scholarly journals Prognostic Efficacy of the RTN1 Gene in Patients with Diffuse Large B-Cell Lymphoma

2021 ◽  
Author(s):  
Mohamad Zamani-Ahmadmahmudi ◽  
Seyed Mahdi Nassiri ◽  
Amir Asadabadi
Keyword(s):  
Gene Expression ◽  
Clinical Outcome ◽  
Gene Expression Data ◽  
Proportional Hazards ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Cox Proportional Hazards ◽  
Expression Data ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Gene expression profiling has been vastly used to extract the genes that can predict the clinical outcome in patients with diverse cancers, including diffuse large B-cell lymphoma (DLBCL). With the aid of bioinformatics and computational analysis on gene expression data, various prognostic gene signatures for DLBCL have been recently developed. The major drawback of the previous signatures is their inability to correctly predict survival in external data sets. In other words, they are not reproducible in other datasets. Hence, in this study, we sought to determine the gene(s) that can reproducibly and robustly predict survival in patients with DLBCL. Gene expression data were extracted from 7 datasets containing 1636 patients (GSE10846 [n=420], GSE31312 [n=470], GSE11318 [n=203], GSE32918 [n=172], GSE4475 [n=123], GSE69051 [n=157], and GSE34171 [n=91]). Genes significantly associated with overall survival were detected using the univariate Cox proportional hazards analysis with a P value <0.001 and a false discovery rate (FDR) <5%. Thereafter, significant genes common between all the datasets were extracted. Additionally, chromosomal aberrations in the corresponding region of the final common gene(s) were evaluated as copy number alterations using the single nucleotide polymorphism (SNP) data of 570 patients with DLBCL (GSE58718 [n=242], GSE57277 [n=148], and GSE34171 [n=180]). Our results indicated that reticulon family gene 1 (RTN1) was the only gene that met our rigorous pipeline criteria and associated with a favorable clinical outcome in all the datasets (P<0.001, FDR<5%). In the multivariate Cox proportional hazards analysis, this gene remained independent of the routine international prognostic index components (i.e., age, stage, lactate dehydrogenase level, Eastern Cooperative Oncology Group [ECOG] performance status, and number of extranodal sites) (P<0.0001). Furthermore, no significant chromosomal aberration was found in the RTN1 genomic region (14q23.1: Start 59,595,976/ End 59,870,966).

scholarly journals Prognostic Efficacy of the RTN1 Gene in Patients with Diffuse Large B-Cell Lymphoma

2019 ◽  
Author(s):  
Mohamad Zamani-Ahmadmahmudi ◽  
Fatemeh Soltani-Nezhad ◽  
Amir Asadabadi
Keyword(s):  
Gene Expression ◽  
Clinical Outcome ◽  
Proportional Hazards ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Cox Proportional Hazards ◽  
Expression Data ◽  
Survival Group ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Background Gene expression profiling has been vastly used to extract genes that can predict the clinical outcome in patients with diverse cancers, including diffuse large B-cell lymphoma (DLBCL). With the aid of bioinformatics and computational analysis on gene expression data, various prognostic gene signatures for DLBCL have been recently developed. The major drawback of the previous signatures is their inability to correctly predict survival in external data sets. In other words, they are not reproducible in other datasets. Hence, in this study, we sought to determine the gene(s) that can reproducibly and robustly predict survival in patients with DLBCL. Methods Gene expression data were extracted from 7 datasets containing 1636 patients (GSE10846 [n=420], GSE31312 [n=470], GSE11318 [n=203], GSE32918 [n=172], GSE4475 [n=123], GSE69051 [n=157], and GSE34171 [n=91]). Genes significantly associated with overall survival were detected using the univariate Cox proportional hazards analysis with a P value <0.001 and a false discovery rate (FDR) <5%. Thereafter, significant genes common between all the datasets were extracted. Additionally, chromosomal aberrations in the corresponding region of final common gene(s) were evaluated as copy number alterations using the single nucleotide polymorphism (SNP) data of 570 patients with DLBCL (GSE58718 [n=242], GSE57277 [n=148], and GSE34171 [n=180]). The results were experimentally confirmed using the quantitative real-time PCR (qRT-PCR) analysis. Results Our results indicated that reticulon family gene 1 (RTN1) was the only gene that met our rigorous pipeline criteria and associated with a favorable clinical outcome in all the datasets (P<0.001, FDR<5%). In the multivariate Cox proportional hazards analysis, this gene remained independent of the routine international prognostic index components (i.e., age, stage, lactate dehydrogenase level, Eastern Cooperative Oncology Group [ECOG] performance status, and number of extranodal sites) (P<0.0001). Our experimental step confirmed the results and revealed that the expression of RTN1 in the long-survival group was significantly higher than that in the short-survival group. Furthermore, no significant chromosomal aberration was found in the RTN1 genomic region (14q23.1: Start 59,595,976/ End 59,870,966). Conclusion In light of the results of present study, RTN1 can be considered a potential prognostic gene that can robustly predict survival in patients with DLBCL.

High risk patients with diffuse large B cell lymphoma are not enrolled on clinical trials.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e19058-e19058
Author(s):  
Alfadel Alshaibani ◽  
Christina Lee ◽  
Sarah Camp Rutherford ◽  
Kah Poh Loh ◽  
Andrea M Baran ◽  
...  
Keyword(s):  
Clinical Trials ◽  
B Cell ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Large B Cell Lymphoma ◽  
Large B Cell

e19058 Background: Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma. In this study, we explore reasons for non-enrollment in clinical trials for DLBCL and implications on trial design and interpretation. Methods: This is a retrospective analysis of patients (pts) with a pathological diagnosis of DLBCL or high grade B-cell lymphoma (HGBL) at University of Rochester (4/14-6/16) and New York-Presbyterian Hospital/Weill Cornell Medicine (NYP/WCM) (4/14-4/17).Ten clinical trials were opened during this time. Participants were divided into 3 groups: those treated in trial, those not enrolled in trial because of need for urgent treatment, and those not enrolled in trial for any other reason. We used a center-stratified Cox proportional hazards model to estimate association of trial enrollment with progression-free survival (PFS; time from start of treatment until progression/death or the last date the pt was known to be progression free) and overall survival (OS). Results: We identified 263 pts; 17% (n = 45) enrolled in a trial. Reasons for non-enrollment included not meeting eligibility criteria (n = 98), physician choice (n = 50), and pt choice (n = 38). For 32 pts, reasons were unclear. Of the 50 pts who were not enrolled because of physician choice, the primary reason for non-enrollment was the need for urgent treatment (n = 46). Pts who needed urgent treatment had higher risk clinical features compared with pts in trial (Table). Compared with those treated in trial and those not enrolled in trial for any other reason, those not enrolled in trial due to need for urgent treatment had an inferior PFS (HR 2.61, 95% CI 1.23–5.16) and OS (HR 2.27, 95% CI 1.21–4.06). Conclusions: At 2 academic institutions, 52% of patients with DLBCL or HGBL required urgent chemotherapy and failed to enroll on trials. Exclusion of such patients limits the applicability and generalizability of clinical trials in DLBCL. This barrier must be overcome so clinical trials may better reflect true DLBCL demographics. [Table: see text]

scholarly journals Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

2021 ◽  
Vol 3 (3) ◽  
pp. 720-739
Author(s):  
Joaquim Carreras ◽  
Rifat Hamoudi
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Hodgkin Lymphoma ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
High Accuracy ◽  
Expression Data ◽  
Non Hodgkin Lymphoma ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were LCE2B, KNG1, IGHV7_81, TG, C6, FGB, ZNF750, CTSV, INGX, and COL4A6 for the whole set; and ARG1, MAGEA3, AKT2, IL1B, S100A7A, CLEC5A, WIF1, TREM1, DEFB1, and GAGE1 for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, n = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series.

scholarly journals Gene expression profiling-based risk prediction and profiles of immune infiltration in diffuse large B-cell lymphoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Selin Merdan ◽  
Kritika Subramanian ◽  
Turgay Ayer ◽  
Johan Van Weyenbergh ◽  
Andres Chang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
B Cell ◽  
Risk Prediction ◽  
Expression Profiling ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Gene Signatures ◽  
Large B Cell Lymphoma ◽  
Large B Cell

AbstractThe clinical risk stratification of diffuse large B-cell lymphoma (DLBCL) relies on the International Prognostic Index (IPI) for the identification of high-risk disease. Recent studies suggest that the immune microenvironment plays a role in treatment response prediction and survival in DLBCL. This study developed a risk prediction model and evaluated the model’s biological implications in association with the estimated profiles of immune infiltration. Gene-expression profiling of 718 patients with DLBCL was done, for which RNA sequencing data and clinical covariates were obtained from Reddy et al. (2017). Using unsupervised and supervised machine learning methods to identify survival-associated gene signatures, a multivariable model of survival was constructed. Tumor-infiltrating immune cell compositions were enumerated using CIBERSORT deconvolution analysis. A four gene-signature-based score was developed that separated patients into high- and low-risk groups. The combination of the gene-expression-based score with the IPI improved the discrimination on the validation and complete sets. The gene signatures were successfully validated with the deconvolution output. Correlating the deconvolution findings with the gene signatures and risk score, CD8+ T-cells and naïve CD4+ T-cells were associated with favorable prognosis. By analyzing the gene-expression data with a systematic approach, a risk prediction model that outperforms the existing risk assessment methods was developed and validated.

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