Dose-escalation study of a second-generation non-ansamycin HSP90 inhibitor, onalespib (AT13387), in combination with imatinib in patients with metastatic gastrointestinal stromal tumour

AbstractBackgroundGastrointestinal stromal tumours (GIST) treated with the tyrosine kinase inhibitor (TKI) imatinib can become resistant when additional mutations in the receptor tyrosine kinases KIT or PDGFRA block imatinib activity. Mutated KIT requires the molecular chaperone heat-shock protein 90 (HSP90) to maintain stability and activity. Onalespib (AT13387) is a potent non-ansamycin HSP90 inhibitor. We hypothesised that the combination of onalespib and imatinib may be safe and effective in managing TKI-resistant GIST.Patients and methodsIn this dose-escalation study, we evaluated the safety and efficacy of combination once-weekly intravenous onalespib for 3 weeks and daily oral imatinib in 28-d cycles. Twenty-six patients with TKI-resistant GIST were enrolled into four sequential dose cohorts of onalespib (dose range, 150–220 mg/m2) and imatinib 400 mg. The relationship between tumour mutational status (KIT/PDGFRA) and efficacy of treatment was explored.ResultsCommon onalespib-related adverse events were diarrhoea (58%), nausea (50%), injection site events (46%), vomiting (39%), fatigue (27%), and muscle spasms (23%). Overall, 81% of patients reported more than one onalespib-related gastrointestinal disorder. Nine patients (35%) had a best response of stable disease, including two patients who had KIT mutations known to be associated with resistance to imatinib and sunitinib. Disease control at 4 months was achieved in five patients (19%), and median progression-free survival was 112 d (95% confidence interval 43–165). One patient with PDGFRA-mutant GIST had a partial response for more than 376 d.ConclusionThe combination of onalespib plus imatinib was well tolerated but exhibited limited antitumour activity as dosed in this TKI-resistant GIST patient population.Trial registration ID: clinicaltrials.gov: NCT0129420

Marine Genomics: A clearing-house for genomic and transcriptomic data of marine organisms

BACKGROUND: The Marine Genomics project is a functional genomics initiative developed to provide a pipeline for the curation of Expressed Sequence Tags (ESTs) and gene expression microarray data for marine organisms. It provides a unique clearing-house for marine specific EST and microarray data and is currently available at . DESCRIPTION: The Marine Genomics pipeline automates the processing, maintenance, storage and analysis of EST and microarray data for an increasing number of marine species. It currently contains 19 species databases (over 46,000 EST sequences) that are maintained by registered users from local and remote locations in Europe and South America in addition to the USA. A collection of analysis tools are implemented. These include a pipeline upload tool for EST FASTA file, sequence trace file and microarray data, an annotative text search, automated sequence trimming, sequence quality control (QA/QC) editing, sequence BLAST capabilities and a tool for interactive submission to GenBank. Another feature of this resource is the integration with a scientific computing analysis environment implemented by MATLAB. CONCLUSION: The conglomeration of multiple marine organisms with integrated analysis tools enables users to focus on the comprehensive descriptions of transcriptomic responses to typical marine stresses. This cross species data comparison and integration enables users to contain their research within a marine-oriented data management and analysis environment

902 EcoGenomics: analysis of complex systems via fractal geometry

Synopsis Ecogenomics is a convenient descriptor for the application of advanced molecular technologies to studies of organismal responses to environmental challenges in their natural settings. The development of molecular tools to survey changes in the transcript profile of thousands of genes has presented scientists with enormous analytical challenges. In the main, these center about the reduction of massively paralleled data to statistics or indices comprehensible to the human mind. Historically, scientists have used linear statistics such as ANOVA to accomplish this task, but the sheer volume of information available from microarrays severely limits this approach. In addition, important information in microarrays may not reside solely in the up or down regulation of individual genes, but rather in their dynamic, and probably nonlinear, interactions. In this presentation, we will explore alternative approaches to extracting of these signals using artificial neural networks and fractal geometry. The goal is to produce predictive models of gene dynamics in individuals and populations under environmental stress and reduce the number of genes that must be surveyed in order to recover transcript profile patterns of environmental challenges