Tumor Vascular Morphology Undergoes Dramatic Changes during Outgrowth of B16 Melanoma While Proangiogenic Gene Expression Remains Unchanged

In established tumors, angiogenic endothelial cells (ECs) coexist next to “quiescent” EC in matured vessels. We hypothesized that angio-gene expression of B16.F10 melanoma would differ depending on the growth stage. Unraveling the spatiotemporal nature thereof is essential for drug regimen design aimed to affect multiple neovascularization stages. We determined the angiogenic phenotype—represented by 52 angio-genes—and vascular morphology of small, intermediate, and large s.c. growing mouse B16.F10 tumors and demonstrated that expression of these genes did not differ between the different growth stages. Yet vascular morphology changed dramatically from small vessels without lumen in small to larger vessels with increased lumen size in intermediate/large tumors. Separate analysis of these vascular morphologies revealed a significant difference in αSMA expression in relation to vessel morphology, while no relation with VEGF, HIF-1α, nor Dll4 expression levels was observed. We conclude that the tumor vasculature remains actively engaged in angiogenesis during B16.F10 melanoma outgrowth and that the major change in tumor vascular morphology does not follow molecular concepts generated in other angiogenesis models

Privacy-preserving dataset combination and Lasso regression for healthcare predictions

Background: Recent developments in machine learning have shown its potential impact for clinical use such as risk prediction, prognosis, and treatment selection. However, relevant data are often scattered across different stakeholders and their use is regulated, e.g. by GDPR or HIPAA. As a concrete use-case, hospital Erasmus MC and health insurance company Achmea have data on individuals in the city of Rotterdam, which would in theory enable them to train a regression model in order to identify high-impact lifestyle factors for heart failure. However, privacy and confdentiality concerns make it unfeasible to exchange these data. Methods: This article describes a solution where vertically-partitioned synthetic data of Achmea and of Erasmus MC are combined using Secure Multi-Party Computation. First, a secure inner join protocol takes place to securely determine the identifiers of the patients that are represented in both datasets. Then, a secure Lasso Regression model is trained on the securely combined data. The involved parties thus obtain the prediction model but no further information on the input data of the other parties. Results: We implement our secure solution and describe its performance and scalability: we can train a prediction model on two datasets with 5000 records each and a total of 30 features in less than one hour, with a minimal difference from the results of standard (non-secure) methods. Conclusions: This article shows that it is possible to combine datasets and train a Lasso regression model on this combination in a secure way. Such a solution thus further expands the potential of privacy-preserving data analysis in the medical domain

Efficient compiler to covert security with public verifiability for honest majority MPC

We present a novel compiler for transforming arbitrary, passively secure MPC protocols into efficient protocols with covert security and public verifiability in the honest majority setting. Our compiler works for protocols with any number of parties > 2 and treats the passively secure protocol in a black-box manner. In multi-party computation (MPC), covert security provides an attractive trade-off between the security of actively secure protocols and the efficiency of passively secure protocols. In this security notion, honest parties are only required to detect an active attack with some constant probability, referred to as the deterrence rate. Extending covert security with public verifiability additionally ensures that any party, even an external one not participating in the protocol, is able to identify the cheaters if an active attack has been detected. Recently, Faust et al. (EUROCRYPT 2021) and Scholl et al. (Pre-print 2021) introduced similar covert security compilers based on computationally expensive time-lock puzzles. At the cost of requiring an honest majority, our work avoids the use of time-lock puzzles completely. Instead, we adopt a much more efficient publicly verifiable secret sharing scheme to achieve a similar functionality. This obviates the need for a trusted setup and a general-purpose actively secure MPC protocol. We show that our computation and communication costs are orders of magnitude lower while achieving the same deterrence rate