Daily clinical practice of fresh tumour tissue freezing and gene expression profiling; logistics pilot study preceding the MINDACT trial

2009 ◽  
Vol 45 (7) ◽  
pp. 1201-1208 ◽  
Author(s):  
S. Mook ◽  
H. Bonnefoi ◽  
G. Pruneri ◽  
D. Larsimont ◽  
J. Jaskiewicz ◽  
...  
Pain ◽  
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Robert Petty ◽  
Farideh Miraki-Moud ◽  
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2015 ◽  
Vol 33 (15_suppl) ◽  
pp. e22071-e22071
Author(s):  
Manuel Sureda ◽  
Joseba Rebollo ◽  
Elena Ma Martínez ◽  
Francisco J. Fernández-Morejón ◽  
Vicente Munoz ◽  
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2017 ◽  
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Kyuwhan Jung ◽  
InSong Koh ◽  
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Hyun Sub Cheong ◽  
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2017 ◽  
Vol 20 (6) ◽  
pp. 785-793 ◽  
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M. Sureda ◽  
J. Rebollo ◽  
E. Mª. Martínez-Navarro ◽  
F. J. Fernández-Morejón ◽  
J. Farré ◽  
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2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Eve Merry ◽  
Khin Thway ◽  
Robin L. Jones ◽  
Paul H. Huang

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.


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