Fc γ receptor IIB (FcγRIIB) is an inhibitory molecule capable of reducing antibody immunotherapy efficacy. We hypothesized its expression could confer resistance in patients with diffuse large B-cell lymphoma (DLBCL) treated with anti-CD20 monoclonal antibody (mAb) chemoimmunotherapy, with outcomes varying depending on mAb (rituximab [R]/obinutuzumab [G]) because of different mechanisms of action. We evaluated correlates between FCGR2B messenger RNA and/or FcγRIIB protein expression and outcomes in 3 de novo DLBCL discovery cohorts treated with R plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) reported by Arthur, Schmitz, and Reddy, and R-CHOP/G-CHOP-treated patients in the GOYA trial (NCT01287741). In the discovery cohorts, higher FCGR2B expression was associated with significantly shorter progression-free survival (PFS; Arthur: hazard ratio [HR], 1.09; 95% confidence interval [CI], 1.01-1.19; P = .0360; Schmitz: HR, 1.13; 95% CI, 1.02-1.26; P = .0243). Similar results were observed in GOYA with R-CHOP (HR, 1.26; 95% CI, 1.00-1.58; P = .0455), but not G-CHOP (HR, 0.91; 95% CI, 0.69-1.20; P = .50). A nonsignificant trend that high FCGR2B expression favored G-CHOP over R-CHOP was observed (HR, 0.67; 95% CI, 0.44-1.02; P = .0622); however, low FCGR2B expression favored R-CHOP (HR, 1.58; 95% CI, 1.00-2.50; P = .0503). In Arthur and GOYA, FCGR2B expression was associated with tumor FcγRIIB expression; correlating with shorter PFS for R-CHOP (HR, 2.17; 95% CI, 1.04-4.50; P = .0378), but not G-CHOP (HR, 1.37; 95% CI, 0.66-2.87; P = .3997). This effect was independent of established prognostic biomarkers. High FcγRIIB/FCGR2B expression has prognostic value in R-treated patients with DLBCL and may confer differential responsiveness to R-CHOP/G-CHOP.
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http://dx.doi.org/10.1182/bloodadvances.2021004770 | DOI Listing |
Background: Considering that the treatment of gout is poor, we performed a Mendelian randomization (MR) study to identify candidate biomarkers and therapeutic targets for gout.
Methods: A drug-targeted MR study was performed for gout by integrating the gout genome-wide association studies (GWAS) summary data and cis expression quantitative trait loci of 2,633 druggable genes from multiple cohorts. Summary data-based Mendelian randomization (SMR) analyses based on transcript and protein levels were further implemented to validate the reliability of the identified potential therapeutic targets for gout.
J Transl Med
December 2024
The First Affiliated Hospital of Chongqing Medical University, Chongqing Branch (Municipality Division) of National Clinical Research Center for Ocular Diseases, Youyi Road 1, Chongqing, 400016, People's Republic of China.
Background: To investigate the associations of methylation, expression, and protein quantitative trait loci (mQTL, eQTL, and pQTL) with ankylosing spondylitis (AS) and find out genetically supported drug targets for AS.
Methods: The summary-data-based Mendelian randomization (SMR) and Bayesian co-localization analysis were used to assess the potential causality between AS and relevant genes. The GWAS data obtained from the International Genetics of Ankylosing Spondylitis Consortium (IGAS) were set as the discovery stage, and the FinnGen and UK Biobank databases were used to replicate the analysis as an external validation.
J Toxicol Sci
November 2024
Laboratory of Veterinary Pathology, Tokyo University of Agriculture and Technology.
J Immunother Cancer
October 2024
Laboratory of Host Defenses, Department of Biological Sciences, KAIST, Daejeon 34141, Republic of Korea
Gene
January 2025
Department of Ophthalmology, Jiujiang No 1 Peoples Hospital, Jiujiang 332000, China. Electronic address:
The early diagnosis of diabetic retinopathy (DR) is challenging, highlighting the urgent need to identify new biomarkers. Immune responses play a crucial role in DR, yet there are currently no reports of machine learning (ML) algorithms being utilized for the development of immune-related molecular markers in DR. Based on the datasets GSE102485 and GSE160306, differentially expressed genes (DEGs) were screened using Weighted Gene Co-expression Network Analysis (WGCNA).
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