Metabolomic analysis of serum may refine 21-gene expression assay risk recurrence stratification

Amelia Mccartney,Angelo Di Leo,Emanuela Risi,Laura Biganzoli,Alessia Vignoli,Claudio Luchinat,Lorenzo Rossi,Monica Fornier,Leonardo Tenori

doi:10.1038/s41523-019-0123-9

Abstract

Despite recent refinements to the 21-gene g score, allowing a better identification of patients who may derive no benefit from the addition of adjuvant chemotherapy to that of endocrine therapy, patients with early breast cancer still stand to be over-treated in the setting of clinical and/or genomic uncertainty or discordance. Here we describe and demonstrate a potential approach of further refining the OncotypeDX risk score by metabolomic analysis of serum. In a clinical dataset (N = 87), the risk of recurrence was further sub-stratified by metabolomic signature, with an effective splitting of each Oncotype risk classification. A total of seven recurrences were recorded, with metabolomic analysis accurately predicting six of these. Contrastingly, the genomic risk score of the seven recurrences ranged across all three Oncotype classifications (one recurrence occurred in the “low”-risk group, three in the “intermediate” group and three in the “high”-risk group).

Highlights

In meeting its primary endpoint of distant recurrent-free survival, the recently published TAILORx study demonstrated that adjuvant endocrine therapy was non-inferior to chemotherapy plus endocrine therapy in women with endocrine receptor-positive, HER2-negative early breast cancer whose OncotypeDX 21gene expression assay risk recurrence scores (RS) was between 11 and 25.1 while many cases of eBC are cured by surgery ± adjuvant endocrine therapy alone, a significant population are still over-treated due to the fear of recurrent disease established by clinicopathological and/or genomic risk factors
Our group has already established a reproducible method of quantifying the individual metabolomic fingerprint, and its ability to accurately discriminate between advanced breast cancer and eBC2 we previously demonstrated that the metabolomic fingerprint can be used to predict the risk of disease recurrence in early disease,[2,3,4] and that subsequent recurrence is characterised by higher serum levels of choline, phenylalanine, leucine, histidine, glutamate, glycine, tyrosine, valine, lactate and isoleucine.[4]
nuclear magnetic resonance (NMR) spectra derived from the sera of 87 patients with eBC were compared with a matched population of 28 metastatic breast cancer patients, previously analysed in a preceding study.[3]

Summary

Introduction

In meeting its primary endpoint of distant recurrent-free survival, the recently published TAILORx study demonstrated that adjuvant endocrine therapy was non-inferior to chemotherapy plus endocrine therapy in women with endocrine receptor-positive, HER2-negative early breast cancer (eBC) whose OncotypeDX 21gene expression assay risk recurrence scores (RS) was between 11 and 25.1 while many cases of eBC are cured by surgery ± adjuvant endocrine therapy alone, a significant population are still over-treated due to the fear of recurrent disease established by clinicopathological and/or genomic risk factors. Metabolomics is the -omic science that deals with the characterisation of the metabolome, in turn defined as the whole set of metabolites in a certain biological system such as a cell, a tissue, an organ or an entire organism.[8] The two leading analytical techniques used to perform metabolomics are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Both techniques have their own strengths and limitations. NMR analysis is high throughput and produces data that are highly reproducible and intrinsically quantitative, and more suitable for the fingerprinting analysis described here.[8,10] Our group has already established a reproducible method of quantifying the individual metabolomic fingerprint, and its ability to accurately discriminate between advanced breast cancer and eBC2 we previously demonstrated that the metabolomic fingerprint can be used to predict the risk of disease recurrence in early disease,[2,3,4] and that subsequent recurrence is characterised by higher (adjusted P < 0.05) serum levels of choline, phenylalanine, leucine, histidine, glutamate, glycine, tyrosine, valine, lactate and isoleucine.[4]

Methods

Results

Conclusion