Gene expression pathway analysis to predict response to neoadjuvant docetaxel and capecitabine for breast cancer

Abstract

Neoadjuvant chemotherapy has been shown to be equivalent to post-operative treatment for breast cancer, and allows for assessment of chemotherapy response. In a pilot trial of docetaxel (T) and capecitabine (X) neoadjuvant chemotherapy for Stage II/III BC, we assessed correlation between baseline gene expression and tumor response to treatment, and examined changes in gene expression associated with treatment. Patients received four cycles of TX. Tumor tissue obtained from Mammotome core biopsies pretreatment (BL) and post-cycle 1 (C1) of TX was FLash frozen and stored at -70 degrees C until processing. Gene expression analysis utilized Affymetrix HG-U133 Plus 2.0 GeneChip arrays. Statistical analysis was performed using BRB Array Tools after RMA normalization. Gene ontology (GO) pathway analysis used random variance t tests with a significance level of P\0.005. For gene categories identified byGO pathway analysis as significant, expression levels of individual genes within those pathways were compared between classes using univariate t tests; those genes with significance level of P\0.05 were reported. PAM50 analyses were performed on tumor samples to investigate biologic subtype and risk of relapse (ROR). Using GO pathway analysis, 39 gene categories discriminated between responders and non-responders,most notably genes involved in microtubule assembly and regulation. When comparing pre- and post-chemotherapy specimens, we identified 71 differentially expressed gene categories, including DNA repair and cell proliferation regulation. There were 45 GO pathways in which the change in expression after one cycle of chemotherapy was significantly different among responders and nonresponders. The majority of tumor samples fell into the basal like and luminal B categories. ROR scores decreased in response to chemotherapy; this change was more evident in samples from patients classified as responders by clinical criteria. GO pathway analysis identified a number of gene categories pertinent to therapeutic response, and may be an informative method for identifying genes important in response to chemotherapy. Larger studies using the methods described here are necessary to fully evaluate gene expression changes in response to chemotherapy.

Fig. 1

Clustering dendrogram of patient specimens. Samples were clustered using agglomerative hierarchical clustering and the mean centered 24,224 genes passing the variance-based screening with centered correlation as distance metric and average as linkage function. Number in “ID” column corresponds to Patient ID number; there are up to three samples per patient

Fig. 2

Change in ROR score after chemotherapy among responders (panel a) and non-responders (panel b)

Fig. 3

Heatmap from PAM50 Hierarchic cluster analysis from using (a) all patient specimens (b) baseline specimens only. Sample key: blue luminal A, light blue luminal B, pink HER2-enriched, red basal-like, green normal-like

Fig. 3

Heatmap from PAM50 Hierarchic cluster analysis from using (a) all patient specimens (b) baseline specimens only. Sample key: blue luminal A, light blue luminal B, pink HER2-enriched, red basal-like, green normal-like