Changes in Expression of Genes Representing Key Biologic Processes after Neoadjuvant Chemotherapy in Breast Cancer, and Prognostic Implications in Residual Disease.
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<h4>Purpose</h4>The primary aim was to derive evidence for or against the clinical importance of several biologic processes in patients treated with neoadjuvant chemotherapy (NAC) by assessing expression of selected genes with prior implications in prognosis or treatment resistance. The secondary aim was to determine the prognostic impact in residual disease of the genes' expression.<h4>Experimental design</h4>Expression levels of 24 genes were quantified by NanoString nCounter on formalin-fixed paraffin-embedded residual tumors from 126 patients treated with NAC and 56 paired presurgical biopsies. The paired t test was used for testing changes in gene expression, and Cox regression and penalized elastic-net Cox Regression for estimating HRs.<h4>Results</h4>After NAC, 12 genes were significantly up- and 8 downregulated. Fourteen genes were significantly associated with time to recurrence in univariable analysis in residual disease. In a multivariable model, ACACB, CD3D, MKI67, and TOP2A added prognostic value independent of clinical ER(-), PgR(-), and HER2(-) status. In ER(+)/HER2(-) patients, ACACB, PAWR, and ERBB2 predicted outcome, whereas CD3D and PAWR were prognostic in ER(-)/HER2(-) patients. By use of elastic-net analysis, a 6-gene signature (ACACB, CD3D, DECORIN, ESR1, MKI67, PLAU) was identified adding prognostic value independent of ER, PgR, and HER2.<h4>Conclusions</h4>Most of the tested genes were significantly enriched or depleted in response to NAC. Expression levels of genes representing proliferation, stromal activation, metabolism, apoptosis, stemcellness, immunologic response, and Ras-ERK activation predicted outcome in residual disease. The multivariable gene models identified could, if validated, be used to identify patients needing additional post-neoadjuvant treatment to improve prognosis. Clin Cancer Res; 22(10); 2405-16. ©2016 AACR.
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Genomic Analysis – Clinical Trials
Medicine (RMH Smith Cunningham)
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Clinical cancer research : an official journal of the American Association for Cancer Research, 2016, 22 (10), pp. 2405 - 2416