Phase II Study of BEZ235 versus Everolimus in Patients with Mammalian Target of Rapamycin Inhibitor-Naïve Advanced Pancreatic Neuroendocrine Tumors.

LESSONS LEARNED: Treatment with BEZ235 has not been shown to demonstrate increased efficacy compared with everolimus and may be associated with a poorer tolerability profile.The hypothesis of dual targeting of the phosphatidylinositol 3-kinase and mammalian target of rapamycin pathways in patients with advanced pancreatic neuroendocrine tumors may warrant further study using other agents. BACKGROUND: This phase II study investigated whether targeting the phosphatidylinositol 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) pathway via PI3K, mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2) inhibition using BEZ235 may be more effective than mTORC1 inhibition with everolimus in patients with advanced pancreatic neuroendocrine tumors (pNET) who are naïve to mTOR inhibitor therapy. METHODS: Patients with advanced pNET were randomized (1:1) to oral BEZ235 400 mg twice daily or oral everolimus 10 mg once daily on a continuous dosing schedule. The primary endpoint was progression-free survival (PFS). Secondary endpoints included safety, overall response rate (ORR), overall survival (OS), and time to treatment failure. RESULTS: Enrollment in this study was terminated early (62 enrolled of the 140 planned). The median PFS was 8.2 months (95% confidence interval [CI]: 5.3 to not evaluable [NE]) with BEZ235 versus 10.8 months (95% CI: 8.1-NE) with everolimus (hazard ratio 1.53; 95% CI: 0.72-3.25). The most commonly reported all-grade adverse events (>50% of patients regardless of study treatment relationship) with BEZ235 were diarrhea (90.3%), stomatitis (74.2%), and nausea (54.8%). CONCLUSION: BEZ235 treatment in mTOR inhibitor-naïve patients with advanced pNET did not demonstrate increased efficacy compared with everolimus and may be associated with a poorer tolerability profile.

Dynamic properties of water in breast pathology depend on the histological compounds: distinguishing tissue malignancy by water diffusion coefficients.

BACKGROUND: The parameters that characterize the intricate water diffusion in tumors may also reveal their distinct pathology. Specifically, characterization of breast cancer could be aided by diffusion magnetic resonance.The present in vitro study aimed to discover connections between the NMR biexponential diffusion parameters [fast diffusion phase (D(FDP)), slow diffusion phase (D(SDP)), and spin population of fast diffusion phase (P1)] and the histological constituents of nonmalignant (control) and malignant human breast tissue. It also investigates whether the diffusion coefficients indicate tissue status. METHODS: Post-surgical specimens of control (mastopathy and peritumoral tissues) and malignant human breast tissue were placed in an NMR spectrometer and diffusion sequences were applied. The resulting decay curves were analyzed by a biexponential model, and slow and fast diffusion parameters as well as percentage signal were identified. The same samples were also histologically examined and their percentage composition of several tissue constituents were measured: parenchyma (P), stroma (St), adipose tissue (AT), vessels (V) , pericellular edema (PCE), and perivascular edema (PVE). Correlations between the biexponential model parameters and tissue types were evaluated for different specimens. The effects of tissue composition on the biexponential model parameters, and the effects of histological and model parameters on cancer probability, were determined by non-linear regression. RESULTS: Meaningful relationships were found among the in vitro data. The dynamic parameters of water in breast tissue are stipulated by the histological constituents of the tissues (P, St, AT, PCE, and V). High coefficients of determination (R2) were obtained in the non-linear regression analysis: D(FDP) (R2 = 0.92), D(SDP) (R2 = 0.81), and P1(R2 = 0.93).In the cancer probability analysis, the informative value (R2) of the obtained equations of cancer probability in distinguishing tissue malignancy depended on the parameters input to the model. In order of increasing value, these equations were: cancer probability (P, St, AT, PCE, V) (R2 = 0.66), cancer probability (D(FDP), D(SDP))(R2 = 0.69), cancer probability (D(FDP), D(SDP), P1) (R2 = 0.85). CONCLUSION: Histological tissue components are related to the diffusion biexponential model parameters. From these parameters, the relative probability of cancer in a given specimen can be determined with some certainty.