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Abstract:
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Previous studies have described subtypes of ovarian cancer using gene expression data only. Since multiple data are available on the same set of tumor samples, we apply recently proposed integrative clustering method based on Non-negative Matrix Factorization (intNMF) on mRNA and DNA methylation data set from two ovarian cancer studies by (i) The Cancer Genome Atlas (TCGA) and (ii) Mayo Clinic. The TCGA and Mayo studies had 489 and 441 samples available with clinical data respectively. Application of intNMF to TCGA data resulted in four optimum clusters which closely overlapped with the gene expression clusters identified by TCGA network (p-value=2.4×10-14). Significant association of the clusters obtained by intNMF with time to recurrence (TTR) was found (p-value=3×10-2, log-rank test) while such association was non-significant with the clusters determined by TCGA network (p-value=0.24). In contrast, application of intNMF to the Mayo data resulted in three clusters which closely overlap with the clusters based on TCGA cluster signature assignment (p-value < 2.2×10-16). The association of TTR in the Mayo data with intNMF clusters was stronger too (p-value=1.2×10-4 vs 3.7×10-3).
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