Phuong Dinh1,2,3, J Dinny Graham1,4,3, Elisabeth N Elder1,3, Masrura Kabir1, Tram B Doan4, James French1,3, Farid Meybodi1,3, Rina Hui1,2,3, Nicholas R Wilcken2,3, Paul R Harnett2,3, Jeremy Hsu1,3, Kirsty E Stuart1,2,3, Tim Wang1,2,3, Verity Ahern2,3, Meagan Brennan2,3, Stephen B Fox5, Rachel F Dear6, Elgene Lim6,7, Michelle White8, G Bruce Mann5,9, Nirmala Pathmanathan10,11,12. 1. Westmead Breast Cancer Institute, Westmead, NSW, 2145, Australia. 2. Crown Princess Mary Cancer Centre, Western Sydney Local Health District, Westmead, NSW, 2145, Australia. 3. Westmead Clinical School, University of Sydney, Westmead, NSW, 2145, Australia. 4. The Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW, 2145, Australia. 5. Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia. 6. St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Darlinghurst, NSW, 2010, Australia. 7. Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia. 8. Monash Health, Clayton, VIC, 3168, Australia. 9. Royal Melbourne Hospital, Parkville, VIC, 3050, Australia. 10. Westmead Breast Cancer Institute, Westmead, NSW, 2145, Australia. Nirmala.pathmanathan@health.nsw.gov.au. 11. Westmead Clinical School, University of Sydney, Westmead, NSW, 2145, Australia. Nirmala.pathmanathan@health.nsw.gov.au. 12. Douglass Hanly Moir Pathology, Macquarie Park, NSW, 2113, Australia. Nirmala.pathmanathan@health.nsw.gov.au.
Abstract
PURPOSE: Genomic tests improve accuracy of risk prediction for early breast cancers but these are expensive. This study evaluated the clinical utility of EndoPredict®, in terms of impact on adjuvant therapy recommendations and identification of parameters to guide selective application. METHODS: Patients with ER-positive, HER2-negative, and early-stage invasive breast cancer were tested with EndoPredict®. Two cohorts were recruited: one consecutively and another at clinical team discretion. Systemic treatment recommendations were recorded before and after EndoPredict® results were revealed to the multidisciplinary team. RESULTS: 233 patients were recruited across five sites: 123 consecutive and 110 at clinical team discretion. In the consecutive cohort 50.6% (62/123) cases were classified high risk of recurrence by EndoPredict®, compared with 62.7% (69/110) in the selective cohort. A change in treatment recommendation was significantly more likely (p < 0.0001) in the selective cohort (43/110, 39.1%) compared to the consecutive group (11/123, 8.9%). The strongest driver of selective recruitment was intermediate grade histology, whilst logistic regression modelling demonstrated that nodal status (p < 0.001), proliferative rate (p = 0.001), and progesterone receptor positivity (p < 0.001) were the strongest discriminators of risk. CONCLUSION: Whilst molecular risk can be predicted by traditional variables in a high proportion of cases, EndoPredict® had a greater impact on treatment decisions in those cases selected for testing at team discretion. This is indicative of the robust ability of the clinical team to identify cases most likely to benefit from testing, underscoring the value of genomic tests in the oncologists' tool kit.
PURPOSE: Genomic tests improve accuracy of risk prediction for early breast cancers but these are expensive. This study evaluated the clinical utility of EndoPredict®, in terms of impact on adjuvant therapy recommendations and identification of parameters to guide selective application. METHODS: Patients with ER-positive, HER2-negative, and early-stage invasive breast cancer were tested with EndoPredict®. Two cohorts were recruited: one consecutively and another at clinical team discretion. Systemic treatment recommendations were recorded before and after EndoPredict® results were revealed to the multidisciplinary team. RESULTS: 233 patients were recruited across five sites: 123 consecutive and 110 at clinical team discretion. In the consecutive cohort 50.6% (62/123) cases were classified high risk of recurrence by EndoPredict®, compared with 62.7% (69/110) in the selective cohort. A change in treatment recommendation was significantly more likely (p < 0.0001) in the selective cohort (43/110, 39.1%) compared to the consecutive group (11/123, 8.9%). The strongest driver of selective recruitment was intermediate grade histology, whilst logistic regression modelling demonstrated that nodal status (p < 0.001), proliferative rate (p = 0.001), and progesterone receptor positivity (p < 0.001) were the strongest discriminators of risk. CONCLUSION: Whilst molecular risk can be predicted by traditional variables in a high proportion of cases, EndoPredict® had a greater impact on treatment decisions in those cases selected for testing at team discretion. This is indicative of the robust ability of the clinical team to identify cases most likely to benefit from testing, underscoring the value of genomic tests in the oncologists' tool kit.
Authors: F Cardoso; S Kyriakides; S Ohno; F Penault-Llorca; P Poortmans; I T Rubio; S Zackrisson; E Senkus Journal: Ann Oncol Date: 2019-08-01 Impact factor: 32.976
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Authors: Joseph A Sparano; Robert J Gray; Della F Makower; Kathleen I Pritchard; Kathy S Albain; Daniel F Hayes; Charles E Geyer; Elizabeth C Dees; Matthew P Goetz; John A Olson; Tracy Lively; Sunil S Badve; Thomas J Saphner; Lynne I Wagner; Timothy J Whelan; Matthew J Ellis; Soonmyung Paik; William C Wood; Peter M Ravdin; Maccon M Keane; Henry L Gomez Moreno; Pavan S Reddy; Timothy F Goggins; Ingrid A Mayer; Adam M Brufsky; Deborah L Toppmeyer; Virginia G Kaklamani; Jeffrey L Berenberg; Jeffrey Abrams; George W Sledge Journal: N Engl J Med Date: 2018-06-03 Impact factor: 91.245
Authors: Patricia R Blank; Martin Filipits; Peter Dubsky; Florian Gutzwiller; Michael P Lux; Jan C Brase; Karsten E Weber; Margaretha Rudas; Richard Greil; Sibylle Loibl; Thomas D Szucs; Ralf Kronenwett; Matthias Schwenkglenks; Michael Gnant Journal: Pharmacoeconomics Date: 2015-02 Impact factor: 4.981
Authors: Ivana Sestak; Miguel Martín; Peter Dubsky; Ralf Kronenwett; Federico Rojo; Jack Cuzick; Martin Filipits; Amparo Ruiz; William Gradishar; Hatem Soliman; Lee Schwartzberg; Richard Buus; Dominik Hlauschek; Alvaro Rodríguez-Lescure; Michael Gnant Journal: Breast Cancer Res Treat Date: 2019-04-30 Impact factor: 4.872
Authors: Miguel Martin; Jan C Brase; Lourdes Calvo; Kristin Krappmann; Manuel Ruiz-Borrego; Karin Fisch; Amparo Ruiz; Karsten E Weber; Blanca Munarriz; Christoph Petry; Cesar A Rodriguez; Ralf Kronenwett; Carmen Crespo; Emilio Alba; Eva Carrasco; Maribel Casas; Rosalia Caballero; Alvaro Rodriguez-Lescure Journal: Breast Cancer Res Date: 2014-04-12 Impact factor: 6.466