Identification of novel kinase targets for the treatment of estrogen receptor-negative breast cancer

Abstract

Purpose: Previous gene expression profiling studies of breast cancer have focused on the entire genome to identify genes differentially expressed between estrogen receptor (ER) alpha-positive and ER-alpha-negative cancers.

Experimental design: Here, we used gene expression microarray profiling to identify a distinct kinase gene expression profile that identifies ER-negative breast tumors and subsets ER-negative breast tumors into four distinct subtypes.

Results: Based on the types of kinases expressed in these clusters, we identify a cell cycle regulatory subset, a S6 kinase pathway cluster, an immunomodulatory kinase-expressing cluster, and a mitogen-activated protein kinase pathway cluster. Furthermore, we show that this specific kinase profile is validated using independent sets of human tumors and is also seen in a panel of breast cancer cell lines. Kinase expression knockdown studies show that many of these kinases are essential for the growth of ER-negative, but not ER-positive, breast cancer cell lines. Finally, survival analysis of patients with breast cancer shows that the S6 kinase pathway signature subtype of ER-negative cancers confers an extremely poor prognosis, whereas patients whose tumors express high levels of immunomodulatory kinases have a significantly better prognosis.

Conclusions: This study identifies a list of kinases that are prognostic and may serve as druggable targets for the treatment of ER-negative breast cancer.

Fig. 1

Supervised hierarchical clustering identifies different subsets of ER-negative breast cancer. (A) Hierarchical clustering analysis of kinases that distinguish ER-positive from ER-negative human breast tumors. Gene expression analysis of 102 human breast tumors reveals 86 kinases and kinase-associated genes that are differentially expressed between ER-negative and ER-positive human breast tumors with a permutation P-value <.05. (B) Unsupervised hierarchical clustering analysis of only ER-negative tumors using kinases and kinase-associated genes overexpressed in ER-negative breast cancers reveals 4 distinct subsets of ER-negative breast cancer. These four subset are defined by kinases that are involved in cell cycle checkpoint control (group 1), S6 kinase signaling (group 2), immunomodulatory (group 3), or paracrine signaling involving many MAPKs (group 4). Subtype refers to the breast cancer subtypes identified by Sotiriou et al. (46). BRCA1 and BRCA2 relative gene expression has been included for comparison.

Fig. 1

Supervised hierarchical clustering identifies different subsets of ER-negative breast cancer. (A) Hierarchical clustering analysis of kinases that distinguish ER-positive from ER-negative human breast tumors. Gene expression analysis of 102 human breast tumors reveals 86 kinases and kinase-associated genes that are differentially expressed between ER-negative and ER-positive human breast tumors with a permutation P-value <.05. (B) Unsupervised hierarchical clustering analysis of only ER-negative tumors using kinases and kinase-associated genes overexpressed in ER-negative breast cancers reveals 4 distinct subsets of ER-negative breast cancer. These four subset are defined by kinases that are involved in cell cycle checkpoint control (group 1), S6 kinase signaling (group 2), immunomodulatory (group 3), or paracrine signaling involving many MAPKs (group 4). Subtype refers to the breast cancer subtypes identified by Sotiriou et al. (46). BRCA1 and BRCA2 relative gene expression has been included for comparison.

Fig. 2

Kinase overexpression validated in independent human tumor sample data sets and in a panel of breast cancer cell lines. (A) The expression of 34 of 34 kinases and kinase-associated genes identified in the array profiling were validated as being more highly expressed in ER-negative tumors compared to ER-positive tumors as measured by Q-RT-PCR in an independent set of breast tumors. Expression data for 6 representative kinases (CHK1, BUB1, PTK7, TTK, TLR1, and RAF1) are shown. Asterisks indicate P-value <0.01. Data are represented as mean ± SEM. (B) The expression of 42 of 42 kinases was significantly higher in ER-negative breast cancer cell lines as compared to ER-positive cell lines. Again, expression data as measured by Q-RT-PCR, this time in a panel of breast cancer cell lines, for 6 representative kinases (CHK1, BUB1, PTK7, TTK, TLR1, and RAF1) are shown. Asterisks indicate P-value <0.01. Data are represented as mean ± SEM. Validation of kinase overexpression was also done in 12 human breast tumor datasets (Table S4).

Fig. 2

Kinase overexpression validated in independent human tumor sample data sets and in a panel of breast cancer cell lines. (A) The expression of 34 of 34 kinases and kinase-associated genes identified in the array profiling were validated as being more highly expressed in ER-negative tumors compared to ER-positive tumors as measured by Q-RT-PCR in an independent set of breast tumors. Expression data for 6 representative kinases (CHK1, BUB1, PTK7, TTK, TLR1, and RAF1) are shown. Asterisks indicate P-value <0.01. Data are represented as mean ± SEM. (B) The expression of 42 of 42 kinases was significantly higher in ER-negative breast cancer cell lines as compared to ER-positive cell lines. Again, expression data as measured by Q-RT-PCR, this time in a panel of breast cancer cell lines, for 6 representative kinases (CHK1, BUB1, PTK7, TTK, TLR1, and RAF1) are shown. Asterisks indicate P-value <0.01. Data are represented as mean ± SEM. Validation of kinase overexpression was also done in 12 human breast tumor datasets (Table S4).

Fig. 3

Effect of siRNA knockdown on the growth of ER-negative and ER-positive breast cancer cells. (A) Knockdown of target kinase expression was achieved using siRNA against the identified kinases, with representative data of DAPK1, PTK7, and RYK knockdown in MDA-MB-468 cells shown. Knockdown was confirmed by Q-PCR at day 2 and day 5 and was >70% in all experiments. (B) DAPK1, PTK7, and RYK knockdown inhibited growth in the ER-negative breast cancer cell lines MDA-MB-468 and MDA-MB-231 but not in the ER-positive breast cancer cell lines MCF-7 and T47D. Asterisk denotes significant difference in curves between kinase of interest knockdown and siLuc transfected growth curves, P-value < 0.05. A complete summary of results of the kinase inhibition cell growth studies are shown in figure S4 and figure S5. Data are represented as mean ± SD.

Fig. 3

Effect of siRNA knockdown on the growth of ER-negative and ER-positive breast cancer cells. (A) Knockdown of target kinase expression was achieved using siRNA against the identified kinases, with representative data of DAPK1, PTK7, and RYK knockdown in MDA-MB-468 cells shown. Knockdown was confirmed by Q-PCR at day 2 and day 5 and was >70% in all experiments. (B) DAPK1, PTK7, and RYK knockdown inhibited growth in the ER-negative breast cancer cell lines MDA-MB-468 and MDA-MB-231 but not in the ER-positive breast cancer cell lines MCF-7 and T47D. Asterisk denotes significant difference in curves between kinase of interest knockdown and siLuc transfected growth curves, P-value < 0.05. A complete summary of results of the kinase inhibition cell growth studies are shown in figure S4 and figure S5. Data are represented as mean ± SD.

Fig. 4

Hierarchical clustering and Kaplan-Meier metastasis free and overall survival analysis of ER-negative tumors in multiple datasets. (A) Hierarchical clustering of only ER-negative tumors identified the 4 clusters of ER-negative breast tumors in the Wang data set (25). The tumors were classified based on the expression level of the kinases identified in the analysis. Tumors that fell into the immunomodulatory cluster had a decreased risk of metastasis, and tumors in the cell cycle regulatory and S6 kinase clusters had a substantially elevated risk of metastasis at 5 years. (B) Similar results were found when hierarchical clustering was done in the van de Vijver data set (3). Overall survival was substantially higher in the immunomodulatory group than in the S6 kinase or cell cycle checkpoint groups. Overall P-value was calculated based on the assumption that there would be no difference between any of the survival curves and was initially used to determine whether any one of the curves were significantly different. Further P-values were calculated comparing the designated two groups with the calculation of Chi square values. Immune refers to immunomodulatory group, CCC to the cell cycle checkpoint group, and S6 kinase to the S6 kinase group.

Fig. 4

Hierarchical clustering and Kaplan-Meier metastasis free and overall survival analysis of ER-negative tumors in multiple datasets. (A) Hierarchical clustering of only ER-negative tumors identified the 4 clusters of ER-negative breast tumors in the Wang data set (25). The tumors were classified based on the expression level of the kinases identified in the analysis. Tumors that fell into the immunomodulatory cluster had a decreased risk of metastasis, and tumors in the cell cycle regulatory and S6 kinase clusters had a substantially elevated risk of metastasis at 5 years. (B) Similar results were found when hierarchical clustering was done in the van de Vijver data set (3). Overall survival was substantially higher in the immunomodulatory group than in the S6 kinase or cell cycle checkpoint groups. Overall P-value was calculated based on the assumption that there would be no difference between any of the survival curves and was initially used to determine whether any one of the curves were significantly different. Further P-values were calculated comparing the designated two groups with the calculation of Chi square values. Immune refers to immunomodulatory group, CCC to the cell cycle checkpoint group, and S6 kinase to the S6 kinase group.