Dataset Information

Performance of Three-Biomarker Immunohistochemistry for Intrinsic Breast Cancer Subtyping in the AMBER Consortium.

ABSTRACT:

Background

Classification of breast cancer into intrinsic subtypes has clinical and epidemiologic importance. To examine accuracy of IHC-based methods for identifying intrinsic subtypes, a three-biomarker IHC panel was compared with the clinical record and RNA-based intrinsic (PAM50) subtypes.

Methods

Automated scoring of estrogen receptor (ER), progesterone receptor (PR), and HER2 was performed on IHC-stained tissue microarrays comprising 1,920 cases from the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. Multiple cores (1-6/case) were collapsed to classify cases, and automated scoring was compared with the clinical record and to RNA-based subtyping.

Results

Automated analysis of the three-biomarker IHC panel produced high agreement with the clinical record (93% for ER and HER2, and 88% for PR). Cases with low tumor cellularity and smaller core size had reduced agreement with the clinical record. IHC-based definitions had high agreement with the clinical record regardless of hormone receptor positivity threshold (1% vs. 10%), but a 10% threshold produced highest agreement with RNA-based intrinsic subtypes. Using a 10% threshold, IHC-based definitions identified the basal-like intrinsic subtype with high sensitivity (86%), although sensitivity was lower for luminal A, luminal B, and HER2-enriched subtypes (76%, 40%, and 37%, respectively).

Conclusion

Three-biomarker IHC-based subtyping has reasonable accuracy for distinguishing basal-like from nonbasal-like, although additional biomarkers are required for accurate classification of luminal A, luminal B, and HER2-enriched cancers.

Impact

Epidemiologic studies relying on three-biomarker IHC status for subtype classification should use caution when distinguishing luminal A from luminal B and when interpreting findings for HER2-enriched cancers.

SUBMITTER: Allott EH

PROVIDER: S-EPMC4779705 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Publications

Performance of Three-Biomarker Immunohistochemistry for Intrinsic Breast Cancer Subtyping in the AMBER Consortium.

Allott Emma H EH Cohen Stephanie M SM Geradts Joseph J Sun Xuezheng X Khoury Thaer T Bshara Wiam W Zirpoli Gary R GR Miller C Ryan CR Hwang Helena H Thorne Leigh B LB O'Connor Siobhan S Tse Chiu-Kit CK Bell Mary B MB Hu Zhiyuan Z Li Yan Y Kirk Erin L EL Bethea Traci N TN Perou Charles M CM Palmer Julie R JR Ambrosone Christine B CB Olshan Andrew F AF Troester Melissa A MA

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20151228 3

<h4>Background</h4>Classification of breast cancer into intrinsic subtypes has clinical and epidemiologic importance. To examine accuracy of IHC-based methods for identifying intrinsic subtypes, a three-biomarker IHC panel was compared with the clinical record and RNA-based intrinsic (PAM50) subtypes.<h4>Methods</h4>Automated scoring of estrogen receptor (ER), progesterone receptor (PR), and HER2 was performed on IHC-stained tissue microarrays comprising 1,920 cases from the African American Bre ...[more]

PMID: 26711328

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Project description:MotivationThe PAM50 signature/method is widely used for intrinsic subtyping of breast cancer samples. However, depending on the number and composition of the samples included in a cohort, the method may assign different subtypes to the same sample. This lack of robustness is mainly due to the fact that PAM50 subtracts a reference profile, which is computed using all samples in the cohort, from each sample before classification. In this paper we propose modifications to PAM50 to develop a simple and robust single-sample classifier, called MPAM50, for intrinsic subtyping of breast cancer. Like PAM50, the modified method uses a nearest centroid approach for classification, but the centroids are computed differently, and the distances to the centroids are determined using an alternative method. Additionally, MPAM50 uses unnormalized expression values for classification and does not subtract a reference profile from the samples. In other words, MPAM50 classifies each sample independently, and so avoids the previously mentioned robustness issue.ResultsA training set was employed to find the new MPAM50 centroids. MPAM50 was then tested on 19 independent datasets (obtained using various expression profiling technologies) containing 9637 samples. Overall good agreement was observed between the PAM50- and MPAM50-assigned subtypes with a median accuracy of 0.792, which (we show) is comparable with the median concordance between various implementations of PAM50. Additionally, MPAM50- and PAM50-assigned intrinsic subtypes were found to agree comparably with the reported clinical subtypes. Also, survival analyses indicated that MPAM50 preserves the prognostic value of the intrinsic subtypes. These observations demonstrate that MPAM50 can replace PAM50 without loss of performance. On the other hand, MPAM50 was compared with 2 previously published single-sample classifiers, and with 3 alternative modified PAM50 approaches. The results indicated a superior performance by MPAM50.ConclusionsMPAM50 is a robust, simple, and accurate single-sample classifier of intrinsic subtypes of breast cancer.

Dataset Information

Performance of Three-Biomarker Immunohistochemistry for Intrinsic Breast Cancer Subtyping in the AMBER Consortium.

Background

Methods

Results

Conclusion

Impact

Publications

Performance of Three-Biomarker Immunohistochemistry for Intrinsic Breast Cancer Subtyping in the AMBER Consortium.

Similar Datasets

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