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PMID: 14711987 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.

Gene expression profiling identifies clinically relevant subtypes of prostate cancer.

Lapointe J, Li C, Higgins JP, van de Rijn M, Bair E, Montgomery K, Ferrari M, Egevad L, Rayford W, Bergerheim U, Ekman P, DeMarzo AM, Tibshirani R, Botstein D, Brown PO, Brooks JD, Pollack JR

Abstract

Prostate cancer, a leading cause of cancer death, displays a broad range of clinical behavior from relatively indolent to aggressive metastatic disease. To explore potential molecular variation underlying this clinical heterogeneity, we profiled gene expression in 62 primary prostate tumors, as well as 41 normal prostate specimens and nine lymph node metastases, using cDNA microarrays containing approximately 26,000 genes. Unsupervised hierarchical clustering readily distinguished tumors from normal samples, and further identified three subclasses of prostate tumors based on distinct patterns of gene expression. High-grade and advanced stage tumors, as well as tumors associated with recurrence, were disproportionately represented among two of the three subtypes, one of which also included most lymph node metastases. To further characterize the clinical relevance of tumor subtypes, we evaluated as surrogate markers two genes differentially expressed among tumor subgroups by using immunohistochemistry on tissue microarrays representing an independent set of 225 prostate tumors. Positive staining for MUC1, a gene highly expressed in the subgroups with "aggressive" clinicopathological features, was associated with an elevated risk of recurrence (P = 0.003), whereas strong staining for AZGP1, a gene highly expressed in the other subgroup, was associated with a decreased risk of recurrence (P = 0.0008). In multivariate analysis, MUC1 and AZGP1 staining were strong predictors of tumor recurrence independent of tumor grade, stage, and preoperative prostate-specific antigen levels. Our results suggest that prostate tumors can be usefully classified according to their gene expression patterns, and these tumor subtypes may provide a basis for improved prognostication and treatment stratification.

MeSH Terms
Biomarkers, Tumor/genetics Case-Control Studies Gene Expression Profiling Humans Lymphatic Metastasis Male Mucin-1/genetics Oligonucleotide Array Sequence Analysis Prognosis Prostatic Neoplasms/classification,genetics,pathology Recurrence Risk Factors Seminal Plasma Proteins/genetics Zn-Alpha-2-Glycoprotein
Chemicals
Biomarkers, Tumor Mucin-1 Seminal Plasma Proteins Zn-Alpha-2-Glycoprotein
Authors & Affiliations
17 authors, click to expand affiliations / ORCID
Lapointe Jacques
Department of Pathology, Stanford University, Stanford, CA 94305, USA.
Li Chunde
Higgins John P
van de Rijn Matt
Bair Eric
Montgomery Kelli
Ferrari Michelle
Egevad Lars
Rayford Walter
Bergerheim Ulf
Ekman Peter
DeMarzo Angelo M
Tibshirani Robert
Botstein David
Brown Patrick O
Brooks James D
Pollack Jonathan R
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Article Info
Journal
Proceedings of the National Academy of Sciences of the United States of America
Abbr.
Proc Natl Acad Sci U S A
ISSN
0027-8424
Published
2004-01-20
Epub
2004-00-07
Pages
811-6
Language
English
Region
United States
NLM ID
7505876
PMCID
PMC321763
Subset
IM
Grants
NCI NIH HHS · P50 CA058236 · United States
NCI NIH HHS · U01 CA085129 · United States
NCI NIH HHS · P50CA58236 · United States
NCI NIH HHS · U01CA85129 · United States
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