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, 17 (1), 35

Integrative Analyses Identify Modulators of Response to Neoadjuvant Aromatase Inhibitors in Patients With Early Breast Cancer

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Integrative Analyses Identify Modulators of Response to Neoadjuvant Aromatase Inhibitors in Patients With Early Breast Cancer

Elena López-Knowles et al. Breast Cancer Res.

Abstract

Introduction: Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach.

Methods: Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n=84), gene expression profiling (n=47), matched pre- and post-AI aCGH (n=19 pairs) and Ki67-based AI-response analysis (n=39).

Results: Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1).

Conclusions: These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response.

Figures

Figure 1
Figure 1
Correlation of the proportion of the genome with copy number aberrations and the patterns of copy number aberrations with Ki67 indices of proliferation and response to aromatase inhibitors. (A) A frequency plot of gains and losses (top panel) or amplifications (bottom panel) in 84 samples of ER-positive breast carcinomas from post-menopausal women before treatment with anastrozole. The proportion of tumors in which each bacterial artificial chromosome (BAC) clone is gained/amplified (green bars) or lost (red bars) is plotted (y-axis) for each BAC clone according to its genomic position (x-axis). (B) Scatter plots showing the correlation between the proportion of the genome with copy number aberrations on the y-axis and baseline Ki67 expression levels (left side) or the percentage decrease in Ki67 expression levels after 2 weeks of anastrozole (right side). Spearman correlation demonstrates a statistically significant positive correlation between proportion of the genome altered and baseline Ki67 expression levels. (C) Dot plots demonstrating the relationship between the distinct patterns of copy number aberrations defined by Hicks et al. and baseline Ki67 expression levels (left side) or the percentage decrease in Ki67 expression levels after 2 weeks of anastrozole (right side).
Figure 2
Figure 2
Grouped and pairwise analysis of matched pre- and post-aromatase inhibitor (AI) therapy copy number profiles. (A) Frequency plot of copy-number gains and losses (top) or amplifications (bottom) in 19 matched pre- and post-letrozole-treated breast cancer samples. The proportion of tumors in which each bacterial artificial chromosome (BAC) clone is gained (green bars) or lost (red bars) is plotted (y-axis) for each BAC clone according to its genomic position (x-axis). No significant differences were identified between the two components. (B) Hierarchical cluster analysis performed with array comparative genomic hybridization (aCGH) categorical states (that is, gains, losses and amplifications) using the Euclidean distance metric and Ward's algorithm of 19 matched pre- and post-letrozole treatment samples from estrogen receptor (ER)-positive breast carcinomas. Matched samples from each patient preferentially cluster together, and have similar patterns of copy number aberrations. In some cases, small regions show differential copy number states between matched samples (green squares), but these are private events. The heatmap displays each case along the x-axis and the genomic position along the y-axis. Amp, amplification; Gain, copy number gain; Loss, copy number loss; NC, no copy number change. (C) Genome plots of pre- and post-letrozole samples from two patients. The genomic position is plotted along the x-axis and circular binary segmentation (cbs)-smoothed log2 ratio on the y-axis; amplifications are shown in bright green, gains in dark green, losses in dark red and normal copy number in black. Red stars denote amplicons present in only one sample of a matched pair.
Figure 3
Figure 3
Integrative analysis of microarray-based comparative genomic hybridization, gene expression and Ki67-based response data. (A) Matched heatmaps of gene expression and aCGH within two amplified loci; 11q13.2-q13.4 and 17q12-q21.1. Bar plots show the result of a Mann-Whitney U-test for expression as a continuous variable and gene amplification as the grouping variable. Bars in red show adjusted P-values <0.05. aCGH, green copy number loss; black, no copy number change; dark red, copy number gain; bright red, gene amplification; gene expression: green, downregulation; red, upregulation; MWU, Mann-Whitney U-test; adjp, adjusted P-value. (B) Venn diagram shows the intersect between the list of genes that are overexpressed when amplified and those genes that are associated with a poor response to AI when amplified. The call-out box lists these genes and their loci, highlighting that only three genes are upregulated in long-term estrogen deprived (LTEDs). (C) Scatter plots demonstrating that for each of the three genes selected for functional validation, significant negative correlation was identified between the aCGH-derived cbs ratios and the percentage decrease in Ki67 following 2 weeks of AI therapy. In each plot, cbs-smoothed ratios are plotted on the y-axis while the percentage decrease in Ki67 at 2 weeks is plotted on the x-axis. Red, CHKA, Blue, LRP5, Green, SAPS3.
Figure 4
Figure 4
Functional validation of genes identified as potential modulators of aromatase inhibitor (AI) response when amplified and overexpressed. (A) Western blotting of lysates from SUM44 cells: each of the three genes, silenced and blotted for, was used to demonstrate siRNA efficacy and antibody specificity. (B) Panel of estrogen receptor (ER)-positive cell lines were used to assess the effect of RNA-interference-induced silencing of CHKA, LRP5 and SAPS3 on cell viability. Cell line names in red font harbor amplification of these genes; those named in black do not. Data for each knockdown (performed using SMARTpools) were normalized to readings from cells transfected with a non-targeting control and grown in dextran charcoal-stripped (DCC) media. Data are representative of six replicates from at least two independent experiments. Numbers indicate t-test P-value. (C) The same panel was used to assess the effect of RNA-interference-induced silencing of CHKA, LRP5 and SAPS3 on ER-driven proliferation. After growing and transfecting cell lines in DCC, cell viability was assessed as a surrogate marker of proliferation in the presence of increasing concentrations of estrogen (E2). Cell line names in red font harbor amplification of these genes; those in black do not. Data for each knockdown (performed using SMARTpools) were normalized to readings from cells transfected with a non-targeting control and grown in DCC media. Drug curves were inferred from non-linear regression. Error bars represent standard error of the mean. Data are representative of six replicates from at least two independent experiments. P-value is for one-way analysis of variance.
Figure 5
Figure 5
Mechanistic assessment of the effect of CHKA in modulating response to aromatase inhibitor (AI) therapy. (A) Following RNA-interference-mediated silencing of CHKA, SUM44 cells were transfected with an estrogen receptor (ER)/ERE luciferase reporter construct, and then treated with E2 or dextran charcoal-stripped media (DCC) for 2 days before reading luciferase activity. Data are normalized to the activity in the DCC-treated control transfected cell lines. *Significant P-value (<0.05) between the indicated column and corresponding siControl-equivalent. (B) To validate effects seen with the ER-ERE reporter assay, expression levels of two well known ER-regulated genes (TFF1 and GREB1) were assessed by quantitative real-time PCR following RNA-interference-induced silencing of CHKA. Data normalized to DCC-treated control transfected cell lines; *P-value <0.01 between indicated column and corresponding siCON equivalent. (C) Following RNA-interference-induced silencing of CHKA, cell lysates were subjected to gel electrophoresis and western blotting using indicated antibodies. Cells were treated with 1nM E2 for 1 h or 24 h following transfection, to represent the two phases of ER dynamics (early active- and late turnover phase). Blots are representative of at least two independent experiments; numbers below each band represent densitometry analysis of intensity, measured as a ratio of the siControl with no siCHKA or E2 treatments.
Figure 6
Figure 6
The effect of siRNA (CHKA) on cell cycle as determined by flow cytometry. (A) Flow cytometry was used to compare DNA content in Sum44 WT cells treated with dextran charcoal-stripped media (DCC) and DCC + E2. There was no significant difference between the cell cycle phases in the cells treated with DCC. CHKA arrests cells at the G1/S, but not at the G2/M phase of the cell cycle in cells treated with DCC + E2. (B) The percentage of cells at each cell phase: G0/G1, S, G2/M are shown on the bar chart as the mean ± SD. The experiment was performed in triplicate; **P <0.005 and *P <0.01.

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