A new computational workflow to guide personalized drug therapy.

OBJECTIVE: Computational models are at the forefront of the pursuit of personalized medicine thanks to their descriptive and predictive abilities. In the presence of complex and heterogeneous data, patient stratification is a prerequisite for effective precision medicine, since disease development is often driven by individual variability and unpredictable environmental events. Herein, we present GreatNectorworkflow as a valuable tool for (i) the analysis and clustering of patient-derived longitudinal data, and (ii) the simulation of the resulting model of patient-specific disease dynamics. METHODS: GreatNectoris designed by combining an analytic strategy composed of CONNECTOR, a data-driven framework for the inspection of longitudinal data, and an unsupervised methodology to stratify the subjects with GreatMod, a quantitative modeling framework based on the Petri Net formalism and its generalizations. RESULTS: To illustrate GreatNectorcapabilities, we exploited longitudinal data of four immune cell populations collected from Multiple Sclerosis patients. Our main results report that the T-cell dynamics after alemtuzumab treatment separate non-responders versus responders patients, and the patients in the non-responders group are characterized by an increase of the Th17 concentration around 36 months. CONCLUSION: GreatNectoranalysis was able to stratify individual patients into three model meta-patients whose dynamics suggested insight into patient-tailored interventions.

CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.

MOTIVATION: The transition from evaluating a single time point to examining the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and clustering challenging. RESULTS: We developed CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. We give a new perspective of mechanism interpretation, specifically, we define novel model aggregations and we identify unanticipated molecular associations with response to clinically approved therapies. AVAILABILITY AND IMPLEMENTATION: CONNECTOR is freely available under GNU GPL license at https://qbioturin.github.io/connector and https://doi.org/10.17504/protocols.io.8epv56e74g1b/v1.

LPHN2 inhibits vascular permeability by differential control of endothelial cell adhesion.

Dynamic modulation of endothelial cell-to-cell and cell-to-extracellular matrix (ECM) adhesion is essential for blood vessel patterning and functioning. Yet the molecular mechanisms involved in this process have not been completely deciphered. We identify the adhesion G protein-coupled receptor (ADGR) Latrophilin 2 (LPHN2) as a novel determinant of endothelial cell (EC) adhesion and barrier function. In cultured ECs, endogenous LPHN2 localizes at ECM contacts, signals through cAMP/Rap1, and inhibits focal adhesion (FA) formation and nuclear localization of YAP/TAZ transcriptional regulators, while promoting tight junction (TJ) assembly. ECs also express an endogenous LPHN2 ligand, fibronectin leucine-rich transmembrane 2 (FLRT2), that prevents ECM-elicited EC behaviors in an LPHN2-dependent manner. Vascular ECs of lphn2a knock-out zebrafish embryos become abnormally stretched, display a hyperactive YAP/TAZ pathway, and lack proper intercellular TJs. Consistently, blood vessels are hyperpermeable, and intravascularly injected cancer cells extravasate more easily in lphn2a null animals. Thus, LPHN2 ligands, such as FLRT2, may be therapeutically exploited to interfere with cancer metastatic dissemination.

Quantifying Polarized Extracellular Matrix Secretion in Cultured Endothelial Cells.

In endothelial cells (ECs), the onset of apicobasal polarity is primarily regulated by the interaction of integrins with the surrounding extracellular matrix (ECM). ECs secrete and polymerize fibronectin (FN), a unique, permissive substrate that allows for vascular morphogenesis and lumen formation. We previously identified a signaling pathway that, under the control of the adhesion site adaptor protein PPFIA1, integrates the polarized secretion of freshly synthesized FN with the recycling of conformationally active α5ß1 integrin, the main FN receptor in ECs. To characterize the functional role of PPFIA1-dependent signaling in ECs, we set up a Transwell-based assay to quantify the polarized secretion of ECM proteins. To this aim, we allowed ECs to form a confluent monolayer on the Transwell membrane and checked its integrity by measuring transendothelial electric resistance and controlling the stability of tight junctions over time by fluorescent confocal microscope analysis. Finally, we quantified apical and basolateral FN secretion in control and PPFIA1-silenced EC culture medium by western blot analysis coupled to spike-in normalization.

MET/HGF Co-Targeting in Pancreatic Cancer: A Tool to Provide Insight into the Tumor/Stroma Crosstalk.

The 'onco-receptor' MET (Hepatocyte Growth Factor Receptor) is involved in the activation of the invasive growth program that is essential during embryonic development and critical for wound healing and organ regeneration during adult life. When aberrantly activated, MET and its stroma-secreted ligand HGF (Hepatocyte Growth Factor) concur to tumor onset, progression, and metastasis in solid tumors, thus representing a relevant target for cancer precision medicine. In the vast majority of tumors, wild-type MET behaves as a 'stress-response' gene, and relies on ligand stimulation to sustain cancer cell 'scattering', invasion, and protection form apoptosis. Moreover, the MET/HGF axis is involved in the crosstalk between cancer cells and the surrounding microenvironment. Pancreatic cancer (namely, pancreatic ductal adenocarcinoma, PDAC) is an aggressive malignancy characterized by an abundant stromal compartment that is associated with early metastases and resistance to conventional and targeted therapies. Here, we discuss the role of the MET/HGF axis in tumor progression and dissemination considering as a model pancreatic cancer, and provide a proof of concept for the application of dual MET/HGF inhibition as an adjuvant therapy in pancreatic cancer patients.