Establishment of the Dutch Nationwide, Interdisciplinary Infrastructure and Biobank for Fundamental and Translational Ovarian Cancer Research: Archipelago of Ovarian Cancer Research.

OBJECTIVES: Ovarian cancer has the worst overall survival rate of all gynecologic malignancies. For the majority of patients, the 5-year overall survival rate of less than 50% has hardly improved over the last decades. To improve the outcome of patients with all subtypes of ovarian cancer, large-scale fundamental and translational research is needed. To accommodate these types of ovarian cancer research, we have established a Dutch nationwide, interdisciplinary infrastructure and biobank: the Archipelago of Ovarian Cancer Research (AOCR). The AOCR will facilitate fundamental and translational ovarian cancer research and enhance interdisciplinary, national, and international collaboration. DESIGN: The AOCR biobank is a prospective ovarian cancer biobank in which biomaterials are collected, processed, and stored in a uniform matter for future (genetic) scientific research. All 19 Dutch hospitals in which ovarian cancer surgery is performed participate and collaborate in the AOCR biobank. PARTICIPANTS/MATERIALS, SETTING, METHODS: Patients of 16 years and older with suspected or diagnosed ovarian, fallopian tube, or primary peritoneal cancer are recruited for participation. Patients who agree to participate give written informed consent for collection, storage, and issue of their biomaterials for future studies. After inclusion, different blood samples are taken at various predefined time points both before and during treatment. In case of a diagnostic paracentesis or biopsy, the residual biomaterials of these procedures are stored in the biobank. During surgery, primary tumor tissue and, if applicable, tissue from metastatic sites are collected and stored. From each patient, a representative histological hematoxylin and eosin stained slide is digitalized for research purposes, including reassessment by a panel of gynecologic pathologists. Clinical and pathological data are obtained on a per-study basis from Dutch registries. Research proposals for the issue of biomaterials and data are evaluated by both the Archipelago Scientific Committee and the Steering Committee. Researchers using the biomaterials from the AOCR biobank are encouraged to enrich the biobank with data and materials resulting from their analyses and experiments. LIMITATIONS: The implementation and first 4 years of collection are financed by an infrastructural grant from the Dutch Cancer Society. Therefore, the main limitation is that the costs for sustaining the biobank after the funding period will have to be covered. This coverage will come from incorporation of budget for biobanking in future grant applications and from fees from external researchers and commercial parties using the biomaterials stored in the AOCR biobank. Moreover, we will apply for grants aimed at sustaining and improving research infrastructures and biobanks. CONCLUSIONS: With the establishment of the Dutch nationwide, interdisciplinary Archipelago of Ovarian Cancer Research infrastructure and biobank, fundamental and translational research on ovarian cancer can be greatly improved. The ultimate aim of this infrastructure is that it will lead to improved diagnostics, treatment, and survival of patients with ovarian cancer.

The Information Technology (IT) Infrastructure of the Multicenter Archipelago of Ovarian Cancer Research Biobank: A Potential Blueprint for Other Biobanks.

Objective: Biobanks play a crucial role in fundamental and translational research by storing valuable biomaterials and data for future analyses. However, the design of their information technology (IT) infrastructures is often customized to specific requirements, thereby lacking the ability to be used for biobanks comprising other (types of) diseases. This results in substantial costs, time, and efforts for each new biobank project. The Dutch multicenter Archipelago of Ovarian Cancer Research (AOCR) biobank has developed an innovative, reusable IT infrastructure capable of adaptation to various biobanks, thereby enabling cost-effective and efficient implementation and management of biobank IT systems. Methods and Results: The AOCR IT infrastructure incorporates preexisting biobank software, mainly managed by Health-RI. The web-based registration tool Ldot is used for secure storage and pseudonymization of patient data. Clinicopathological data are retrieved from the Netherlands Cancer Registry and the Dutch nationwide pathology databank (Palga), both established repositories, reducing administrative workload and ensuring high data quality. Metadata of collected biomaterials are stored in the OpenSpecimen system. For digital pathology research, a hematoxylin and eosin-stained slide from each patient's tumor is digitized and uploaded to Slide Score. Furthermore, adhering to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, genomic data derived from the AOCR samples are stored in cBioPortal. Conclusion: The IT infrastructure of the AOCR biobank represents a new standard for biobanks, offering flexibility to handle diverse diseases and types of biomaterials. This infrastructure bypasses the need for disease-specific, custom-built software, thereby being cost- and time-effective while ensuring data quality and legislative compliance. The adaptability of this infrastructure highlights its potential to serve as a blueprint for the development of IT infrastructures in both new and existing biobanks.

FAIR Genomes metadata schema promoting Next Generation Sequencing data reuse in Dutch healthcare and research.

The genomes of thousands of individuals are profiled within Dutch healthcare and research each year. However, this valuable genomic data, associated clinical data and consent are captured in different ways and stored across many systems and organizations. This makes it difficult to discover rare disease patients, reuse data for personalized medicine and establish research cohorts based on specific parameters. FAIR Genomes aims to enable NGS data reuse by developing metadata standards for the data descriptions needed to FAIRify genomic data while also addressing ELSI issues. We developed a semantic schema of essential data elements harmonized with international FAIR initiatives. The FAIR Genomes schema v1.1 contains 110 elements in 9 modules. It reuses common ontologies such as NCIT, DUO and EDAM, only introducing new terms when necessary. The schema is represented by a YAML file that can be transformed into templates for data entry software (EDC) and programmatic interfaces (JSON, RDF) to ease genomic data sharing in research and healthcare. The schema, documentation and MOLGENIS reference implementation are available at https://fairgenomes.org .

The iCRF Generator: Generating interoperable electronic case report forms using online codebooks.

Semantic interoperability of clinical data is essential to preserve its meaning and intent when the data is exchanged, re-used or integrated with other data. Achieving semantic operability requires the use of a communication standard, such as HL7, as well as (functional) information standards. Manually mapping clinical data to a medical thesaurus, such as SNOMED CT, is complicated and requires expert knowledge of both the dataset, including its context, and the thesaurus. As an alternative, the (re-)use of codebooks, data definitions which may already have been mapped to a thesaurus, can be a viable approach. We've developed the iCRF Generator, a Java program that can generate the core of an interoperable electronic case report form (iCRF) for several of the major electronic data capture systems (EDCs). To build their CRFs, users can select one or more items from established codebooks, available from an online system called ART-DECOR. By providing an easy to use method to create CRFs for multiple EDCs based on the same codebooks, interoperability can be more easily attained.

Type I interferon response gene expression in established rheumatoid arthritis is not associated with clinical parameters.

BACKGROUND: A peripheral blood interferon (IFN) signature (i.e., elevated type I interferon response gene [IRG] expression) has been described in a subset of patients with rheumatoid arthritis (RA). In the present study, we systematically assessed the association between this IRG expression and clinical parameters. METHODS: Expression of 19 IRGs was determined in peripheral blood from 182 consecutive patients with RA and averaged into an IFN score per individual. Correlation and unpaired analyses were performed on the complete patient group. The analyses were internally validated by using an algorithm to randomize the patient group 1000 times into two equally sized sets, and then analyses were performed on both sets. RESULTS: Associations were assessed between IFN score and disease duration, 28-joint Disease Activity Score and its components, the occurrence of erosions and nodules, autoantibody positivity, and immunosuppressive treatment. This analysis revealed lower IFN scores in patients using hydroxychloroquine, prednisone, and/or sulfasalazine, but it did not show significant associations between the other parameters and the IFN score. Selecting patients who were not treated with hydroxychloroquine, prednisone, and/or sulfasalazine (n = 95) did not reveal any significant associations either. CONCLUSIONS: IRG expression in RA is affected by immunosuppressive treatment with prednisone, hydroxychloroquine, and/or sulfasalazine, but it is not evidently associated with other clinical parameters. Hence, the IFN signature appears to describe a subgroup of patients with RA but does not seem to reflect disease activity.

Changes in peripheral blood lymphocyte subsets during arthritis development in arthralgia patients.

BACKGROUND: Multiple lymphocyte subsets like T and B cells have been connected to joint infiltration and inflammation in rheumatoid arthritis (RA). Identification of leucocyte subsets that are dysregulated in arthritis development could provide insight into the aetiology of RA. This study aimed to investigate the composition of the peripheral blood components, i.e. CD14(+) monocytes, CD4(+) and CD8(+) T lymphocytes (CD3(+)), CD80(+), C-X-C chemokine receptor 3 (CXCR3)(+) and CD27(+) B lymphocytes (CD19(+)), CD16(+)CD56(+)CD3(-) natural killer (NK) cells and activated CD56(+)CD3(+) T cells, for association with arthritis development in patients with arthralgia. METHODS: Peripheral blood was collected from 89 patients with early RA (disease duration <6 months), 37 healthy controls (HC) and 113 patients with arthralgia (22 developed arthritis within ≤1 year, 18 developed arthritis after >1 year and 73 did not develop arthritis). Absolute numbers of monocytes and lymphocyte subsets in whole heparinized blood were determined with flow cytometry using quantification beads in combination with fluorescent labelled antibodies for T cells, B cells, monocytes, NK cells and activated T cells. RESULTS: In patients with early RA, significant decreases in numbers of (activated) T cells, CD80(+) and memory B cells and a trend towards smaller numbers of CD8(+) T cells was observed compared to HC. Similar differences were seen in patients with arthralgia who developed or did not develop arthritis (non-converters), with significantly decreased CD8(+) T cells and memory B cells. Patients with arthralgia who developed arthritis were split into groups that developed arthritis within 1 year (early converters) or after 1 year (late converters). Late converters had a significantly decreased number of CD8(+) T cells compared to non-converters; early converters had a decreased number of memory B cells. Longitudinal analysis of converters showed a significant relative increase in CD80(+) B cells towards the conversion time point compared to 24 months prior to conversion. CONCLUSIONS: This study revealed that patients with arthralgia who develop arthritis demonstrate a change in cellular immune parameters apparent in the periphery, starting with a decrease in cytotoxic T cells 24 months prior to arthritis development, followed by a decrease in the number of memory B cells 12 months prior to disease onset.

Physiological evidence for diversification of IFNα- and IFNß-mediated response programs in different autoimmune diseases.

BACKGROUND: Activation of the type I interferon (IFN) response program is described for several autoimmune diseases, including systemic lupus erythematosus (SLE), multiple sclerosis (MS), myositis (IIM) and rheumatoid arthritis (RA). While IFNα contributes to SLE pathology, IFNß therapy is often beneficial in MS, implying different immunoregulatory roles for these IFNs. This study was aimed to investigate potential diversification of IFNα-and IFNß-mediated response programs in autoimmune diseases. METHODS: Peripheral blood gene expression of 23 prototypical type I IFN response genes (IRGs) was determined in 54 healthy controls (HCs), 69 SLE (47 test, 22 validation), 149 IFNß-treated MS (71 test, 78 validation), 160 untreated MS, 78 IIM and 76 RA patients. Patients with a type I IFN signature were selected for analysis. RESULTS: We identified IFNα- and IFNß-specific response programs (GC-A and GC-B, respectively) in SLE and IFNß-treated MS patients. Concordantly, the GC-A/GC-B log-ratio was positive for all SLE patients and negative for virtually all IFNß-treated MS patients, which was confirmed in additional cohorts. Applying this information to other autoimmune diseases, IIM patients displayed positive GC-A/GC-B log-ratios, indicating predominant IFNα activity. The GC-A/GC-B log-ratio in RA was lower and approached zero in part of the patients, implying relative importance of both clusters. Remarkably, GC-A/GC-B log-ratios appeared most heterogeneous in untreated MS; half of the patients displayed GC-A dominance, whereas others showed GC-B dominance or log-ratios near zero. CONCLUSIONS: Our findings show diversification of the type I IFN response in autoimmune diseases, suggesting different pathogenic roles of the type I IFNs.

Independently outgrowing neurons and geometry-based synapse formation produce networks with realistic synaptic connectivity.

Neuronal signal integration and information processing in cortical networks critically depend on the organization of synaptic connectivity. During development, neurons can form synaptic connections when their axonal and dendritic arborizations come within close proximity of each other. Although many signaling cues are thought to be involved in guiding neuronal extensions, the extent to which accidental appositions between axons and dendrites can already account for synaptic connectivity remains unclear. To investigate this, we generated a local network of cortical L2/3 neurons that grew out independently of each other and that were not guided by any extracellular cues. Synapses were formed when axonal and dendritic branches came by chance within a threshold distance of each other. Despite the absence of guidance cues, we found that the emerging synaptic connectivity showed a good agreement with available experimental data on spatial locations of synapses on dendrites and axons, number of synapses by which neurons are connected, connection probability between neurons, distance between connected neurons, and pattern of synaptic connectivity. The connectivity pattern had a small-world topology but was not scale free. Together, our results suggest that baseline synaptic connectivity in local cortical circuits may largely result from accidentally overlapping axonal and dendritic branches of independently outgrowing neurons.

The interferon type I signature towards prediction of non-response to rituximab in rheumatoid arthritis patients.

INTRODUCTION: B cell depletion therapy is efficacious in rheumatoid arthritis (RA) patients failing on tumor necrosis factor (TNF) blocking agents. However, approximately 40% to 50% of rituximab (RTX) treated RA patients have a poor response. We investigated whether baseline gene expression levels can discriminate between clinical non-responders and responders to RTX. METHODS: In 14 consecutive RA patients starting on RTX (test cohort), gene expression profiling on whole peripheral blood RNA was performed by Illumina® HumanHT beadchip microarrays. Supervised cluster analysis was used to identify genes expressed differentially at baseline between responders and non-responders based on both a difference in 28 joints disease activity score (ΔDAS28 < 1.2) and European League against Rheumatism (EULAR) response criteria after six months RTX. Genes of interest were measured by quantitative real-time PCR and tested for their predictive value using receiver operating characteristics (ROC) curves in an independent validation cohort (n = 26). RESULTS: Genome-wide microarray analysis revealed a marked variation in the peripheral blood cells between RA patients before the start of RTX treatment. Here, we demonstrated that only a cluster consisting of interferon (IFN) type I network genes, represented by a set of IFN type I response genes (IRGs), that is, LY6E, HERC5, IFI44L, ISG15, MxA, MxB, EPSTI1 and RSAD2, was associated with ΔDAS28 and EULAR response outcome (P = 0.0074 and P = 0.0599, respectively). Based on the eight IRGs an IFN-score was calculated that reached an area under the curve (AUC) of 0.82 to separate non-responders from responders in an independent validation cohort of 26 patients using Receiver Operator Characteristics (ROC) curves analysis according to ΔDAS28 < 1.2 criteria. Advanced classifier analysis yielded a three IRG-set that reached an AUC of 87%. Comparable findings applied to EULAR non-response criteria. CONCLUSIONS: This study demonstrates clinical utility for the use of baseline IRG expression levels as a predictive biomarker for non-response to RTX in RA.

An algorithm for finding candidate synaptic sites in computer generated networks of neurons with realistic morphologies.

Neurons make synaptic connections at locations where axons and dendrites are sufficiently close in space. Typically the required proximity is based on the dimensions of dendritic spines and axonal boutons. Based on this principle one can search those locations in networks formed by reconstructed neurons or computer generated neurons. Candidate synapses are then located where axons and dendrites are within a given criterion distance from each other. Both experimentally reconstructed and model generated neurons are usually represented morphologically by piecewise-linear structures (line pieces or cylinders). Proximity tests are then performed on all pairs of line pieces from both axonal and dendritic branches. Applying just a test on the distance between line pieces may result in local clusters of synaptic sites when more than one pair of nearby line pieces from axonal and dendritic branches is sufficient close, and may introduce a dependency on the length scale of the individual line pieces. The present paper describes a new algorithm for defining locations of candidate synapses which is based on the crossing requirement of a line piece pair, while the length of the orthogonal distance between the line pieces is subjected to the distance criterion for testing 3D proximity.