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Leveraging high-res...
Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors
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- Limeta, Angelo, 1996 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Gatto, Francesco, 1987 (författare)
- Karolinska Institutet,Chalmers tekniska högskola,Chalmers University of Technology
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- Herrgard, M. J. (författare)
- BioInnovation Institute (BII)
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visa fler...
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- Ji, Boyang, 1983 (författare)
- BioInnovation Institute (BII)
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- Nielsen, Jens B, 1962 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,BioInnovation Institute (BII)
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visa färre...
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(creator_code:org_t)
- 2023
- 2023
- Engelska.
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Ingår i: Computational and Structural Biotechnology Journal. - 2001-0370. ; 21, s. 3912-3919
- Relaterad länk:
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https://research.cha... (primary) (free)
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https://doi.org/10.1...
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https://research.cha...
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http://kipublication...
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Abstract
Ämnesord
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- A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Nyckelord
- Immune related adverse events
- Omics
- Predictive models
- Immune-checkpoint inhibitors
- Biomarkers
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- for (ämneskategori)
- ref (ämneskategori)
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