Experiments: 2. Subtyping and Integrative Analysis of Cellular Morphometric Context

Hierarchical clustering was adopted as the clustering algorithm for consensus clustering, which is implemented via R Bioconductor ConsensusClusterPlus package with chi-square distance as the distance function. The procedure was run for 500 iterations with a sampling rate of 0.8 on 203 patients, and the corresponding consensus clustering matrices with 2 to 9 clusters are shown in Fig. 2, where the matrices with 2 to 5 clusters reveal different levels of similarity among patients and matrices with 6 to 9 clusters provide little further information. Thus, we use the five-cluster result for integrative analysis with clinical outcomes and genomic signatures, where, due to insufficient patients in subtypes #1 (1 patient) and #2 (2 patients), we focus on the remaining three subtypes.

Fig. 3(a) shows the Kaplan-Meier survival plot for three major subtypes of the five-cluster consensus clustering result. The log-rank test p-value of 2.82e-5 indicates that the difference between survival times of subtype #5 patients and subtypes #3&#4 patients is statistically significant. The integration of genome-wide data from multiple platforms uncovered three molecular classes of lower-grade gliomas that were best represented by IDH and 1p/19q status: wild-type IDH, IDH mutation with 1p/19q codeletion, and IDH mutation without 1p/19q codeletion. Further Fisher's exact test reveals no enrichment between the cellular morphometric subtypes and these molecular subtypes. On the other hand, differential expressed genes between subtype #5 and subtypes #3&#4 (Fig. 3(b)), indicate enrichment of genes that mediate programmed cell death (apoptosis) by activation of caspases, and genes defining epithelial-mesenchymal transition, as in wound healing, fibrosis and metastasis (via MSigDB).