Background: TNBC is divided into basal and non-basal subclasses. To further subclassify TNBC we performed microRNA (miR) expression profiles and linked them to patient overall survival. Methods: During 1996-2005, 365 consecutive TNBC (phenotypically estrogen, progesterone and HER2 negative by immunohistochemistry [IHC]) were identified from the NCCN Breast Cancer Data Base/Tumor Registry at OSU Medical Center. One hundred fifty-eight (43%) formalin-fixed paraffin embedded (FFPE) breast cancer and 40 normal breast tissue blocks were available and tissue cores were obtained for RNA. RNA was isolated using the Ambion recoverall total nucleic acid isolation kit and the expression of ~700 miRs was assessed for each sample using the nanoString nCounter method. A consensus-clustering algorithm (ConsensusClusterPlus, Bioconductor www.bioconductor.org) was used to identify subclasses of TNBC and Kaplan-Meier overall survival curves were compared using the log-rank test. Censoring occurred at the date of death from causes other than breast cancer or at time of the last known follow-up, whichever occurred first. The median follow-up was 67 mo. (range 4-171 mo.). Results: The median age was 52 yrs. (range 20-84 yrs.); 81% white and 9% African-American; stages I, II, and III were 31%, 54% and 15%, respectively; and most patients received adjuvant anthracycline-based regimens with (25%) or without taxanes (75%). The algorithm identified 5 distinct subclasses; 1 clustering with normal breast miR expression whereas the other 4 each had a unique pattern of deregulated miRs. The median overall survivals were significantly different across the 5 cancer subclasses (log-rank p=0.028) (Table). Conclusions: miR expression profiling identifies and discriminates 5 TNBC subclasses, which do not coincide with those identified as basal and non-basal by IHC. Molecular analyses are ongoing to associate the miR-based subclasses with specific clinical features or the expression of specific pathways.

Use of microRNA (miR) expression profiling to identify distinct subclasses of triple-negative breast cancers (TNBC).

PULVIRENTI, ALFREDO;
2012

Abstract

Background: TNBC is divided into basal and non-basal subclasses. To further subclassify TNBC we performed microRNA (miR) expression profiles and linked them to patient overall survival. Methods: During 1996-2005, 365 consecutive TNBC (phenotypically estrogen, progesterone and HER2 negative by immunohistochemistry [IHC]) were identified from the NCCN Breast Cancer Data Base/Tumor Registry at OSU Medical Center. One hundred fifty-eight (43%) formalin-fixed paraffin embedded (FFPE) breast cancer and 40 normal breast tissue blocks were available and tissue cores were obtained for RNA. RNA was isolated using the Ambion recoverall total nucleic acid isolation kit and the expression of ~700 miRs was assessed for each sample using the nanoString nCounter method. A consensus-clustering algorithm (ConsensusClusterPlus, Bioconductor www.bioconductor.org) was used to identify subclasses of TNBC and Kaplan-Meier overall survival curves were compared using the log-rank test. Censoring occurred at the date of death from causes other than breast cancer or at time of the last known follow-up, whichever occurred first. The median follow-up was 67 mo. (range 4-171 mo.). Results: The median age was 52 yrs. (range 20-84 yrs.); 81% white and 9% African-American; stages I, II, and III were 31%, 54% and 15%, respectively; and most patients received adjuvant anthracycline-based regimens with (25%) or without taxanes (75%). The algorithm identified 5 distinct subclasses; 1 clustering with normal breast miR expression whereas the other 4 each had a unique pattern of deregulated miRs. The median overall survivals were significantly different across the 5 cancer subclasses (log-rank p=0.028) (Table). Conclusions: miR expression profiling identifies and discriminates 5 TNBC subclasses, which do not coincide with those identified as basal and non-basal by IHC. Molecular analyses are ongoing to associate the miR-based subclasses with specific clinical features or the expression of specific pathways.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/62264
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