Phase II study of the HSP90-inhibitor BIIB021 in gastrointestinal stromal tumors.

BACKGROUND: HSP90 inhibition leads to proteosomal degradation of activated KIT and has in vitro activity against gastrointestinal stromal tumors (GIST). BIIB021 is an oral non-ansamycin HSP90 inhibitor. We carried out a phase II study of BIIB021 in patients with GIST refractory to imatinib and sunitinib. PATIENTS AND METHODS: The primary end-point was metabolic partial response (mPR) as assessed by fluorodeoxyglucose positron emission tomography (FDG-PET). The secondary end-points were pharmacokinetic assessments of BIIB021 and pharmacodynamic assessments of HSP70. Twenty-three patients were treated on two schedules: 12 pts received 600 mg twice a week (BIW) and 11 patients received 400 mg three times a week (TIW). All had prior imatinib and sunitinib but stopped>14 days before starting BIIB021. RESULTS: The median age was 59 years (33-88 years), 61% male, 44% Eastern Cooperative Oncology Group 1 (ECOG1). The best response was PR by FDG-PET for five patients (3/12 at 600 mg BIW and 2/9 at 400 TIW) for an overall response rate of 22%. The response duration was 25-138 days. Adverse events (AEs) were mild to moderate. The mean Cmax was 1.5 µmol and the mean AUC was 2.9 µmol h. Cmax>1.5 µmol was associated with a decrease in standardized uptake value (SUVmax). HSP70 increased substantially following treatment. CONCLUSIONS: This study met its primary end-point. BIIB021 leads to objective responses in refractory GIST patients. Pharmacodynamic studies confirmed HSP90 inhibition. Further evaluation of BIIB021 in GIST is warranted.

Using an external gating signal to estimate noise in PET with an emphasis on tracer avid tumors.

The purpose of this study is to establish and validate a methodology for estimating the standard deviation of voxels with large activity concentrations within a PET image using replicate imaging that is immediately available for use in the clinic. To do this, ensembles of voxels in the averaged replicate images were compared to the corresponding ensembles in images derived from summed sinograms. In addition, the replicate imaging noise estimate was compared to a noise estimate based on an ensemble of voxels within a region. To make this comparison two phantoms were used. The first phantom was a seven-chamber phantom constructed of 1 liter plastic bottles. Each chamber of this phantom was filled with a different activity concentration relative to the lowest activity concentration with ratios of 1:1, 1:1, 2:1, 2:1, 4:1, 8:1 and 16:1. The second phantom was a GE Well-Counter phantom. These phantoms were imaged and reconstructed on a GE DSTE PET/CT scanner with 2D and 3D reprojection filtered backprojection (FBP), and with 2D- and 3D-ordered subset expectation maximization (OSEM). A series of tests were applied to the resulting images that showed that the region and replicate imaging methods for estimating standard deviation were equivalent for backprojection reconstructions. Furthermore, the noise properties of the FBP algorithms allowed scaling the replicate estimates of the standard deviation by a factor of 1/square root N, where N is the number of replicate images, to obtain the standard deviation of the full data image. This was not the case for OSEM image reconstruction. Due to nonlinearity of the OSEM algorithm, the noise is shown to be both position and activity concentration dependent in such a way that no simple scaling factor can be used to extrapolate noise as a function of counts. The use of the Well-Counter phantom contributed to the development of a heuristic extrapolation of the noise as a function of radius in FBP. In addition, the signal-to-noise ratio for high uptake objects was confirmed to be higher with backprojection image reconstruction methods. These techniques were applied to several patient data sets acquired in either 2D or 3D mode, with (18)F (FLT and FDG). Images of the standard deviation and signal-to-noise ratios were constructed and the standard deviations of the tumors' uptake were determined. Finally, a radial noise extrapolation relationship deduced in this paper was applied to patient data.