p53 Status Identifies Two Subgroups of Triple-negative Breast Cancers with Distinct Biological Features

Biganzoli, Elia; Coradini, Danila; Ambrogi, Federico; Garibaldi, Jonathan M.; Lisboa, Paulo J. G.; Soria, Daniele; Green, Andrew; Pedriali, Massimo; Piantelli, Mauro; Querzoli, Patrizia; Demicheli, Romano; Boracchi, Patrizia

doi:10.1093/jjco/hyq227

Cited by 60 publications

(64 citation statements)

References 32 publications

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“…Effective molecular characterization of patients' tumors (26,32), for example, for alterations of signal transducing molecules, further extended these approaches. For example, EGFR mutations predict better response to the kinase inhibitor erlotinib, whereas mutated KRAS associates to worse prognosis and lack of response to erlotinib (47).…”

Section: Discussionmentioning

confidence: 99%

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Trop-2 Induces Tumor Growth Through AKT and Determines Sensitivity to AKT Inhibitors

Guerra

Trerotola

Tripaldi

et al. 2016

Clinical Cancer Research

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Purpose: Inhibition of AKT is a key target area for personalized cancer medicine. However, predictive markers of response to AKT inhibitors are lacking. Correspondingly, the AKT-dependent chain of command for tumor growth, which will mediate AKTdependent therapeutic responses, remains unclear.Experimental Design: Proteomic profiling was utilized to identify nodal hubs of the Trop-2 cancer growth-driving network. Kinase-specific inhibitors were used to dissect Trop-2-dependent from Trop-2-independent pathways. In vitro assays, in vivo preclinical models, and case series of primary human breast cancers were utilized to define the mechanisms of Trop-2-driven growth and the mode of action of Trop-2-predicted AKT inhibitors.Results: Trop-2 and AKT expression was shown to be tightly coordinated in human breast cancers, with virtual overlap with AKT activation profiles at T308 and S473, consistent with functional interaction in vivo. AKT allosteric inhibitors were shown to only block the growth of Trop-2-expressing tumor cells, both in vitro and in preclinical models, being ineffective on Trop-2-null cells. Consistently, AKTtargeted siRNA only impacted on Trop-2-expressing cells. Lentiviral downregulation of endogenous Trop-2 abolished tumor response to AKT blockade, indicating Trop-2 as a mandatory activator of AKT.Conclusions: Our findings indicate that the expression of Trop-2 is a stringent predictor of tumor response to AKT inhibitors. They also support the identification of target-activatory pathways, as efficient predictors of response in precision cancer therapy. Clin Cancer Res; 22(16); 4197-205. Ó2016 AACR.

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Section: Discussionmentioning

confidence: 99%

“…Clinical and pathologic status, tumor histotype, histologic grade, levels of expression of estrogen receptor a and progesterone receptor (PGR), and fraction of proliferating cells (% Ki-67) were recorded (25). Tumor samples were analyzed for correlated expression of Trop-2, AKT, and TP53 (26,27) …”

Section: Tumor Patient Case Seriesmentioning

confidence: 99%

Trop-2 Induces Tumor Growth Through AKT and Determines Sensitivity to AKT Inhibitors

Guerra

Trerotola

Tripaldi

et al. 2016

Clinical Cancer Research

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“…Positivity for p53 is associated with a worse prognosis and diminished response to therapy. 31,32 Status of p53 has been proposed to divide TNBC into 2 biologically distinct subgroups: a p53-negative normal breast-like TN subgroup, and a p53-positive basal-like subgroup with worse overall and event-free survival. 31,33,34 AR is expressed in up to 90% of breast cancers and up 50% of TNBCs.…”

mentioning

confidence: 99%

“…31,32 Status of p53 has been proposed to divide TNBC into 2 biologically distinct subgroups: a p53-negative normal breast-like TN subgroup, and a p53-positive basal-like subgroup with worse overall and event-free survival. 31,33,34 AR is expressed in up to 90% of breast cancers and up 50% of TNBCs. 35 AR expression is associated with better overall survival and disease-free survival, irrespective of coexpression of ER.…”

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confidence: 99%

Immunohistochemical Surrogates for Molecular Classification of Breast Carcinoma: A 2015 Update

Tang¹,

Tse

2016

Archives of Pathology &Amp; Laboratory Medicine

129

109

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Context.-The pioneering works on molecular classification (MC) by Perou and Sorlie et al in the early 2000s using global gene expression profiling identified 5 intrinsic subtypes of invasive breast cancers (IBCs): luminal A, luminal B, normal breast-like, HER2-enriched, and basallike subtypes, each unique in incidence, survival, and response to therapy. Because the application of gene expression profiling in daily practice is not economical or practical at the present time, many investigators have studied the use of immunohistochemical (IHC) surrogates as a substitute for determining the MC of IBC.Objective.-To discuss the continuing efforts that have been made to develop clinically significant and readily available IHC surrogates for the MC of IBC.Data Sources.-Data were obtained from pertinent peer-reviewed English-language literature.Conclusions.-The most commonly used IHC surrogates are estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2), dividing IBC into luminal, HER2, and triple-negative subtypes. The addition of Ki-67, cytokeratin 5, and epidermal growth factor receptor (EGFR) separates luminal B from luminal A subtypes, and basal-like subtype from triple-negative breast cancer. More recently, biomarkers such as androgen receptor and p53 have been shown to further stratify these molecular subtypes. Although many studies of IHC-based MC have shown clinical significance similar to gene expression profiling-defined MC, its critical limitations are: (1) a lack of standardization in terminology, (2) a lack of standardization in biomarkers used for each subtype, and (3) the lack of a uniform cutoff for each biomarker. A panel of IHC surrogates for each subtype of IBC is proposed.

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“…The Nottingham Tenovus Breast Cancer (NTBC) dataset has been used in studies to understand the mechanism of breast cancer and characteristics of its subgroups [1,2]. The dataset contains 25 immunohistochemical features for 1076 patients.…”

Section: Introductionmentioning

confidence: 99%

Investigating Distance Metrics in Semi-supervised Fuzzy c-Means for Breast Cancer Classification

Lai

Garibaldi

2013

Computational Intelligence Methods for Bioinformatics and Biostatistics

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Abstract. In previous work, semi-supervised Fuzzy c-means (ssFCM) was used as an automatic classification technique to classify the Nottingham Tenovus Breast Cancer (NTBC) dataset as no method to do this currently exists. However, the results were poor when compared with semi-manual classification. It is known that the NTBC data is highly non-normal and it was suspected that this affected the poor results. This motivated a further investigation into alternative distance metrics to explore their effect on classification results. Mahalanobis, Euclidean and kernel-based distance metrics were used on 100 sets of randomly-selected labelled data. It was found that ssFCM with Euclidean distance successfully and automatically identified the six classes in close agreement with those of Soria et al. We showed that there is also high agreement in the key features that define the breast cancer classes with those of Soria et al. The superiority of Euclidean distance to Mahalanobis distance is unexpected as it can only generate spherical clusters while Mahalanobis distance can generate hyperellipsoidal ones including spherical ones. We expected Mahalanobis distance to generate the hyperellipsoidal clusters that would best fit NTBC data.

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p53 Status Identifies Two Subgroups of Triple-negative Breast Cancers with Distinct Biological Features

Cited by 60 publications

References 32 publications

Trop-2 Induces Tumor Growth Through AKT and Determines Sensitivity to AKT Inhibitors

Trop-2 Induces Tumor Growth Through AKT and Determines Sensitivity to AKT Inhibitors

Immunohistochemical Surrogates for Molecular Classification of Breast Carcinoma: A 2015 Update

Investigating Distance Metrics in Semi-supervised Fuzzy c-Means for Breast Cancer Classification

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