High SET Domain Bifurcated 1 (SETDB1) Expression Predicts Poor Prognosis in Breast Carcinoma
MEDICAL SCIENCE MONITOR
Authors: Zhou, Zhaoping; Wu, Baojin; Tang, Xinjie; Yang, Wenlin; Zou, Qiang; Wang, Hongying
Abstract
Background: SETDB1, an H3K9-specific histone methyltransferase, plays important roles in the progression of various hu- man cancers. However, the expression patterns and its clinical roles of SETDB1 remain elusive in breast cancer (BC). Material/Methods: The transcriptional level of SETDB1 and survival data in BC were analyzed through UALCAN, ONCOMINE, and Pan Cancer Prognostics Database. SETDB1 protein expression was assessed by immunohistochemistry (IHC) in 159 BC tissue samples. The associations between SETDB1 expression and clinical pathological characteristics of patients were analyzed. The GEO dataset GSE108656 was downloaded and analyzed to identify the differentially expressed genes (DEGs) between control and BC cells targeting interference with SETDB. The DEGs were further integrated by bioinformatics analysis to decipher the key signaling pathways and hub genes that are regulated by SETDB. Results: The public databases showed the level of SETDB1 mRNA was significantly upregulated in BC Our IHC results demonstrated the level of SETDB1 protein was associated with tumor size (P=0.028), histopathological grading (P=0.012), lymph node metastasis (P<0.001), and TNM stage (P<0.001). High expression of SETDBI indicated worse overall survival (P=0.015) and shorter relapse-free survival (P=0.027). The bioinformatic analysis of GSE108656 suggested that the SETDB1-related DEGs was mainly enriched in antigen processing and presentation, as well as immune networks in BC. The cytoHubba analysis suggested the top 10 hub genes were IL6, BMP4, CD74, PECAM1, HLA-DPA1, HLA-DRA, LAMC1, CTSB, SERPINA1, and CTSD. Conclusions: The results suggest that SETDB1 is an oncogene and can serve as a prognostic biomarker for BC However, the mechanisms of SETDB1 in BC remain to be explored.
Whole-blood RNA transcript-based models can predict clinical response in two large independent clinical studies of patients with advanced melanoma treated with the checkpoint inhibitor, tremelimumab
JOURNAL FOR IMMUNOTHERAPY OF CANCER
Authors: Friedlander, Philip; Wassmann, Karl; Christenfeld, Alan M.; Fisher, David; Kyi, Chrisann; Kirkwood, John M.; Bhardwaj, Nina; Oh, William K.
Abstract
Background: Tremelimumab is an antibody that blocks CTLA-4 and demonstrates clinical efficacy in a subset of advanced melanoma patients. An unmet clinical need exists for blood-based response-predictive gene signatures to facilitate clinically effective and cost-efficient use of such immunotherapeutic interventions. Methods: Peripheral blood samples were collected in PAXgene (R) tubes from 210 treatment-naive melanoma patients receiving tremelimumab in a worldwide, multicenter phase III study (discovery dataset). A central panel of radiologists determined objective response using RECIST criteria. Gene expression for 169 mRNA transcripts was measured using quantitative PCR. A 15-gene pre-treatment response-predictive classifier model was identified. An independent population (N = 150) of refractory melanoma patients receiving tremelimumab after chemotherapy enrolled in a worldwide phase II study (validation dataset). The classifier model, using the same genes, coefficients and constants for objective response and one-year survival after treatment, was applied to the validation dataset. Results: A 15-gene pre-treatment classifier model (containing ADAM17, CDK2, CDKN2A, DPP4, ERBB2, HLA-DRA, ICOS, ITGA4, LARGE, MYC, NAB2, NRAS, RHOC, TGFB1, and TIMP1) achieved an area under the curve (AUC) of 0.86 (95% confidence interval 0.81 to 0.91, p < 0.0001) for objective response and 0.6 (95% confidence interval 0.54 to 0.67, p = 0.0066) for one-year survival in the discovery set. This model was validated in the validation set with AUCs of 0.62 (95% confidence interval 0.54 to 0.70 p = 0.0455) for objective response and 0.68 for one-year survival (95% confidence interval 0.59 to 0.75 p = 0.0002). Conclusions: To our knowledge, this is the largest blood-based biomarker study of a checkpoint inhibitor, tremelimumab, which demonstrates a validated pre-treatment mRNA classifier model that predicts clinical response. The data suggest that the model captures a biological signature representative of genes needed for a robust anti-cancer immune response. It also identifies non-responders to tremelimumab at baseline prior to treatment.