Comprehensive Profiling of DNA Repair Defects in Breast Cancer Identifies a Novel Class of Endocrine Therapy Resistance Drivers

Abstract

Purpose: This study was undertaken to conduct a comprehensive investigation of the role of DNA damage repair (DDR) defects in poor outcome ER⁺ disease.Experimental Design: Expression and mutational status of DDR genes in ER⁺ breast tumors were correlated with proliferative response in neoadjuvant aromatase inhibitor therapy trials (discovery dataset), with outcomes in METABRIC, TCGA, and Loi datasets (validation datasets), and in patient-derived xenografts. A causal relationship between candidate DDR genes and endocrine treatment response, and the underlying mechanism, was then tested in ER⁺ breast cancer cell lines.Results: Correlations between loss of expression of three genes: CETN2 (P < 0.001) and ERCC1 (P = 0.01) from the nucleotide excision repair (NER) and NEIL2 (P = 0.04) from the base excision repair (BER) pathways were associated with endocrine treatment resistance in discovery dataset, and subsequently validated in independent patient cohorts. Complementary mutation analysis supported associations between mutations in NER and BER genes and reduced endocrine treatment response. A causal role for CETN2, NEIL2, and ERCC1 loss in intrinsic endocrine resistance was experimentally validated in ER⁺ breast cancer cell lines, and in ER⁺ patient-derived xenograft models. Loss of CETN2, NEIL2, or ERCC1 induced endocrine treatment resistance by dysregulating G₁-S transition, and therefore, increased sensitivity to CDK4/6 inhibitors. A combined DDR signature score was developed that predicted poor outcome in multiple patient cohorts.Conclusions: This report identifies DDR defects as a new class of endocrine treatment resistance drivers and indicates new avenues for predicting efficacy of CDK4/6 inhibition in the adjuvant treatment setting. Clin Cancer Res; 24(19); 4887-99. ©2018 AACR.

Figure 1.. Study outline.

A) Network view of different DDR pathways along with the shared genes (gray nodes) and unique genes (indicated by name, adjacent to pathway name). Pathways associated with SSBR are denoted as yellow nodes and DSBR denoted as orange nodes. Lines indicate pathways that share common genes. Mismatch repair (MMR), nucleotide excision repair (NER), base excision repair (BER), non-homologous end joining (NHEJ), homologous recombination (HR), Fanconi Anemia (FA), trans-lesion synthesis (TLS) and direct repair (DR). B) Schematic representation of design of screening approach to identify DDR pathways and genes associated with ET response. Grey boxes indicate data accrued at baseline and cyan boxes indicate data accrued on-AI treatment. Supporting data with detailed schema is presented in Figure S1.

Figure 2.. RNA levels of MMR, BER and NER genes associate inversely with on-endocrine treatment Ki67.

A) Table describing 13 candidate genes with significant correlation between RNA levels and on-treatment Ki67 in ER+ tumors from NeoAI. Pearson correlation analysis was used to determine the correlation coefficient. False discovery rate (FDR) is denoted in the table for specific correlations. Blue boxes indicate correlations where FDR<20%. Pathways to which candidate genes belong are noted in the DDR function column. Yellow boxes indicate SSBR pathways and orange boxes, DSBR. As a positive control for the analysis, three genes previously implicated in response to ET: ESR1, GATA3 and RUNX1, are included. B) Bar graph indicating enrichment for SSBR genes in the list of 13 candidate genes. Fisher’s exact test determined p-value. C) Venn diagram depicting proportion of genes from each DDR pathway that was implicated in ET resistance from correlation analysis in A. Blue circle indicates candidate gene population. D) KEGG pathway enrichment analysis of the candidate gene list against all DDR genes used in the analysis revealed significant enrichment of indicated pathways. Number of genes from candidate list contributing to each enriched pathway is listed along the bars. A –log10 (p-value) of 1.2 denotes a p<0.05.

Figure 3.. CETN2, NEIL2 and ERCC1 loss associates with poor survival of ER+ breast cancer patients.

A) Forest plot summarizing results of multivariate analysis of the 13 candidate genes in METABRIC. Other factors included in the analysis were tumor size, grade and node positivity. Boxes denote hazard ratio (HR) based on overall survival outcome, and error bars the 95% confidence interval. HR for genes whose dysregulation associated with poor survival (p≤0.05) by univariate analysis (presented in Figure S2) are shown as red boxes. B-D) Kaplan-Meier curves depicting disease specific survival of patients with luminal breast cancer treated with ET whose tumors have low (mean-1.5 standard deviation) CETN2 (B), NEIL2 (C) and ERCC1 (D) expression (red) in METABRIC data set. Kaplan-Meier curves for HER2-enriched and basal-like tumors are presented in Figure S3. (E) Kaplan-Meier curves depicting recurrence free survival of tamoxifen treated ER+ breast cancer patients whose tumors had low expression of CETN2, ERCC1 and NEIL2 (CEN Low in red) in Loi data set. Individual Kaplan-Meier curves presented in Figure S4. All HRs were calculated using Cox Regression and log-rank p-value determined significance of differences in survival.

Figure 4.. NER, BER and NHEJ genes are enriched for damaging mutations in endocrine treatment resistant tumors.

A) Enrichment analysis for prevalence of predicted damaging mutations (based on SIFT scores: lower the SIFT score, the more damaging the mutation is predicted to be) in SSBR and DSBR pathways compared to genome-wide prevalence in tumors from NeoAI. Significant p-values were determined by Wilcoxon test analysis. Similar analysis for each individual DDR pathway is presented in Figure S5. B) Pie charts comparing proportion of missense (light yellow – SSBR, light orange - DSBR) and frameshift/nonsense (yellow – SSBR, orange - DSBR) mutations in SSBR and DSBR genes relative to proportion in control gene set (grey). Z-statistic for two population proportions was used to determine significant differences in proportion of missense to frameshift/nonsense mutations in patients who remained alive to maintain adequate sample size for the test. C) Forest plots depicting hazard ratios for overall survival of patients from TCGA (above) and MSKCC-IMPACT (below) with ER+ tumors harboring non-synonymous mutations in indicated pathways. Log rank test was used to determine significance and Cox Regression Proportional Hazards generated univariate hazard ratios. Supporting data investigating a role for NHEJ gene mutation in ER+ breast cancer survival is presented in Figure S6, and analyses controlling for replication defects, genome instability and mutation load are presented in Figures S7–8. D) Venn diagram and word cloud (yellow text, SSBR and orange text, DSBR) summarizing candidate pathways that significantly associate with poor survival of ER+ breast cancer patients (red) based on mutational (green) or transcriptomic (violet) dysregulation. MMR, NER and BER pathways are identified at the intersection of all analyses. Larger font size indicates greater confidence.

Figure 5.. Inhibition of CETN2, NEIL2 and ERCC1 induces resistance to all classes of endocrine therapy in ER+ breast cancer cells and PDXs.

(A) Western blot validation of siRNA-mediated knockdown of CETN2, NEIL2 and ERCC1 respectively in MCF7 cells. Results from three independent experiments are depicted. Columns represent the mean and error bars the standard deviation. RNA level validation of knockdown is presented in Figure S9A. (B-D) Dose response curves of MCF7 cells with transient inhibition of CETN2, NEIL2 or ERCC1 treated with fulvestrant (B) or 4-hydroxy-tamoxifen (C). Dose response to estrogen stimulation is presented in Figure S9B. IC50 values were calculated from three independent dose curves for each condition and Student’s t-test used to determine significant differences in IC50 values. nM, nanomolar. Independent validation in a second cell line is presented in Figures S9C–F, and orthogonal validation of knockdown results are presented in Figure S10. (D) Box plot depicting tumor viability in vivo after anastrozole treatment of 7 ER+ PDX lines from BCaPE, calculated using area under the curve (AUC) measurements. CEN: CETN2, ERCC1, NEIL2; MP: MLH1, PMS2. Wilcoxon Rank Sum test determined p-value. (E) Working model indicating peak expression levels of NEIL2, ERCC1, MLH1, and CETN2 genes across the cell cycle. Data generated from two independent double thymidine block experiments (www.dnarepairgenes.com). Cumulative peak expression level of all genes in NHEJ, HR and FA pathways also indicated. Y-axis indicates relative gene expression level and X-axis is plotted based on number of hours post release of double thymidine block. Implication of CDKs and estrogen stimulation in the cell cycle is based on published reports. Supporting mechanistic data is presented in Figures S11–12. (F) Bar graphs represent growth inhibition, relative to vehicle treated cells, in response to 100nM of fulvestrant or 1 μM of Palbociclib, CDK4/6 inhibitor in MCF7 cells stably expressing pooled RNAi oligos against CETN2, ERCC1, NEIL2 or scrambled control. Student’s t-test determined p-values by comparing growth inhibition in response to Palbociclib against that in response to fulvestrant.

Figure 6.. Cumulative incidence and predictive potential of CETN2, NEIL2, ERCC1, MLH1 and PMS2 (CENMP) deficiency.

A-B) Stacked columns indicating cumulative frequency of dysregulation (mutation or underexpression) of CETN2, ERCC1, NEIL2 (CEN-); MLH1, PMS2 (MutL-); and PRKDC mutation (mut) or copy number loss (cnl) in ER+ breast tumors from METABRIC (A) and TCGA (B). Fisher’s exact test determined p-values. C) Box plots describing CENMP expression signature score in tumors from patients based on their response to AI-treatment. Wilcoxon Rank Sum test determined p-values. D-E) Kaplan-Meier survival curves evaluating separation based on CENMP score in ET treated ER+ patients from METABRIC (D) and Loi (E) data sets. Cox Regression identified hazard ratio (HR) and log rank test determined p-values for survival analyses.