Multiomics Analyses to Deliver the Most Effective Treatment to Every Patient With Inflammatory Bowel Disease

Weersma, Rinse K.; Xavier, Ramnik J.; Vermeire, Séverine; Barrett, Jeffrey C.

doi:10.1053/j.gastro.2018.07.039

Cited by 27 publications

(19 citation statements)

References 9 publications

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“…In addition, early childhood exposure to antibiotics, birth mode and limited childhood exposure to environmental microorganisms can also influence susceptibility to IBD development [16]. Although a great progress has been made in our understanding of IBD pathogenesis, translating this knowledge into a personalized clinical decision is still far from being achieved [17,18]. Progress to date indicates that IBD is a multifactorial disease, therefore, a systems biology approach aiming to integrate biological omics and non-omics datasets can be a solution to resolve the complexity of the disease etiology and its heterogenous clinical outcomes.…”

Section: Introductionmentioning

confidence: 99%

Integrating omics for a better understanding of Inflammatory Bowel Disease: a step towards personalized medicine

2019

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Background: Inflammatory Bowel Disease (IBD) is a multifactorial chronic disease. Understanding only one aspect of IBD pathogenesis does not reflect the complex nature of IBD nor will it improve its clinical management. Therefore, it is vital to dissect the interactions between the different players in IBD pathogenesis in order to understand the biology of the disease and enhance its clinical outcomes. Aims:To provide an overview of the available omics data used to assess the potential mechanisms through which various players are contributing to IBD pathogenesis and propose a precision medicine model to fill the current knowledge gap in IBD.Results: Several studies have reported microbial dysbiosis, immune and metabolic dysregulation in IBD patients, however, this data is not sufficient to create signatures that can differentiate between the disease subtypes or between disease relapse and remission. Conclusions:We summarized the current knowledge in the application of omics in IBD patients, and we showed that the current knowledge gap in IBD hinders the improvements of clinical decision for treatment as well as the prediction of disease relapse. We propose one way to fill this gap by implementing integrative analysis of various omics datasets generated from one patient at a single time point.

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Section: Introductionmentioning

confidence: 99%

Integrating omics for a better understanding of Inflammatory Bowel Disease: a step towards personalized medicine

2019

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“…This was exemplified to some extent by the RISK cohort findings [34]. Innovative efforts at integrating different data sets could provide important new insights into risk identification and optimized treatment for IBD in the near future [112]. Another important direction is to characterize IBD disease progression and response to treatment at an in-depth cellular level for a deeper definition of the pathophysiological context.…”

Section: Discussionmentioning

confidence: 99%

Molecular Profiling of Inflammatory Bowel Disease: Is It Ready for Use in Clinical Decision-Making?

Lee

Cleynen

2019

Cells

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Inflammatory bowel disease (IBD) is a heterogeneous disorder in terms of age at onset, clinical phenotypes, severity, disease course, and response to therapy. This underlines the need for predictive and precision medicine that can optimize diagnosis and disease management, provide more cost-effective strategies, and minimize the risk of adverse events. Ideally, we can leverage molecular profiling to predict the risk to develop IBD and disease progression. Despite substantial successes of genome-wide association studies in the identification of genetic variants affecting IBD susceptibility, molecular profiling of disease onset and progression as well as of treatment responses has lagged behind. Still, thanks to technological advances and good study designs, predicting phenotypes using genomics and transcriptomics in IBD has been rapidly evolving. In this review, we summarize the current status of prediction of disease risk, clinical course, and response to therapy based on clinical case presentations. We also discuss the potential and limitations of the currently used approaches.

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“…Precision medicine holds great promise to improve the landscape of IBD course of care for an individual patient, providing the most beneficial therapy while minimising the risk. The ultimate goals of precision medicine include stratifying patients based on disease subtypes and severity, disease progression and treatment response using personal and clinical data coupled with molecular profiling data of patients 1 2. IBD, with its two main subtypes, Crohn’s disease (CD) and UC, is a complex inflammatory disease with a wide range of contributing factors including host genetics, immune system, environmental exposures and the gut microbiome 3–5.…”

Section: Introductionmentioning

confidence: 99%

“…Most important sources of data in IBD comprise study cohorts, clinical trials, administrative and electronic health record databases, patient-reported outcomes databases, medical imaging databases and omics datasets (including genomics, transcriptomics, proteomics and metabolomics, as well as environmental omics) (figure 1). The use of big data in IBD allows medical researchers to reveal disease-related trends, associations and patterns to propel our understanding of IBD forward and to inform clinical practice 2 8. However, due to the high complexity of big data and the long list of confounding factors, interpreting these data is not trivial and warrants approaches that can uncover hidden patterns in these large and complex datasets 9…”

Section: Introductionmentioning

confidence: 99%

“…The use of big data in IBD allows medical researchers to reveal disease-related trends, associations and patterns to propel our understanding of IBD forward and to inform clinical practice. 2 8 However, due to the high complexity of big data and the long list of confounding factors, interpreting these data is not trivial and warrants approaches that can uncover hidden patterns in these large and complex datasets. 9 …”

Section: Introductionmentioning

confidence: 99%

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Big data in IBD: big progress for clinical practice

Tabib

Madgwick

Sudhakar³

et al. 2020

Gut

139

108

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IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.

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Multiomics Analyses to Deliver the Most Effective Treatment to Every Patient With Inflammatory Bowel Disease

Cited by 27 publications

References 9 publications

Integrating omics for a better understanding of Inflammatory Bowel Disease: a step towards personalized medicine

Integrating omics for a better understanding of Inflammatory Bowel Disease: a step towards personalized medicine

Molecular Profiling of Inflammatory Bowel Disease: Is It Ready for Use in Clinical Decision-Making?

Big data in IBD: big progress for clinical practice

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