Lymph node involvement in immunohistochemistry-based molecular classifications of breast cancer
Abstract Background Prognosis and treatment options differ for each molecular subtype of breast cancer, but risk of regional lymph node (LN) metastasis for each subtype has not been well studied. Since LN status is the most important predictor for prognosis, the aim of this study is to investigate t...
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| Veröffentlicht in: | The Journal of surgical research 2013-12, Vol.185 (2), p.697-703 |
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| creator | Howland, Nicholas K., MD Driver, Teryn D., BS Sedrak, Michael P., MD Wen, Xianfeng, MD Dong, Wenli, MS Hatch, Sandra, MD Eltorky, Mahmoud A., MD, PhD Chao, Celia, MD, FACS |
| description | Abstract Background Prognosis and treatment options differ for each molecular subtype of breast cancer, but risk of regional lymph node (LN) metastasis for each subtype has not been well studied. Since LN status is the most important predictor for prognosis, the aim of this study is to investigate the propensity for LN metastasis in each of the five breast cancer molecular subtypes. Methods Under an institutional review board–approved protocol, we retrospectively reviewed the charts of all pathologically confirmed breast cancer cases from January 2004 to June 2012. Five subtypes were defined as luminal A (hormone receptor positive, Ki-67 low), luminal B (hormone receptor positive, Ki-67 high), luminal human epidermal growth factor receptor 2 (HER2), HER2-enriched (hormone receptor negative), and triple negative (TN). Results A total of 375 patients with complete data were classified by subtype: 95 (25.3%) luminal A, 120 (32%) luminal B, 69 (18.4%) luminal HER2, 26 (6.9%) HER2-enriched, and 65 (17.3%) TN. On univariate analysis, age ( |
| doi_str_mv | 10.1016/j.jss.2013.06.048 |
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| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_3923595</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0022480413006744</els_id><sourcerecordid>1458185283</sourcerecordid><originalsourceid>FETCH-LOGICAL-c506t-c84d58bf81cbc19e9116be6b10707aa5a90116780c9a003c515ba4f89bf600d13</originalsourceid><addsrcrecordid>eNp9UsuO1DAQtBCIHRY-gAvKkUtCO44TR0groRUvaSQOwLnlOB3Gg2MPdjLS_P16NMsKOHBqP6rK7qpm7CWHigNv3-yrfUpVDVxU0FbQqEdsw6GXpWo78ZhtAOq6bBQ0V-xZSnvI-74TT9lV3WQU1HLDcHuaD7vCh5EK64_BHWkmv-R1Yed59WFn0xLMjuZc46kcdKKxmIMjszodC-N0SnayRi82-FSEqRgi6bQURntD8Tl7MmmX6MV9vWbfP7z_dvup3H75-Pn23bY0EtqlNKoZpRomxc1geE895-1A7cChg05rqXvIJ50C02sAYSSXg24m1Q9TCzBycc1uLrqHdZhpNLmHqB0eop11PGHQFv--8XaHP8IRRV8L2css8PpeIIZfK6UFc8eGnNOewpqQN1JxJWslMpRfoCaGlCJND89wwHMwuMccDJ6DQWgxB5M5r_783wPjdxIZ8PYCoOzS0VLEZCxlC0cbySw4Bvtf-Zt_2MZZn1NxP-lEaR_W6LP9yDHVCPj1PBnnweACoO2aRtwB2vi2Qw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1458185283</pqid></control><display><type>article</type><title>Lymph node involvement in immunohistochemistry-based molecular classifications of breast cancer</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><creator>Howland, Nicholas K., MD ; Driver, Teryn D., BS ; Sedrak, Michael P., MD ; Wen, Xianfeng, MD ; Dong, Wenli, MS ; Hatch, Sandra, MD ; Eltorky, Mahmoud A., MD, PhD ; Chao, Celia, MD, FACS</creator><creatorcontrib>Howland, Nicholas K., MD ; Driver, Teryn D., BS ; Sedrak, Michael P., MD ; Wen, Xianfeng, MD ; Dong, Wenli, MS ; Hatch, Sandra, MD ; Eltorky, Mahmoud A., MD, PhD ; Chao, Celia, MD, FACS</creatorcontrib><description>Abstract Background Prognosis and treatment options differ for each molecular subtype of breast cancer, but risk of regional lymph node (LN) metastasis for each subtype has not been well studied. Since LN status is the most important predictor for prognosis, the aim of this study is to investigate the propensity for LN metastasis in each of the five breast cancer molecular subtypes. Methods Under an institutional review board–approved protocol, we retrospectively reviewed the charts of all pathologically confirmed breast cancer cases from January 2004 to June 2012. Five subtypes were defined as luminal A (hormone receptor positive, Ki-67 low), luminal B (hormone receptor positive, Ki-67 high), luminal human epidermal growth factor receptor 2 (HER2), HER2-enriched (hormone receptor negative), and triple negative (TN). Results A total of 375 patients with complete data were classified by subtype: 95 (25.3%) luminal A, 120 (32%) luminal B, 69 (18.4%) luminal HER2, 26 (6.9%) HER2-enriched, and 65 (17.3%) TN. On univariate analysis, age (<50), higher tumor grade, HER2+ status, tumor size, and molecular subtype were significant for LN positivity. Molecular subtype correlated strongly with tumor size (χ2 ; P = 0.0004); therefore, multivariable logistic regression did not identify molecular subtype as an independent variable to predict LN positivity. Conclusions Luminal A tumors have the lowest risk of LN metastasis, whereas luminal HER2 subtype has the highest risk of LN metastasis. Immunohistochemical-based molecular classification can be readily performed and knowledge of the factors that affect LN status may help with treatment decisions.</description><identifier>ISSN: 0022-4804</identifier><identifier>EISSN: 1095-8673</identifier><identifier>DOI: 10.1016/j.jss.2013.06.048</identifier><identifier>PMID: 24095025</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Biomarkers, Tumor - metabolism ; Breast Neoplasms - classification ; Breast Neoplasms - epidemiology ; Breast Neoplasms - secondary ; Female ; Humans ; Immunohistochemistry ; Immunohistochemistry - methods ; Ki-67 Antigen - metabolism ; Logistic Models ; Lymphatic Metastasis - pathology ; Middle Aged ; Molecular subtypes of breast cancer ; Multivariate Analysis ; Predictive Value of Tests ; Prognosis ; Receptor, Epidermal Growth Factor - metabolism ; Receptor, ErbB-2 - metabolism ; Retrospective Studies ; Risk Factors ; Surgery ; Triple Negative Breast Neoplasms - classification ; Triple Negative Breast Neoplasms - epidemiology ; Triple Negative Breast Neoplasms - secondary</subject><ispartof>The Journal of surgical research, 2013-12, Vol.185 (2), p.697-703</ispartof><rights>2013</rights><rights>Copyright © 2013. Published by Elsevier Inc.</rights><rights>2013 Published by Elsevier Inc. 2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c506t-c84d58bf81cbc19e9116be6b10707aa5a90116780c9a003c515ba4f89bf600d13</citedby><cites>FETCH-LOGICAL-c506t-c84d58bf81cbc19e9116be6b10707aa5a90116780c9a003c515ba4f89bf600d13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0022480413006744$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24095025$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Howland, Nicholas K., MD</creatorcontrib><creatorcontrib>Driver, Teryn D., BS</creatorcontrib><creatorcontrib>Sedrak, Michael P., MD</creatorcontrib><creatorcontrib>Wen, Xianfeng, MD</creatorcontrib><creatorcontrib>Dong, Wenli, MS</creatorcontrib><creatorcontrib>Hatch, Sandra, MD</creatorcontrib><creatorcontrib>Eltorky, Mahmoud A., MD, PhD</creatorcontrib><creatorcontrib>Chao, Celia, MD, FACS</creatorcontrib><title>Lymph node involvement in immunohistochemistry-based molecular classifications of breast cancer</title><title>The Journal of surgical research</title><addtitle>J Surg Res</addtitle><description>Abstract Background Prognosis and treatment options differ for each molecular subtype of breast cancer, but risk of regional lymph node (LN) metastasis for each subtype has not been well studied. Since LN status is the most important predictor for prognosis, the aim of this study is to investigate the propensity for LN metastasis in each of the five breast cancer molecular subtypes. Methods Under an institutional review board–approved protocol, we retrospectively reviewed the charts of all pathologically confirmed breast cancer cases from January 2004 to June 2012. Five subtypes were defined as luminal A (hormone receptor positive, Ki-67 low), luminal B (hormone receptor positive, Ki-67 high), luminal human epidermal growth factor receptor 2 (HER2), HER2-enriched (hormone receptor negative), and triple negative (TN). Results A total of 375 patients with complete data were classified by subtype: 95 (25.3%) luminal A, 120 (32%) luminal B, 69 (18.4%) luminal HER2, 26 (6.9%) HER2-enriched, and 65 (17.3%) TN. On univariate analysis, age (<50), higher tumor grade, HER2+ status, tumor size, and molecular subtype were significant for LN positivity. Molecular subtype correlated strongly with tumor size (χ2 ; P = 0.0004); therefore, multivariable logistic regression did not identify molecular subtype as an independent variable to predict LN positivity. Conclusions Luminal A tumors have the lowest risk of LN metastasis, whereas luminal HER2 subtype has the highest risk of LN metastasis. Immunohistochemical-based molecular classification can be readily performed and knowledge of the factors that affect LN status may help with treatment decisions.</description><subject>Biomarkers, Tumor - metabolism</subject><subject>Breast Neoplasms - classification</subject><subject>Breast Neoplasms - epidemiology</subject><subject>Breast Neoplasms - secondary</subject><subject>Female</subject><subject>Humans</subject><subject>Immunohistochemistry</subject><subject>Immunohistochemistry - methods</subject><subject>Ki-67 Antigen - metabolism</subject><subject>Logistic Models</subject><subject>Lymphatic Metastasis - pathology</subject><subject>Middle Aged</subject><subject>Molecular subtypes of breast cancer</subject><subject>Multivariate Analysis</subject><subject>Predictive Value of Tests</subject><subject>Prognosis</subject><subject>Receptor, Epidermal Growth Factor - metabolism</subject><subject>Receptor, ErbB-2 - metabolism</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>Surgery</subject><subject>Triple Negative Breast Neoplasms - classification</subject><subject>Triple Negative Breast Neoplasms - epidemiology</subject><subject>Triple Negative Breast Neoplasms - secondary</subject><issn>0022-4804</issn><issn>1095-8673</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9UsuO1DAQtBCIHRY-gAvKkUtCO44TR0groRUvaSQOwLnlOB3Gg2MPdjLS_P16NMsKOHBqP6rK7qpm7CWHigNv3-yrfUpVDVxU0FbQqEdsw6GXpWo78ZhtAOq6bBQ0V-xZSnvI-74TT9lV3WQU1HLDcHuaD7vCh5EK64_BHWkmv-R1Yed59WFn0xLMjuZc46kcdKKxmIMjszodC-N0SnayRi82-FSEqRgi6bQURntD8Tl7MmmX6MV9vWbfP7z_dvup3H75-Pn23bY0EtqlNKoZpRomxc1geE895-1A7cChg05rqXvIJ50C02sAYSSXg24m1Q9TCzBycc1uLrqHdZhpNLmHqB0eop11PGHQFv--8XaHP8IRRV8L2css8PpeIIZfK6UFc8eGnNOewpqQN1JxJWslMpRfoCaGlCJND89wwHMwuMccDJ6DQWgxB5M5r_783wPjdxIZ8PYCoOzS0VLEZCxlC0cbySw4Bvtf-Zt_2MZZn1NxP-lEaR_W6LP9yDHVCPj1PBnnweACoO2aRtwB2vi2Qw</recordid><startdate>20131201</startdate><enddate>20131201</enddate><creator>Howland, Nicholas K., MD</creator><creator>Driver, Teryn D., BS</creator><creator>Sedrak, Michael P., MD</creator><creator>Wen, Xianfeng, MD</creator><creator>Dong, Wenli, MS</creator><creator>Hatch, Sandra, MD</creator><creator>Eltorky, Mahmoud A., MD, PhD</creator><creator>Chao, Celia, MD, FACS</creator><general>Elsevier Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20131201</creationdate><title>Lymph node involvement in immunohistochemistry-based molecular classifications of breast cancer</title><author>Howland, Nicholas K., MD ; Driver, Teryn D., BS ; Sedrak, Michael P., MD ; Wen, Xianfeng, MD ; Dong, Wenli, MS ; Hatch, Sandra, MD ; Eltorky, Mahmoud A., MD, PhD ; Chao, Celia, MD, FACS</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c506t-c84d58bf81cbc19e9116be6b10707aa5a90116780c9a003c515ba4f89bf600d13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Biomarkers, Tumor - metabolism</topic><topic>Breast Neoplasms - classification</topic><topic>Breast Neoplasms - epidemiology</topic><topic>Breast Neoplasms - secondary</topic><topic>Female</topic><topic>Humans</topic><topic>Immunohistochemistry</topic><topic>Immunohistochemistry - methods</topic><topic>Ki-67 Antigen - metabolism</topic><topic>Logistic Models</topic><topic>Lymphatic Metastasis - pathology</topic><topic>Middle Aged</topic><topic>Molecular subtypes of breast cancer</topic><topic>Multivariate Analysis</topic><topic>Predictive Value of Tests</topic><topic>Prognosis</topic><topic>Receptor, Epidermal Growth Factor - metabolism</topic><topic>Receptor, ErbB-2 - metabolism</topic><topic>Retrospective Studies</topic><topic>Risk Factors</topic><topic>Surgery</topic><topic>Triple Negative Breast Neoplasms - classification</topic><topic>Triple Negative Breast Neoplasms - epidemiology</topic><topic>Triple Negative Breast Neoplasms - secondary</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Howland, Nicholas K., MD</creatorcontrib><creatorcontrib>Driver, Teryn D., BS</creatorcontrib><creatorcontrib>Sedrak, Michael P., MD</creatorcontrib><creatorcontrib>Wen, Xianfeng, MD</creatorcontrib><creatorcontrib>Dong, Wenli, MS</creatorcontrib><creatorcontrib>Hatch, Sandra, MD</creatorcontrib><creatorcontrib>Eltorky, Mahmoud A., MD, PhD</creatorcontrib><creatorcontrib>Chao, Celia, MD, FACS</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The Journal of surgical research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Howland, Nicholas K., MD</au><au>Driver, Teryn D., BS</au><au>Sedrak, Michael P., MD</au><au>Wen, Xianfeng, MD</au><au>Dong, Wenli, MS</au><au>Hatch, Sandra, MD</au><au>Eltorky, Mahmoud A., MD, PhD</au><au>Chao, Celia, MD, FACS</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Lymph node involvement in immunohistochemistry-based molecular classifications of breast cancer</atitle><jtitle>The Journal of surgical research</jtitle><addtitle>J Surg Res</addtitle><date>2013-12-01</date><risdate>2013</risdate><volume>185</volume><issue>2</issue><spage>697</spage><epage>703</epage><pages>697-703</pages><issn>0022-4804</issn><eissn>1095-8673</eissn><abstract>Abstract Background Prognosis and treatment options differ for each molecular subtype of breast cancer, but risk of regional lymph node (LN) metastasis for each subtype has not been well studied. Since LN status is the most important predictor for prognosis, the aim of this study is to investigate the propensity for LN metastasis in each of the five breast cancer molecular subtypes. Methods Under an institutional review board–approved protocol, we retrospectively reviewed the charts of all pathologically confirmed breast cancer cases from January 2004 to June 2012. Five subtypes were defined as luminal A (hormone receptor positive, Ki-67 low), luminal B (hormone receptor positive, Ki-67 high), luminal human epidermal growth factor receptor 2 (HER2), HER2-enriched (hormone receptor negative), and triple negative (TN). Results A total of 375 patients with complete data were classified by subtype: 95 (25.3%) luminal A, 120 (32%) luminal B, 69 (18.4%) luminal HER2, 26 (6.9%) HER2-enriched, and 65 (17.3%) TN. On univariate analysis, age (<50), higher tumor grade, HER2+ status, tumor size, and molecular subtype were significant for LN positivity. Molecular subtype correlated strongly with tumor size (χ2 ; P = 0.0004); therefore, multivariable logistic regression did not identify molecular subtype as an independent variable to predict LN positivity. Conclusions Luminal A tumors have the lowest risk of LN metastasis, whereas luminal HER2 subtype has the highest risk of LN metastasis. Immunohistochemical-based molecular classification can be readily performed and knowledge of the factors that affect LN status may help with treatment decisions.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>24095025</pmid><doi>10.1016/j.jss.2013.06.048</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Biomarkers, Tumor - metabolism Breast Neoplasms - classification Breast Neoplasms - epidemiology Breast Neoplasms - secondary Female Humans Immunohistochemistry Immunohistochemistry - methods Ki-67 Antigen - metabolism Logistic Models Lymphatic Metastasis - pathology Middle Aged Molecular subtypes of breast cancer Multivariate Analysis Predictive Value of Tests Prognosis Receptor, Epidermal Growth Factor - metabolism Receptor, ErbB-2 - metabolism Retrospective Studies Risk Factors Surgery Triple Negative Breast Neoplasms - classification Triple Negative Breast Neoplasms - epidemiology Triple Negative Breast Neoplasms - secondary |
| title | Lymph node involvement in immunohistochemistry-based molecular classifications of breast cancer |
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