Article
Quantitative expression profiling of highly degraded RNA from formalin-fixed, paraffin-embedded breast tumor biopsies by oligonucleotide microarrays

https://doi.org/10.1038/labinvest.2008.11Get rights and content
Under an Elsevier user license
open archive

Abstract

Microarray-based gene expression profiling is well suited for parallel quantitative analysis of large numbers of RNAs, but its application to cancer biopsies, particularly formalin-fixed, paraffin-embedded (FFPE) archived tissues, is limited by the poor quality of the RNA recovered. This represents a serious drawback, as FFPE tumor tissue banks are available with clinical and prognostic annotations, which could be exploited for molecular profiling studies, provided that reliable analytical technologies are found. We applied and evaluated here a microarray-based cDNA-mediated annealing, selection, extension and ligation (DASL) assay for analysis of 502 mRNAs in highly degraded total RNA extracted from cultured cells or FFPE breast cancer (MT) biopsies. The study included quantitative and qualitative comparison of data obtained by analysis of the same RNAs with genome-wide oligonucleotide microarrays vs DASL arrays and, by DASL, before and after extensive in vitro RNA fragmentation. The DASL-based expression profiling assay applied to RNA extracted from MCF-7 cells, before or after 24 h stimulation with a mitogenic dose of 17β-estradiol, consistently allowed to detect hormone-induced gene expression changes following extensive RNA degradation in vitro. Comparable results where obtained with tumor RNA extracted from FFPE MT biopsies (6 to 19 years old). The method proved itself sensitive, reproducible and accurate, when compared to results obtained by microarray analysis of RNA extracted from snap-frozen tissue of the same tumor.

Keywords

breast cancer
estrogen
expression profiling
FFPE
microarrays

Cited by (0)

Supplementary Information accompanies the paper on the Laboratory Investigation website (http://www.laboratoryinvestigation.org)

Disclosure

The authors have no conflict of interest.

Maria Ravo and Margherita Mutarelli: These authors contributed equally to this work, and should thereby both considered as first author.

Supplementary information

The online version of this article (doi:10.1038/labinvest.2008.11) contains supplementary material, which is available to authorized users.