Inhibition Of Inflammation Driven Leukemia Stem Cell Self-Renewal With a Selective JAK2 Antagonist
Bibliographic record
Abstract
Abstract Chronic myeloid leukemia (CML) represents an important paradigm for identifying the molecular events that promote malignant reprogramming of progenitors into therapeutically recalcitrant leukemia stem cells (LSC) during blast crisis (BC) transformation. To elucidate mechanisms of human BC LSC generation, whole transcriptome RNA sequencing (RNA Seq), lentiviral BCR-ABL and JAK2 transduction, quantitative RT-PCR (qRT-PCR) and serial xenotransplantation studies were performed. In human BC LSC, RNA seq revealed extensive upregulation of inflammation-responsive genes in conjunction with JAK/STAT signaling pathway activation and splice isoform specific qRT-PCR uncovered a predilection for selective STAT5a isoform expression. While lentiviral BCR-ABL1 expression in cord blood progenitors enhanced JAK2 activation and expression of specific STAT5a splice isoforms, lentiviral human JAK2 overexpression globally activated inflammation-response genes and expression of adenosine deaminase RNA associated (ADAR1), a primate specific RNA editase previously shown to activate self-renewal in response to inflammation. Notably, inhibition of BC LSC self-renewal with dasatinib, a BCR-ABL inhibitor, combined with a potent JAK2 inhibitor, SAR302503, was associated with reduced STAT5a isoform expression and phospho-STAT5 activation as well as ADAR1 expression and activity. These results highlight a novel JAK/STAT pathway driven niche-responsive mechanism of human BC LSC generation that can be targeted, at least in part, with a selective JAK2 inhibitor and may be utilized as an RNA editing-based biomarker of cancer stem cell generation and therapeutic resistance. Disclosures: Jamieson: Sanofi: Consultancy.
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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 | 0.000 |
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".