Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach

Donald M Miller¹, Kavitha Yadanapudi², Veeresh Rai³, Shesh N Rai⁴, Joseph Chen⁵, Hermann B Frieboes⁶, Adrianna Masters⁷, Abigail McCallum⁸, Brian J Williams⁸

Affiliations

¹ Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY, USA. Electronic address: donaldmi@ulh.org.
² Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY, USA.
³ Brown Cancer Center, University of Louisville, Louisville, KY, USA.
⁴ Brown Cancer Center, University of Louisville, Louisville, KY, USA; Biostatistics and Informatics Shared Resources, University of Cincinnati Cancer Center, Cincinnati, OH, USA; Cancer Data Science Center of University of Cincinnati College of Medicine, Cincinnati, OH, USA.
⁵ Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, USA.
⁶ Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, USA; Center for Preventative Medicine, University of Louisville, Louisville, KY, USA.
⁷ Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Radiation Oncology, University of Louisville, Louisville, KY, USA.
⁸ Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Neurosurgery, University of Louisville, Louisville, KY, USA.

PMID: 37330006
DOI: 10.1016/j.amjms.2023.06.010

Review

Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach

Donald M Miller et al. Am J Med Sci. 2023 Sep.

. 2023 Sep;366(3):185-198.

doi: 10.1016/j.amjms.2023.06.010. Epub 2023 Jun 15.

Authors

Donald M Miller¹, Kavitha Yadanapudi², Veeresh Rai³, Shesh N Rai⁴, Joseph Chen⁵, Hermann B Frieboes⁶, Adrianna Masters⁷, Abigail McCallum⁸, Brian J Williams⁸

Affiliations

¹ Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY, USA. Electronic address: donaldmi@ulh.org.
² Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Medicine, School of Medicine, University of Louisville, Louisville, KY, USA.
³ Brown Cancer Center, University of Louisville, Louisville, KY, USA.
⁴ Brown Cancer Center, University of Louisville, Louisville, KY, USA; Biostatistics and Informatics Shared Resources, University of Cincinnati Cancer Center, Cincinnati, OH, USA; Cancer Data Science Center of University of Cincinnati College of Medicine, Cincinnati, OH, USA.
⁵ Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, USA.
⁶ Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, USA; Center for Preventative Medicine, University of Louisville, Louisville, KY, USA.
⁷ Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Radiation Oncology, University of Louisville, Louisville, KY, USA.
⁸ Brown Cancer Center, University of Louisville, Louisville, KY, USA; Department of Neurosurgery, University of Louisville, Louisville, KY, USA.

PMID: 37330006
DOI: 10.1016/j.amjms.2023.06.010

Abstract

Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.

Keywords: Glioblastoma; Immune cells; Metabolomics; Multi-omic analysis; Proteomics; Tumor microenvironment.

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Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach

Affiliations

Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach

Authors

Affiliations

Abstract

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Research Materials

Miscellaneous