The use of gene expression data from patient tumor samples to determine better treatment options is becoming increasingly common in clinical practice.10–12
However, inconsistencies in glioma molecular classification across various studies makes expression profiling a challenging endeavor for routine use in clinical practices outside major hospitals or commercial laboratories.13
Previous reports attempting glioma classification are all based on whole genome profiling of glioma samples derived from patients in Western countries.3–5,14
However, there exists no dataset generated from a large number of samples from an East Asian population that could be used for glioma classification. This study investigated a large number of samples from Chinese patients in an attempt to complement and/or validate existing molecular subtyping systems. In our study, a total of 225 samples were subjected to whole genome gene expression profiling. The data identified 3 major groups of gliomas.
The urgent need for an objective, molecularly based classification system for gliomas is highlighted by the high rate of divergent diagnoses, inexact prognostic capabilities, and poor therapeutic predictive properties based on the current histopathologic classification schemes.15–17
The TCGA network described a robust gene expression–based molecular classiﬁcation of GBMs that divided them into proneural, neural, classical, and mesenchymal subtypes.4
Phillips et al. defined 3 subtypes (mesenchymal, proneural, and proliferative) when they molecularly profiled several high-grade glioma samples.4
Other results from Li et al. identified 2 main subtypes, which they defined as GBM-rich (mesenchymal) and oligodendroglioma-rich (proneural) with use of an unsupervised clustering method.5
With use of consensus clustering, our results have identified 3 subtypes with robust differences in clinical characteristics. The G1 subgroup was characterized by good clinical outcome, young age, low malignant behaviors, and extraordinary high IDH1 mutation. G3 groups exhibited the opposite effect. The G2 subtype is the middle class of the aforementioned 2 subtypes. Of interest, every sample in the G1 group carried the IDH1 mutation. Also of note, the G2 subgroup showed a higher percentage loss of 1p and 19q. The G3 subgroup consisted of more GBMs than did either G1 or G2. Only 3 primary GBMs and 1 secondary GBM were included in the G1 subtype, and all 4 GBMs presented with mutations in the IDH1 gene. Our classification scheme also identified a spatial difference in glioma development, with G1 and G2 tumors occurring predominantly in the frontal lobe as opposed to the G3 subtype. IDH1 mutations are early events in the development of gliomas.13,18–20
All samples in the G1 group were accompanied with IDH1 mutation and young age. On the basis of IDH1 mutation status in our molecular classification, our classification system may more accurately reflect the process of glioma development.
We next aimed to validate our classification system with the use of 2 external datasets from the TCGA dataset containing GBMs only and a Rembrandt dataset containing all gliomas.3,21
Our classification system effectively classified the 202 TCGA GBM samples into the G1, G2, and G3 subtypes. The G1 samples were enriched with the proneural subtype, whereas the G2 samples were enriched with the neural subtype. The G3 samples were enriched with the classical and mesenchymal subtypes. Furthermore, patient survival analysis and age distribution in the G1, G2, and G3 subtypes from TCGA GBM samples closely mirrored the survival and age distribution found in the CGGA samples with all grades of gliomas. Additionally, our classification scheme could divide 475 samples from Rembrandt dataset into G1, G2, and G3 subgroups with different prognoses very clearly. These results indicate that our classification system based on CGGA samples can effectively group independent glioma samples into their respective subtypes based on their different characteristics.
To analyze our dataset in greater depth, we annotated the CGGA samples with the use of the TCGA system of classification. Proneural, neural, and mesenchymal glioma subtypes and their respective gene signatures were clearly identified. However, no classical subtype–associated gene signature was identified in the heat map. Only the proneural and mesenchymal subtypes were consistently identifiable across various studies. In our study, we report that proneural, mesenchymal, and neural, but not classical, gene signatures significantly existed in the CGGA glioma dataset when the TCGA classification system was applied. Moreover, the proneural subtype was associated with significantly better survival for all cases. The neural subtype was also associated with a better prognosis when compared with the classical and mesenchymal subtype. It should be highlighted that the gene expression pattern and clinical characteristics of the classical subtype resemble those of the mesenchymal subtype. Thus, we may treat them as one mesenchymal subtype. In addition, the proneural, neural, and mesenchymal subtypes were enriched in the G1, G2, and G3 subgroups, respectively. The difference in age distribution, IDH1 mutation, and loss of 1p and 19q in the proneural, neural, and mesenchymal groups was less significant than that of the G1, G2, and G3 subtypes in CGGA samples. This indicates that our classification system may more accurately classify gliomas based on clinical and genetic characteristics. Of note, G2 or neural subgroup has the modest prognosis in all gliomas in our dataset. However, G2 or neural subgroup has the poorest prognosis when only GBMs were considered. We may postulate that G2 or neural subgroup may have a more rapid progression when at a different stage of development.
In summary, our results have identified 3 subtypes of glioma based on whole genome gene expression profiling using a large number of samples from Chinese patients with glioma. Furthermore, our results were validated on an independent dataset from GBMs in the TCGA. We annotated our samples with use of the TCGA classification system. Of note, no significant classic gene signature was identified in our dataset, potentially highlighting differences between Chinese gliomas and gliomas of other cultures. We also found that the G1, G2, and G3 subtypes were enriched with proneural, neural, and mesenchymal subgroups, respectively. Our classification scheme may discriminate more clearly between clinical and genetic alterations than when the TCGA subtyping system is applied to the CGGA dataset. This finding indicates that only 3 main subtypes clearly exist in our dataset regardless of whether the TCGA or CGGA classification system is used.