scholarly journals Gene expression profiling in digestive tract tumors: From basic research to clinical practice

2018 ◽  
Vol 26 (34) ◽  
pp. 1966-1978
Author(s):  
Jian-Bo Lu ◽  
Ru-Yi Li
Keyword(s):  
Gene Expression ◽  
Clinical Practice ◽  
Gene Expression Profiling ◽  
Digestive Tract ◽  
Expression Profiling ◽  
Basic Research

scholarly journals Predictive and prognostic transcriptomic biomarkers in soft tissue sarcomas

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Eve Merry ◽  
Khin Thway ◽  
Robin L. Jones ◽  
Paul H. Huang
Keyword(s):  
Gene Expression ◽  
Clinical Practice ◽  
Soft Tissue ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Cancer Biology ◽  
Treatment Strategies ◽  
Soft Tissue Sarcomas ◽  
Selective Treatment ◽  
Genomic Grade Index

AbstractSoft tissue sarcomas (STS) are rare and heterogeneous tumours comprising over 80 different histological subtypes. Treatment options remain limited in advanced STS with high rates of recurrence following resection of localised disease. Prognostication in clinical practice relies predominantly on histological grading systems as well as sarcoma nomograms. Rapid developments in gene expression profiling technologies presented opportunities for applications in sarcoma. Molecular profiling of sarcomas has improved our understanding of the cancer biology of these rare cancers and identified potential novel therapeutic targets. In particular, transcriptomic signatures could play a role in risk classification in sarcoma to aid prognostication. Unlike other solid and haematological malignancies, transcriptomic signatures have not yet reached routine clinical use in sarcomas. Herein, we evaluate early developments in gene expression profiling in sarcomas that laid the foundations for transcriptomic signature development. We discuss the development and clinical evaluation of key transcriptomic biomarker signatures in sarcomas, including Complexity INdex in SARComas (CINSARC), Genomic Grade Index, and hypoxia-associated signatures. Prospective validation of these transcriptomic signatures is required, and prospective trials are in progress to evaluate reliability for clinical application. We anticipate that integration of these gene expression signatures alongside existing prognosticators and other Omics methodologies, including proteomics and DNA methylation analysis, could improve the identification of ‘high-risk’ patients who would benefit from more aggressive or selective treatment strategies. Moving forward, the incorporation of these transcriptomic prognostication signatures in clinical practice will undoubtedly advance precision medicine in the routine clinical management of sarcoma patients.

scholarly journals Evaluation of Myocardial Gene Expression Profiling for Superior Diagnosis of Idiopathic Giant-Cell Myocarditis and Clinical Feasibility in a Large Cohort of Patients with Acute Cardiac Decompensation

2020 ◽  
Vol 9 (9) ◽  
pp. 2689
Author(s):  
Felicitas Escher ◽  
Heiko Pietsch ◽  
Ganna Aleshcheva ◽  
Philip Wenzel ◽  
Friedrich Fruhwald ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Practice ◽  
Gene Expression Profiling ◽  
Giant Cell ◽  
Expression Profiling ◽  
Sampling Error ◽  
Gene Profiling ◽  
Giant Cell Myocarditis ◽  
Cardiac Decompensation ◽  
Myocardial Gene Expression

Aims: The diagnostic approach to idiopathic giant-cell myocarditis (IGCM) is based on identifying various patterns of inflammatory cell infiltration and multinucleated giant cells (GCs) in histologic sections taken from endomyocardial biopsies (EMBs). The sampling error for detecting focally located GCs by histopathology is high, however. The aim of this study was to demonstrate the feasibility of gene profiling as a new diagnostic method in clinical practice, namely in a large cohort of patients suffering from acute cardiac decompensation. Methods and Results: In this retrospective multicenter study, EMBs taken from n = 427 patients with clinically acute cardiac decompensation and suspected acute myocarditis were screened (mean age: 47.03 ± 15.69 years). In each patient, the EMBs were analyzed on the basis of histology, immunohistology, molecular virology, and gene-expression profiling. Out of the total of n = 427 patient samples examined, GCs could be detected in 26 cases (6.1%) by histology. An established myocardial gene profile consisting of 27 genes was revealed; this was narrowed down to a specified profile of five genes (CPT1, CCL20, CCR5, CCR6, TLR8) which serve to identify histologically proven IGCM with high specificity in 25 of the 26 patients (96.2%). Once this newly established profiling approach was applied to the remaining patient samples, an additional n = 31 patients (7.3%) could be identified as having IGCM without any histologic proof of myocardial GCs. In a subgroup analysis, patients diagnosed with IGCM using this gene profiling respond in a similar fashion to immunosuppressive therapy as patients diagnosed with IGCM by conventional histology alone. Conclusions: Myocardial gene-expression profiling is a promising new method in clinical practice, one which can predict IGCM even in the absence of any direct histologic proof of GCs in EMB sections. Gene profiling is of great clinical relevance in terms of (a) overcoming the sampling error associated with purely histologic examinations and (b) monitoring the effectiveness of therapy.

2425Evaluation of myocardial gene expression profiling for superior diagnosis of idiopathic giant cell myocarditis in a large cohort of patients with acute cardiac decompensation

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
F Escher ◽  
H P Pietsch ◽  
U G Gross ◽  
D L Lassner ◽  
H P Schultheiss
Keyword(s):  
Gene Expression ◽  
Clinical Practice ◽  
Gene Expression Profiling ◽  
Giant Cell ◽  
Chemokine Receptor ◽  
Expression Profiling ◽  
Sampling Error ◽  
Giant Cell Myocarditis ◽  
Cardiac Decompensation ◽  
Myocardial Gene Expression

Abstract Background Idiopathic giant cell myocarditis (IGCM) diagnostics is based on differential patterns of inflammatory cell infiltration, and multinucleated giant cells (GCs) in histological sections of endomyocardial biopsies (EMBs). However, the sampling error is high for the detection of focally GCs by histopathology. We report on a clinical evaluation of a recently published method for improved differential diagnosis of this frequently fatal disease by myocardial gene expression profiling. Objective This is to improve the diagnostics of IGCM by gene expression profiling, and to demonstrate the feasibility of this method in clinical practice in a large cohort of patients. Methods In this multicenter study, EMBs of n=427 patients with clinically acute cardiac decompensation and suspected acute myocarditis were screened (mean age 47.03±15.69 years). In each patient, EMBs were analyzed by histology, immunohistology, molecular virology, and gene expression profiling. Results Out of the total of n=427 patient samples examined, GCs could be detected in 26 cases (6.0%) by histology. An established myocardial gene profile – consisting of 27 genes – was revealed resulting in a specified profile of 5 genes (chemokine receptor 5 (CCR5), chemokine receptor 6 (CCR6); carnitine palmitoyltransferase I (CPT1), toll-like receptor 8 (TLR8), and chemokine (C-C motif) ligand 20 (CCL20)) which identified histologically proven IGCM with high specificity in 25 of the 26 patients (96.2%). Applying this newly established profiling on the remaining patient samples, additional n=31 (7.3%) patients were identified for IGCM without any histological proof of myocardial GCs. Conclusions Myocardial gene expression profiling is a reliable method in clinical practice to predict IGCM even without direct histological proof of GCs in EMB section. The gene profiling is of high clinical relevance to overcome the sampling error of purely histological examination, and to control the effectiveness of the therapy. The data clearly show the importance to take EMB in unexplained acute decompensation to get a diagnosis and improve the prognosis.

Use and Costs for Tumor Gene Expression Profiling Panels in the Management of Breast Cancer From 2006 to 2012: Implications for Genomic Test Adoption Among Private Payers

2015 ◽  
Vol 11 (4) ◽  
pp. 273-277 ◽  
Author(s):  
Megan C. Roberts ◽  
Stacie B. Dusetzina
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Clinical Practice ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Genomic Test ◽  
Tumor Gene Expression ◽  
Tumor Gene ◽  
Over Time ◽  
Out Of Pocket Costs

Gene expression profiling has diffused into clinical practice. Reimbursements by insurers have increased, and average out-of-pocket costs to patients have decreased, seemingly driven by improved coverage for testing over time.

Gene expression profiling in follicular lymphoma and its implication for clinical practice

2010 ◽  
Vol 52 (1) ◽  
pp. 59-68 ◽  
Author(s):  
Andrea Janikova ◽  
Boris Tichy ◽  
Jana Supikova ◽  
Katerina Stano-Kozubik ◽  
Sarka Pospisilova ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Practice ◽  
Follicular Lymphoma ◽  
Gene Expression Profiling ◽  
Expression Profiling

Custom microarray for glycobiologists: considerations for glycosyltransferase gene expression profiling

2002 ◽  
Vol 69 ◽  
pp. 135-142 ◽  
Author(s):  
Elena M. Comelli ◽  
Margarida Amado ◽  
Steven R. Head ◽  
James C. Paulson
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Affymetrix Genechip ◽  
Microarray Technology ◽  
Gene Arrays ◽  
Glycosyltransferase Gene

The development of microarray technology offers the unprecedented possibility of studying the expression of thousands of genes in one experiment. Its exploitation in the glycobiology field will eventually allow the parallel investigation of the expression of many glycosyltransferases, which will ultimately lead to an understanding of the regulation of glycoconjugate synthesis. While numerous gene arrays are available on the market, e.g. the Affymetrix GeneChip® arrays, glycosyltransferases are not adequately represented, which makes comprehensive surveys of their gene expression difficult. This chapter describes the main issues related to the establishment of a custom glycogenes array.

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