICs hold great promise for developing an effective cure for ALL.
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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) | 0.001 | 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 | 0.003 |
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".