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      Subtyping of Breast Cancer by Immunohistochemistry to Investigate a Relationship between Subtype and Short and Long Term Survival: A Collaborative Analysis of Data for 10,159 Cases from 12 Studies

      1 , 1 , 2 , 2 , 2 , 2 , 3 , 3 , 3 , 4 , 5 , 6 , 6 , 7 , 8 , 9 , 10 , 10 , 11 , 11 , 12 , 12 , 12 , 13 , 14 , 15 , 15 , 15 , 16 , 17 , 16 , 18 , 19 , 20 , 1 , 1 , 1 , 21 , 21 , 1 , 21 , 16 , 1 , 1 , 21 , * , 3
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          Paul Pharoah and colleagues evaluate the prognostic significance of immunohistochemical subtype classification in more than 10,000 breast cancer cases with early disease, and examine the influence of a patient's survival time on the prediction of future survival.



          Immunohistochemical markers are often used to classify breast cancer into subtypes that are biologically distinct and behave differently. The aim of this study was to estimate mortality for patients with the major subtypes of breast cancer as classified using five immunohistochemical markers, to investigate patterns of mortality over time, and to test for heterogeneity by subtype.

          Methods and Findings

          We pooled data from more than 10,000 cases of invasive breast cancer from 12 studies that had collected information on hormone receptor status, human epidermal growth factor receptor-2 (HER2) status, and at least one basal marker (cytokeratin [CK]5/6 or epidermal growth factor receptor [EGFR]) together with survival time data. Tumours were classified as luminal and nonluminal tumours according to hormone receptor expression. These two groups were further subdivided according to expression of HER2, and finally, the luminal and nonluminal HER2-negative tumours were categorised according to expression of basal markers. Changes in mortality rates over time differed by subtype. In women with luminal HER2-negative subtypes, mortality rates were constant over time, whereas mortality rates associated with the luminal HER2-positive and nonluminal subtypes tended to peak within 5 y of diagnosis and then decline over time. In the first 5 y after diagnosis the nonluminal tumours were associated with a poorer prognosis, but over longer follow-up times the prognosis was poorer in the luminal subtypes, with the worst prognosis at 15 y being in the luminal HER2-positive tumours. Basal marker expression distinguished the HER2-negative luminal and nonluminal tumours into different subtypes. These patterns were independent of any systemic adjuvant therapy.


          The six subtypes of breast cancer defined by expression of five markers show distinct behaviours with important differences in short term and long term prognosis. Application of these markers in the clinical setting could have the potential to improve the targeting of adjuvant chemotherapy to those most likely to benefit. The different patterns of mortality over time also suggest important biological differences between the subtypes that may result in differences in response to specific therapies, and that stratification of breast cancers by clinically relevant subtypes in clinical trials is urgently required.

          Please see later in the article for the Editors' Summary

          Editors' Summary


          Each year, more than one million women discover they have breast cancer. Breast cancer begins when cells in the breast's milk-producing glands or in the tubes (ducts) that take milk to the nipples acquire genetic changes that allow them to divide uncontrollably and to move around the body (metastasize). The uncontrolled cell division leads to the formation of a lump that can be detected by mammography (a breast X-ray) or by manual breast examination. Breast cancer is treated by surgical removal of the lump or, if the cancer has started to spread, by removal of the whole breast (mastectomy). Surgery is usually followed by radiotherapy or chemotherapy. These “adjuvant” therapies are designed to kill any remaining cancer cells but can make women very ill. Generally speaking, the outlook (prognosis) for women with breast cancer is good. In the United States, for example, nearly 90% of affected women are still alive five years after their diagnosis.

          Why Was This Study Done?

          Because there are several types of cells in the milk ducts and glands, there are several subtypes of breast cancer. Luminal tumors, for example, begin in the cells that line the ducts and glands and usually grow slowly; basal-type tumors arise in deeper layers of the ducts and glands and tend to grow quickly. Clinicians need to distinguish between different breast cancer subtypes so that they can give women a realistic prognosis and can give adjuvant treatments to those women who are most likely to benefit. One way to distinguish between different subtypes is to stain breast cancer samples using antibodies (immune system proteins) that recognize particular proteins (antigens). This “immunohistochemical” approach can identify several breast cancer subtypes but its prognostic value and the best way to classify breast tumors remains unclear. In this study, the researchers investigate the survival over time of women with six major subtypes of breast cancer classified using five immunohistochemical markers: the estrogen receptor and the progesterone receptor (two hormone receptors expressed by luminal cells), the human epidermal growth factors receptor-2 (HER2, a protein marker used to select specific adjuvant therapies), and CK5/6 and EGFR (proteins expressed by basal cells).

          What Did the Researchers Do and Find?

          The researchers pooled data on survival time and on the expression of the five immunohistochemical markers from more than 10,000 cases of breast cancer from 12 studies. They then divided the tumors into six subtypes on the basis of their marker expression: luminal (hormone receptor-positive), HER2-positive tumors; luminal, HER2-negative, basal marker-positive tumors; luminal, HER2-negative, basal marker-negative tumors; nonluminal (hormone receptor-negative), HER2-positive tumors; nonluminal, HER2-negative, basal marker-positive tumors; and nonluminal, HER2-negative, basal marker-negative tumors. In the first five years after diagnosis, women with nonluminal tumor subtypes had the worst prognosis but at 15 years after diagnosis, women with luminal HER2-positive tumors had the worst prognosis. Furthermore, death rates (the percentage of affected women dying each year) differed by subtype over time. Thus, women with the two luminal HER2-negative subtypes were as likely to die soon after diagnosis as at later times whereas the death rates associated with nonluminal subtypes peaked within five years of diagnosis and then declined.

          What Do These Findings Mean?

          These and other findings indicate that the six subtypes of breast cancer defined by the expression of five immunohistochemical markers have distinct biological characteristics that are associated with important differences in short-term and long-term outcomes. Because different laboratories measured the immunohistochemical markers using different methods, it is possible that some of the tumors included in this study were misclassified. However, the finding of clear differences in the behavior of the immunochemically classified subtypes suggests that the use of the five markers for tumor classification might be robust enough for routine clinical practice. The application of these markers in the clinical setting, suggest the researchers, could improve the targeting of adjuvant therapies to those women most likely to benefit. Furthermore, note the researchers, these findings strongly suggest that subtype-specific responses should be evaluated in future clinical trials of treatments for breast cancer.

          Additional Information

          Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000279.

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          Most cited references28

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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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            Breast cancer molecular subtypes respond differently to preoperative chemotherapy.

            Molecular classification of breast cancer has been proposed based on gene expression profiles of human tumors. Luminal, basal-like, normal-like, and erbB2+ subgroups were identified and were shown to have different prognoses. The goal of this research was to determine if these different molecular subtypes of breast cancer also respond differently to preoperative chemotherapy. Fine needle aspirations of 82 breast cancers were obtained before starting preoperative paclitaxel followed by 5-fluorouracil, doxorubicin, and cyclophosphamide chemotherapy. Gene expression profiling was done with Affymetrix U133A microarrays and the previously reported "breast intrinsic" gene set was used for hierarchical clustering and multidimensional scaling to assign molecular class. The basal-like and erbB2+ subgroups were associated with the highest rates of pathologic complete response (CR), 45% [95% confidence interval (95% CI), 24-68] and 45% (95% CI, 23-68), respectively, whereas the luminal tumors had a pathologic CR rate of 6% (95% CI, 1-21). No pathologic CR was observed among the normal-like cancers (95% CI, 0-31). Molecular class was not independent of conventional cliniocopathologic predictors of response such as estrogen receptor status and nuclear grade. None of the 61 genes associated with pathologic CR in the basal-like group were associated with pathologic CR in the erbB2+ group, suggesting that the molecular mechanisms of chemotherapy sensitivity may vary between these two estrogen receptor-negative subtypes. The basal-like and erbB2+ subtypes of breast cancer are more sensitive to paclitaxel- and doxorubicin-containing preoperative chemotherapy than the luminal and normal-like cancers.
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              Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype.

              Basal-like breast cancer is associated with high grade, poor prognosis, and younger patient age. Clinically, a triple-negative phenotype definition [estrogen receptor, progesterone receptor, and human epidermal growth factor receptor (HER)-2, all negative] is commonly used to identify such cases. EGFR and cytokeratin 5/6 are readily available positive markers of basal-like breast cancer applicable to standard pathology specimens. This study directly compares the prognostic significance between three- and five-biomarker surrogate panels to define intrinsic breast cancer subtypes, using a large clinically annotated series of breast tumors. Four thousand forty-six invasive breast cancers were assembled into tissue microarrays. All had staging, pathology, treatment, and outcome information; median follow-up was 12.5 years. Cox regression analyses and likelihood ratio tests compared the prognostic significance for breast cancer death-specific survival (BCSS) of the two immunohistochemical panels. Among 3,744 interpretable cases, 17% were basal using the triple-negative definition (10-year BCSS, 6 7%) and 9% were basal using the five-marker method (10-year BCSS, 62%). Likelihood ratio tests of multivariable Cox models including standard clinical variables show that the five-marker panel is significantly more prognostic than the three-marker panel. The poor prognosis of triple-negative phenotype is conferred almost entirely by those tumors positive for basal markers. Among triple-negative patients treated with adjuvant anthracycline-based chemotherapy, the additional positive basal markers identified a cohort of patients with significantly worse outcome. The expanded surrogate immunopanel of estrogen receptor, progesterone receptor, human HER-2, EGFR, and cytokeratin 5/6 provides a more specific definition of basal-like breast cancer that better predicts breast cancer survival.

                Author and article information

                Role: Academic Editor
                PLoS Med
                PLoS Medicine
                Public Library of Science (San Francisco, USA )
                May 2010
                May 2010
                25 May 2010
                : 7
                : 5
                : e1000279
                [1 ]Department of Oncology, University of Cambridge, United Kingdom
                [2 ]Netherlands Cancer Institute, Amsterdam, The Netherlands
                [3 ]Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, Canada
                [4 ]Department of Oncology, Helsinki University Central Hospital, Helsinki, Finland
                [5 ]Department of Pathology, Helsinki University Central Hospital, Helsinki, Finland
                [6 ]Department of Obstetrics and Gynecology, Helsinki University Central Hospital, Helsinki, Finland
                [7 ]The Gade Institute, Section for Pathology, University of Bergen, Haukeland University Hospital, Bergen, Norway
                [8 ]Department of Pathology, McGill University and Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
                [9 ]Program in Cancer Genetics, Departments of Oncology and Human Genetics, McGill University, Montreal, Quebec, Canada
                [10 ]Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, United States of America
                [11 ]Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
                [12 ]Cancer Epidemiology Centre, The Cancer Council Victoria, Melbourne, Australia
                [13 ]The Alfred Hospital, Melbourne, Australia
                [14 ]Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Australia
                [15 ]Departments of Histopathology and Surgery, The Breast Unit, Nottingham City Hospital NHS Trust and University of Nottingham, Nottingham, United Kingdom
                [16 ]Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, United States of America
                [17 ]M. Sklodowska-Curie Memorial Cancer Center & Institute of Oncology, Warsaw, Poland
                [18 ]Institute for Cancer Studies, University of Sheffield School of Medicine, Sheffield, United Kingdom
                [19 ]Academic Unit of Pathology, University of Sheffield School of Medicine, Sheffield, United Kingdom
                [20 ]Academic Unit of Surgical Oncology, University of Sheffield School of Medicine, Sheffield, United Kingdom
                [21 ]Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
                National Institutes of Health, United States of America
                Author notes

                ICMJE criteria for authorship read and met: FMB KED MKS AB FEvL JW MCC KG TON CB PH TH HN LA LRB WF FJC XW VC JEO LB GGG GS CAM MCS ER ARG IOE MES JL WA AC SSC MWRR EP SJD AMD MKH DFE MGC CC PDP DH. Agree with the manuscript's results and conclusions: FMB KED MKS AB FEvL JW MCC KG TON CB PH TH HN LA LRB WF FJC XW VC JEO LB GGG GS CAM MCS ER ARG IOE MES JL WA AC SSC MWRR EP SJD AMD MKH DFE MGC CC PDP DH. Designed the experiments/the study: TON PH MCS IOE JL AMD MGC CC PDP DH. Analyzed the data: FMB KED PH LRB XW MCS MES WA CC PDP. Collected data/did experiments for the study: FMB KED MKS AB JW MCC TON CB PH TH HN LA LRB WF FJC XW VC LB GGG GS CAM MCS ER ARG MES JL AC SSC EP SJD MKH DFE MGC CC PDP DH. Enrolled patients: FEvL KG FJC JEO LB GGG MCS IOE JL MWRR DFE MGC PDP DH. Wrote the first draft of the paper: FMB KED. Contributed to the writing of the paper: MKS FEvL JW KG TON CB PH TH HN LA WF FJC XW JEO LB GGG GS CAM MCS IOE MES JL WA AC SSC MWRR EP SJD DFE MGC CC PDP. Co-ordinated data collation: FMB KED. Contributed to database building and checking of data: MKS. Enrolled patients are derived from a cohort built by me and others: FEvL. Responsible for clinical data integrity: VC. Assistance with TMA construction and scoring of immunohistochemistry: EP. Responsible for pooling data and data cleaning: MH.

                This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
                : 28 September 2009
                : 12 April 2010
                Page count
                Pages: 12
                Research Article
                Oncology/Breast Cancer



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