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      Molecular characteristics of screen-detected vs symptomatic breast cancers and their impact on survival

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          Abstract

          Background:

          Several recent studies have shown that screen detection remains an independent prognostic factor after adjusting for disease stage at presentation. This study compares the molecular characteristics of screen-detected with symptomatic breast cancers to identify if differences in tumour biology may explain some of the survival benefit conferred by screen detection.

          Methods:

          A total of 1379 women (aged 50–70 years) with invasive breast cancer from a large population-based case–control study were included in the analysis. Individual patient data included tumour size, grade, lymph node status, adjuvant therapy, mammographic screening status and mortality. Immunohistochemistry was performed on tumour samples using 11 primary antibodies to define five molecular subtypes. The effect of screen detection compared with symptomatic diagnosis on survival was estimated after adjustment for grade, nodal status, Nottingham Prognostic Index (NPI) and the molecular markers.

          Results:

          Fifty-six per cent of the survival benefit associated with screen-detected breast cancer was accounted for by a shift in the NPI, a further 3–10% was explained by the biological variables and more than 30% of the effect remained unexplained.

          Conclusion:

          Currently known biomarkers remain limited in their ability to explain the heterogeneity of breast cancer fully. A more complete understanding of the biological profile of breast tumours will be necessary to assess the true impact of tumour biology on the improvement in survival seen with screen detection.

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          Tissue microarrays for high-throughput molecular profiling of tumor specimens.

          Juha Kononen, Lukas Bubendorf, Anne Kallionimeni … (1998)
          Many genes and signalling pathways controlling cell proliferation, death and differentiation, as well as genomic integrity, are involved in cancer development. New techniques, such as serial analysis of gene expression and cDNA microarrays, have enabled measurement of the expression of thousands of genes in a single experiment, revealing many new, potentially important cancer genes. These genome screening tools can comprehensively survey one tumor at a time; however, analysis of hundreds of specimens from patients in different stages of disease is needed to establish the diagnostic, prognostic and therapeutic importance of each of the emerging cancer gene candidates. Here we have developed an array-based high-throughput technique that facilitates gene expression and copy number surveys of very large numbers of tumors. As many as 1000 cylindrical tissue biopsies from individual tumors can be distributed in a single tumor tissue microarray. Sections of the microarray provide targets for parallel in situ detection of DNA, RNA and protein targets in each specimen on the array, and consecutive sections allow the rapid analysis of hundreds of molecular markers in the same set of specimens. Our detection of six gene amplifications as well as p53 and estrogen receptor expression in breast cancer demonstrates the power of this technique for defining new subgroups of tumors.
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            Prognostic markers in triple-negative breast cancer.

            Emad Rakha, Maysa E El-Sayed, Andrew R. Green … (2007)
            Triple-negative breast cancer (estrogen receptor-negative, progesterone receptor-negative, and HER2-negative) is a high risk breast cancer that lacks the benefit of specific therapy that targets these proteins. In this study, the authors examined a large and well characterized series of invasive breast carcinoma (n = 1944) with a long-term clinical follow-up (median, 56 months) by using tissue microarray. The series were also stained with concurrent immunohistochemical prognostic panels (estrogen receptor, progesterone receptor, HER-2, androgen receptor, epidermal growth factor receptor (EGFR), P-cadherin, E-cadherin, and basal (CK5/6, CK14), and p53), to characterize this specific subgroup of breast cancer and to identify prognostic markers that can identify tumors with more aggressive behavior. Of informative cases, 16.3% were of the triple-negative phenotype. The majority of these tumors were grade 3, ductal/no-specific-type carcinomas. There were positive associations with larger size, pushing margins, poorer Nottingham Prognostic Index, development of recurrence and distant metastasis, and poorer outcome. In addition, associations were found with loss of expression of androgen receptor and E-cadherin, and positive expression of basal cytokeratins (basal phenotype), P-cadherin, p53, and EGFR. In all tumors, tumor size, lymph node stage, and androgen receptor were the most useful prognostic markers. In the lymph node-positive subgroup, both size and androgen receptor retained their prognostic significance. However, in the lymph node-negative tumors, basal phenotype was the sole prognostic marker identified in this subgroup. Other parameters including age, histological grade, tumor size, vascular invasion or other biomarkers included in the current study were not significant. The authors concluded that assessment of androgen receptor and basal phenotype, in addition to the established pathologic variables, mainly lymph node status and tumor size, can be used to select high-risk and low-risk patients at the time of primary surgery and can provide valuable information on treatment options in these triple-negative tumors. (c) 2006 American Cancer Society.
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              High-throughput protein expression analysis using tissue microarray technology of a large well-characterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses.

              Douglas MacMillan, R W Blamey, E Paish … (2005)
              Recent studies on gene molecular profiling using cDNA microarray in a relatively small series of breast cancer have identified biologically distinct groups with apparent clinical and prognostic relevance. The validation of such new taxonomies should be confirmed on larger series of cases prior to acceptance in clinical practice. The development of tissue microarray (TMA) technology provides methodology for high-throughput concomitant analyses of multiple proteins on large numbers of archival tumour samples. In our study, we have used immunohistochemistry techniques applied to TMA preparations of 1,076 cases of invasive breast cancer to study the combined protein expression profiles of a large panel of well-characterized commercially available biomarkers related to epithelial cell lineage, differentiation, hormone and growth factor receptors and gene products known to be altered in some forms of breast cancer. Using hierarchical clustering methodology, 5 groups with distinct patterns of protein expression were identified. A sixth group of only 4 cases was also identified but deemed too small for further detailed assessment. Further analysis of these clusters was performed using multiple layer perceptron (MLP)-artificial neural network (ANN) with a back propagation algorithm to identify key biomarkers driving the membership of each group. We have identified 2 large groups by their expression of luminal epithelial cell phenotypic characteristics, hormone receptors positivity, absence of basal epithelial phenotype characteristics and lack of c-erbB-2 protein overexpression. Two additional groups were characterized by high c-erbB-2 positivity and negative or weak hormone receptors expression but showed differences in MUC1 and E-cadherin expression. The final group was characterized by strong basal epithelial characteristics, p53 positivity, absent hormone receptors and weak to low luminal epithelial cytokeratin expression. In addition, we have identified significant differences between clusters identified in this series with respect to established prognostic factors including tumour grade, size and histologic tumour type as well as differences in patient outcomes. The different protein expression profiles identified in our study confirm the biologic heterogeneity of breast cancer and demonstrate the clinical relevance of classification in this manner. These observations could form the basis of revision of existing traditional classification systems for breast cancer.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Br J Cancer
                Title: British Journal of Cancer
                Publisher: Nature Publishing Group
                ISSN (Print): 0007-0920
                ISSN (Electronic): 1532-1827
                Publication date (Electronic): 22 September 2009
                Publication date Collection: 13 October 2009
                Publication date (Print): 20 October 2009
                Volume: 101
                Issue: 8
                Pages: 1338-1344
                Affiliations
                [1 ]Department of Oncology, Strangeways Research Laboratories, University of Cambridge Cambridge CB1 8RN, UK
                [2 ]Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre Robinson Way, Cambridge CB2 0RE, UK
                [3 ]Cambridge Breast Unit, Addenbrooke's Hospital, Cambridge University Hospital NHS Foundation Trust Cambridge CB2 2QQ, UK
                [4 ]Cambridge Biomedical Research Centre, National Institute of Health Research Cambridge CB2 2QQ, UK
                [5 ]Cancer Research UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry Charterhouse Square, London EC1 M 6BQ, UK
                [6 ]Eastern Cancer Registration and Information Centre, Unit C, Magog Court Shelford Bottom, Hinton Way, Cambridge CB22 3AD, UK
                Author notes
                [* ]Author for correspondence: gordon.wishart@ 123456addenbrookes.nhs.uk
                Article
                Publisher Item ID: 6605317
                DOI: 10.1038/sj.bjc.6605317
                PMC ID: 2768460
                PubMed ID: 19773756
                SO-VID: 5312ee78-8d50-4eb0-84ae-e2a3cc037f9c
                Copyright © Copyright 2009, Cancer Research UK
                History
                Date received : 09 July 2009
                Date accepted : 19 August 2009
                Categories
                Subject: Molecular Diagnostics

                ScienceOpen disciplines: Oncology & Radiotherapy
                Keywords: survival,biomarkers,breast screening,immunohistochemistry,breast cancer
                Data availability:
                ScienceOpen disciplines: Oncology & Radiotherapy
                Keywords: survival, biomarkers, breast screening, immunohistochemistry, breast cancer

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