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

Prediction of 60 day case-fatality after aneurysmal subarachnoid haemorrhage: results from the International Subarachnoid Aneurysm Trial (ISAT)

Aneurysmal subarachnoid haemorrhage (aSAH) is a devastating event with substantial case-fatality. Our purpose was to examine which clinical and neuro-imaging characteristics, available on admission, predict 60 day case-fatality in aSAH and to evaluate performance of our prediction model. We performed a secondary analysis of patients enrolled in the International Subarachnoid Aneurysm Trial (ISAT), a randomised multicentre trial to compare coiling with clipping in aSAH patients. Multivariable logistic regression analysis was used to develop a prognostic model to estimate the risk of dying within 60 days from aSAH based on clinical and neuro-imaging characteristics. The model was internally validated with bootstrapping techniques. The study population comprised of 2,128 patients who had been randomised to either endovascular coiling or neurosurgical clipping. In this population 153 patients (7.2%) died within 60 days. World Federation of Neurosurgical Societies (WFNS) grade was the most important predictor of case-fatality, followed by age, lumen size of the aneurysm and Fisher grade. The model discriminated reasonably between those who died within 60 days and those who survived (c statistic = 0.73), with minor optimism according to bootstrap re-sampling (optimism corrected c statistic = 0.70). Several strong predictors are available to predict 60 day case-fatality in aSAH patients who survived the early stage up till a treatment decision; after external validation these predictors could eventually be used in clinical decision making

Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download

OREMPdb: a semantic dictionary of computational pathway models

<p>Abstract</p> <p>Background</p> <p>The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. In fact, such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Indeed, having different objectives and different abstraction levels, most of these resources "speak" different languages. Semantic web technologies are here explored as a means to address some of these problems.</p> <p>Methods</p> <p>Employing an extensible collection of interpreters, we developed OREMP (Ontology Reasoning Engine for Molecular Pathways), a system that abstracts the information from different resources and combines them together into a coherent ontology. Continuing this effort we present OREMPdb; once different pathways are fed into OREMP, species are linked to the external ontologies referred and to reactions in which they participate. Exploiting these links, the system builds species-sets, which encapsulate species that operate together. Composing all of the reactions together, the system computes all of the reaction paths from-and-to all of the species-sets.</p> <p>Results</p> <p>OREMP has been applied to the curated branch of BioModels (2011/04/15 release) which overall contains 326 models, 9244 reactions, and 5636 species. OREMPdb is the semantic dictionary created as a result, which is made of 7360 species-sets. For each one of these sets, OREMPdb links the original pathway and the link to the original paper where this information first appeared. </p

Avapritinib versus regorafenib in locally advanced unresectable or metastatic GI stromal tumor: A randomized, open-label phase III study

PURPOSE Primary or secondary mutations in KIT or platelet-derived growth factor receptor alpha (PDGFRA) underlie tyrosine kinase inhibitor resistance in most GI stromal tumors (GISTs). Avapritinib selectively and potently inhibits KIT- and PDGFRA-mutant kinases. In the phase I NAVIGATOR study (NCT02508532), avapritinib showed clinical activity against PDGFRA D842V–mutant and later-line KIT-mutant GIST. VOYAGER (NCT03465722), a phase III study, evaluated efficacy and safety of avapritinib versus regorafenib as third-line or later treatment in patients with unresectable or metastatic GIST. PATIENTS AND METHODS VOYAGER randomly assigned patients 1:1 to avapritinib 300 mg once daily (4 weeks continuously) or regorafenib 160 mg once daily (3 weeks on and 1 week off). Primary end point was progression-free survival (PFS) by central radiology per RECIST version 1.1 modified for GIST. Secondary end points included objective response rate, overall survival, safety, disease control rate, and duration of response. Regorafenib to avapritinib crossover was permitted upon centrally confirmed disease progression. RESULTS Four hundred seventy-six patients were randomly assigned (avapritinib, n 5 240; regorafenib, n 5 236). Median PFS was not statistically different between avapritinib and regorafenib (hazard ratio, 1.25; 95% CI, 0.99 to 1.57; 4.2 v 5.6 months; P 5 .055). Overall survival data were immature at cutoff. Objective response rates were 17.1% and 7.2%, with durations of responses of 7.6 and 9.4 months for avapritinib and regorafenib; disease control rates were 41.7% (95% CI, 35.4 to 48.2) and 46.2% (95% CI, 39.7 to 52.8). Treatment-related adverse events (any grade, grade $ 3) were similar for avapritinib (92.5% and 55.2%) and regorafenib (96.2% and 57.7%). CONCLUSION Primary end point was not met. There was no significant difference in median PFS between avapritinib and regorafenib in patients with molecularly unselected, late-line GIST

Automatic Filtering and Substantiation of Drug Safety Signals

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions