Abstract P3-07-14: Multigene signatures based risk estimates in early ER+/HER2- breast cancer: The predictive value of inexpensive statistical models and changes in adjuvant chemotherapy use

Abstract Background Multigene signatures (MGS) select women with estrogen receptor positive human epidermal growth factor receptor 2 negative (ER+/HER2-) breast cancers where adjuvant chemotherapy (aCT) can be avoided. However, MGS are expensive and not always reimbursed. We investigated the predictive value of six inexpensive statistical models in tumors with low or high risk of relapse based on MGS and investigated the change in decision to add chemotherapy following MGS results. Patients and Methods In this retrospective study, we evaluated patients diagnosed with primary operable ER+/HER2- lymph node negative or positive breast cancer diagnosed at University Hospitals Leuven between 2013 and 2018. Tumor tissue of the patients was analyzed by MammaPrint® (MP) (n=25), OncotypeDX® (ODX) (n=44) or Prosigna® (n=57) as there was uncertainty about benefit of aCT during multidisciplinary meeting (MDM). Magee equations (ME), Memorial Sloan Kettering simplified score (MSK), Breast Cancer Recurrence Score Estimator (BCRSE), OncotypeDXCalculator (ODXC), new Adjuvant! Online (nAOL) and PREDICT v2.0 were computed. TAILORx cut-offs were used for ODX. A 85% cut-off was used for the probability of a low (0-25) or high risk (26-100) ODX recurrence score for ODXC and a 5% cut-off was used for 10-year survival benefit with aCT for nAOL and PREDICT. Results All ME- and BCRSE-high cases were classified by MGS as high or intermediate and not as MGS-low risk (Table 1). None of the low risk classifications by ME and nAOL resulted in MGS-high risk with ODX. High risk classification with nAOL showed strong concordance with all MGS-high risk results. A switch in chemotherapy recommendation based on MDM decisions, was observed in 46% (58/126) of patients after MGS results. Following MGS testing, aCT was given to 57 patients which resulted in 17% relative and 10% absolute reduction. Conclusion Inexpensive statistical models based on clinico-pathological parameters can be useful in selecting patients that may need MGS testing. The use of MGS resulted in a substantial decisional switch and reduction in aCT-use. Table 1 Predictive value of inexpensive statistical models in MGS tested tumors.MGS high risk (n=53)MGS low risk (n=52)ODX (n=17)MP (n=11)Prosigna (n=25)ODX (n=27)MP (n=14)Prosigna (n=11)MSK high (n=32)1038150ME high (n=7)411000BCRSE high (n=6)311000ODXC high (n=4)110010nAOL high (n=105)1772423123PREDICT high (n=47)7512850MSK low (n=37)3361145ME low (n=9)012202BCRSE low (n=50)3310967ODXC low (n=67)35131469nAOL low (n=21)041428PREDICT low (n=79)1061319911 Citation Format: Laurence Slembrouck, Isabelle Vanden Bempt, Hans Wildiers, Ann Smeets, Erik Van Limbergen, Philippe Moerman, Caroline Weltens, Kevin Punie, Griet Hoste, Els Van Nieuwenhuysen, Sileny Han, Ines Nevelsteen, Lynn Jongen, Patrick Neven, Giuseppe Floris. Multigene signatures based risk estimates in early ER+/HER2- breast cancer: The predictive value of inexpensive statistical models and changes in adjuvant chemotherapy use [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-07-14.