Citations

271 Background: MEDI-573 is a dual-targeting human antibody that neutralizes IGF-I/-II ligands and inhibits IGF-1R and insulin receptor-A (IR-A) signaling pathways, which play a role in breast and other epithelial cancers. By sparing IR-B and its hybrid receptors, MEDI-573 is expected to achieve antitumor activity without perturbing glucose homeostasis. Methods: In a 3 + 3 dose-escalation trial ( NCT00816361 ), patients with advanced solid tumors, KPS ≥ 60, received MEDI-573 as a weekly 1-hr IV infusion at 0.5, 1.5, 5, 10, or 15 mg/kg every 21 days. Objectives included determination of MTD, safety profile, pharmacokinetics (PK), pharmacodynamics, and tumor response. In a separate biomarker study, mRNA expression of IR-A and IR-B was compared in HR+/HER- breast cancers. Results: Patients included 13M/12F (17 evaluable for efficacy at data cutoff); median age 59 yrs (range 37-78). No DLTs, drug-related SAEs, or significant toxicity patterns were reported. Forty-five AEs were considered treatment-related (% of patients): decreased appetite (28%), fatigue (24%), nausea (20%), diarrhea (16%), and anemia (12%). Hyperglycemia occurred in 2 patients, 1 with evidence of prestudy insulin resistance. No other significant changes in plasma glucose levels occurred. MEDI-573 had dose-proportionality, with AUC of 415 ± 165, 597 ± 298, and 1940 ± 904 d*μg/mL at 5, 10, and 15 mg/kg, respectively. Suppression of free IGF-1 and -2 was fully achieved at doses ≥ 5 mg/kg. Stable disease spanning ≥ 12 wks (range 12-36+) was seen in 6/17 patients. No antidrug antibodies were reported. In a study to identify breast cancer subsets that are dependent on IGF signaling, the IR-A:IR-B ratio was significantly higher in HR+/HER2- breast cancer than matched adjacent normal tissues and in proliferative luminal B than in luminal A breast cancer. Conclusions: MEDI-573 showed acceptable safety and favorable PK profiles without significant changes in glucose levels. The IR-A:IR-B ratio supports a biomarker approach for MEDI-573 patient stratification. A biomarker-rich phase Ib/II study of MEDI-573 with an aromatase inhibitor in patients with advanced HR+/HER2- breast cancer opens in 2011.

Background and purposeIntensity‐modulated proton therapy (IMPT) is highly sensitive to range uncertainties and uncertainties caused by setup variation. The conventional inverse treatment planning of IMPT based on the planning target volume (PTV) is not often sufficient to ensure robustness of treatment plans. We applied a probabilistic framework (chance‐constrained optimization) in IMPT planning to hedge against the influence of uncertainties.Material and methodsWe retrospectively selected one patient with lung cancer, one patient with head and neck (H&N) cancer, and one with prostate cancer for this analysis. Using their original images and prescriptions, we created new IMPT plans using two methods: (1) a robust chance‐constrained treatment planning method with the clinical target volume (CTV) as the target; (2) the margin‐based method with PTV as the target, which was solved by commercial software, CPLEX, using linear programming. For the first method, we reformulated the model into a tractable mixed‐integer programming problem and sped up the calculation using Benders decomposition. The dose‐volume histograms (DVHs) from the nominal and perturbed dose distributions were used to assess and compare plan quality. DVHs for all uncertain scenarios along with the nominal DVH were plotted. The width of the “bands” of DVHs was used to quantify the plan sensitivity to uncertainty. The newly developed Benders decomposition method was compared with a commercial solution to demonstrate its computational efficiency. The trade‐off between nominal plan quality and plan robustness was investigated.ResultsOur chance‐constrained model outperformed the PTV method in terms of tumor coverage, tumor dose homogeneity, and plan robustness. Our model was shown to produce IMPT plans to meet the dose‐volume constraints of organs at risk (OARs) and had better sparing of OARs than the PTV method in the three clinical cases included in this study. The chance‐constrained model provided a flexible tool for users to balance between plan robustness and plan quality. In addition, our in‐house developed method was found to be much faster than the commercial solution.ConclusionWith explicit control of plan robustness, the chance‐constrained robust optimization model generated superior IMPT plans compared to the PTV‐based method.

AbstractAlzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion‐weighted imaging (DWI) offers a non‐invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm—autoMATE (automated Multi‐Atlas Tract Extraction); we then extracted multiple DWI‐derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method—FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191–1207, 2017. © 2016 Wiley Periodicals, Inc.

ABSTRACT The widespread dissemination of carbapenem-resistant Acinetobacter spp. has created significant therapeutic challenges. At present, rapid molecular diagnostics (RMDs) that can identify this phenotype are not commercially available. Two RMD platforms, PCR combined with electrospray ionization mass spectrometry (PCR/ESI-MS) and molecular beacons (MB), for detecting genes conferring resistance/susceptibility to carbapenems in Acinetobacter spp. were evaluated. An archived collection of 200 clinical Acinetobacter sp. isolates was tested. Predictive values for susceptibility and resistance were estimated as a function of susceptibility prevalence and were based on the absence or presence of beta-lactamase ( bla ) NDM, VIM, IMP, KPC, and OXA carbapenemase genes (e.g., bla OXA-23 , bla OXA-24/40 , and bla OXA-58 found in this study) against the reference standard of MIC determinations. According to the interpretation of MICs, 49% ( n = 98) of the isolates were carbapenem resistant (as defined by either resistance or intermediate resistance to imipenem). The susceptibility sensitivities (95% confidence interval [CI]) for imipenem were 82% (74%, 89%) and 92% (85%, 97%) for PCR/ESI-MS and MB, respectively. Resistance sensitivities (95% CI) for imipenem were 95% (88%, 98%) and 88% (80%, 94%) for PCR/ESI-MS and MB, respectively. PRIMERS III establishes that RMDs can discriminate between carbapenem resistance and susceptibility in Acinetobacter spp. In the context of a known prevalence of resistance, SPVs and RPVs can inform clinicians regarding the best choice for empiric antimicrobial therapy against this multidrug-resistant pathogen.