Comparative survival analysis of multiparametric tests\u2014when molecular tests disagree\u2014A TEAM Pathology study

John M S Bartlett,Elizabeth Kornaga,Paul C Boutros,Greg R Pond,Annette Hasenburg,Keying Xu,Robert C Stein,Christos Markopoulos,Daniel Rea,Cindy Q Yao,Jane Bayani,Elizabeth Mallon,Cornelis J H Van De Velde,Luc Dirix,Caroline Seynaeve,Tammy Piper,J A Dunn

doi:10.1038/s41523-021-00297-7

Abstract

Multiparametric assays for risk stratification are widely used in the management of both node negative and node positive hormone receptor positive invasive breast cancer. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. The TEAM pathology study consists of 3284 postmenopausal ER+ve breast cancers treated with endocrine therapy Using genes comprising the following multi-parametric tests OncotypeDx®, Prosigna™ and MammaPrint® signatures were trained to recapitulate true assay results. Patients were then classified into risk groups and survival assessed. Whilst likelihood χ2 ratios suggested limited value for combining tests, Kaplan–Meier and LogRank tests within risk groups suggested combinations of tests provided statistically significant stratification of potential clinical value. Paradoxically whilst Prosigna-trained results stratified Oncotype-trained subgroups across low and intermediate risk categories, only intermediate risk Prosigna-trained cases were further stratified by Oncotype-trained results. Both Oncotype-trained and Prosigna-trained results further stratified MammaPrint-trained low risk cases, and MammaPrint-trained results also stratified Oncotype-trained low and intermediate risk groups but not Prosigna-trained results. Comparisons between existing multiparametric tests are challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. Detailed analysis of the TEAM pathology study suggests a complex inter-relationship between test results in the same patient cohorts which requires careful evaluation regarding test utility. Further prognostic improvement appears both desirable and achievable.

Highlights

Multi-parametric molecular tests are central to the treatment management of early breast cancer and their use is incorporated into most major guidelines[1] as a pre-requisite for the staging of breast cancer patients, to direct prognostication and to select patients for chemotherapy treatment[2,3]
We compared the ability of trained signatures to predict DMFS10 using the likelihood ratio χ2(LRχ2) based on the Cox models as a measure of the overall prognostic information provided by each model
When combining tests with Mammaprint-trained results adding Prosigna-trained results showed a greater increase in LRχ2 (ΔLRχ2 = 49.3) than did combining Mammaprint-trained results with Oncotype-trained results (ΔLRχ2 = 26.3)

Summary

Introduction

Multi-parametric molecular tests are central to the treatment management of early breast cancer and their use is incorporated into most major guidelines[1] as a pre-requisite for the staging of breast cancer patients, to direct prognostication and to select patients for chemotherapy treatment[2,3]. Recent results from the MINDACT and TAILORx studies validate the utility of tests to direct chemotherapy use in node-negative patients[2,5,6], which may be extended as new evidence emerges from retrospective[3] or prospective studies[7,8]. In this context an error in assigning appropriate risk classifications would have significant impact on patient treatment and outcomes. These results may reflect the relatively modest performance of individual multiparametric tests[17]

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