Intratumoral heterogeneity as a source of discordance in breast cancer biomarker classification.
Background: Spatial heterogeneity in biomarker expression may impact breast cancer classification. The aims of this study were to estimate the frequency of spatial heterogeneity in biomarker expression within tumors, to identify technical and biological factors contributing to spatial heterogeneity,...
| Publicado en: | Breast Cancer Research Vol. 18; pp. 68 - 69 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
6/28/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=116580220&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 116580220 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14655411 8UYJ jtl: Breast Cancer Research issn: 14655411 maglogo: N pubinfo: dt: 6/28/2016 vid: 18 pid: 24147 pub: BioMed Central artinfo: ui: 116580220 116580220 NLM27349894 116580220 10.1186/s13058-016-0725-1 NLM27349894 PMC4924300 116580220 ppf: 68 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Intratumoral heterogeneity as a source of discordance in breast cancer biomarker classification. aug: au: Allott, Emma H. Geradts, Joseph Xuezheng Sun Cohen, Stephanie M. Zirpoli, Gary R. Khoury, Thaer Bshara, Wiam Mengjie Chen Sherman, Mark E. Palmer, Julie R. Ambrosone, Christine B. Olshan, Andrew F. Troester, Melissa A. Sun, Xuezheng Chen, Mengjie affil: Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA sug: subj: Breast Neoplasms Pathology Breast Neoplasms Metabolism Tissue Array Analysis Methods Proteins Metabolism Receptors, Cell Surface Metabolism Sensitivity and Specificity Proteins Female Immunohistochemistry Biopsy Prognosis Receptors, Cell Surface Breast Neoplasms Diagnosis Human Funding Source Female ab: Background: Spatial heterogeneity in biomarker expression may impact breast cancer classification. The aims of this study were to estimate the frequency of spatial heterogeneity in biomarker expression within tumors, to identify technical and biological factors contributing to spatial heterogeneity, and to examine the impact of discordant biomarker status within tumors on clinical record agreement.Methods: Tissue microarrays (TMAs) were constructed using two to four cores (1.0 mm) for each of 1085 invasive breast cancers from the Carolina Breast Cancer Study, which is part of the AMBER Consortium. Immunohistochemical staining for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) was quantified using automated digital imaging analysis. The biomarker status for each core and for each case was assigned using clinical thresholds. Cases with core-to-core biomarker discordance were manually reviewed to distinguish intratumoral biomarker heterogeneity from misclassification of biomarker status by the automated algorithm. The impact of core-to-core biomarker discordance on case-level agreement between TMAs and the clinical record was evaluated.Results: On the basis of automated analysis, discordant biomarker status between TMA cores occurred in 9 %, 16 %, and 18 % of cases for ER, PR, and HER2, respectively. Misclassification of benign epithelium and/or ductal carcinoma in situ as invasive carcinoma by the automated algorithm was implicated in discordance among cores. However, manual review of discordant cases confirmed spatial heterogeneity as a source of discordant biomarker status between cores in 2 %, 7 %, and 8 % of cases for ER, PR, and HER2, respectively. Overall, agreement between TMA and clinical record was high for ER (94 %), PR (89 %), and HER2 (88 %), but it was reduced in cases with core-to-core discordance (agreement 70 % for ER, 61 % for PR, and 57 % for HER2).Conclusions: Intratumoral biomarker heterogeneity may impact breast cancer classification accuracy, with implications for clinical management. Both manually confirmed biomarker heterogeneity and misclassification of biomarker status by automated image analysis contribute to discordant biomarker status between TMA cores. Given that manually confirmed heterogeneity is uncommon (<10 % of cases), large studies are needed to study the impact of heterogeneous biomarker expression on breast cancer classification and outcomes. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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