A mounting body of evidence in cancer research suggests that the local microenvironment of tumor cells has a profound influence on cancer progression and metastasis. In vitro studies on the tumor microenvironment and its pharmacological modulation, however, are often hampered by the technical challenges associated with creating physiological cell culture environments that integrate cancer cells with the key components of their native niche such as neighboring cells and extracellular matrix (ECM) to mimic complex microarchitecture of cancerous tissue. Using early-stage breast cancer as a model disease, here we describe a biomimetic microengineering strategy to reconstitute three-dimensional (3D) structural organization and microenvironment of breast tumors in human cell-based in vitro models. Specifically, we developed a microsystem that enabled co-culture of breast tumor spheroids with human mammary ductal epithelial cells and mammary fibroblasts in a compartmentalized 3D microfluidic device to replicate microarchitecture of breast ductal carcinoma in situ (DCIS). We also explored the potential of this breast cancer-on-a-chip system as a drug screening platform by evaluating the efficacy and toxicity of an anticancer drug (paclitaxel). Our microengineered disease model represents the first critical step towards recapitulating pathophysiological complexity of breast cancer, and may serve as an enabling tool to systematically examine the contribution of the breast cancer microenvironment to the progression of DCIS to an invasive form of the disease.

and AW is currently with the NUTECH

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This study aims to evaluate the value of perfusion MRI as a predictive/prognostic biomarker and a pharmacodynamic biomarker in patients with recurrent glioma treated with a bevacizumab-based regimen. We identified thirteen literature reports that investigated dynamic susceptibility-contrast (DSC) MRI or dynamic contrast-enhanced (DCE) MRI for predicting the patient outcome and analyzing the anti-angiogenic effect of bevacizumab by performing a systematic search of MEDLINE and EMBASE. The relative cerebral volume (rCBV) of DSC-MRI is currently the most common perfusion MRI parameter used as a predictive/prognostic biomarker. Pooled hazard ratios between responders and non-responders, as determined by rCBV, were 0.46 (95 % CI 0.28-0.76) for progression-free survival from five articles with a total 226 patients and 0.47 (95 % CI 0.29-0.76) for overall survival from six articles with a total 247 patients, and thus indicating that rCBV is helpful for predicting disease progression and the eventual outcome after treatment. Regarding the pharmacodynamic value of perfusion MRI parameters derived from either DSC-MRI or DCE-MRI, most perfusion MRI parameters (rCBV, Ktrans, CBVmax, Kpsmax, fpv, Ve and Kep) demonstrated a consistent decrease on the follow-up MRI after treatment, indicating that perfusion MRI may be helpful for evaluating the anti-angiogenic effect of a bevacizumab-based treatment regimen. However, the lack of standardization of imaging acquisition and analysis techniques for various perfusion MRI parameters needs to be resolved in the future. Despite these unsolved issues, the current evidence favoring the use of perfusion MRI as a predictive/prognostic or pharmacodynamic biomarker should be considered in patients with glioma treated using a bevacizumab-based regimen.