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  • Limeta, Angelo,1996Chalmers tekniska högskola,Chalmers University of Technology (author)

Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors

  • Article/chapterEnglish2023

Publisher, publication year, extent ...

  • 2023
  • electronicrdacarrier

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  • LIBRIS-ID:oai:research.chalmers.se:dba13e4b-207a-46bc-b71b-ee7ee759181d
  • https://doi.org/10.1016/j.csbj.2023.07.032DOI
  • https://research.chalmers.se/publication/536993URI
  • http://kipublications.ki.se/Default.aspx?queryparsed=id:153620002URI

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  • Language:English
  • Summary in:English

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  • Subject category:for swepub-publicationtype
  • Subject category:ref swepub-contenttype

Notes

  • 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.

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  • Gatto, Francesco,1987Karolinska Institutet,Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)gatto (author)
  • Herrgard, M. J.BioInnovation Institute (BII) (author)
  • Ji, Boyang,1983BioInnovation Institute (BII)(Swepub:cth)boyang (author)
  • Nielsen, Jens B,1962Chalmers tekniska högskola,Chalmers University of Technology,BioInnovation Institute (BII)(Swepub:cth)nielsenj (author)
  • Chalmers tekniska högskolaKarolinska Institutet (creator_code:org_t)

Related titles

  • In:Computational and Structural Biotechnology Journal21, s. 3912-39192001-0370

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