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Supplementary Methods, Results and Tables from Subtype-Specific Metagene-Based Prediction of Outcome after Neoadjuvant and Adjuvant Treatment in Breast Cancer

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posted on 2023-03-31, 18:42 authored by Maurizio Callari, Vera Cappelletti, Francesca D'Aiuto, Valeria Musella, Antonio Lembo, Fabien Petel, Thomas Karn, Takayuki Iwamoto, Paolo Provero, Maria Grazia Daidone, Luca Gianni, Giampaolo Bianchini

Table S1: Clinico-pathological features for samples in the PROGNOSTIC collection; Table S2: Clinico-pathological features for samples in the TAM collection; Table S3: Clinico-pathological features for samples in the CHEMO collection; Table S4: Output of immune cluster refinement in ER-HER2- samples. Five years DMFS for high, intermediate and low expression groups and log-rank test p-values (average and standard deviation of 100 10-fold cross-validations) are reported as a function of the number of genes; Table S5: Output of immune cluster refinement in HER2+ samples. Five years DMFS for high, intermediate and low expression groups and log-rank test p-values (average and standard deviation of 100 10-fold cross-validations) are reported as a function of the number of genes; Table S6: Output of proliferation cluster refinement in 508 ER+HER2- samples. Five-years DMFS for high, intermediate and low expression groups and log-rank test p-values (average and standard deviation of 100 10-fold cross-validations) are reported as a function of the number of genes; Table S7: Output of ER-related cluster refinement in 394 high proliferation ER+HER2- samples. Five-years DMFS for high, intermediate and low expression groups and log-rank test p-values (average and standard deviation of 100 10-fold cross-validations) are reported as a function of the number of genes; Table S8: Univariable Cox regression analysis for the CTM in the combined dataset or in each dataset for ER- or HER2+ cases or separately for ER-HER2- and HER2+ subgroups; Table S9: Univariable and multivariable Cox regression analysis in 205 ER-HER2- and HER2+ samples from the CHEMO collection (all biomarkers were used as continous variables); Table S10: Univariable Cox regression analysis for the combined proliferation and ER-related metagenes in ER+HER2- cases from the combined dataset or from each dataset;

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ARTICLE ABSTRACT

Purpose: In spite of improvements of average benefit from adjuvant/neoadjuvant treatments, there are still individual patients with early breast cancer at high risk of relapse. We explored the association with outcome of robust gene cluster–based metagenes linked to proliferation, ER-related genes, and immune response to identify those high-risk patients.Experimental Design: A total of 3,847 publicly available gene-expression profiles were analyzed (untreated, N = 826; tamoxifen-treated, N = 685; chemotherapy-treated, N = 1,150). Genes poorly performing in formalin-fixed samples were removed. Outcomes of interest were pathologic-complete response (pCR) and distant metastasis-free survival (DMFS). In ER+HER2−, the proliferation and ER-related metagenes were combined to define three risk groups. In HER2+ and ER−HER2− risk groups were defined by tertiles of an immune-related metagene.Results: The high-proliferation/low-ER group of ER+HER2− breast cancer had significantly higher pCR rate [OR, 5.01 (1.76–17.99), P = 0.005], but poorer outcome [HR = 3.73 (1.63–8.51), P = 0.0018] than the low-proliferation/high-ER. A similar association with outcome applied to patients with residual disease (RD) after neoadjuvant chemotherapy (P = 0.01). In ER−HER2− and HER2+ breast cancer, immune metagene in the high tertile was linked to higher pCR [33.7% vs. 11.6% in high and low tertile, respectively; OR, 3.87 (1.79–8.95); P = 0.0009]. In ER−HER2−, after adjuvant/neoadjuvant chemotherapy, 5-year DMFS was 85.4% for high-tertile immune metagene, and 43.9% for low tertile. The outcome association was similar in patients with RD (P = 0.0055). In HER2+ breast cancer treated with chemotherapy the association with risk of relapse was not significant.Conclusions: We developed metagene-based predictors able to define low and high risk of relapse after adjuvant/neoadjuvant therapy. High-risk patients so defined should be preferably considered for trials with investigational agents. Clin Cancer Res; 22(2); 337–45. ©2015 AACR.