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.

Gene-expression profiling to classify soft-tissue sarcomas

The Lancet ◽  
2002 ◽  
Vol 359 (9314) ◽  
pp. 1263-1264 ◽  
Author(s):  
Luc Y Dirix ◽  
Allan T van Oosterom
Keyword(s):  
Gene Expression ◽  
Soft Tissue ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Soft Tissue Sarcomas

Gene Expression Profiling for Discovery of Novel Targets in Human Traumatic Brain Injury

2010 ◽  
Vol 13 (2) ◽  
pp. 140-153 ◽  
Author(s):  
Taura L. Barr ◽  
Sheila Alexander ◽  
Yvette Conley
Keyword(s):  
Gene Expression ◽  
Traumatic Brain Injury ◽  
Clinical Trials ◽  
Brain Injury ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Treatment Strategies ◽  
Biological Pathways ◽  
Human Response ◽  
Novel Targets

Several clinical trials have failed to demonstrate a significant effect on outcome following human traumatic brain injury (TBI) despite promising results obtained in preclinical animal studies. These failures may be due in part to a misinterpretation of the findings obtained in preclinical animal models of TBI, a misunderstanding of the complexity of the human response to TBI, limited knowledge about the biological pathways that interact to contribute to good and bad outcomes after brain injury, and the effects of genomic variability and environment on individual recovery. Recent publications suggest that data obtained from gene expression profiling studies of complex neurological diseases such as stroke, multiple sclerosis (MS), Alzheimer’s and Parkinson’s may contribute to a more informed understanding of what affects outcome following TBI. These data may help to bridge the gap between successful preclinical studies and negative clinical trials in humans to reveal novel targets for therapy. Gene expression profiling has the capability to identify biomarkers associated with response to TBI, elucidate complex genetic interactions that may play a role in outcome following TBI, and reveal biological pathways related to brain health. This review highlights the current state of the literature on gene expression profiling for neurological disease and discusses its ability to aid in unraveling the variable human response to TBI and the potential for it to offer treatment strategies in an area where we currently have limited therapeutic options primarily based on supportive care.

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.

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