Clinical Value of RNA Sequencing–Based Classifiers for Prediction of the Five Conventional Breast Cancer Biomarkers: A Report From the Population-Based Multicenter Sweden Cancerome Analysis Network—Breast Initiative

Author:

Brueffer Christian1,Vallon-Christersson Johan,Grabau Dorthe1,Ehinger Anna1,Häkkinen Jari1,Hegardt Cecilia1,Malina Janne1,Chen Yilun1,Bendahl Pär-Ola1,Manjer Jonas1,Malmberg Martin1,Larsson Christer1,Loman Niklas,Rydén Lisa1,Borg Åke1,Saal Lao H.1

Affiliation:

1. Christian Brueffer, Johan Vallon-Christersson, Anna Ehinger, Jari Häkkinen, Cecilia Hegardt, Yilun Chen, Pär-Ola Bendahl, Jonas Manjer, Christer Larsson, Niklas Loman, Lisa Rydén, Åke Borg, and Lao H. Saal, Lund University, Lund; Dorthe Grabau, Anna Ehinger, Martin Malmberg, Niklas Loman, and Lisa Rydén, Skåne University Hospital Lund, Lund; Anna Ehinger, Blekinge County Hospital, Karlskrona; and Janne Malina and Jonas Manjer, Skåne University Hospital Malmö, Malmö, Sweden.

Abstract

Purpose In early breast cancer (BC), five conventional biomarkers—estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), Ki67, and Nottingham histologic grade (NHG)—are used to determine prognosis and treatment. We aimed to develop classifiers for these biomarkers that were based on tumor mRNA sequencing (RNA-seq), compare classification performance, and test whether such predictors could add value for risk stratification. Methods In total, 3,678 patients with BC were studied. For 405 tumors, a comprehensive multi-rater histopathologic evaluation was performed. Using RNA-seq data, single-gene classifiers and multigene classifiers (MGCs) were trained on consensus histopathology labels. Trained classifiers were tested on a prospective population-based series of 3,273 BCs that included a median follow-up of 52 months (Sweden Cancerome Analysis Network—Breast [SCAN-B], ClinicalTrials.gov identifier: NCT02306096), and results were evaluated by agreement statistics and Kaplan-Meier and Cox survival analyses. Results Pathologist concordance was high for ER, PgR, and HER2 (average κ, 0.920, 0.891, and 0.899, respectively) but moderate for Ki67 and NHG (average κ, 0.734 and 0.581). Concordance between RNA-seq classifiers and histopathology for the independent cohort of 3,273 was similar to interpathologist concordance. Patients with discordant classifications, predicted as hormone responsive by histopathology but non–hormone responsive by MGC, had significantly inferior overall survival compared with patients who had concordant results. This extended to patients who received no adjuvant therapy (hazard ratio [HR], 3.19; 95% CI, 1.19 to 8.57), or endocrine therapy alone (HR, 2.64; 95% CI, 1.55 to 4.51). For cases identified as hormone responsive by histopathology and who received endocrine therapy alone, the MGC hormone-responsive classifier remained significant after multivariable adjustment (HR, 2.45; 95% CI, 1.39 to 4.34). Conclusion Classification error rates for RNA-seq–based classifiers for the five key BC biomarkers generally were equivalent to conventional histopathology. However, RNA-seq classifiers provided added clinical value in particular for tumors determined by histopathology to be hormone responsive but by RNA-seq to be hormone insensitive.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

Cancer Research,Oncology

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