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

Amelia McCartneyAlessia VignoliLeonardo TenoriMonica FornierLorenzo RossiEmanuela RisiClaudio LuchinatLaura BiganzoliAngelo Di Leo
Published in: NPJ breast cancer (2019)
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).
Keyphrases
  • gene expression
  • copy number
  • early breast cancer
  • dna methylation
  • machine learning
  • deep learning
  • genome wide
  • transcription factor
  • single cell
  • replacement therapy