70 research outputs found
Converging organoids and extracellular matrix::New insights into liver cancer biology
Primary liver cancer, consisting primarily of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), is a heterogeneous malignancy with a dismal prognosis, resulting in the third leading cause of cancer mortality worldwide [1, 2]. It is characterized by unique histological features, late-stage diagnosis, a highly variable mutational landscape, and high levels of heterogeneity in biology and etiology [3-5]. Treatment options are limited, with surgical intervention the main curative option, although not available for the majority of patients which are diagnosed in an advanced stage. Major contributing factors to the complexity and limited treatment options are the interactions between primary tumor cells, non-neoplastic stromal and immune cells, and the extracellular matrix (ECM). ECM dysregulation plays a prominent role in multiple facets of liver cancer, including initiation and progression [6, 7]. HCC often develops in already damaged environments containing large areas of inflammation and fibrosis, while CCA is commonly characterized by significant desmoplasia, extensive formation of connective tissue surrounding the tumor [8, 9]. Thus, to gain a better understanding of liver cancer biology, sophisticated in vitro tumor models need to incorporate comprehensively the various aspects that together dictate liver cancer progression. Therefore, the aim of this thesis is to create in vitro liver cancer models through organoid technology approaches, allowing for novel insights into liver cancer biology and, in turn, providing potential avenues for therapeutic testing. To model primary epithelial liver cancer cells, organoid technology is employed in part I. To study and characterize the role of ECM in liver cancer, decellularization of tumor tissue, adjacent liver tissue, and distant metastatic organs (i.e. lung and lymph node) is described, characterized, and combined with organoid technology to create improved tissue engineered models for liver cancer in part II of this thesis. Chapter 1 provides a brief introduction into the concepts of liver cancer, cellular heterogeneity, decellularization and organoid technology. It also explains the rationale behind the work presented in this thesis. In-depth analysis of organoid technology and contrasting it to different in vitro cell culture systems employed for liver cancer modeling is done in chapter 2. Reliable establishment of liver cancer organoids is crucial for advancing translational applications of organoids, such as personalized medicine. Therefore, as described in chapter 3, a multi-center analysis was performed on establishment of liver cancer organoids. This revealed a global establishment efficiency rate of 28.2% (19.3% for hepatocellular carcinoma organoids (HCCO) and 36% for cholangiocarcinoma organoids (CCAO)). Additionally, potential solutions and future perspectives for increasing establishment are provided. Liver cancer organoids consist of solely primary epithelial tumor cells. To engineer an in vitro tumor model with the possibility of immunotherapy testing, CCAO were combined with immune cells in chapter 4. Co-culture of CCAO with peripheral blood mononuclear cells and/or allogenic T cells revealed an effective anti-tumor immune response, with distinct interpatient heterogeneity. These cytotoxic effects were mediated by cell-cell contact and release of soluble factors, albeit indirect killing through soluble factors was only observed in one organoid line. Thus, this model provided a first step towards developing immunotherapy for CCA on an individual patient level. Personalized medicine success is dependent on an organoids ability to recapitulate patient tissue faithfully. Therefore, in chapter 5 a novel organoid system was created in which branching morphogenesis was induced in cholangiocyte and CCA organoids. Branching cholangiocyte organoids self-organized into tubular structures, with high similarity to primary cholangiocytes, based on single-cell sequencing and functionality. Similarly, branching CCAO obtain a different morphology in vitro more similar to primary tumors. Moreover, these branching CCAO have a higher correlation to the transcriptomic profile of patient-paired tumor tissue and an increased drug resistance to gemcitabine and cisplatin, the standard chemotherapy regimen for CCA patients in the clinic. As discussed, CCAO represent the epithelial compartment of CCA. Proliferation, invasion, and metastasis of epithelial tumor cells is highly influenced by the interaction with their cellular and extracellular environment. The remodeling of various properties of the extracellular matrix (ECM), including stiffness, composition, alignment, and integrity, influences tumor progression. In chapter 6 the alterations of the ECM in solid tumors and the translational impact of our increased understanding of these alterations is discussed. The success of ECM-related cancer therapy development requires an intimate understanding of the malignancy-induced changes to the ECM. This principle was applied to liver cancer in chapter 7, whereby through a integrative molecular and mechanical approach the dysregulation of liver cancer ECM was characterized. An optimized agitation-based decellularization protocol was established for primary liver cancer (HCC and CCA) and paired adjacent tissue (HCC-ADJ and CCA-ADJ). Novel malignancy-related ECM protein signatures were found, which were previously overlooked in liver cancer transcriptomic data. Additionally, the mechanical characteristics were probed, which revealed divergent macro- and micro-scale mechanical properties and a higher alignment of collagen in CCA. This study provided a better understanding of ECM alterations during liver cancer as well as a potential scaffold for culture of organoids. This was applied to CCA in chapter 8 by combining decellularized CCA tumor ECM and tumor-free liver ECM with CCAO to study cell-matrix interactions. Culture of CCAO in tumor ECM resulted in a transcriptome closely resembling in vivo patient tumor tissue, and was accompanied by an increase in chemo resistance. In tumor-free liver ECM, devoid of desmoplasia, CCAO initiated a desmoplastic reaction through increased collagen production. If desmoplasia was already present, distinct ECM proteins were produced by the organoids. These were tumor-related proteins associated with poor patient survival. To extend this method of studying cell-matrix interactions to a metastatic setting, lung and lymph node tissue was decellularized and recellularized with CCAO in chapter 9, as these are common locations of metastasis in CCA. Decellularization resulted in removal of cells while preserving ECM structure and protein composition, linked to tissue-specific functioning hallmarks. Recellularization revealed that lung and lymph node ECM induced different gene expression profiles in the organoids, related to cancer stem cell phenotype, cell-ECM integrin binding, and epithelial-to-mesenchymal transition. Furthermore, the metabolic activity of CCAO in lung and lymph node was significantly influenced by the metastatic location, the original characteristics of the patient tumor, and the donor of the target organ. The previously described in vitro tumor models utilized decellularized scaffolds with native structure. Decellularized ECM can also be used for creation of tissue-specific hydrogels through digestion and gelation procedures. These hydrogels were created from both porcine and human livers in chapter 10. The liver ECM-based hydrogels were used to initiate and culture healthy cholangiocyte organoids, which maintained cholangiocyte marker expression, thus providing an alternative for initiation of organoids in BME. Building upon this, in chapter 11 human liver ECM-based extracts were used in combination with a one-step microfluidic encapsulation method to produce size standardized CCAO. The established system can facilitate the reduction of size variability conventionally seen in organoid culture by providing uniform scaffolding. Encapsulated CCAO retained their stem cell phenotype and were amendable to drug screening, showing the feasibility of scalable production of CCAO for throughput drug screening approaches. Lastly, Chapter 12 provides a global discussion and future outlook on tumor tissue engineering strategies for liver cancer, using organoid technology and decellularization. Combining multiple aspects of liver cancer, both cellular and extracellular, with tissue engineering strategies provides advanced tumor models that can delineate fundamental mechanistic insights as well as provide a platform for drug screening approaches.<br/
Managing healthcare transformation towards P5 medicine (Published in Frontiers in Medicine)
Health and social care systems around the world are facing radical organizational, methodological and technological paradigm changes to meet the requirements for improving quality and safety of care as well as efficiency and efficacy of care processes. In this they’re trying to manage the challenges of ongoing demographic changes towards aging, multi-diseased societies, development of human resources, a health and social services consumerism, medical and biomedical progress, and exploding costs for health-related R&D as well as health services delivery. Furthermore, they intend to achieve sustainability of global health systems by transforming them towards intelligent, adaptive and proactive systems focusing on health and wellness with optimized quality and safety outcomes.
The outcome is a transformed health and wellness ecosystem combining the approaches of translational medicine, 5P medicine (personalized, preventive, predictive, participative precision medicine) and digital health towards ubiquitous personalized health services realized independent of time and location. It considers individual health status, conditions, genetic and genomic dispositions in personal social, occupational, environmental and behavioural context, thus turning health and social care from reactive to proactive. This requires the advancement communication and cooperation among the business actors from different domains (disciplines) with different methodologies, terminologies/ontologies, education, skills and experiences from data level (data sharing) to concept/knowledge level (knowledge sharing). The challenge here is the understanding and the formal as well as consistent representation of the world of sciences and practices, i.e. of multidisciplinary and dynamic systems in variable context, for enabling mapping between the different disciplines, methodologies, perspectives, intentions, languages, etc. Based on a framework for dynamically, use-case-specifically and context aware representing multi-domain ecosystems including their development process, systems, models and artefacts can be consistently represented, harmonized and integrated. The response to that problem is the formal representation of health and social care ecosystems through an system-oriented, architecture-centric, ontology-based and policy-driven model and framework, addressing all domains and development process views contributing to the system and context in question.
Accordingly, this Research Topic would like to address this change towards 5P medicine. Specifically, areas of interest include, but are not limited:
• A multidisciplinary approach to the transformation of health and social systems
• Success factors for sustainable P5 ecosystems
• AI and robotics in transformed health ecosystems
• Transformed health ecosystems challenges for security, privacy and trust
• Modelling digital health systems
• Ethical challenges of personalized digital health
• Knowledge representation and management of transformed health ecosystems
Table of Contents:
04 Editorial: Managing healthcare transformation towards P5
medicine
Bernd Blobel and Dipak Kalra
06 Transformation of Health and Social Care Systems—An
Interdisciplinary Approach Toward a Foundational
Architecture
Bernd Blobel, Frank Oemig, Pekka Ruotsalainen and Diego M. Lopez
26 Transformed Health Ecosystems—Challenges for Security,
Privacy, and Trust
Pekka Ruotsalainen and Bernd Blobel
36 Success Factors for Scaling Up the Adoption of Digital
Therapeutics Towards the Realization of P5 Medicine
Alexandra Prodan, Lucas Deimel, Johannes Ahlqvist, Strahil Birov,
Rainer Thiel, Meeri Toivanen, Zoi Kolitsi and Dipak Kalra
49 EU-Funded Telemedicine Projects – Assessment of, and
Lessons Learned From, in the Light of the SARS-CoV-2
Pandemic
Laura Paleari, Virginia Malini, Gabriella Paoli, Stefano Scillieri,
Claudia Bighin, Bernd Blobel and Mauro Giacomini
60 A Review of Artificial Intelligence and Robotics in
Transformed Health Ecosystems
Kerstin Denecke and Claude R. Baudoin
73 Modeling digital health systems to foster interoperability
Frank Oemig and Bernd Blobel
89 Challenges and solutions for transforming health ecosystems
in low- and middle-income countries through artificial
intelligence
Diego M. LĂłpez, Carolina Rico-Olarte, Bernd Blobel and Carol Hullin
111 Linguistic and ontological challenges of multiple domains
contributing to transformed health ecosystems
Markus Kreuzthaler, Mathias Brochhausen, Cilia Zayas, Bernd Blobel
and Stefan Schulz
126 The ethical challenges of personalized digital health
Els Maeckelberghe, Kinga Zdunek, Sara Marceglia, Bobbie Farsides
and Michael Rigb
From Mouse Models to Patients: A Comparative Bioinformatic Analysis of HFpEF and HFrEF
Heart failure (HF) represents an immense health burden with currently no curative
therapeutic strategies. Study of HF patient heterogeneity has led to the recognition of
HF with preserved (HFpEF) and reduced ejection fraction (HFrEF) as distinct syndromes
regarding molecular characteristics and clinical presentation. Until the recent past,
HFrEF represented the focus of research, reflected in the development of a number of
therapeutic strategies. However, the pathophysiological concepts applicable to HFrEF
may not be necessarily applicable to HFpEF. HF induces a series of ventricular
modeling processes that involve, among others, hallmarks of hypertrophy, fibrosis,
inflammation, all of which can be observed to some extent in HFpEF and HFrEF. Thus,
by direct comparative analysis between HFpEF and HFrEF, distinctive features can be
uncovered, possibly leading to improved pathophysiological understanding and
opportunities
for
therapeutic
intervention.
Moreover,
recent
advances
in
biotechnologies, animal models, and digital infrastructure have enabled large-scale
collection of molecular and clinical data, making it possible to conduct a bioinformatic
comparative analysis of HFpEF and HFrEF.
Here, I first evaluated the field of HF transcriptome research by revisiting published
studies and data sets to provide a consensus gene expression reference. I discussed the
patient clientele that was captured, revealing that HFpEF patients were not represented.
Thus, I applied alternative approaches to study HFpEF. I utilized a mouse surrogate
model of HFpEF and analyzed single cell transcriptomics to gain insights into the
interstitial tissue remodeling. I contrasted this analysis by comparison of fibroblast
activation patterns found in mouse models resembling HFrEF. The human reference
was used to further demonstrate similarities between models and patients and a novel
possible biomarker for HFpEF was introduced.
Mouse models only capture selected aspects of HFpEF but largely fail to imitate the
complex multi-factor and multi-organ syndrome present in humans. To account for
this complexity, I performed a top-down analysis in HF patients by analyzing
phenome-wide comorbidity patterns. I derived clinical insights by contrasting HFpEF
and HFrEF patients and their comorbidity profiles. These profiles were then used to
predict associated genetic profiles, which could be also recovered in the HFpEF mouse
model, providing hypotheses about the molecular links of comorbidity profiles.
My work provided novel insights into HFpEF and HFrEF syndromes and exemplified an
interdisciplinary bioinformatic approach for a comparative analysis of both syndromes
using different data modalities
Recommended from our members
Generating Reliable and Responsive Observational Evidence: Reducing Pre-analysis Bias
A growing body of evidence generated from observational data has demonstrated the potential to influence decision-making and improve patient outcomes. For observational evidence to be actionable, however, it must be generated reliably and in a timely manner. Large distributed observational data networks enable research on diverse patient populations at scale and develop new sound methods to improve reproducibility and robustness of real-world evidence. Nevertheless, the problems of generalizability, portability and scalability persist and compound. As analytical methods only partially address bias, reliable observational research (especially in networks) must address the bias at the design stage (i.e., pre-analysis bias) including the strategies for identifying patients of interest and defining comparators.
This thesis synthesizes and enumerates a set of challenges to addressing pre-analysis bias in observational studies and presents mixed-methods approaches and informatics solutions for overcoming a number of those obstacles. We develop frameworks, methods and tools for scalable and reliable phenotyping including data source granularity estimation, comprehensive concept set selection, index date specification, and structured data-based patient review for phenotype evaluation. We cover the research on potential bias in the unexposed comparator definition including systematic background rates estimation and interpretation, and definition and evaluation of the unexposed comparator.
We propose that the use of standardized approaches and methods as described in this thesis not only improves reliability but also increases responsiveness of observational evidence. To test this hypothesis, we designed and piloted a Data Consult Service - a service that generates new on-demand evidence at the bedside. We demonstrate that it is feasible to generate reliable evidence to address clinicians’ information needs in a robust and timely fashion and provide our analysis of the current limitations and future steps needed to scale such a service
Cancer Biomarker Research and Personalized Medicine
Biomarkers are measures of a biological state. The treatment of individual patients based on particular factors, such as biomarkers, distinguishes standard, generalized treatment plans from personalized medicine. Even though personalized medicine is applicable to most branches of medicine, the field of oncology is perhaps where it is most easily employed. Cancer is a heterogeneous disease; although patients may be diagnosed histologically with the same cancer type, their tumors can comprise varying tumor microenvironments and molecular characteristics that can impact treatment response and prognosis. There has been a major drive over the past decade to try and realize personalized cancer medicine through the discovery and use of disease-specific biomarkers. This book, entitled “Cancer Biomarker Research and Personalized Medicine”, encompasses 22 publications from colleagues working on a diverse range of cancers, including prostate, breast, ovarian, head and neck, liver, gastric, bladder, colorectal, and kidney. The biomarkers assessed in these studies include genes, intracellular or secreted proteins, exosomes, DNA, RNA, miRNA, circulating tumor cells, circulating immune cells, in addition to radiomic features
- …