Discordance in 21-gene recurrence scores between paired breast cancer samples is inversely associated with patient age

Sarah M Bernhardt 1 2, Pallave Dasari 1 2, Joseph Wrin 1 2, Wendy Raymond 3, Suzanne Edwards 4, David Walsh 1, Amanda R Townsend 5 6, Timothy J Price 5 6, Wendy V Ingman 7 8

Affiliations

PMID: 32811558
PMCID: PMC7437067
DOI: 10.1186/s13058-020-01327-1

Free PMC article

Abstract

Background: The Oncotype DX 21-gene Recurrence Score is a genomic-based algorithm that guides adjuvant chemotherapy treatment decisions for women with early-stage, oestrogen receptor (ER)-positive breast cancer. However, there are age-related differences in chemotherapy benefit for women with intermediate Oncotype DX Recurrence Scores that are not well understood. Menstrual cycling in younger women is associated with hormonal fluctuations that might affect the expression of genomic predictive biomarkers and alter Recurrence Scores. Here, we use paired human breast cancer samples to demonstrate that the clinically employed Oncotype DX algorithm is critically affected by patient age.

Methods: RNA was extracted from 25 pairs of formalin-fixed paraffin-embedded, invasive ER-positive breast cancer samples that had been collected approximately 2 weeks apart. A 21-gene signature analogous to the Oncotype DX platform was assessed through quantitative real-time PCR, and experimental recurrence scores were calculated using the Oncotype DX algorithm.

Results: There was a significant inverse association between patient age and discordance in the recurrence score. For every 1-year decrease in age, discordance in recurrence scores between paired samples increased by 0.08 units (95% CI - 0.14, - 0.01; p = 0.017). Discordance in recurrence scores for women under the age of 50 was driven primarily by proliferation- and HER2-associated genes.

Conclusion: The Oncotype DX 21-gene Recurrence Score algorithm is critically affected by patient age. These findings emphasise the need for the consideration of patient age, particularly for women younger than 50, in the development and application of genomic-based algorithms for breast cancer care.

Keywords: Age; Genomics; Menstrual cycle; Predictive biomarkers; Premenopausal breast cancer.