Validation of (serous) ovarian cancer gene expression signatures for survival and subtype in formalin fixed paraffin embedded tissues

Abstract

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Introduction

Gene expression signatures have been identified for epithelial ovarian cancer survival (TCGA) and intrinsic subtypes (Tothill et al.). One obstacle to clinical translation is that these signatures were developed using frozen tissue, whereas usually only formalin-fixed, paraffin embedded (FFPE) tissue is available. The aim of this study was to determine if gene expression signatures can be translated to fixed archival tissues.

Methods

RNA extracted from FFPE sections from 240 primary ovarian cancers was analyzed by DASL on Illumina BeadChip arrays. Concordance of expression at the individual gene level was assessed by comparing array data from the same cancers (30 frozen samples analyzed on Affymetrix arrays versus FFPE DASL).

Results

The correlation between FFPE and frozen survival signature estimates was 0.774. The TCGA signature using DASL was predictive of survival in 106 advanced stage high grade serous ovarian cancers
(median survival 33 versus 60 months, estimated hazard ratio for death 2.30, P = 0.0007). Similar to Tothill, we found using DASL that most high grade serous ovarian cancers (102/110, 93%) were assigned to subtypes 1, 2, 4 and 5, whereas most endometrioid, clear cell, mucinous and low grade serous cases (39/57, 68%) were assigned to subtypes 3 and 6 (P < 10e-15).

Conclusions

Although individual probe estimates of microarrays may be weakly correlated between FFPE and frozen samples, combinations of probes have robust ability to predict survival and subtype. This suggests that it may be possible to use these signatures for prognostic and predictive purposes as we seek to individualize the treatment of ovarian cancer.

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Highlights

► Gene expression profiles of serous cancer demonstrate biologic and prognostic heterogeneity that we discerned from archival paraffin specimens.
► This approach facilitates retrospective and prospective analyses of the disease using more population-based cohorts