IDH mutation-specific radiomic signature in lower-grade gliomas

Abstract

Unravelling the heterogeneity is the central challenge for glioma precession oncology. In this study, we extracted quantitative image features from T2-weighted MR images and revealed that the isocitrate dehydrogenase (IDH) wild type and mutant lower grade gliomas (LGGs) differed in their expression of 146 radiomic descriptors. The logistic regression model algorithm further reduced these to 86 features. The classification model could discriminate the two types in both the training and validation sets with area under the curve values of 1.0000 and 0.9932, respectively. The transcriptome-radiomic analysis revealed that these features were associated with the immune response, biological adhesion, and several malignant behaviors, all of which are consistent with biological processes that are differentially expressed in IDH wild type and IDH mutant LGGs. Finally, a prognostic signature showed an ability to stratify IDH mutant LGGs into high and low risk groups with distinctive outcomes. By extracting a large number of radiomic features, we identified an IDH mutation-specific radiomic signature with prognostic implications. This radiomic signature may provide a way to non-invasively discriminate lower-grade gliomas as with or without the IDH mutation.

Keywords: isocitrate dehydrogenase; lower grade gliomas; prognostic signature; radiomic signature; transcriptome-radiomic analysis.

Figure 1

Radiomic patterns of 431 features in LGGs. Each column corresponds to one patient in the training cohort, and each row corresponds to one z-score-normalized radiomic feature. Unsupervised clustering between radiomic features and LGG samples revealed two distinct radiomic patterns. The second cluster showed a higher frequency of the IDH mutation (**, P < 0.01).

Figure 2

Identification and validation of the IDH mutation-specific radiomic signature using the logistic regression. (A) A total of 146 radiomic features were selected using SAM methods. The mean value and the corresponding groups of the differentially expressed features are listed. (B and C) In the training set, the logistic regression-derived radiomic features was able to separate LGGs into two groups with high sensitivity and specificity. The AUCs were 0.86, 0.92, 0.98 and 1.00 for 10, 20, 50 and 86 radiomic features, respectively. (D) Importantly, these 86 features comprised a signature enabling the distinction of LGGs into IDH^MUT and IDH^WT groups with an AUC of 0.9932. (E) A Western Blot assay confirmed the expression of the mutant IDH1 protein (IDH1^R132H, 1:200, DIA-H05, Dianova). (F) The radiogenomic analysis of xenograft gliomas of nude mice. Differential radiomic features between LGGs patients could be used to distinguish the IDH mutation phenotype in the xenograft model as well.

Figure 3

Identification of a prognostic signature based on differential features between IDH^WT and IDH^MUT LGGs. (A) The expression pattern of 16 radiomic features along with the elevation of the risk score. The corresponding survival data and IDH status are listed. (B) In 158 LGGs cohort, the IDH^MUT patients survived longer than the IDH^WT patients (P = 0.0045, HR = 0.4024, 95%CI:0.16–0.70). (C) The risk score divided the LGGs into two groups with distinct outcomes (P = 0.0017, HR = 0.3207, 95%CI:0.19–0.68). (D) IDH^MUT LGGs with a low risk score showed a favorable prognosis compared with the IDH^WT patients (P = 0.0483, HR = 0.4232, 95%, CI:0.17–0.99). (E) Further, the overall survival time of the IDH^MUT patients with a high risk score was not significantly different from that of the IDH^WT (P = 0.199).

Figure 4

Gene annotation of 48 patients with radiomic and transcriptome data. (A) The radiomic features, clinical characteristics, and associated genes are presented. (B) The positively associated genes (blue) that participated in GO in terms (yellow) of apoptosis, cell growth, and metabolic processes.

Figure 5

Case examples of LGG patients with T2-weighted images. Case 1 was a 39-year-old male with an IDH mutant LGG. This patient was classified into the IDH^MUT group with a relatively low risk score based on the radiomic features. In contrast, case 2 was a 46-year-old male with an IDH wildtype LGG, who was correctly classified into the IDH^WT group with a high risk score.

Figure 6. The workflow of the radiogenomic analysis for the identification and validation of the IDH mutation-specific radiomic signature in lower grade gliomas.