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


METHODS
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.


CONCLUSIONS
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.
Agarwal
Roshan
R
The University of Texas M. D. Anderson Cancer Center (MDACC), Houston, Texas, USA.
Gonzalez-Angulo
Ana-Maria
AM
Myhre
Simen
S
Carey
Mark
M
Lee
Ju-Seog
JS
Overgaard
Jens
J
Alsner
Jan
J
Stemke-Hale
Katherine
K
Lluch
Ana
A
Neve
Richard M
RM
Kuo
Wen Lin
WL
Sorlie
Therese
T
Sahin
Aysegul
A
Valero
Vicente
V
Keyomarsi
Khandan
K
Gray
Joe W
JW
Borresen-Dale
Anne-Lise
AL
Mills
Gordon B
GB
Hennessy
Bryan T
BT
D001940:Breast / Q000378:metabolism / Q000473:pathology; D001943:Breast Neoplasms / Q000145:classification / Q000235:genetics / Q000473:pathology*; D045744:Cell Line, Tumor; D058534:Class I Phosphatidylinositol 3-Kinases; D019926:Cyclin B / Q000235:genetics* / Q000378:metabolism; D056744:Cyclin B1; D019938:Cyclin D1 / Q000235:genetics* / Q000378:metabolism; D019927:Cyclin E / Q000235:genetics* / Q000378:metabolism; D004252:DNA Mutational Analysis; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D007150:Immunohistochemistry; D009154:Mutation; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data; D015513:Oncogene Proteins / Q000235:genetics* / Q000378:metabolism; D019869:Phosphatidylinositol 3-Kinases / Q000235:genetics; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D040901:Proteomics / Q000379:methods / Q000706:statistics & numerical data; D011960:Receptors, Estrogen / Q000378:metabolism; D011980:Receptors, Progesterone / Q000378:metabolism; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D016019:Survival Analysis
C534699:CCNB1 protein, human; C475820:CCNE1 protein, human; D019926:Cyclin B; D056744:Cyclin B1; D019927:Cyclin E; D015513:Oncogene Proteins; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; D019938:Cyclin D1; D019869:Phosphatidylinositol 3-Kinases; D058534:Class I Phosphatidylinositol 3-Kinases; C484760:PIK3CA protein, human