Transcriptome analysis of G protein-coupled receptors in distinct genetic subgroups of acute myeloid leukemia: identification of potential disease-specific targets
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is associated with poor clinical outcome and the development of more effective therapies is urgently needed. G protein-coupled receptors (GPCRs) represent attractive therapeutic targets, accounting for approximately 30% of all targets of marketed drugs. Using next-generation sequencing, we studied the expression of 772 GPCRs in 148 genetically diverse AML specimens, normal blood and bone marrow cell populations as well as cord blood-derived CD34-positive cells. Among these receptors, 30 are overexpressed and 19 are downregulated in AML samples compared with normal CD34-positive cells. Upregulated GPCRs are enriched in chemokine (CCR1, CXCR4, CCR2, CX3CR1, CCR7 and CCRL2), adhesion (CD97, EMR1, EMR2 and GPR114) and purine (including P2RY2 and P2RY13) receptor subfamilies. The downregulated receptors include adhesion GPCRs, such as LPHN1, GPR125, GPR56, CELSR3 and GPR126, protease-activated receptors (F2R and F2RL1) and the Frizzled family receptors SMO and FZD6. Interestingly, specific deregulation was observed in genetically distinct subgroups of AML, thereby identifying different potential therapeutic targets in these frequent AML subgroups.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".