Gliomas are highly fatal and heterogeneous brain tumors. Molecular subtyping is critical for accurate diagnosis and prediction of patient outcomes, with isocitrate dehydrogenase (IDH) mutations being the most informative tumor feature. Molecular subtyping currently relies on resected tumor samples, highlighting the need for non-invasive, preoperative biomarkers. We investigated the integration of glioma polygenic risk scores (PRS) and radiomic features for prediction of IDH mutation status. The elastic net classifier was trained on a panel of 256 radiomic features from preoperative MRI scans, a germline PRS for IDH mutation and demographic information from 159 glioma cases in The Cancer Genome Atlas. Combining radiomics features with the PRS increased the area under the receiver operating characteristic curve (AUC) for distinguishing IDH-wildtype vs. IDH-mutant glioma from 0.824 to 0.890 (PΔAUC=0.0016). Incorporating age at diagnosis and sex further improved the classifier (AUC=0.920). Our multimodal classifier also predicted survival. Patients predicted to have IDH-mutant vs. IDH-wildtype tumors had significantly lower mortality risk (hazard ratio (HR)=0.27, 95% CI: 0.14-0.51, P=6.3×10-5), comparable to prognostic trajectories observed for biopsy-confirmed IDH mutation status. In conclusion, our study shows that augmenting imaging-based classifiers with genetic risk profiles may help delineate molecular subtypes and improve the timely, non-invasive clinical assessment of glioma patients.
Competing Interest StatementThe authors have declared no competing interest.
Funding StatementThis study did not receive any funding.
Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
Genotype data of glioma cases from The Cancer Genome Atlas (TCGA) are available from the Database of Genotypes and Phenotypes (dbGaP; https://www.ncbi.nlm.nih.gov/gap/) under accession phs000178. Radiomic data of glioma cases from TCGA can be obtained from The Cancer Imaging Archive (https://www.cancerimagingarchive.net).
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.
Yes
Data AvailabilityGenotype data of glioma cases from The Cancer Genome Atlas (TCGA) are available from the Database of Genotypes and Phenotypes (dbGaP) under accession phs000178. Radiomic data of glioma cases from TCGA can be obtained from The Cancer Imaging Archive (https://www.cancerimagingarchive.net). The data required for fitting polygenic risk scores for glioma are available at: https://zenodo.org/records/10790748.
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