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Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets
The European Union multi-disciplinary Personalised RNA interference to Enhance the Delivery of Individualised Cytotoxic and Targeted therapeutics (PREDICT) consortium has recently initiated a framework to accelerate the development of predictive biomarkers of individual patient response to anti-canc...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2945010/ https://www.ncbi.nlm.nih.gov/pubmed/20701793 http://dx.doi.org/10.1186/gm174 |
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author | Swanton, Charles Larkin, James M Gerlinger, Marco Eklund, Aron C Howell, Michael Stamp, Gordon Downward, Julian Gore, Martin Futreal, P Andrew Escudier, Bernard Andre, Fabrice Albiges, Laurence Beuselinck, Benoit Oudard, Stephane Hoffmann, Jens Gyorffy, Balázs Torrance, Chris J Boehme, Karen A Volkmer, Hansjuergen Toschi, Luisella Nicke, Barbara Beck, Marlene Szallasi, Zoltan |
author_facet | Swanton, Charles Larkin, James M Gerlinger, Marco Eklund, Aron C Howell, Michael Stamp, Gordon Downward, Julian Gore, Martin Futreal, P Andrew Escudier, Bernard Andre, Fabrice Albiges, Laurence Beuselinck, Benoit Oudard, Stephane Hoffmann, Jens Gyorffy, Balázs Torrance, Chris J Boehme, Karen A Volkmer, Hansjuergen Toschi, Luisella Nicke, Barbara Beck, Marlene Szallasi, Zoltan |
author_sort | Swanton, Charles |
collection | PubMed |
description | The European Union multi-disciplinary Personalised RNA interference to Enhance the Delivery of Individualised Cytotoxic and Targeted therapeutics (PREDICT) consortium has recently initiated a framework to accelerate the development of predictive biomarkers of individual patient response to anti-cancer agents. The consortium focuses on the identification of reliable predictive biomarkers to approved agents with anti-angiogenic activity for which no reliable predictive biomarkers exist: sunitinib, a multi-targeted tyrosine kinase inhibitor and everolimus, a mammalian target of rapamycin (mTOR) pathway inhibitor. Through the analysis of tumor tissue derived from pre-operative renal cell carcinoma (RCC) clinical trials, the PREDICT consortium will use established and novel methods to integrate comprehensive tumor-derived genomic data with personalized tumor-derived small hairpin RNA and high-throughput small interfering RNA screens to identify and validate functionally important genomic or transcriptomic predictive biomarkers of individual drug response in patients. PREDICT's approach to predictive biomarker discovery differs from conventional associative learning approaches, which can be susceptible to the detection of chance associations that lead to overestimation of true clinical accuracy. These methods will identify molecular pathways important for survival and growth of RCC cells and particular targets suitable for therapeutic development. Importantly, our results may enable individualized treatment of RCC, reducing ineffective therapy in drug-resistant disease, leading to improved quality of life and higher cost efficiency, which in turn should broaden patient access to beneficial therapeutics, thereby enhancing clinical outcome and cancer survival. The consortium will also establish and consolidate a European network providing the technological and clinical platform for large-scale functional genomic biomarker discovery. Here we review our current understanding of molecular mechanisms driving resistance to anti-angiogenesis agents, the current limitations of laboratory and clinical trial strategies and how the PREDICT consortium will endeavor to identify a new generation of predictive biomarkers. |
format | Text |
id | pubmed-2945010 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-29450102011-08-11 Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets Swanton, Charles Larkin, James M Gerlinger, Marco Eklund, Aron C Howell, Michael Stamp, Gordon Downward, Julian Gore, Martin Futreal, P Andrew Escudier, Bernard Andre, Fabrice Albiges, Laurence Beuselinck, Benoit Oudard, Stephane Hoffmann, Jens Gyorffy, Balázs Torrance, Chris J Boehme, Karen A Volkmer, Hansjuergen Toschi, Luisella Nicke, Barbara Beck, Marlene Szallasi, Zoltan Genome Med Correspondence The European Union multi-disciplinary Personalised RNA interference to Enhance the Delivery of Individualised Cytotoxic and Targeted therapeutics (PREDICT) consortium has recently initiated a framework to accelerate the development of predictive biomarkers of individual patient response to anti-cancer agents. The consortium focuses on the identification of reliable predictive biomarkers to approved agents with anti-angiogenic activity for which no reliable predictive biomarkers exist: sunitinib, a multi-targeted tyrosine kinase inhibitor and everolimus, a mammalian target of rapamycin (mTOR) pathway inhibitor. Through the analysis of tumor tissue derived from pre-operative renal cell carcinoma (RCC) clinical trials, the PREDICT consortium will use established and novel methods to integrate comprehensive tumor-derived genomic data with personalized tumor-derived small hairpin RNA and high-throughput small interfering RNA screens to identify and validate functionally important genomic or transcriptomic predictive biomarkers of individual drug response in patients. PREDICT's approach to predictive biomarker discovery differs from conventional associative learning approaches, which can be susceptible to the detection of chance associations that lead to overestimation of true clinical accuracy. These methods will identify molecular pathways important for survival and growth of RCC cells and particular targets suitable for therapeutic development. Importantly, our results may enable individualized treatment of RCC, reducing ineffective therapy in drug-resistant disease, leading to improved quality of life and higher cost efficiency, which in turn should broaden patient access to beneficial therapeutics, thereby enhancing clinical outcome and cancer survival. The consortium will also establish and consolidate a European network providing the technological and clinical platform for large-scale functional genomic biomarker discovery. Here we review our current understanding of molecular mechanisms driving resistance to anti-angiogenesis agents, the current limitations of laboratory and clinical trial strategies and how the PREDICT consortium will endeavor to identify a new generation of predictive biomarkers. BioMed Central 2010-08-11 /pmc/articles/PMC2945010/ /pubmed/20701793 http://dx.doi.org/10.1186/gm174 Text en Copyright ©2010 Swanton et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Correspondence Swanton, Charles Larkin, James M Gerlinger, Marco Eklund, Aron C Howell, Michael Stamp, Gordon Downward, Julian Gore, Martin Futreal, P Andrew Escudier, Bernard Andre, Fabrice Albiges, Laurence Beuselinck, Benoit Oudard, Stephane Hoffmann, Jens Gyorffy, Balázs Torrance, Chris J Boehme, Karen A Volkmer, Hansjuergen Toschi, Luisella Nicke, Barbara Beck, Marlene Szallasi, Zoltan Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets |
title | Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets |
title_full | Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets |
title_fullStr | Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets |
title_full_unstemmed | Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets |
title_short | Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets |
title_sort | predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets |
topic | Correspondence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2945010/ https://www.ncbi.nlm.nih.gov/pubmed/20701793 http://dx.doi.org/10.1186/gm174 |
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