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

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.

[1]  P. Grambsch,et al.  Proportional hazards tests and diagnostics based on weighted residuals , 1994 .

[2]  M. Fernö,et al.  Reproducibility of human epidermal growth factor receptor 2 analysis in primary breast cancer – A national survey performed at pathology departments in Sweden , 2009, Acta oncologica.

[3]  I. Ellis,et al.  Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. , 2002, Histopathology.

[4]  A. Nobel,et al.  Supervised risk predictor of breast cancer based on intrinsic subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[5]  John M S Bartlett,et al.  An international Ki67 reproducibility study. , 2013, Journal of the National Cancer Institute.

[6]  M. Cronin,et al.  A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. , 2004, The New England journal of medicine.

[7]  E. Rutgers,et al.  Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. , 2015, Annals of oncology : official journal of the European Society for Medical Oncology.

[8]  Poul Boiesen, Pär-Ola Bendahl, Lola Anagnostaki, H,et al.  Histologic Grading in Breast Cancer: Reproducibility Between Seven Pathologic Departments , 2000 .

[9]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[10]  J Bogaerts,et al.  High concordance of protein (by IHC), gene (by FISH; HER2 only), and microarray readout (by TargetPrint) of ER, PgR, and HER2: results from the EORTC 10041/BIG 03-04 MINDACT trial. , 2014, Annals of oncology : official journal of the European Society for Medical Oncology.

[11]  Peter A Kaufman,et al.  HER2 testing by local, central, and reference laboratories in specimens from the North Central Cancer Treatment Group N9831 intergroup adjuvant trial. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Christophe Lemetre,et al.  MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neu receptor status in breast cancer , 2009, Breast Cancer Research.

[14]  Lee T. Sam,et al.  Personalized Oncology Through Integrative High-Throughput Sequencing: A Pilot Study , 2011, Science Translational Medicine.

[15]  A. Glas,et al.  High concordance of protein (by IHC), gene (by FISH; HER2 only), and microarray readout (by TargetPrint) of ER, PgR, and HER2: results from the EORTC 10041/BIG 03-04 MINDACT trial. , 2014 .

[16]  M. J. van de Vijver,et al.  Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. , 2006, Journal of the National Cancer Institute.

[17]  N. Harbeck,et al.  St. Gallen/Vienna 2015: A Brief Summary of the Consensus Discussion , 2015, Breast Care.

[18]  Jari Häkkinen,et al.  Implementation of an Open Source Software solution for Laboratory Information Management and automated RNAseq data analysis in a large-scale Cancer Genomics initiative using BASE with extension package Reggie , 2016, bioRxiv.

[19]  G. Hampton,et al.  Development of a robust RNA-based classifier to accurately determine ER, PR, and HER2 status in breast cancer clinical samples , 2014, Breast Cancer Research and Treatment.

[20]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[21]  Jan Gorodkin,et al.  Comparing two K-category assignments by a K-category correlation coefficient , 2004, Comput. Biol. Chem..

[22]  Jack Cuzick,et al.  Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. , 2011, Journal of the National Cancer Institute.

[23]  Javed Siddiqui,et al.  The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing , 2015, Genome research.

[24]  A. Viera,et al.  Understanding interobserver agreement: the kappa statistic. , 2005, Family medicine.

[25]  Joshy George,et al.  Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. , 2006, Cancer research.

[26]  K. Czene,et al.  Sequencing-based breast cancer diagnostics as an alternative to routine biomarkers , 2016, Scientific Reports.

[27]  R. Greiner,et al.  A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status , 2013, PloS one.

[28]  M. Fernö,et al.  Highly reproducible results of breast cancer biomarkers when analysed in accordance with national guidelines – a Swedish survey with central re-assessment , 2015, Acta oncologica.

[29]  M. Ringnér,et al.  Poor prognosis in carcinoma is associated with a gene expression signature of aberrant PTEN tumor suppressor pathway activity , 2007, Proceedings of the National Academy of Sciences.

[30]  Graham R. Ball,et al.  Estrogen receptor status prediction for breast cancer using artificial neural network , 2011, 2011 International Conference on Machine Learning and Cybernetics.

[31]  A. Feinstein,et al.  High agreement but low kappa: II. Resolving the paradoxes. , 1990, Journal of clinical epidemiology.

[32]  Anthony Rhodes,et al.  American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. , 2010, Archives of pathology & laboratory medicine.

[33]  M. J. van de Vijver,et al.  Microarray-Based Determination of Estrogen Receptor, Progesterone Receptor, and HER2 Receptor Status in Breast Cancer , 2009, Clinical Cancer Research.

[34]  Jinha M. Park,et al.  Diagnostic Evaluation of HER-2 as a Molecular Target: An Assessment of Accuracy and Reproducibility of Laboratory Testing in Large, Prospective, Randomized Clinical Trials , 2005, Clinical Cancer Research.

[35]  John M S Bartlett,et al.  Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. , 2014, Archives of pathology & laboratory medicine.

[36]  Nadia Harbeck,et al.  A Brief Summary of the Consensus Discussion , 2015 .

[37]  Shu Ichihara,et al.  Breast cancer prognostic classification in the molecular era: the role of histological grade , 2010, Breast Cancer Research.

[38]  Vladimir B Bajic,et al.  Classifying the estrogen receptor status of breast cancers by expression profiles reveals a poor prognosis subpopulation exhibiting high expression of the ERBB2 receptor. , 2003, Human molecular genetics.

[39]  B. Ljung,et al.  Breast cancer version 3.2014. , 2014, Journal of the National Comprehensive Cancer Network : JNCCN.

[40]  L. V. van't Veer,et al.  70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer. , 2016, The New England journal of medicine.

[41]  Christian Brueffer,et al.  The Sweden Cancerome Analysis Network - Breast (SCAN-B) Initiative: a large-scale multicenter infrastructure towards implementation of breast cancer genomic analyses in the clinical routine , 2015, Genome Medicine.