title: Clinical pharmacogenomics and transcriptional profiling in early phase oncology clinical trials.
abstract: Microarray-based expression profiling studies in the field of oncology have demonstrated encouraging correlations between tumor transcriptional profiles and eventual patient outcomes. These findings have fueled great interest in the application of transcriptional profiling to samples available from real-time clinical trials, and clinical pharmacogenomic objectives utilizing transcriptional profiling strategies are becoming increasingly incorporated into clinical trial study designs. Over the last few years several retrospective studies based on the profiling of archival tumor tissues suggest that transcriptional analysis of oncology samples may provide general prognosis measures, and in some cases may even predict response to specific therapies. Recently the FDA released a voluntary genomic data guidance meant to assist both regulatory agencies and pharmaceutical companies alike in evaluating the potential benefit of implementing expression profiling studies during the preclinical and clinical phases of drug development. Despite the great promise afforded by this technology, the ultimate benefit of applying transcriptional profiling in prospective clinical trials has yet to be realized because a number of practical impediments to this process exist. The multi-fold purpose of the current review is to highlight the increasing evidence from studies that have identified transcriptional signatures in archived tumors prognostic of patient outcome, to describe some of the drivers for the implementation of transcriptional profiling strategies in real-time drug development, to discuss the use of transcriptional profiling in the context of increasingly complex translational medicine strategies, and to highlight the practical issues and potential approaches involved in the successful application of clinical pharmacogenomic objectives during real-time clinical trials. Strategic implementation of transcriptional profiling in early oncology clinical trials can provide an opportunity to identify predictive markers of clinical response and eventually provide a substantial step forward towards the era of personalized medicine.
authors.affiliation: Discovery Medicine, Wyeth Research, Cambridge, MA 01810, USA. mburczynski@wyeth.com
authors.forename: Michael E
authors.initials: ME
authors.lastname: Burczynski
authors.forename: Judith L
authors.initials: JL
authors.lastname: Oestreicher
authors.forename: Monica J
authors.initials: MJ
authors.lastname: Cahilly
authors.forename: Deborah P
authors.initials: DP
authors.lastname: Mounts
authors.forename: Maryann Z
authors.initials: MZ
authors.lastname: Whitley
authors.forename: Lisa A
authors.initials: LA
authors.lastname: Speicher
authors.forename: William L
authors.initials: WL
authors.lastname: Trepicchio
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use; D002986:Clinical Trials as Topic; D030541:Databases, Genetic; D015195:Drug Design; D020869:Gene Expression Profiling; D006801:Humans; D016247:Information Storage and Retrieval; D009369:Neoplasms / Q000188:drug therapy / Q000235:genetics / Q000473:pathology; D020411:Oligonucleotide Array Sequence Analysis; D010597:Pharmacogenetics; D011379:Prognosis; D015996:Survival Rate; D013678:Technology, Pharmaceutical; D014158:Transcription, Genetic
chemical_list: D000970:Antineoplastic Agents