Selaru FM, Yin J, Olaru A, Mori Y, Xu Y, Epstein SH, Sato F, Deacu E, Wang S, Sterian A, Fulton A, Abraham JM, Shibata D, Baquet C, Stass SA, Meltzer SJ. An unsupervised approach to identify molecular phenotypic components influencing breast cancer features.
Cancer Res 2004;
64:1584-8. [PMID:
14996713 DOI:
10.1158/0008-5472.can-03-3208]
[Citation(s) in RCA: 16] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
To discover a biological basis for clinical subgroupings within breast cancers, we applied principal components (PCs) analysis to cDNA microarray data from 36 breast cancers. We correlated the resulting PCs with clinical features. The 35 PCs discovered were ranked in order of their impact on gene expression patterns. Interestingly, PC 7 identified a unique subgroup consisting of estrogen receptor (ER); (+) African-American patients. This group exhibited global molecular phenotypes significantly different from both ER (-) African-American women and ER (+) or ER (-) Caucasian women (P < 0.001). Additional significant PCs included PC 4, correlating with lymph node metastasis (P = 0.04), and PC 10, with tumor stage (stage 2 versus stage 3; P = 0.007). These results provide a molecular phenotypic basis for the existence of a biologically unique subgroup comprising ER (+) breast cancers from African-American patients. Moreover, these findings illustrate the potential of PCs analysis to detect molecular phenotypic bases for relevant clinical or biological features of human tumors in general.
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