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


CONCLUSIONS
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.
Mattie
Michael D
MD
UCSF Comprehensive Cancer Center, Department of Urology, San Francisco, California 94115, USA. michael.d.mattie@gsk.com
Benz
Christopher C
CC
Bowers
Jessica
J
Sensinger
Kelly
K
Wong
Linda
L
Scott
Gary K
GK
Fedele
Vita
V
Ginzinger
David
D
Getts
Robert
R
Haqq
Chris
C
D014408:Biomarkers, Tumor / Q000032:analysis*; D001706:Biopsy; D001943:Breast Neoplasms / Q000175:diagnosis*; D016000:Cluster Analysis; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D018734:Genes, erbB-2; D006801:Humans; D008297:Male; D035683:MicroRNAs / Q000378:metabolism*; D010641:Phenotype; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D011960:Receptors, Estrogen / Q000032:analysis; D015203:Reproducibility of Results; D020133:Reverse Transcriptase Polymerase Chain Reaction / Q000379:methods; D012680:Sensitivity and Specificity; D014407:Tumor Cells, Cultured
D014408:Biomarkers, Tumor; D035683:MicroRNAs; D011960:Receptors, Estrogen