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      Cripto-1 as a novel therapeutic target for triple negative breast cancer

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          Abstract

          Triple-negative breast cancer (TNBC) presents the poorest prognosis among the breast cancer subtypes and no current standard therapy. Here, we performed an in-depth molecular analysis of a mouse model that establishes spontaneous lung metastasis from JygMC(A) cells. These primary tumors resembled the triple-negative breast cancer (TNBC) both phenotypically and molecularly. Morphologically, primary tumors presented both epithelial and spindle-like cells but displayed only adenocarcinoma-like features in lung parenchyma. The use of laser-capture microdissection combined with Nanostring mRNA and microRNA analysis revealed overexpression of either epithelial and miRNA-200 family or mesenchymal markers in adenocarcinoma and mesenchymal regions, respectively. Cripto-1, an embryonic stem cell marker, was present in spindle-like areas and its promoter showed activity in primary tumors. Cripto-1 knockout by the CRISPR-Cas9 system inhibited tumor growth and pulmonary metastasis. Our findings show characterization of a novel mouse model that mimics the TNBC and reveal Cripto-1 as a TNBC target hence may offer alternative treatment strategies for TNBC.

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          Evolution of the cancer stem cell model.

          Antonija Kreso, John E. Dick (2014)
          Genetic analyses have shaped much of our understanding of cancer. However, it is becoming increasingly clear that cancer cells display features of normal tissue organization, where cancer stem cells (CSCs) can drive tumor growth. Although often considered as mutually exclusive models to describe tumor heterogeneity, we propose that the genetic and CSC models of cancer can be harmonized by considering the role of genetic diversity and nongenetic influences in contributing to tumor heterogeneity. We offer an approach to integrating CSCs and cancer genetic data that will guide the field in interpreting past observations and designing future studies. Copyright © 2014 Elsevier Inc. All rights reserved.
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            Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles.

            Daniel Rhodes, Shanker Kalyana-Sundaram, Vasudeva Mahavisno … (2007)
            DNA microarrays have been widely applied to cancer transcriptome analysis; however, the majority of such data are not easily accessible or comparable. Furthermore, several important analytic approaches have been applied to microarray analysis; however, their application is often limited. To overcome these limitations, we have developed Oncomine, a bioinformatics initiative aimed at collecting, standardizing, analyzing, and delivering cancer transcriptome data to the biomedical research community. Our analysis has identified the genes, pathways, and networks deregulated across 18,000 cancer gene expression microarrays, spanning the majority of cancer types and subtypes. Here, we provide an update on the initiative, describe the database and analysis modules, and highlight several notable observations. Results from this comprehensive analysis are available at http://www.oncomine.org.
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              Strong Time Dependence of the 76-Gene Prognostic Signature for Node-Negative Breast Cancer Patients in the TRANSBIG Multicenter Independent Validation Series

              C. Desmedt, F Piette, S Loi … (2007)
              Recently, a 76-gene prognostic signature able to predict distant metastases in lymph node-negative (N(-)) breast cancer patients was reported. The aims of this study conducted by TRANSBIG were to independently validate these results and to compare the outcome with clinical risk assessment. Gene expression profiling of frozen samples from 198 N(-) systemically untreated patients was done at the Bordet Institute, blinded to clinical data and independent of Veridex. Genomic risk was defined by Veridex, blinded to clinical data. Survival analyses, done by an independent statistician, were done with the genomic risk and adjusted for the clinical risk, defined by Adjuvant! Online. The actual 5- and 10-year time to distant metastasis were 98% (88-100%) and 94% (83-98%), respectively, for the good profile group and 76% (68-82%) and 73% (65-79%), respectively, for the poor profile group. The actual 5- and 10-year overall survival were 98% (88-100%) and 87% (73-94%), respectively, for the good profile group and 84% (77-89%) and 72% (63-78%), respectively, for the poor profile group. We observed a strong time dependence of this signature, leading to an adjusted hazard ratio of 13.58 (1.85-99.63) and 8.20 (1.10-60.90) at 5 years and 5.11 (1.57-16.67) and 2.55 (1.07-6.10) at 10 years for time to distant metastasis and overall survival, respectively. This independent validation confirmed the performance of the 76-gene signature and adds to the growing evidence that gene expression signatures are of clinical relevance, especially for identifying patients at high risk of early distant metastases.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Oncotarget
                Journal ID (iso-abbrev): Oncotarget
                Journal ID (publisher-id): ImpactJ
                Title: Oncotarget
                Publisher: Impact Journals LLC
                ISSN (Electronic): 1949-2553
                Publication date Collection: 20 May 2015
                Publication date (Electronic): 19 May 2014
                Volume: 6
                Issue: 14
                Pages: 11910-11929
                Affiliations
                1 Tumor Growth Factor Section, Mouse Cancer Genetics Program, National Cancer Institute, Frederick, MD, USA
                2 CCRIFX Bioinformatics Core, National Cancer Institute, Bethesda, MD, USA
                3 Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
                4 Genetics of Cancer Susceptibility Section, Mouse Cancer Genetics Program, National Cancer Institute, Frederick, MD, USA
                Author notes
                Correspondence to: David S. Salomon, salomond@ 123456mail.nih.gov
                Article
                DOI: 10.18632/oncotarget.4182
                PMC ID: 4494913
                PubMed ID: 26059540
                SO-VID: 0d91045b-5486-4792-b921-c707e3386e32
                Copyright © Copyright: © 2015 Castro et al.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 17 April 2015
                Date accepted : 9 May 2015
                Categories
                Subject: Priority Research Paper

                ScienceOpen disciplines: Oncology & Radiotherapy
                Keywords: cripto-1,notch4,epithelial-mesenchymal plasticity,mouse model,triple-negative breast cancer
                Data availability:
                ScienceOpen disciplines: Oncology & Radiotherapy
                Keywords: cripto-1, notch4, epithelial-mesenchymal plasticity, mouse model, triple-negative breast cancer

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