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      A Metabolomic Approach to Predict Breast Cancer Behavior and Chemotherapy Response

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          Abstract

          Although the classification of breast carcinomas into molecular or immunohistochemical subtypes has contributed to a better categorization of women into different therapeutic regimens, breast cancer nevertheless still progresses or recurs in a remarkable number of patients. Identifying women who would benefit from chemotherapy could potentially increase treatment effectiveness, which has important implications for long-term survival. Metabolomic analyses of fluids and tissues from cancer patients improve our knowledge of the reprogramming of metabolic pathways involved in resistance to chemotherapy. This review evaluates how recent metabolomic approaches have contributed to understanding the relationship between breast cancer and the acquisition of resistance. We focus on the advantages and challenges of cancer treatment and the use of new strategies in clinical care, which helps us comprehend drug resistance and predict responses to treatment.

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          On the origin of cancer cells.

          O WARBURG (1956)
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            Ki67 Index, HER2 Status, and Prognosis of Patients With Luminal B Breast Cancer

            Background Gene expression profiling of breast cancer has identified two biologically distinct estrogen receptor (ER)-positive subtypes of breast cancer: luminal A and luminal B. Luminal B tumors have higher proliferation and poorer prognosis than luminal A tumors. In this study, we developed a clinically practical immunohistochemistry assay to distinguish luminal B from luminal A tumors and investigated its ability to separate tumors according to breast cancer recurrence-free and disease-specific survival. Methods Tumors from a cohort of 357 patients with invasive breast carcinomas were subtyped by gene expression profile. Hormone receptor status, HER2 status, and the Ki67 index (percentage of Ki67-positive cancer nuclei) were determined immunohistochemically. Receiver operating characteristic curves were used to determine the Ki67 cut point to distinguish luminal B from luminal A tumors. The prognostic value of the immunohistochemical assignment for breast cancer recurrence-free and disease-specific survival was investigated with an independent tissue microarray series of 4046 breast cancers by use of Kaplan–Meier curves and multivariable Cox regression. Results Gene expression profiling classified 101 (28%) of the 357 tumors as luminal A and 69 (19%) as luminal B. The best Ki67 index cut point to distinguish luminal B from luminal A tumors was 13.25%. In an independent cohort of 4046 patients with breast cancer, 2847 had hormone receptor–positive tumors. When HER2 immunohistochemistry and the Ki67 index were used to subtype these 2847 tumors, we classified 1530 (59%, 95% confidence interval [CI] = 57% to 61%) as luminal A, 846 (33%, 95% CI = 31% to 34%) as luminal B, and 222 (9%, 95% CI = 7% to 10%) as luminal–HER2 positive. Luminal B and luminal–HER2-positive breast cancers were statistically significantly associated with poor breast cancer recurrence-free and disease-specific survival in all adjuvant systemic treatment categories. Of particular relevance are women who received tamoxifen as their sole adjuvant systemic therapy, among whom the 10-year breast cancer–specific survival was 79% (95% CI = 76% to 83%) for luminal A, 64% (95% CI = 59% to 70%) for luminal B, and 57% (95% CI = 47% to 69%) for luminal–HER2 subtypes. Conclusion Expression of ER, progesterone receptor, and HER2 proteins and the Ki67 index appear to distinguish luminal A from luminal B breast cancer subtypes.
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              Tailoring therapies—improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015

              The 14th St Gallen International Breast Cancer Conference (2015) reviewed new evidence on locoregional and systemic therapies for early breast cancer. This manuscript presents news and progress since the 2013 meeting, provides expert opinion on almost 200 questions posed to Consensus Panel members, and summarizes treatment-oriented classification of subgroups and treatment recommendations.
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                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                21 February 2018
                February 2018
                : 19
                : 2
                : 617
                Affiliations
                [1 ]Hospital da Mulher Prof. Dr. José Aristodemo Pinotti—Centro de Atenção Integral à Saúde da Mulher (CAISM), University of Campinas (UNICAMP), Campinas, São Paulo 13083-881, Brazil; macardoso86@ 123456hotmail.com (M.R.C.); mcecilia_r@ 123456hotmail.com (M.C.R.T.); lala.viana311088@ 123456gmail.com (L.R.V.); sophie.derchain@ 123456gmail.com (S.F.M.D.)
                [2 ]Clinical Pharmacology and Gastroenterology Unit, São Francisco University, Bragança Paulista, São Paulo 13083-881, Brazil; marcelo.ribeiro@ 123456usf.edu.br
                Author notes
                [* ]Correspondence: santos.j.c@ 123456outlook.com ; Tel.: +55-(11)-997-661-068
                Author information
                https://orcid.org/0000-0001-5913-102X
                Article
                ijms-19-00617
                10.3390/ijms19020617
                5855839
                29466297
                a9e39759-982f-4d97-aa39-c39d84c7d2a6
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 November 2017
                : 31 January 2018
                Categories
                Review

                Molecular biology
                breast cancer,drug resistance,metabolomics
                Molecular biology
                breast cancer, drug resistance, metabolomics

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