Intratumoral heterogeneity as a source of discordance in breast cancer biomarker classification

Abstract

BackgroundSpatial 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.MethodsTissue 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.ResultsOn 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).ConclusionsIntratumoral 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-016-0725-1) contains supplementary material, which is available to authorized users.

Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer

“…As HER2 heterogeneity has been reported to be a source of inter-pathologist variability1240, computer-aided diagnosis could be particularly useful to bring objective and accurate biomarker quantification for these difficult cases. Here, we identified that the discordant cases between automated scoring and the pathologist were significantly associated with HER2 staining heterogeneity.…”

Future Clinical Trials

“…Clonal tumor heterogeneity poses additional diagnostic and longitudinal genetic surveillance challenges to the design of clinical trials. The rate of discrepancies in HER2 status, for example in breast cancer biopsied at different time points, can be as high as around 10% 31 . Similar challenges are encountered in gastroesophageal carcinomas in which co-occurrence of HER2, EGFR and MET amplifications have been reported in the chromosomal instability subset of carcinomas 4 .…”

Absent progesterone receptor expression in the lymph node metastases of ER-positive, HER2-negative breast cancer is associated with relapse on tamoxifen

“…All studies that have measured the relative benefit of AIs over tamoxifen have done so by assessing ER and PR status on the primary tumour, where there is often significant intratumoral heterogeneity of PR expression,21 and differences in receptor expression between the primary tumour and axillary lymph node metastatic disease 12. The lymph node metastatic disease may represent the potentially recurrent breast cancer cells better than the primary tumour.…”