Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital – A real life proof of concept

AbstractPurposeOne of the major hurdles in enabling personalized medicine is obtaining sufficient patient data to feed into predictive models. Combining data originating from multiple hospitals is difficult because of ethical, legal, political, and administrative barriers associated with data sharing. In order to avoid these issues, a distributed learning approach can be used. Distributed learning is defined as learning from data without the data leaving the hospital.Patients and methodsClinical data from 287 lung cancer patients, treated with curative intent with chemoradiation (CRT) or radiotherapy (RT) alone were collected from and stored in 5 different medical institutes (123 patients at MAASTRO (Netherlands, Dutch), 24 at Jessa (Belgium, Dutch), 34 at Liege (Belgium, Dutch and French), 48 at Aachen (Germany, German) and 58 at Eindhoven (Netherlands, Dutch)).A Bayesian network model is adapted for distributed learning (watch the animation: http://youtu.be/nQpqMIuHyOk). The model predicts dyspnea, which is a common side effect after radiotherapy treatment of lung cancer.ResultsWe show that it is possible to use the distributed learning approach to train a Bayesian network model on patient data originating from multiple hospitals without these data leaving the individual hospital. The AUC of the model is 0.61 (95%CI, 0.51–0.70) on a 5-fold cross-validation and ranges from 0.59 to 0.71 on external validation sets.ConclusionDistributed learning can allow the learning of predictive models on data originating from multiple hospitals while avoiding many of the data sharing barriers. Furthermore, the distributed learning approach can be used to extract and employ knowledge from routine patient data from multiple hospitals while being compliant to the various national and European privacy laws

Integrative and perturbation-based analysis of the transcriptional dynamics of TGFβ/BMP system components in transition from embryonic stem cells to neural progenitors

Cooperative actions of extrinsic signals and cell-intrinsic transcription factors alter gene regulatory networks enabling cells to respond appropriately to environmental cues. Signaling by transforming growth factor type β (TGFβ) family ligands (eg, bone morphogenetic proteins [BMPs] and Activin/Nodal) exerts cell-type specific and context-dependent transcriptional changes, thereby steering cellular transitions throughout embryogenesis. Little is known about coordinated regulation and transcriptional interplay of the TGFβ system. To understand intrafamily transcriptional regulation as part of this system's actions during development, we selected 95 of its components and investigated their mRNA-expression dynamics, gene-gene interactions, and single-cell expression heterogeneity in mouse embryonic stem cells transiting to neural progenitors. Interrogation at 24 hour intervals identified four types of temporal gene transcription profiles that capture all stages, that is, pluripotency, epiblast formation, and neural commitment. Then, between each stage we performed esiRNA-based perturbation of each individual component and documented the effect on steady-state mRNA levels of the remaining 94 components. This exposed an intricate system of multilevel regulation whereby the majority of gene-gene interactions display a marked cell-stage specific behavior. Furthermore, single-cell RNA-profiling at individual stages demonstrated the presence of detailed co-expression modules and subpopulations showing stable co-expression modules such as that of the core pluripotency genes at all stages. Our combinatorial experimental approach demonstrates how intrinsically complex transcriptional regulation within a given pathway is during cell fate/state transitions

Position statement on the role of healthcare professionals, patient organizations and industry in European Reference Networks

A call from the EU for the set-up of European Reference Networks (ERNs) is expected to be launched in the first quarter of 2016. ERNs are intended to improve the care for patients with low prevalent or rare diseases throughout the EU by, among other things, facilitating the pooling and exchange of experience and knowledge and the development of protocols and guidelines. In the past, for example where costly orphan drugs have been concerned, industry has played an important role in facilitating consensus meetings and publication of guidelines. The ERNs should provide a unique opportunity for healthcare professionals and patients to lead these activities in an independent way. However, currently costs for networking activities are not to be covered by EU funds and alternative sources of funding are being explored. There is growing concern that any involvement of the industry in the funding of ERNs and their core activities may create a risk of undue influence. To date, the European Commission has not been explicit in how industry will be engaged in ERNs. We believe that public funding and a conflict of interest policy are needed at the level of the ERNs, Centers of Expertise (CEs), healthcare professionals and patient organizations with the aim of maintaining scientific integrity and independence. Specific attention is needed where it concerns the development of clinical practice guidelines. A proposal for a conflict of interest policy is presented, which may support the development of a framework to facilitate collaboration, safeguard professional integrity and to establish and maintain public acceptability and trust among patients, their organizations and the general public

Geometry sensing by dendritic cells dictates spatial organization and PGE2-induced dissolution of podosomes

Assembly and disassembly of adhesion structures such as focal adhesions (FAs) and podosomes regulate cell adhesion and differentiation. On antigen-presenting dendritic cells (DCs), acquisition of a migratory and immunostimulatory phenotype depends on podosome dissolution by prostaglandin E2 (PGE2). Whereas the effects of physico-chemical and topographical cues have been extensively studied on FAs, little is known about how podosomes respond to these signals. Here, we show that, unlike for FAs, podosome formation is not controlled by substrate physico-chemical properties. We demonstrate that cell adhesion is the only prerequisite for podosome formation and that substrate availability dictates podosome density. Interestingly, we show that DCs sense 3-dimensional (3-D) geometry by aligning podosomes along the edges of 3-D micropatterned surfaces. Finally, whereas on a 2-dimensional (2-D) surface PGE2 causes a rapid increase in activated RhoA levels leading to fast podosome dissolution, 3-D geometric cues prevent PGE2-mediated RhoA activation resulting in impaired podosome dissolution even after prolonged stimulation. Our findings indicate that 2-D and 3-D geometric cues control the spatial organization of podosomes. More importantly, our studies demonstrate the importance of substrate dimensionality in regulating podosome dissolution and suggest that substrate dimensionality plays an important role in controlling DC activation, a key process in initiating immune responses

Research priorities in prehabilitation for patients undergoing cancer surgery: an international Delphi study

Background Recently, the number of prehabilitation trials has increased significantly. The identification of key research priorities is vital in guiding future research directions. Thus, the aim of this collaborative study was to define key research priorities in prehabilitation for patients undergoing cancer surgery. Methods The Delphi methodology was implemented over three rounds of surveys distributed to prehabilitation experts from across multiple specialties, tumour streams and countries via a secure online platform. In the first round, participants were asked to provide baseline demographics and to identify five top prehabilitation research priorities. In successive rounds, participants were asked to rank research priorities on a 5-point Likert scale. Consensus was considered if > 70% of participants indicated agreement on each research priority. Results A total of 165 prehabilitation experts participated, including medical doctors, physiotherapists, dieticians, nurses, and academics across four continents. The first round identified 446 research priorities, collated within 75 unique research questions. Over two successive rounds, a list of 10 research priorities reached international consensus of importance. These included the efficacy of prehabilitation on varied postoperative outcomes, benefit to specific patient groups, ideal programme composition, cost efficacy, enhancing compliance and adherence, effect during neoadjuvant therapies, and modes of delivery. Conclusions This collaborative international study identified the top 10 research priorities in prehabilitation for patients undergoing cancer surgery. The identified priorities inform research strategies, provide future directions for prehabilitation research, support resource allocation and enhance the prehabilitation evidence base in cancer patients undergoing surgery

Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT

Machine learning applications for personalized medicine are highly dependent on access to sufficient data. For personalized radiation oncology, datasets representing the variation in the entire cancer patient population need to be acquired and used to learn prediction models. Ethical and legal boundaries to ensure data privacy hamper collaboration between research institutes. We hypothesize that data sharing is possible without identifiable patient data leaving the radiation clinics and that building machine learning applications on distributed datasets is feasible. We developed and implemented an IT infrastructure in five radiation clinics across three countries (Belgium, Germany, and The Netherlands). We present here a proof-of-principle for future ‘big data’ infrastructures and distributed learning studies. Lung cancer patient data was collected in all five locations and stored in local databases. Exemplary support vector machine (SVM) models were learned using the Alternating Direction Method of Multipliers (ADMM) from the distributed databases to predict post-radiotherapy dyspnea grade P 2. The discriminative performance was assessed by the area under the curve (AUC) in a five-fold cross-validation (learning on four sites and validating on the fifth). The perfor- mance of the distributed learning algorithm was compared to centralized learning where datasets of all institutes are jointly analyzed. The euroCAT infrastructure has been successfully implemented in five radiation clinics across three countries. SVM models can be learned on data distributed over all five clinics. Furthermore, the infrastructure provides a general framework to execute learning algorithms on distributed data. The ongoing expansion of the euroCAT network will facilitate machine learning in radiation oncology. The resulting access to larger datasets with sufficient variation will pave the way for generalizable prediction models and personalized medicine

Single-Crystal Rutile TiO ₂ Nanocylinders are Highly Effective Transducers of Optical Force and Torque

Optical trapping of (sub)micron-sized particles is broadly employed in nanoscience and engineering. The materials commonly employed for these particles, however, have physical properties that limit the transfer of linear or angular momentum (or both). This reduces the magnitude of forces and torques, and the spatiotemporal resolution, achievable in linear and angular traps. Here, we overcome these limitations through the use of single-crystal rutile TiO 2 , which has an exceptionally large optical birefringence, a high index of refraction, good chemical stability, and is amenable to geometric control at the nanoscale. We show that rutile TiO 2 nanocylinders form powerful joint force and torque transducers in aqueous environments by using only moderate laser powers to apply nN·nm torques at kHz rotational frequencies to tightly trapped particles. In doing so, we demonstrate how rutile TiO 2 nanocylinders outperform other materials and offer unprecedented opportunities to expand the control of optical force and torque at the nanoscale. BN/Nynke Dekker LabImPhys/OpticsBN/Marileen Dogterom La