Transcriptome Analysis Reveals That G Protein-Coupled Receptors Are Potential Diagnostic Markers or Therapeutic Targets in Acute Myeloid Leukemia
Bibliographic record
Abstract
Abstract Acute myeloid leukemia (AML) is associated with poor overall survival and the development of more effective therapies is urgently needed. G Protein-Coupled Receptors (GPCRs) represent the largest family of membrane receptors, with an estimated 800 members in humans, and are attractive therapeutic targets, accounting for approximately 30% of targets of marketed drugs. These receptors are key transducers that bind a vast diversity of ligands (e.g. glycoproteins, peptides, amino acids, nucleotides, nucleosides, ions) allowing the cells to adapt to their environment by regulating a wide variety of physiological processes including the control of blood pressure, heart rate, digestive processes, hormone secretion, cell growth and migration, as well as vision and olfaction. Binding to their ligands leads to conformational rearrangements promoting the engagement and modulation of many distinct downstream signaling effectors that are both G protein-dependent and independent. Several GPCRs are critical for cell proliferation and survival, and can be aberrantly expressed in cancer cells. For example, the chemokine receptor CXCR4 plays an important role in metastasis and angiogenesis in breast cancer and many other types of tumors. In AML, CXCR4 overexpression has been associated with poor outcome. Moreover, in vivo mouse studies have shown that the use of a small molecule antagonist of CXCR4 increases the mobilization of AML cells into the peripheral blood and improves the apoptotic effects of chemotherapy. This activity has been explored in a phase I/II clinical study showing that the addition of CXCR4 antagonists to chemotherapy in AML might improve the remission rate. The role of GPCRs in mouse leukemogenesis has also been suggested in a transcriptome analysis of two related leukemia clones that differ in their stem cell frequency. In this study, GPCRs were the most differentially expressed class of genes between the two clones. Currently, an extensive assessment of GPCR expression in human AML is lacking. To address this issue, we studied the expression of GPCRs in a large cohort of AML samples, as well as in normal blood cells, bone marrow cell populations, and cord blood-derived CD34+ cells as controls.The 772 GPCRs analyzed in this study consist of all the GPCR members included in the International Union of Basic and Clinical Pharmacology (IUPHAR) database, as well as 370 olfactory, 24 taste and 4 vomeronasal receptors. RNA sequencing data analysis was performed as previously described (Lavallée et al, Blood 2015 Jan 1;125(1):140-3) and revealed that 240 GPCRs are expressed in cells from this AML cohort. Among these receptors, 30 are upregulated and 19 are downregulated in AML samples compared to CD34+ normal 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 sub-families. The downregulated members 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, a subset of GPCRs being differentially expressed in normal karyotype AML with NPM1 or FLT3 -ITD mutations, and in specimens with Core Binding Factor and MLL rearrangements, thereby representing promising therapeutic targets or diagnostic markers. In conclusion, our results demonstrate that several GPCR members are deregulated in AML with a clear enrichment in distinct classes, providing the rationale for functional assays using available agonists or antagonists to leukemia-enriched GPCRs. Since these receptors are the targets of several US Federal and Drug Administration approved drugs, our results pave the way to explore selected GPCRs as novel AML therapeutic targets. Disclosures Bouvier: American Society of Nephrology: Other: speaker; Domain Therapeutics: Other: Company SAB member, Research Funding; Vertex Pharmaceutical: Research Funding; Ontario Genomic Institute: Other: SAB meeting; BMS: Research Funding; DalCor Pharmaceutics: Other: Company SAB member; Pfizer: Other: Speaker, Research Funding; Novo-Nordisc: Research Funding.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".