Clinical evidence supporting genomic tests in early breast cancer: Do all genomic tests provide the same information?

Riccardo Masetti, C. Markopoulos, C. Van De Velde, D. Zarca, V. Ozmen

Risultato della ricerca: Contributo in rivistaArticolo in rivista

17 Citazioni (Scopus)

Abstract

Breast cancer (BC) has historically been treated as a single disease entity; however, in the last decade, insights into its molecular heterogeneity have underpinned the development/commercialisation of several genomic tools whose goal is to guide patient management in early BC. These include the Oncotype DX® Breast Recurrence Score™ assay, MammaPrint®, Prosigna®, and EndoPredict®. Although these assays are similar in that they are all multigene assays reflecting risk of recurrence, they differ substantially in the technological platform used to measure gene expression; the number and identity of genes assessed; the patient populations used for development and validation; and the level of evidence supporting clinical utility. They also differ in the amount of evidence demonstrating their impact on treatment decisions and cost effectiveness in different countries. This review discusses these 4 assays, highlighting the clinical evidence that supports each of them, while focussing on the Recurrence Score assay. This assay has the greatest body of evidence supporting its clinical utility and decision impact/effectiveness, and currently is the only one validated as a predictor of response to adjuvant chemotherapy in hormone-receptor positive early BC patients treated with endocrine therapy and to be included as such in international/national BC treatment guidelines. The review also discusses ongoing prospective trials investigating the 4 assays, recent outcome studies, as well as analyses comparing different assays on the same tumour blocks.
Lingua originaleEnglish
pagine (da-a)909-920
Numero di pagine12
RivistaEuropean Journal of Surgical Oncology
Volume43
DOI
Stato di pubblicazionePubblicato - 2017

Keywords

  • Adjuvant chemotherapy
  • Breast cancer
  • Genomics
  • Personalized medicine

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