Open Access Highly Accessed Correspondence

Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets

Charles Swanton12*, James M Larkin2, Marco Gerlinger13, Aron C Eklund4, Michael Howell1, Gordon Stamp12, Julian Downward1, Martin Gore2, P Andrew Futreal5, Bernard Escudier6, Fabrice Andre6, Laurence Albiges6, Benoit Beuselinck7, Stephane Oudard7, Jens Hoffmann8, Balázs Gyorffy9, Chris J Torrance10, Karen A Boehme11, Hansjuergen Volkmer11, Luisella Toschi12, Barbara Nicke12, Marlene Beck4 and Zoltan Szallasi4

Author Affiliations

1 Cancer Research UK London Research Institute, 44 Lincoln's Inn Fields, London, WC2A 3PX, UK

2 Department of Medicine, Royal Marsden Hospital, Fulham Road, London, SW3 6JJ, UK

3 Institute of Cancer, Barts and the London School of Medicine and Dentistry, Charterhouse Square, London, EC1M 6BQ, UK

4 Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark

5 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK

6 Institut Gustave Roussy, 114 rue Edouard Vaillant, 94805 Villejuif, France

7 Hôpital Européen Georges Pompidou, 20 Rue Leblanc, 75015 Paris, France

8 EPO-Berlin GmbH, Robert-Rössle-Str.10, 13125 Berlin, Germany

9 Joint Research Laboratory of the Hungarian Academy of Sciences and the Semmelweis University, Semmelweis University 1st Department of Pediatrics, Bokay u. 53-54. H-1083 Budapest, Hungary

10 Horizon Discovery Ltd, Building 7300, IQ Cambridge, CB25 9TL, UK

11 Department of Molecular Biology, NMI Natural and Medical Sciences Institute at the University of Tübingen, Markwiesenstrasse 55, 72770 Reutlingen, Germany

12 Bayer Schering Pharma AG Müllerstraße 178, 13353 Berlin, Germany

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Genome Med 2010, 2:53  doi:10.1186/gm174

Published: 11 August 2010

Abstract

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.