Adhesion GPCR ADGRE2 Maintains Proteostasis to Promote Progression in Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is an aggressive and heterogeneous hematologic malignancy. In elderly patients, AML incidence is high and has a poor prognosis due to a lack of effective therapies. G protein-coupled receptors (GPCR) play integral roles in physiologic processes and human diseases. Particularly, one third of adhesion GPCRs, the second largest group of GPCRs, are highly expressed in hematopoietic stem and progenitor cells or lineage cells. Here, we investigate the role of adhesion GPCRs in AML and whether they could be harnessed as antileukemia targets. Systematic screening of the impact of adhesion GPCRs on AML functionality by bioinformatic and functional analyses revealed high expression of ADGRE2 in AML, particularly in leukemic stem cells, which is associated with poor patient outcomes. Silencing ADGRE2 not only exerts antileukemic effects in AML cell lines and cells derived from patients with AML in vitro, but also delays AML progression in xenograft models in vivo. Mechanistically, ADGRE2 activates phospholipase Cß/protein kinase C/MEK/ERK signaling to enhance the expression of AP1 and transcriptionally drive the expression of DUSP1, a protein phosphatase. DUSP1 dephosphorylates Ser16 in the J-domain of the co-chaperone DNAJB1, which facilitates the DNAJB1-HSP70 interaction and maintenance of proteostasis in AML. Finally, combined inhibition of MEK, AP1, and DUSP1 exhibits robust therapeutic efficacy in AML xenograft mouse models. Collectively, this study deciphers the roles and mechanisms of ADGRE2 in AML and provides a promising therapeutic strategy for treating AML. Significance: Increased expression of the adhesion GPCR member ADGRE2 in AML supports leukemia stem cell self-renewal and leukemogenesis by modulating proteostasis via an MEK/AP1/DUSP1 axis, which can be targeted to suppress AML progression.

Development of algorithms to estimate the EQ-5D-5L from the FACT-L in patients with lung cancer: a mapping study.

OBJECTIVE: This study aimed to develop a mapping algorithm to evaluate the EQ-5D-5L according to the FACT-L when the EQ-5D-5L is not available. METHODS: EQ-5D-5L and FACT-L data were collected from patients with lung cancer in Departments of Thoracic Surgery, Medical Oncology, Radiation Oncology, Sichuan Cancer Hospital. We used the ordinary least squares model (OLS), Tobit model (Tobit), two-part model (TPM), beta mixture regression (BM), and censored least absolute deviation model (CLAD) to map the results of the FACT-L according to EQ-5D-5L scores. To establish these models, the total score, dimension scores, squared items, and interaction items were introduced. Performance metrics including Adjusted R2, root mean square error (RMSE), and mean absolute error (MAE) were used to select the optimized model. RESULTS: The model with the best mapping performance was the BM model (BETAMIX4) with the PWB (physical well-being) dimension, FWB (functional well-being) dimension, the squared term of the PWB dimension, and the squared term of the FWB dimension as covariates. The final prediction metrics were Adjusted R2 = 0.695, RMSE = 0.206, and MAE = 0.109. Fivefold cross-validation (CV) results also demonstrated that the BM model had the best mapping power. CONCLUSIONS: This study developed an optimized mapping algorithm to predict the utility index from the FACT-L to the EQ-5D-5L, which provides an effective alternative reference for EQ-5D-5L estimation when the preference-based health utility values were unavailable.