Abstract
Abstract Background: Recent evidence confirms the importance of both biological and pathological risk markers in predicting early relapse for breast cancer patients treated with endocrine therapy. Most studies use a two step process integrating biological markers into a “biological predictor (e.g. Oncotype Dx, “IHC4” etc) followed by assessment of the predictive value of such tests in the context of pathological markers (grade, nodal status etc). We have taken a one step process integrating both biological and pathological markers into a single model to assess key factors for predicting outcome at 2.75 years and 5 years of endocrine therapy; to inform choices between switching, upfront and extended adjuvant treatment with AIs. Patients & Methods: Pathology blocks from 4598 TEAM patients were collected and tissue microarrays constructed. Quantitative analysis ER, PgR, Ki67, HER1, HER2, and HER3 was performed centrally. A prognostic model, integrating data from biological and pathological markers was created to assess risk (disease-free survival) after 2.75 and 5 years of follow up in the TEAM trial. Results: Of 4595 eligible cases samples received, 16 were excluded, and 3993 had complete biomarker data for all markers for the final biomarker analysis. In univariate analysis nodal status, grade, size, age at diagnosis, HER1, HER2, PgR, ER and Ki67 were all prognostic. At 2.75 years nodal status, age, PgR histoscore, size, grade, HER2, ER histoscore and HER1 positivity were significant prognostic variables (ranked by WaldX2 statistic), Ki67 and HER3 were not included in this model. At 5 years median follow up; age, nodal status, size, PgR histoscore, grade, Ki67, HER2, and HER1 positivity were significant prognostic variables (ranked by WaldX2 statistic), ER and HER3 were not included in this model. Conclusion: Combined biological and pathological marker panels are of significant value in predicting early relapse in breast cancer patients treated with endocrine therapy, however duration of follow-up may impact on the inclusion of variables in the model. This provides significant information relevant to the choice of different adjuvant endocrine therapies. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P3-10-04.
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