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Radiogenomics to characterize the immune-related prognostic signature associated with biological functions in glioblastoma

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

An Editorial Comment to this article was published on 28 October 2022

Abstract

Objectives

The tumor microenvironment and immune cell infiltration (ICI) associated with glioblastoma (GBM) play a vital role in cancer development, progression, and prognosis. This study aimed to establish an ICI-related prognostic biomarker and explore the associations between ICI signatures and radiomic features in patients with GBM.

Methods

The gene expression and survival data of patients with GBM were obtained from three databases. Based on the ICI pattern, an individualized ICI score for each GBM patient was developed in the discovery set (n = 400) and independently verified in the validation set (n = 374). A total of 5915 radiomic features were extracted from the intratumoral and peritumoral regions. Recursive feature elimination and support vector machine methods were performed to select the key features and generate a model predictive of low- or high- ICI scores. The prognostic value of the identified radio genomic model was examined in an independent dataset (n = 149) using imaging and survival data.

Results

We found that higher ICI scores often indicated worse patient prognosis (multivariable hazard ratio: 0.48 and 0.63 in discovery and validation set, respectively) and higher expression levels of immune checkpoint-related genes. A model that combined 11 radiomic features could well distinguish tumors with different ICI scores (AUC = 0.96, accuracy = 94%). This model was proven to be helpful for noninvasive prognostic stratification in an independent validation cohort.

Conclusions

ICI scores may serve as an effective prognostic biomarker to characterize potential biological processes in patients with GBM. This ICI signature can be evaluated noninvasively through radiogenomic analysis.

Key Points

• Immune cell infiltration (ICI) scores can serve as an effective prognostic biomarker in patients with glioblastoma.

• The ICI signature can be evaluated noninvasively through radiomic features derived from the intratumoral and peritumoral regions.

• The prognostic value of the radiogenomic model can be verified by independent survival and MRI data.

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Abbreviations

AUC:

Area under the curve

CGGA:

Chinese Glioma Genome Atlas

DEGs:

Differentially Expressed Genes

FPKM:

Fragments per kilobase per million

GBM:

Glioblastoma

GO:

Gene Ontology

GSEA:

Gene set enrichment analysis

ICCs:

Intraclass correlation coefficients

ICI:

Immune cell infiltration

MAPK:

Mitogen-activated protein kinase

RFECV:

Recursive feature elimination with cross-validation

ROIs:

Regions of interest

SVM:

Support vector machine

T1CE:

T1 contrast-enhanced MRI sequences

T2:

T2-weighted MRI sequences

TMB:

Tumor mutation burden

TCGA:

The Cancer Genome Atlas

TME:

Tumor microenvironment

VEGF:

Vascular endothelial growth factor

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Acknowledgements

The anonymous data from Nanjing Brain Hospital was de-identified and collected as part of routine clinical practice. Also, most of the imaging and genomic data were collected from public databases. Therefore, the local ethics board waived the need for written informed consent.

Funding

This study has received funding by the grants from the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. SJCX21_0643), the project of Jiangsu Provincial Medical Youth Talent (No. QNRC2016047), the Jiangsu Provincial Medical Innovation Team (No. CXTDA2017050), the Nanjing Municipal Health Commission (YKK20097), and Jiangsu Provincial Health Commission (M2021003).

Author information

Author notes

  1. Dongming Liu and Jiu Chen contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China

    Dongming Liu, Honglin Ge, Zhen Yan, Bei Luo, Xinhua Hu, Kun Yang, Yong Liu, Hongyi Liu & Wenbin Zhang

  2. Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, 210029, Jiangsu, China

    Jiu Chen

  3. Institute of Brain Sciences, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China

    Jiu Chen, Xinhua Hu, Hongyi Liu & Wenbin Zhang

Authors
  1. Dongming Liu
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Corresponding authors

Correspondence to Hongyi Liu or Wenbin Zhang.

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Guarantor

The scientific guarantor of this publication is Prof. Hongyi Liu.

Conflicts of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Transcriptome data and partial imaging data were obtained from The Cancer Genome Atlas (TCGA) project and The Cancer Imaging Archive (TCIA) database.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Liu, D., Chen, J., Ge, H. et al. Radiogenomics to characterize the immune-related prognostic signature associated with biological functions in glioblastoma. Eur Radiol 33, 209–220 (2023). https://doi.org/10.1007/s00330-022-09012-x

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  • DOI: https://doi.org/10.1007/s00330-022-09012-x

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