Gene Expression, Molecular Class Changes, and Pathway Analysis after Neoadjuvant Systemic Therapy for Breast Cancer

Purpose: To examine gene expression differences between pre- and post-neoadjuvant systemic therapy (NST) specimens of breast cancers and identify biologic changers that may lead to new therapeutic insights. Methods: Gene expression data from prechemotherapy fine needle aspiration specimens were compared with resected residual cancers in 21 patients after 4 to 6 months of NST. We removed stroma-associated genes to minimize confounding effects. PAM50 was used to assign molecular class. Paired t test and gene set analysis were used to identify differentially expressed genes and pathways. Results: The ER and HER2 status based on mRNA expression remained stable in all but two cases, and there were no changes in proliferation metrics (Ki67 and proliferating cell nuclear antigen expression). Molecular class changed in 8 cases (33.3%), usually to normal-like class, which was associated with low residual cancer cell cellularity. The expression of 200 to 600 probe sets changed between baseline and post-NST samples. In basal-like cancers, pathways driven by increased expression of phosphoinositide 3-kinase, small G proteins, and calmodulin-dependent protein kinase II and energy metabolism were enriched, whereas immune cell–derived and the sonic hedgehog pathways were depleted in residual cancer. In non–basal-like breast cancers, notch signaling and energy metabolism (e.g., fatty acid synthesis) were enriched and sonic hedgehog signaling and immune-related pathways were depleted in residual cancer. There was no increase in epithelial–mesenchymal transition or cancer stem cell signatures. Conclusions: Our data indicate that energy metabolism related processes are upregulated and immune-related signals are depleted in residual cancers. Targeting these biologic processes may represent promising adjuvant treatment strategies for patients with residual cancer. Clin Cancer Res; 18(4); 1109–19. ©2012 AACR.

[1]  Hui Gao,et al.  Expression of epithelial–mesenchymal transition‐inducing transcription factors in primary breast cancer: The effect of neoadjuvant therapy , 2011, International journal of cancer.

[2]  T. Mak,et al.  Regulation of cancer cell metabolism , 2011, Nature Reviews Cancer.

[3]  Yuan Qi,et al.  Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and -negative cancers. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[4]  Jeffrey M. Rosen,et al.  Residual breast cancers after conventional therapy display mesenchymal as well as tumor-initiating features , 2009, Proceedings of the National Academy of Sciences.

[5]  A. Nobel,et al.  Supervised risk predictor of breast cancer based on intrinsic subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[6]  Achim Rody,et al.  T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers , 2009, Breast Cancer Research.

[7]  Achim Rody,et al.  T cell marker metagene predicts a favourable prognosis in estrogen receptor negative and Her2 positive breast cancers. , 2009 .

[8]  K. Hess,et al.  Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[9]  G. Hortobagyi,et al.  HER2 expression and efficacy of preoperative paclitaxel/FAC chemotherapy in breast cancer , 2008, Breast Cancer Research and Treatment.

[10]  D. Berry,et al.  Research issues affecting preoperative systemic therapy for operable breast cancer. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[11]  Christos Hatzis,et al.  Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  Lajos Pusztai,et al.  Determination of oestrogen-receptor status and ERBB2 status of breast carcinoma: a gene-expression profiling study. , 2007, The Lancet. Oncology.

[13]  Zhen Jiang,et al.  Bioconductor Project Bioconductor Project Working Papers Year Paper Extensions to Gene Set Enrichment , 2013 .

[14]  R. Tibshirani,et al.  On testing the significance of sets of genes , 2006, math/0610667.

[15]  Lajos Pusztai,et al.  Reproducibility of Gene Expression Signature–Based Predictions in Replicate Experiments , 2006, Clinical Cancer Research.

[16]  G. Hortobagyi,et al.  Prognostic value of pathologic complete response after primary chemotherapy in relation to hormone receptor status and other factors. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  Roman Rouzier,et al.  Breast Cancer Molecular Subtypes Respond Differently to Preoperative Chemotherapy , 2005, Clinical Cancer Research.

[18]  K. Coombes,et al.  Comparison of the predictive accuracy of DNA array-based multigene classifiers across cDNA arrays and Affymetrix GeneChips. , 2005, The Journal of molecular diagnostics : JMD.

[19]  J. V. Von Roenn,et al.  Final results of a phase II trial of preoperative TAC (docetaxel/doxorubicin/cyclophosphamide) in stage III breast cancer. , 2005, Clinical breast cancer.

[20]  Terry L. Smith,et al.  Significantly higher pathologic complete remission rate after neoadjuvant therapy with trastuzumab, paclitaxel, and epirubicin chemotherapy: results of a randomized trial in human epidermal growth factor receptor 2-positive operable breast cancer. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[21]  J. Stec,et al.  Total RNA yield and microarray gene expression profiles from fine‐needle aspiration biopsy and core‐needle biopsy samples of breast carcinoma , 2003, Cancer.

[22]  G. Hortobagyi,et al.  Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicin-based neoadjuvant chemotherapy. , 1999, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[23]  Kevin R Coombes,et al.  Melanoma antigen family A identified by the bimodality index defines a subset of triple negative breast cancers as candidates for immune response augmentation. , 2012, European journal of cancer.

[24]  Carsten Denkert,et al.  Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[25]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[26]  Baljit Singh,et al.  A prospective randomized pilot study to evaluate predictors of response in serial core biopsies to single agent neoadjuvant doxorubicin or paclitaxel for patients with locally advanced breast cancer. , 2003, Clinical Cancer Research.