Deciphering the relative contribution of vascular inflammation and blood rheology in metastatic spreading.

Vascular adhesion of circulating tumor cells (CTCs) is a key step in cancer spreading. If inflammation is recognized to favor the formation of vascular "metastatic niches," little is known about the contribution of blood rheology to CTC deposition. Herein, a microfluidic chip, covered by a confluent monolayer of endothelial cells, is used for analyzing the adhesion and rolling of colorectal (HCT-15) and breast (MDA-MB-231) cancer cells under different biophysical conditions. These include the analysis of cell transport in a physiological solution and whole blood over a healthy and a TNF-α inflamed endothelium with a flow rate of 50 and 100 nl/min. Upon stimulation of the endothelial monolayer with TNF-α (25 ng/ml), CTC adhesion increases from 2 to 4 times whilst cell rolling velocity only slightly reduces. Notably, whole blood also enhances cancer cell deposition from 2 to 3 times, but only on the unstimulated vasculature. For all tested conditions, no statistically significant difference is observed between the two cancer cell types. Finally, a computational model for CTC transport demonstrates that a rigid cell approximation reasonably predicts rolling velocities while cell deformability is needed to model adhesion. These results would suggest that, within microvascular networks, blood rheology and inflammation contribute similarly to CTC deposition, thereby facilitating the formation of metastatic niches along the entire network, including the healthy endothelium. In microfluidic-based assays, neglecting blood rheology would significantly underestimate the metastatic potential of cancer cells.

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Differential diagnosis of pleural mesothelioma using Logic Learning Machine.

BACKGROUND: Tumour markers are standard tools for the differential diagnosis of cancer. However, the occurrence of nonspecific symptoms and different malignancies involving the same cancer site may lead to a high proportion of misclassifications. Classification accuracy can be improved by combining information from different markers using standard data mining techniques, like Decision Tree (DT), Artificial Neural Network (ANN), and k-Nearest Neighbour (KNN) classifier. Unfortunately, each method suffers from some unavoidable limitations. DT, in general, tends to show a low classification performance, whereas ANN and KNN produce a "black-box" classification that does not provide biological information useful for clinical purposes. METHODS: Logic Learning Machine (LLM) is an innovative method of supervised data analysis capable of building classifiers described by a set of intelligible rules including simple conditions in their antecedent part. It is essentially an efficient implementation of the Switching Neural Network model and reaches excellent classification accuracy while keeping low the computational demand. LLM was applied to data from a consecutive cohort of 169 patients admitted for diagnosis to two pulmonary departments in Northern Italy from 2009 to 2011. Patients included 52 malignant pleural mesotheliomas (MPM), 62 pleural metastases (MTX) from other tumours and 55 benign diseases (BD) associated with pleurisies. Concentration of three tumour markers (CEA, CYFRA 21-1 and SMRP) was measured in the pleural fluid of each patient and a cytological examination was also carried out. The performance of LLM and that of three competing methods (DT, KNN and ANN) was assessed by leave-one-out cross-validation. RESULTS: LLM outperformed all other considered methods. Global accuracy was 77.5% for LLM, 72.8% for DT, 54.4% for KNN, and 63.9% for ANN, respectively. In more details, LLM correctly classified 79% of MPM, 66% of MTX and 89% of BD. The corresponding figures for DT were: MPM = 83%, MTX = 55% and BD = 84%; for KNN: MPM = 58%, MTX = 45%, BD = 62%; for ANN: MPM = 71%, MTX = 47%, BD = 76%. Finally, LLM provided classification rules in a very good agreement with a priori knowledge about the biological role of the considered tumour markers. CONCLUSIONS: LLM is a new flexible tool potentially useful for the differential diagnosis of pleural mesothelioma.

Stretching of DNA confined in nanochannels with charged walls.

There is currently a growing interest in control of stretching of DNA inside nanoconfined regions due to the possibility to analyze and manipulate single biomolecules for applications such as DNA mapping and barcoding, which are based on stretching the DNA in a linear fashion. In the present work, we couple Finite Element Methods and Monte Carlo simulations in order to study the conformation of DNA molecules confined in nanofluidic channels with neutral and charged walls. We find that the electrostatic forces become more and more important when lowering the ionic strength of the solution. The influence of the nanochannel cross section geometry is also studied by evaluating the DNA elongation in square, rectangular, and triangular channels. We demonstrate that coupling electrostatically interacting walls with a triangular geometry is an efficient way to stretch DNA molecules at the scale of hundreds of nanometers. The paper reports experimental observations of λ-DNA molecules in poly(dimethylsiloxane) nanochannels filled with solutions of different ionic strength. The results are in good agreement with the theoretical predictions, confirming the crucial role of the electrostatic repulsion of the constraining walls on the molecule stretching.

Modulating DNA translocation by a controlled deformation of a PDMS nanochannel device.

Several strategies have been developed for the control of DNA translocation in nanopores and nanochannels. However, the possibility to reduce the molecule speed is still challenging for applications in the field of single molecule analysis, such as ultra-rapid sequencing. This paper demonstrates the possibility to alter the DNA translocation process through an elastomeric nanochannel device by dynamically changing its cross section. More in detail, nanochannel deformation is induced by a macroscopic mechanical compression of the polymeric device. This nanochannel squeezing allows slowing down the DNA molecule passage inside it. This simple and low cost method is based on the exploitation of the elastomeric nature of the device, can be coupled with different sensing techniques, is applicable in many research fields, such as DNA detection and manipulation, and is promising for further development in sequencing technology.

DNA detection with a polymeric nanochannel device.

We present the development and the electrical characterization of a polymeric nanochannel device. Standard microfabrication coupled to Focused Ion Beam (FIB) nanofabrication is used to fabricate a silicon master, which can be then replicated in a polymeric material by soft lithography. Such an elastomeric nanochannel device is used to study DNA translocation events during electrophoresis experiments. Our results demonstrate that an easy and low cost fabrication technique allows creation of a low noise device for single molecule analysis.

DNA manipulation with elastomeric nanostructures fabricated by soft-moulding of a FIB-patterned stamp.

A Focused Ion Beam (FIB)-patterned silicon mould is used to fabricate elastomeric nanostructures, whose cross-section can be dynamically and reversibly tuned by applying a controlled mechanical stress. Direct-write, based on FIB milling, allows the fabrication of nanostructures with a variety of different geometries, aspect ratio, spacing and distribution offering a higher flexibility compared to other nanopatterning approaches. Moreover, a simple double replication process based on poly(dimethylsiloxane) permits a strong reduction of the fabrication costs that makes this approach well-suited for the production of low cost nanofluidic devices. DNA stretching and single molecule manipulation capabilities of these platforms have been successfully demonstrated.