A test utilising diagnostic and on-treatment biomarkers to improve prediction of response to endocrine therapy in breast cancer.

Arran Turnbull, Yan Lee, Dominic Pearce, Carlos Martinez-Perez, Sonya Uddin, Hannah Webb, Anu Fernando, Jeremy Thomas, Lorna Renshaw, Andrew Sims, Mike Dixon

Research output: Contribution to journalMeeting abstractpeer-review

Abstract / Description of output

Background: Identifying breast cancer (BC) patients likely to recur on endocrine therapy (ET) is a major challenge. Many multigene classifiers are available for predicting prognosis. These are used at diagnosis to determine whether adjuvant ET alone is sufficient for estrogen receptor (ER) positive patients to remain disease-free, or if additional therapy is required. We developed a test measuring diagnostic IL6ST and 2 week proliferation, either at transcript level or by immunohistochemistry (IHC), which accurately predicts neoadjuvant response (NR), recurrence free survival (RFS) and BC specific survival (BCS). Methods: We compared predictions using our test with those of established clinicopathological markers or estimations of multigene classifier scores (OncotypeDX, EndoPredict, Prosigna, MammaPrint) before and/or on-treatment with aromatase inhibitors (AIs) in BC patients. We analysed diagnostic and 2 week biopsies from 3 patient cohorts: A: n = 73, treated in a single centre with neoadjuvant (3 months) then adjuvant letrozole, B: n = 44, treated in another centre with neoadjuvant anastrozole (3 months), and C: n = 84, received 2 weeks of AIs prior to surgery and continued adjuvantly. NR was determined using periodic tumour ultrasound. Patients in cohorts A and C had 10 years follow-up. Results: Prediction accuracy of NR at 3 months was poor (30-70%) with all methods tested using pre-treatment biopsies alone. On-treatment was associated with improved predictive power, however the most accurate predictions all utilised IL6ST level at diagnosis and a proliferation marker (MCM4 or Ki67) on-treatment (70-96%). Improvements were also observed with outcome prediction: P = 0.0005 (RFS) and P = 0.0002 (BCS) in series A and P textless 0.0001 for both RFS and BCS in series C, assessed by IHC. Hazard ratios calculated using our test ranged from 2.9-8.0 (95%CI 0.2-33), significantly higher than with all other methods tested either pre- or on-treatment (HR: 0.5-2.0, 95%CI 0.1-11). Conclusions: Estimating IL6ST at diagnosis and proliferation at 2 weeks outperforms all current predictive classifiers and clinical tools for both short term (NR) and long term (RFS and BCS) outcomes.
Original languageEnglish
Pages (from-to)555
Number of pages1
JournalJournal of Clinical Oncology
Issue number15suppl
Publication statusPublished - 2016


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