Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer

Abstract

Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding "angiogenesis signature" was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials.

Figure 1. Four binary classifications of high grade ovarian serous cancer.

The ISIS algorithm identified four independent binary partition classifications (splits) of 129 ovarian cancer samples. Each binary classification is supported by an independently selected set of 100 genes (module). The top panel of this figure shows four horizontal bars representing the classification of the 129 tumor samples (columns) with respect to the gene modules. Red indicates that a patient was classified into the smaller group resulting from the respective split (g1) and white indicates the classification into the larger group (g0). The heatmap in the lower panel represents the expression profiles of the gene modules supporting the four binary classifications. Each row represents a gene, each column a patient and each cell correspond to a gene and its expression level; yellow indicates an expression level of a gene above its mean across the patients and blue below its mean.

Figure 2. Validation of angiogenic ovarian cancer classification in our dataset and ten independent validation datasets.

Panels A, D and G display the gene expression of the 100 genes used to classify ovarian tumors into angiogenic and non-angiogenic subtypes in our dataset (129 patients), the high grade, late stage, serous tumors (1,090 patients) and all tumors (1,606 patients) in the validation set, respectively. Panels D, E and F report the corresponding distribution of the scaled subtype scores. Panels B, D and F reports the (overall) survival curves of patients having tumors of angiogenic or non-angiogenic subtype in the corresponding datasets.