Work packages
1.1 Data infrastructure
Work package leader:
Ederveen (RUMC)
Involved partners:
- ‘t Hoen (RUMC, X-omics)
- Vreeken (MU)
- Rissmann (LACDR)
- BIOMAP
Objectives:
1.1.1 To define specifications of the data infrastructure and specific components
1.1.2 To build the data infrastructure for (meta)data collection, FAIRification, storage, access and analysis
1.1.3 To define interoperability standards, SOPs and tools for standardization and FAIRification
1.1.4 To validate and continuously optimize the infrastructure in test- and production environment
Methodology:
To set-up the data infrastructure, we link and build upon existing and validated FAIR-based and state-of-art (bio)informatics resources and initiatives available in the field. Further details are given below.
Contribution to project:
This WP develops the infrastructure backbone for data collection, storage, access and advanced analysis for discovery of biomarkers and individual signatures. The tools and services enable creation and storage of standardized, interoperable metadata, clinical data, and omics data, complying with FAIR, legal, confidentiality and ethical requirements. Meta, clinical and omics data storages will be linked to (cloud-based and remote) computing environments as private data analysis workspaces, to empower researchers to perform integrative data analyses.
1.1 Data infrastructure
Work package leader:
Ederveen
Involved partners:
- ‘t Hoen (RUMC, X-omics)
- Vreeken (MU)
- Rissmann (LACDR)
- BIOMAP
Objectives:
1.1.1 To define specifications of the data infrastructure and specific components
1.1.2 To build the data infrastructure for (meta)data collection, FAIRification, storage, access and analysis
1.1.3 To define interoperability standards, SOPs and tools for standardization and FAIRification
1.1.4 To validate and continuously optimize the infrastructure in test- and production environment
Methodology:
To set-up the data infrastructure, we link and build upon existing and validated FAIR-based and state-of-art (bio)informatics resources and initiatives available in the field. Further details are given below.
Contribution to project:
This WP develops the infrastructure backbone for data collection, storage, access and advanced analysis for discovery of biomarkers and individual signatures. The tools and services enable creation and storage of standardized, interoperable metadata, clinical data, and omics data, complying with FAIR, legal, confidentiality and ethical requirements. Meta, clinical and omics data storages will be linked to (cloud-based and remote) computing environments as private data analysis workspaces, to empower researchers to perform integrative data analyses.
1.2 Modular (omics)platforms
Work package leader:
Vreeken (MU - Mass Spectrometry Imaging)
Involved partners:
- Vermeer (LUMC - Imaging Mass Cytometry)
- Bouwstra & Rissmann (UL - UPLC-MS/MS)
- Cuypers & Heeren (MU - Mass Spectrometry Imaging)
- Exadaktylos (CHDR – tele-health sensors)
- Hankemeier (UL UPLC-MS/MS)
- Kaal/Lindenburg (HSL – UPLC-MS/MS, GC-MS)
- all UMCs
Objectives:
1.2.1 To develop Standard Operating Procedures (SOPs) for sample harvesting, processing and storage
1.2.2 To optimize analytical performance of omics technologies and assure adequate data pre-processing
1.2.3 To develop Quality Controls for non-invasive and invasive analysis
1.2.4 To employ the NGID-platform in clinical trials and confirm data quality for upload in data infrastructure
Methodology:
Sample analysis with omics-technologies will be performed at central facilities (e.g. M4I, LUMC, LACDR, HSL, EMC). Other technologies are performed at several of the UMCs, as well as peripheral clinics (e.g. line-field confocal optical coherence tomography, trans-epidermal water loss). Systems will be made ‘fit for purpose’ by optimizing sample-preparation, -introduction and instrument specific settings, allowing smooth sample processing, analysis and retrieval of high content data. We will analyse various samples (e.g. different stage of disease or treatment, multiple samples per patients to accommodate for heterogeneity of the lesions) from circa 50 patients for rare diseases (MF, CLE) and circa 100 patients for more prevalent diseases (psoriasis, atopic dermatitis, HS).
Contribution to project:
This WP makes it possible to obtain the high-resolution and high-quality data needed for advanced data analysis and biomarker identification (WP3.1, WP3.2), by using the modular (omics)platform in clinical trials (WP2.2). In particular (spatial)omics analyses allow for a multitude of (un-)targeted molecules, like e.g. lipids, metabolites, amino acids, free fatty acids, proteins and associated enzymes.
2.1 Patient perspective and psychological markers
Work package leader:
van Beugen, van Laarhoven (UL)
Involved partners:
- Olde, van der Pool, Trampe (HAN)
- Rissmann (LACDR)
- Hijnen (EMC)
- Evers (UL)
- Seyger, van den Reek (RUMC)
- Kemperman (AUMC)
- Psoriasispatiënten Nederland
- VCME
- Stichting Huidlymfoom
- Huid Nederland
Objectives:
2.1.1 To develop screening tools for predictive psychological and behavioral markers
2.1.2 To identify psychological and behavioural markers for effective treatment of disease endotypes
2.1.3 To integrate the patient perspective in all aspects of the project
2.1.4 To gain insights into barriers and facilitating factors for patient recruitment in clinical trials
2.1.5 To determine needs, experiences and preferences of all stakeholders
Methodology:
To identify predictive markers from clinical trial data (WP2.2), we will analyse the influence of screened markers at baseline and at setpoints during trials (T2.1.1) on biomedical characteristics.
To gain insights in patient and other stakeholder wishes and needs, we will use: stakeholder workshops, semi-structured interview and focus groups. Systematic input from patient representatives is also acquired via joint project meetings. There will be an extensive assessment of future users and the current provision of care in which the infrastructure will be integrated. A user group is established with key stakeholders (e.g. patient representatives) that will take part in the stakeholder workshops.
To gain insights in barriers and facilitating factors regarding clinical trial participation, we conduct stakeholder workshops with e.g. patient representatives and clinicians to obtain feedback on standardized information on study characteristics.
Contribution to project:
This WP identifies markers that predict that influence of psychological and behavioural patient characteristics (biomarkers) on disease outcomes. These are incorporated into individual signatures to predict the most effective, personalized treatment for patients.
In addition, this WP safeguards the integration of patient wishes and needs throughout the project, particularly regarding clinical trials and implementation. We will also expedite patient recruitment for clinical trials.
2.2 Clinical data generation
Work package leader:
Rissmann (LACDR), van Doorn (EMC)
Involved partners:
- Hijnen, Damman, van der Zee, Caspers (EMC)
- de Jong, van den Reek, Seyger (RUMC)
- Röckmann, de Bruin-Weller (UMCU)
- Middelkamp-Hup, Spuls (AUMC)
- Horvath (UMCG)
- Gostyński (MUMC)
- Vermeer, Huizinga (LUMC)
- Olde, van der Pool (HAN)
- CONNECTED
- CHDR
- SciBase
- Psoriasispatiënten Nederland
- VCME
- Stichting Huidlymfoom
- Perimed
- Clinical Microbiomics
- Damae Medical
Objectives:
2.2.1 To finalize study designs and set-up of disease-specific observational trials
2.2.2 To initiate and execute observational trials
2.2.3 To finalize design and setup of disease-specific interventional trials
2.2.4 To initiate and execute interventional clinical trials
Methodology:
To establish highest level of medical-scientific evidence for each of the six diseases, we will conduct prospective, randomized controlled trials. Cross sectional cohorts of healthy volunteers will be used for comparison.
The trials will be performed in different iterative cycles: 1st observational and 2nd interventional, with a broad spectrum of disease severities (mild, moderate and severe). During the observational part the lesional and non-lesional skin of untreated patients is investigated using multimodular NGID platform (WP1.2). Data is used for a first biomarker analysis (WP3.1, WP3.2). Based in this output, the multimodular NGID platform can be refined for execution of the interventional part of this WP. For each disease different treatments are evaluated, e.g. targeted biologics, topical glucocorticoids, immunosuppressive drugs, anti-histamines etc. Validated efficacy parameters will be used to determine (non)responders. With the 2nd interventional step, validated biomarkers will be obtained. The trials are conducted per disease in different centralized locations: psoriasis (RUMC, MUMC referring), atopic dermatitis (EMC; UMCU, AUMC referring), HS (EMC, UMCG referring), urticaria (EMC, UMCU referring), CLE (LUMC; AUMC, EMC referring), MF (LUMC; AUMC, EMC referring). A dedicated research physician will perform the outward patient clinic in multiple hospitals and refer to one of the dedicated centers.
Contribution to project:
WP2.2 generates the clinical data that is required for WP3.1 and WP3.2 and is therefore critical to the project. In addition, WP4.1. and WP4.2. rely on the data generated within this WP.
3.1 Biomarker discovery by machine learning
Work package leader:
Lelieveldt (LUMC-TUD)
Involved partners:
- Westerhuis (UvA)
- Wee (MU)
- Ederveen, 't Hoen (RUMC)
- BIOMAP
- SRIS
- Hippocrates
- ImmUniverse
Objectives:
3.1.1 To develop data fusion and machine learning methods for biomarker discovery
3.1.2 Integration in data infrastructure and application
3.1.3 To support data analytics tasks in other WPs
Methodology:
Data visualization and machine learning for biomarker discovery. Due to the complexity of the multi-omics and image-based biomarker discovery (see WP2.2 and WP1.2), a triangulation approach is taken. This means that two completely different approached are used to tackle the integration problem (subtasks i and ii) and the consistency of the outcomes serves then as a validation of the putative biomarkers (subtask iii). Of special interest is the fine-tuning of the biomarker selection to a limited set which is as small and noninvasive as possible thereby guiding future experiments (subtask iii and in conjunction with WP 1.2, 2.2, 3.2).
Contribution to project:
This WP analyses the project data and is essential to identify potential biomarkers, that will be translated into individual signatures for use in daily practice. The WP is thus instrumental to generate hypotheses and using the unique, high-resolution data set generated in NGID.
3.2 Translational disease modeling
Work package leader:
van den Bogaard (RUMC), El Ghalbzouri (LUMC)
Involved partners:
- Dermatological expert for each disease
- Ederveen (RUMC)
- Kutmon (MU)
- Vreeken (M4I)
- Vermeer (LUMC)
- Caspers (EMC)
- CELLnTEC
- Proefdiervrij
- Scibase
- Janssen
- Almirall
- Maruho
- CHDR
Objectives:
3.2.1 To set-up a repository for disease-specific cell types and mediators for modelling of skin inflammation
3.2.2 Biomarker identification, analysis and verification of skin model phenotypes
3.2.3 To implement xeno-free methodologies to obtain animal-free translational organotypic models
3.2.4 To develop benign and diseased immunocompetent organotypic human models
3.2.5 To identify molecular mechanisms and validate treatment biomarkers
3.2.6 To provide a proof of concept for skin model-guided personalized treatment strategies
3.2.7 To secure valorisation opportunities for large-scale implementation of models in pre-clinical research
Methodology:
All tasks revolve around the Build-Measure-Learn cycle principle. Systems biology approaches (e.g. pathway and network analysis) are used to explore the underlying molecular mechanisms to enable the search for relevant cell types, inflammatory mediators and suitable models that are required to analyse the respective biomarkers of choice. The online repository is based on current publicly available data through systematic reviews.
Contribution to project:
Human disease models are critical for the evaluation and validation of the identified patient or disease biomarkers in disease pathophysiology and predication of response to treatment. NGID technologies are implemented for data-driven development and refinement of novel, completely animal-free human skin models to maximize their biological relevance, in vivo resemblance and prediction potential for identified biomarkers in the NGID consortium. In addition, they provide additional relevant biomarker-related datasets for NGID.
4.1 Tele-Health
Work package leader:
Rissmann (LACDR), Exadaktylos (CHDR)
Involved partners:
- Hankemeier (UL)
- Rispens (Sanquin)
- Hijnen, van Doorn, van der Zee (EMC)
- de Jong, van den Reek, Seyger (RUMC)
- Röckmann, de Bruin-Weller (UMCU)
- Middelkamp-Hup, Spuls, Bekkenk (AUMC)
- Horvath (UMCG)
- Gostyński (MUMC)
- Vermeer, Huizinga (LUMC)
- van der Pool (HAN)
- CONNECTED
- CHDR
- SciBase
Objectives:
4.1.1 To co-develop (non-)invasive tools for tele-health (with WP1.2)
4.1.2 To select, setup and apply disease-specific integrated tele-health module in clinical trials (T.2.2.3 & T2.2.5)
4.1.3 To refine the tele-health modules based in trial results
4.1.4 Broad scale implementation from trial setting into clinical practice (in combination with T4.2.2)
Methodology:
We will use various sensors, mobile apps and tools, e.g. actigraphy for objective itch monitoring, matrasses with sensors for sleep monitoring, watches for movement tracking, adherence applications and photography applications. These methods can be used together with (non-)invasive techniques for sampling e.g. for gut and cutaneous microbiome, blood, microenvironment of the (lesional) skin. All these devices need to be tested, validated prior to use in the clinical trials (T.2.2.2 and T.2.2.6)
Contribution to project:
This WP essential for implementation of the NGID-approach outside of hospital-settings.
4.2 Implementation, consolidation and real-world evidence
Work package leader:
Seyger, van den Reek (RUMC)
Involved partners:
- Olde, van der Pool, Trampe (HAN)
- de Jong (RUMC)
- van Beugen, van Laarhoven (UL)
- Horvath (UMCG)
- de Bruin-Weller (UMCU)
- NVDV
- NVED
- Psoriasispatiënten Nederland
- VMCE
- Stichting Huidlymfoom
- Huid Nederland
Objectives:
4.2.1 To co-develop engagement activities for stakeholders using inclusive communication
4.2.2 To implement individual signatures into clinical practice
4.2.3 To develop an NGID-associated Real World Evidence registry, with a focus on cost-effectiveness
4.2.4 To continue and consolidate the NGID platform beyond the scope of the NWA-ORC grant
Methodology:
The NGID-associated Real World Evidence registry will be a web-based electronic data capture system (e.g. Castor) in which data is prospectively collected via electronic case record forms. The methodology of communication, implementation and consolidation is described in § 4.2 and in the description of research activities below.
Contribution to project:
National implementation and consolidation of NGID-infrastructure and individual signatures within real-world dermatology practice. We will facilitate implementation via various strategic activities, incl. workshops, training, and early adopter implementation. This WP paves the path for cost-effectiveness evaluation of developed biomarker tool. A fully operational NGID-RWE registry integrating clinical, biomarker and survey data from real-world dermatology practice, and translate into outcomes relevant for individual patient (biomarker-driven treatment choice) and society ((cost-)effectiveness of biomarker-driven care).