Prognostic and therapeutic implications of distinct kinase expression patterns in different subtypes of breast cancer.

Different kinases are expressed in different clinical subsets of breast cancer. In this study, we assessed kinase expression patterns in different clinical subtypes of breast cancer, evaluated the prognostic and predictive values of kinase metagenes, and investigated their functions in vitro. Four hundred twenty-eight protein kinases in gene expression data were examined from 684 cases of breast cancer and 51 breast cancer cell lines to identify kinase expression patterns. We tested the prognostic value of kinase metagenes in 684 node-negative patients who received no adjuvant therapy and the predictive value in 233 patients who received uniform neoadjuvant chemotherapy. Twelve kinases were overexpressed in estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative, 7 in HER2(+), and 28 in ER(-)/HER2(-) cancers, respectively. We examined the functional role of 22 kinases overexpressed in ER(-)/HER2(-) cancers using siRNA. Downregulation of these kinases caused significant subtype-specific inhibition of cell growth in vitro. Two robust kinase clusters, including an immune kinase cluster and a mitosis kinase cluster, were present in all clinical subgroups. High mitosis kinase score was associated with worse prognosis but higher pathologic complete response (pCR) in ER(+)/HER2(-) cancers, but not in ER(-)/HER2(-) or HER2(+) cancers, in univariate and multivariate analyses including other genomic predictors (MammaPrint, genomic grade index, and the 76-gene signature). Conversely, higher immune kinase score was associated with better survival in ER(+)/HER2(-) and HER2(+) tumors and also predicted higher probability of pCR in HER2(+) cancers. Taken together, our results indicate that kinases regulating mitosis and immune functions convey distinct prognostic information that varies by clinical subtype.

Molecular characterization of breast cancer with high-resolution oligonucleotide comparative genomic hybridization array.

PURPOSE: We used high-resolution oligonucleotide comparative genomic hybridization (CGH) arrays and matching gene expression array data to identify dysregulated genes and to classify breast cancers according to gene copy number anomalies. EXPERIMENTAL DESIGN: DNA was extracted from 106 pretreatment fine needle aspirations of stage II-III breast cancers that received preoperative chemotherapy. CGH was done using Agilent Human 4 x 44K arrays. Gene expression data generated with Affymetrix U133A gene chips was also available on 103 patients. All P values were adjusted for multiple comparisons. RESULTS: The average number of copy number abnormalities in individual tumors was 76 (range 1-318). Eleven and 37 distinct minimal common regions were gained or lost in >20% of samples, respectively. Several potential therapeutic targets were identified, including FGFR1 that showed high-level amplification in 10% of cases. Close correlation between DNA copy number and mRNA expression levels was detected. Nonnegative matrix factorization (NMF) clustering of DNA copy number aberrations revealed three distinct molecular classes in this data set. NMF class I was characterized by a high rate of triple-negative cancers (64%) and gains of 6p21. VEGFA, E2F3, and NOTCH4 were also gained in 29% to 34% of triple-negative tumors. A gain of ERBB2 gene was observed in 52% of NMF class II and class III was characterized by a high rate of estrogen receptor-positive tumors (73%) and a low rate of pathologic complete response to preoperative chemotherapy (3%). CONCLUSION: The present study identified dysregulated genes that could classify breast cancer and may represent novel therapeutic targets for molecular subsets of cancers.