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

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.

Impact of the menstrual cycle on commercial prognostic gene signatures in oestrogen receptor-positive primary breast cancer

“…There is very little previous work examining the effect of menstrual cycle on gene expression-based prognostic signatures, such as RS, ROR and EP, which are widely used in ER-positive breast cancer to estimate the risk of distant recurrence for patients receiving endocrine therapy and help guide the use of adjuvant chemotherapy. A recent study by Bernhardt et al [ 28 ] in 25 women reported a higher discordance of RS score when measured in paired samples from the 16 women < 50 years of age. Eight of the 16 cases < 50 years showed differences of > 4 U in the recurrence score between the paired biopsy compared with none of the 9 cases from older women.…”

Clinical utility of the 21-gene assay in predicting response to neoadjuvant endocrine therapy in breast cancer: A systematic review and meta-analysis

“…Recent data from Bernhardt et al. suggests there may be discrepancies between RS values on core biopsy and resected tumour specimens, albeit impacted by patient age [ 59 ]. Patient data from two separate cohorts (both Ueno et al.)…”

“…While this analysis suggests that NET prescription following RS may be reasonable in tumour downstaging, 90% of cancers had T1/T2 disease limiting conclusions which may be drawn in relation to downstaging T3 or locally advanced disease. Recent data from Bernhardt et al suggests there may be discrepancies between RS values on core biopsy and resected tumour specimens, albeit impacted by patient age [59].…”

Steroid Ligands, the Forgotten Triggers of Nuclear Receptor Action; Implications for Acquired Resistance to Endocrine Therapy

“…Despite breast cancer subtype classification being based on the fairly ubiquitous presence of hormone receptors (ER, PR); the presence of ligands are somewhat overlooked. The relevance of age-related endocrine changes in driving transcriptomic and proteomic alterations in breast cancer has recently garnered attention, particularly with regards the robustness of biomarker assessment platforms such as OncotypeDX (47,48). This is a burgeoning area of research and it is clear that inclusion of the patient endocrine landscape may enhance robustness in the application of biomarkerbased algorithms (47).…”