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

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.

Conflict of interest statement

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center. Christian BruefferEmployment: SAGA Diagnostics ABJohan Vallon-ChristerssonNo relationship to discloseDorthe GrabauNo relationship to discloseAnna EhingerNo relationship to discloseJari HäkkinenNo relationship to discloseCecilia HegardtNo relationship to discloseJanne MalinaEmployment: Unilabs Honoraria: AstraZenecaYilun ChenNo relationship to disclosePär-Ola BendahlNo relationship to discloseJonas ManjerNo relationship to discloseMartin MalmbergNo relationship to discloseChrister LarssonHonoraria: Lilly (I) Research Funding: Diamyd Medical AB (I) Travel, Accommodations, Expenses: Lilly (I)Niklas LomanHonoraria: AstraZeneca Consulting or Advisory Role: AmgenLisa RydénResearch Funding: RocheÅke BorgHonoraria: Roche, AstraZeneca Travel, Accommodations, Expenses: Roche, AstraZenecaLao H. SaalEmployment: SAGA Diagnostics AB Leadership: SAGA Diagnostics AB Stock and Other Ownership Interests: SAGA Diagnostics AB Patents, Royalties, Other Intellectual Property: Patent filed for methods related to ultrasensitive quantification of nucleotide sequence variants.

© 2018 by American Society of Clinical Oncology.

Figures

Fig 1.

Study design flow diagram. ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; Ki67, proliferation antigen Ki67; MGC, multigene classifier; NHG, Nottingham histologic grade; PgR, progesterone receptor; SGC, single-gene classifier.

Fig 2.

Performance of trained classifiers in the 3,273-tumor independent validation cohort. (A) Forest plots of concordance statistics for histopathologic evaluation in the training set (blue square markers), and single-gene classifiers (SGCs; gold circles) and multigene classifiers (MGCs; gray diamonds) in the validation cohort, which plots overall agreement with 95% CIs, specific agreements (positive and negative agreements for estrogen receptor [ER], progesterone receptor [PgR], human epidermal growth factor receptor 2 [HER2], and Ki67) and Nottingham histologic grade (NHG) category agreements (grade [G] 1, G2, and G3), and κ values with 95% CIs. Overall agreement is defined as the number of concordant determinations (assigned to the same class) divided by the total sample size. Positive, negative, and G1/G2/G3 agreements are the proportions of agreement specific to the given category (Data Supplement). (B) Overall agreement of classifiers from the literature compared with our SGCs and MGCs. SCAN-B, Sweden Cancerome Analysis Network—Breast.

Fig 3.

Kaplan-Meier overall survival estimates and Cox regression survival analysis for multigene classifiers (MGCs) within the independent validation cohort. (A) Histopathologically hormone responsive (defined as estrogen receptor [ER] positive and progesterone receptor [PgR] positive) group stratified by MGC hormone responsive classification (concordant [blue curve] or discordant [gold curve] to histopathology) within the subgroup of patients who received (left) no adjuvant systemic therapy, (middle) endocrine therapy alone, or (right) chemotherapy with or without trastuzumab or endocrine therapy. (B) Human epidermal growth factor receptor 2 [HER2]–negative histopathology group stratified by HER2 MGC for the same three treatment subgroups as in A. (C) Ki67-high histopathology group stratified by Ki67 MGC for the same three treatment subgroups as in A. (D) Nottingham histologic grade (NHG) combined grade [G] 1 and G2 histopathology group stratified by NHG MGC for the same three treatment subgroups as in A. In each Kaplan-Meier plot, the histopathology to MGC concordant tumor cases are plotted in blue, the discordant tumor cases are plotted in gold, the log-rank P value is given, and the hazard ratio (HR) for discordant-versus-concordant result is given with a 95% CI and after multivariable (MV) Cox regression adjustment. Covariables included in the MV analysis were age at diagnosis, lymph node status, tumor size, and the variables denoted by the following symbols: †, ER, PgR, and NHG; ‡, ER, PgR, HER2, and NHG; §, HER2 and NHG; #, ER, PgR, and HER2.

Fig A1.

Flow diagram for Sweden Cancerome Analysis Network—Breast (SCAN-B) population-based 3,273-tumor independent validation cohort. (*) Nonmetastatic primary unilateral breast cancer, which excluded patient cases that had a diagnosis of synchronous (

Fig A2.

Prediction of biomarker status in the 3,273-case independent validation cohort. For estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki67 clinical histopathology diagnostic results (y-axis), the single-gene classifier (SGC) gene expression (x-axis) (A) or the transformed multigene classifier (MGC) score (x-axis) (B) is plotted for the validation cohort (circles). Within a biomarker prediction, gold circles were concordantly biomarker negative, blue circles were concordantly positive, and gray circles were discordant by the classifier or histopathology. Vertical dotted (SGC) and dashed (MGC) lines represent the classifier threshold that distinguished the classes. FPKM, fragments per kilobase of transcript per million mapped reads.

Fig A3.

Transformed multigene classifier (MGC) score (x-axis) versus single-gene classifier (SGC) gene expression (y-axis) in the 3,273 samples of the independent validation cohort (circles) for (A) estrogen receptor (ER), (B) progesterone receptor (PgR), (C) human epidermal growth factor receptor 2 (HER2), and (D) Ki67. Gold circles are negative or low by histopathology, and blue circles are positive or high by histopathology. Vertical dashed lines are drawn at the MGC score threshold of 0 to distinguish the classes, and horizontal dotted lines are drawn at the SGC gene expression thresholds determined from the training cohort. FPKM, fragments per kilobase of transcript per million mapped reads.

Fig A4.

Kaplan-Meier overall survival estimates for histopathology, single-gene classifiers (SGCs), and multigene classifiers (MGCs) within the validation cohort (neg, classified as negative; pos, classified as positive; grade [G]1, G2, or G3). The biomarker is indicated at the far left, and the number of tumor cases with complete data across pathology, SGC, and MGC for a given biomarker is shown below each biomarker name. In columns are plotted the Kaplan-Meier survival curves for each classification: (left) pathology, (middle) SGC, and (right column) MGC. The log-rank P value is displayed, and horizontal dashed lines are drawn to aid identification of Kaplan-Meier estimates with the poorest outcome classification group within each row. Generally, histopathology and SGCs had similar curves, whereas the MGCs had noticeably improved stratification, for the hormone receptors, in particular.

Fig A5.

Kaplan-Meier overall survival estimates for groups defined by pathology (path) versus multigene classifiers (MGCs) within the validation cohort; the log-rank P value is given. (A) The entire validation cohort stratified by concordance or discordance between estrogen receptor (ER) histopathology and the ER MGC. (B) Progesterone receptor (PgR) status stratified by histopathology and PgR MGC. (C) Hormone responsiveness status stratified by histopathology and MGC; responsive is defined as ER and PgR positive; nonresponsive, as ER negative or PgR negative.