Integrative analysis of cyclin protein levels identifies cyclin b1 as a classifier and predictor of outcomes in breast cancer

Abstract

Purpose: We studied the expression levels of cyclins B1, D1, and E1 and the implications of cyclin overexpression for patient outcomes in distinct breast cancer subtypes defined by clinical variables and transcriptional profiling.

Experimental design: The expression levels of cyclins B1, D1, and E1 were quantified in 779 breast tumors and 53 cell lines using reverse phase protein arrays and/or transcriptional profiling.

Results: Whereas cyclin E1 overexpression was a specific marker of triple-negative and basal-like tumors, cyclin B1 overexpression occurred in poor prognosis hormone receptor-positive, luminal B and basal-like breast cancers. Cyclin D1 overexpression occurred in luminal and normal-like cancers. Breast cancer subgroups defined by integrated expression of cyclins B1, D1, and E1 correlated significantly (P < 0.000001) with tumor subtypes defined by transcriptional profiling and clinical criteria. Across three hormone receptor-positive data sets, cyclin B1 was the dominant cyclin associated with poor prognosis in univariate and multivariate analyses. Although CCNE1 was present in significantly higher copy numbers in basal-like versus other subtypes (ANOVA P < 0.001), CCNB1 gene copy number did not show gain in breast cancer. Instead, cyclin B1 expression was increased in tumors with co-occurrence of TP53 mutations and MYC amplification, a combination that seems to characterize basal-like and luminal B tumors. CCNB1 gene expression was significantly correlated with PLK, CENPE, and AURKB gene expression.

Conclusion: Cyclins B1, D1, and E1 have distinct expressions in different breast cancer subtypes. Novel PLK, CENPE, and AURKB inhibitors should be assessed for therapeutic utility in poor prognosis cyclin B1-overexpressing breast cancers.

Fig. 1

Expression of cyclin B1 in distinct subtypes and grade groupings of 390 human breast tumors (cohort A; B–D) and 53 breast cancer cell lines (A). Cyclin B1 was quantified using RPPA and the quantification data were log 2 transformed and mean centered for expression on the Y axis. HER, HER2–positive; HR, hormone receptor–positive; TN, triple-negative.

Fig. 2

Expression of cyclin E1 in distinct subtypes and grade groupings of 390 human breast tumors (cohort A; B–D) and 53 breast cancer cell lines (A). Cyclin E1 was quantified using RPPA and the quantification data were log 2 transformed and mean centered for expression on the Y axis.

Fig. 3

Cyclin B1, cyclin D1, and cyclin E1 protein expression levels were compared using RPPA between breast cancer subtypes defined by the intrinsic gene lists in cohort B composed of 168 breast cancers, of which 128 tumors had available transcriptional profiling data (Supplementary Table S2). The cyclins were quantified using RPPA and the log 2 transformed mean centered quantification data were graphed on the Y axis. Erbb2, HER2-positive; Lum A, luminal A; Lum B, luminal B; Normal, normal-like.

Fig. 4

In multivariable Cox models (A and B), including cyclin B1 protein expression, tumor stage, grade and patient age, high primary tumor cyclin B1 protein expression assessed as a continuous variable by RPPA was a significant predictor of hormone receptor–positive breast tumor recurrence and patient death in 195 patients treated only with adjuvant tamoxifen after definitive locoregional therapy.

Fig. 5

An integrated analysis of cyclin B1, cyclin D1, and cyclin E1 expression in breast cancer. In human breast tumor cohort B, cyclin B1, cyclin D1, and cyclin E1 protein expression levels were integrated in the heat map. This analysis defined three large breast cancer subgroups (i, ii and iii) that were associated with significantly different outcomes in all breast tumors and when the analysis was confined to ER-positive (ER+) breast cancers only (color coding matches each subgroup name to the corresponding survival curves). The cyclins were quantified using RPPA and quantification data were log 2 transformed and mean centered for clustering in the heat map.