Unsupervised analysis reveals two molecular subgroups of serous ovarian cancer with distinct gene expression profiles and survival

Abstract

Purpose: Ovarian cancer is typically diagnosed at late stages, and thus, patients' prognosis is poor. Improvement in treatment outcomes depends, at least partly, on better understanding of ovarian cancer biology and finding new molecular markers and therapeutic targets.

Methods: An unsupervised method of data analysis, singular value decomposition, was applied to analyze microarray data from 101 ovarian cancer samples; then, selected genes were validated by quantitative PCR.

Results: We found that the major factor influencing gene expression in ovarian cancer was tumor histological type. The next major source of variability was traced to a set of genes mainly associated with extracellular matrix, cell motility, adhesion, and immunological response. Hierarchical clustering based on the expression of these genes revealed two clusters of ovarian cancers with different molecular profiles and distinct overall survival (OS). Patients with higher expression of these genes had shorter OS than those with lower expression. The two clusters did not derive from high- versus low-grade serous carcinomas and were unrelated to histological (ovarian vs. fallopian) origin. Interestingly, there was considerable overlap between identified prognostic signature and a recently described invasion-associated signature related to stromal desmoplastic reaction. Several genes from this signature were validated by quantitative PCR; two of them-DSPG3 and LOX-were validated both in the initial and independent sets of samples and were significantly associated with OS and disease-free survival.

Conclusions: We distinguished two molecular subgroups of serous ovarian cancers characterized by distinct OS. Among differentially expressed genes, some may potentially be used as prognostic markers. In our opinion, unsupervised methods of microarray data analysis are more effective than supervised methods in identifying intrinsic, biologically sound sources of variability. Moreover, as histological type of the tumor is the greatest source of variability in ovarian cancer and may interfere with analyses of other features, it seems reasonable to use histologically homogeneous groups of tumors in microarray experiments.

Keywords: Dermatan sulfate proteoglycan 3 (DSPG3); Gene expression analysis; Lysyl oxidase (LOX); Ovarian cancer; Prognostic biomarkers; Singular value decomposition (SVD).

Fig. 1

Hierarchical clustering of samples based on transcript expression levels from the first SVD mode. The SVD was done on all 101 cancer samples: 74 serous (pink), 12 endometrioid (dark blue), 9 clear cell (light blue), and 6 undifferentiated (green). Clear-cell and endometrioid cancers grouped together and showed common gene expression patterns that were distinct from those of the remaining tumor samples. Undifferentiated cancers were dispersed mostly among and had gene expression patterns similar to neighboring serous samples

Fig. 2

Relationship between SVD modes. Venn diagram shows the numbers of probe sets and genes (in brackets) obtained in SVD. All 116 probe sets in the second mode of SVD carried out on all tumors (orange) were among the 332 in the first mode of SVD, which was carried out on serous and undifferentiated tumors (blue). This suggests that the second mode of SVD done on all cancer samples, corresponded to the same biological feature(s) as the first mode of SVD done only on serous and undifferentiated cancers

Fig. 3

a Hierarchical clustering based on the expression of the 151-probe set signature revealed two clusters of ovarian cancer with distinct molecular profiles. Four undifferentiated and 68 serous samples with complete clinical and molecular data were used for clustering. b The Kaplan–Meier survival analysis of patient OS was carried out using the log-rank test for each cluster. The two clusters were characterized by different OS (p = 0.021). Patients who had tumors with higher expression of the 151 transcripts (cluster 2) had shorter OS [median value = 735, 1 quartile range (QR) = 652, 3 QR = 897], while those with tumors showing lower expression of these genes (cluster 1) had longer OS (median value = 1194.5, 1 QR = 767.25, 3 QR = 1867.75)

Fig. 4

Hierarchical clustering of cancer and normal samples from (Marquez et al. 2005) based on the expression levels of our 151-probe set signature [only 73 probe sets matched due to the older version of the array used in (Marquez et al. 2005)]. Serous ovarian cancers from Marquez study were divided into two clusters; however, normal controls were not, and there was no relationship between the expression patterns of either cluster and particular type of normal control

Fig. 5

a Hierarchical clustering of serous and undifferentiated cancer samples from our experiment using a previously reported gene signature for the malignant potential of ovarian tumors (Ouellet et al. 2005). The clustering pattern was very similar to that obtained using our 151-probe set signature owing to the expression patterns of the only two genes common to the two signatures (COL11A1 and MMP2). Similar expression patterns were observed for laminin beta 1 and homeobox B7, but other genes showed random patterns. Dots indicate tumor samples that were clustered in a different manner from the analysis carried out using our signature: red and black dots indicate samples that were previously included in clusters 2 and 1, respectively. b Kaplan–Meier survival analysis of patient OS (log-rank test) based on cluster (P = 0.015)