Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies

Prajwal Ghimire; Ben Kinnersley; Golestan Karami; Prabhu Arumugam; Richard Houlston; Keyoumars Ashkan; Marc Modat; Thomas C Booth

doi:10.1093/noajnl/vdae055

Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies

Neurooncol Adv. 2024 Apr 5;6(1):vdae055. doi: 10.1093/noajnl/vdae055. eCollection 2024 Jan-Dec.

Authors

Prajwal Ghimire^{1

2}, Ben Kinnersley³, Golestan Karami⁴, Prabhu Arumugam⁴, Richard Houlston⁵, Keyoumars Ashkan¹, Marc Modat², Thomas C Booth²

Affiliations

¹ Department of Neurosurgery, Kings College Hospital NHS Foundation Trust, London, UK.
² School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
³ Department of Oncology, University College London, London, UK.
⁴ Genomics England, London, UK.
⁵ Division of Genetics and Epidemiology, The Institute of Cancer Research, Sutton, UK.

Abstract

Background: Immunotherapy is an effective "precision medicine" treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma.

Methods: A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered: CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis.

Results: Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed.

Conclusions: Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability.

Keywords: deep learning; glioblastoma; immunotherapy; machine learning; radiogenomics.