Research

Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies

Michael D Mattie¹ , Christopher C Benz⁴ , Jessica Bowers² , Kelly Sensinger² , Linda Wong³ , Gary K Scott⁴ , Vita Fedele¹ , David Ginzinger³ , Robert Getts² and Chris Haqq¹

¹  UCSF Comprehensive Cancer Center, Department of Urology, San Francisco, California 94115, USA

²  Genisphere Inc., Hatfield, Pennsylvania 19440, USA

³  Applied Biosystems, Foster City, California 94404, USA

⁴  Buck Institute for Age Research, Novato, California 94945, USA

author email corresponding author email

Molecular Cancer 2006, 5:24doi:10.1186/1476-4598-5-24

Published:	19 June 2006

Abstract

Background

Recent studies indicate that microRNAs (miRNAs) are mechanistically involved in the development of various human malignancies, suggesting that they represent a promising new class of cancer biomarkers. However, previously reported methods for measuring miRNA expression consume large amounts of tissue, prohibiting high-throughput miRNA profiling from typically small clinical samples such as excision or core needle biopsies of breast or prostate cancer. Here we describe a novel combination of linear amplification and labeling of miRNA for highly sensitive expression microarray profiling requiring only picogram quantities of purified microRNA.

Results

Comparison of microarray and qRT-PCR measured miRNA levels from two different prostate cancer cell lines showed concordance between the two platforms (Pearson correlation R² = 0.81); and extension of the amplification, labeling and microarray platform was successfully demonstrated using clinical core and excision biopsy samples from breast and prostate cancer patients. Unsupervised clustering analysis of the prostate biopsy microarrays separated advanced and metastatic prostate cancers from pooled normal prostatic samples and from a non-malignant precursor lesion. Unsupervised clustering of the breast cancer microarrays significantly distinguished ErbB2-positive/ER-negative, ErbB2-positive/ER-positive, and ErbB2-negative/ER-positive breast cancer phenotypes (Fisher exact test, p = 0.03); as well, supervised analysis of these microarray profiles identified distinct miRNA subsets distinguishing ErbB2-positive from ErbB2-negative and ER-positive from ER-negative breast cancers, independent of other clinically important parameters (patient age; tumor size, node status and proliferation index).

Conclusion

In sum, these findings demonstrate that optimized high-throughput microRNA expression profiling offers novel biomarker identification from typically small clinical samples such as breast and prostate cancer biopsies.