Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets | Swanton et al. Genome Med 2010 2 53 http content 2 8 53 Genome Medicine CORRESPONDENCE Open Access Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets t 2t 4t 1 Charles Swanton James M Larkin Marco Gerlinger Aron C Eklund Michael Howell Gordon Stamp Il I I I l A z A A zz 1 1 h ỉ I rGz rz 2 D A z l r M A I Cl I -f- r z- - 15 I z Z 1 c I- r I I z l I zxr6 c L r I r ĩ z l rz 6l iir z Al L I Z t z r 6 Julian Downward Martin Gore P Andrew Futreal Bernard Escudier Fabrice Andre Laurence Albiges Benoit Beuselinck7 Stephane Oudard7 Jens Hoffmann8 Balazs Gyorffy9 Chris J Torrance10 Karen A Boehme11 Hansjuergen Volkmer11 Luisella Toschi12 Barbara Nicke12 Marlene Beck4 Zoltan Szallasi4 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 preoperative 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 transcrip-tomic 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
