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Chromatin regulators mediate anthracycline sensitivity in breast cancer

Abstract

Anthracyclines are a highly effective component of curative breast cancer chemotherapy but are associated with substantial morbidity1,2. Because anthracyclines work in part by inhibiting topoisomerase-II (TOP2) on accessible DNA3,4, we hypothesized that chromatin regulatory genes (CRGs) that mediate DNA accessibility might predict anthracycline response. We studied the role of CRGs in anthracycline sensitivity in breast cancer through integrative analysis of patient and cell line data. We identified a consensus set of 38 CRGs associated with anthracycline response across ten cell line datasets. By evaluating the interaction between expression and treatment in predicting survival in a metacohort of 1006 patients with early-stage breast cancer, we identified 54 CRGs whose expression levels dictate anthracycline benefit across the clinical subgroups; of these CRGs, 12 overlapped with those identified in vitro. CRGs that promote DNA accessibility, including Trithorax complex members, were associated with anthracycline sensitivity when highly expressed, whereas CRGs that reduce accessibility, such as Polycomb complex proteins, were associated with decreased anthracycline sensitivity. We show that KDM4B modulates TOP2 accessibility to chromatin, elucidating a mechanism of TOP2 inhibitor sensitivity. These findings indicate that CRGs mediate anthracycline benefit by altering DNA accessibility, with implications for the stratification of patients with breast cancer and treatment decision making.

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Fig. 1: Schematic overview of study design, the analytical and experimental framework and derivation of an anthracycline response signature.
Fig. 2: The expression levels of key chromatin regulatory genes are associated with anthracycline response.
Fig. 3: Chromatin state mediates anthracycline response.
Fig. 4: KDM4B mediates TOP2 inhibitor response in breast cancer cells by modulating TOP2 accessibility to chromatin.

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Data availability

All data used in this study are available publicly. Clinical cohorts are available under the following GEO identifiers: GSE20685, GSE45255, GSE65194, GSE3494 and GSE1456. Cell line molecular and dose–response data are available from Costello et al.6 and the PharmacoGX38 package.

Code availability

The code used for the analysis in this study was implemented using R v3.5.3 and is publicly available from the following repository: https://github.com/cancersysbio/chromatinAnthra.

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Acknowledgements

We thank breast cancer research advocates S. Brain, D. Heditsian and V. Lee for helpful discussions. We thank E. Chory for illustrating Fig. 3b. The results shown here are in part based on data generated by the TCGA Research Network: http://cancergenome.nih.gov/. This work was supported in part by a Stanford Cancer Institute–Translational Research Award, a Department of Defense grant (W81XWH-16-1-0084) and a V Foundation for Cancer Research grant to C.C. and G.R.C. C.C. is also supported by the National Cancer Institute (R01CA 182514, U01CA217851) and the Breast Cancer Research Foundation. G.R.C. is also supported by the National Cancer Institute (R01CA163915) and the Howard Hughes Medical Institute. J.A.S. is supported by a Susan G. Komen Postdoctoral Fellowship (PDF16377256). J.G.K. is supported by PHS grant number T32 CA09151, awarded by the National Cancer Institute, Department of Health and Human Services. J.L.C. is supported by an American Society of Clinical Oncology Young Investigator Award from the Conquer Cancer Foundation and a Damon Runyon Physician-Scientist Training Award.

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Authors and Affiliations

Authors

Contributions

J.A.S. and C.C. designed and performed the data analysis. J.G.K. and G.R.C. designed and performed the experiments. J.L.C.-J. provided clinical expertise. G.R.C. and C.C. conceptualized the study. All authors contributed to data interpretation. J.A.S., J.G.K., J.L.C.-J. and C.C. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Gerald R. Crabtree or Christina Curtis.

Ethics declarations

Competing interests

G.R.C. is the founder and a stockholder of Foghorn Therapeutics. C.C. is a scientific advisor to GRAIL and reports stock options as well as consulting for GRAIL and Genentech. J.A.S., J.G.K. and J.L.C.-J. declare no competing interests.

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Peer review information Javier Carmona was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Distribution of somatic mutations and copy number alterations and network centrality measures of chromatin regulatory genes.

Panel A: Comparison of somatic alterations in chromatin regulatory genes (CRGs) in breast cancer versus pan-cancer. Top panel: Frequency of mutations in CRGs in breast cancer (BRCA, x-axis) versus pancancer (pancan; 32 cancer types excluding breast) based on non-synonymous mutations in whole exome sequencing data from TCGA. Bottom panel: Frequency of copy number amplification and deletion events in CRGs in breast cancer versus pancancer based on TCGA SNP array data. Panel B: Centrality of CRGs. Genome-wide breast cancer network generated from the TCGA breast cancer RNA-seq dataset using ARACNE. Centrality measures were computed for each gene. A null distribution of centrality scores was generated using 10,000 combinations of 404 non-CRGs by summing their degree, betweenness and page rank values (non CRGs) compared with the sum of degree, betweenness and page rank of the CRGs. The comparison between the null distribution and CRG centrality scores highlights the central role of CRGs in the breast cancer network. Panel C: Distribution of network centrality measures among CRGs and non CRGs. Panel D: CRGs are significantly enriched among ‘influencers’ as compared to other genes (Methods).

Extended Data Fig. 2 Breast cancer chromatin regulatory network and doxorubicin response signature.

Panel A: Derivation of a breast cancer chromatin regulatory network: A breast cancer chromatin regulatory network was derived from TCGA RNA-seq data using the ARACNE algorithm (Methods). The inset panel shows a region mapping to chromatin regulatory genes (CRGs) with high degree centrality. Each hub of the network is a CRG, while the terminal nodes correspond to their target genes. The histogram and barplot show the degree centrality for all CRGs, where three of the largest network hubs correspond to FOXA1, KDM4B and BCL11A. Panel B: Doxorubicin signature in breast cancer cell lines from Heiser microarray dataset (N=46 independent cell lines). Heatmap of gene expression profiles among resistant (1/3) and sensitive (1/3) breast cancer cell lines where cell lines are sorted by GI50. Panel C: Volcano plot of genes differentially expressed (moderate T-statistic from limma) between sensitive versus resistant cell lines.

Extended Data Fig. 3 Summary of adjuvant breast cancer metacohort (N=1006).

Top panels describe the 5 individual cohorts used to build the metacohort: UPP (GSE3494), IRB/JNR/NUH (GSE45255), KAO (GSE20685), MAIRE (GSE65194) and STK (GSE1456). All cohorts were profiled on Affymetrix gene expression arrays. Center panels describe the metacohort comparing anthracycline versus non-anthracycline (no therapy, hormone therapy or other chemotherapy), anthracycline versus taxane or anthracycline versus CMF. Bottom panels describe the metacohort stratified by the breast cancer clinical subgroups, namely ER+/HER2-, HER2+ and ER-/HER2- (triple negative). In the ER+/HER2- subgroup, hormonal therapy was included a covariate. *The STK cohort does not report size, t-stage, n-stage or lymph node status, but in the original paper (PMC1410752) the reported mean size is 22 mm and 62% samples are <21 mm, so t-stage=2 was inferred for all samples. Similarly, 38% of samples are lymph node positive but are unmatched to individuals so we treated all samples as LN−.

Extended Data Fig. 4 Chromatin regulatory genes associated with anthracycline response in the metacohort (a total of 760 individuals treated with anthracycline versus a non-anthracycline-containing regimen were included in this analysis).

The forest plots show the log-2 hazard (boxes) of overall survival (OS) based on the expression level of chromatin regulatory genes (CRGs) where error bars represent 95% confidence intervals. P-values (unadjusted for multiple hypothesis testing) are based on the Cox Proportional Hazards model of the interaction between drug and gene expression, adjusted by hormone receptor status, HER2 status, tumor size, lymph node status, MKI67 expression (from the array data) and cohort. Genes shown in bold were also significant in the in vitro analysis.

Extended Data Fig. 5 Enrichment of chromatin regulatory genes among genes associated with anthracycline response.

Panel A: Histogram of the –log10 p-values of the association of random gene sets, each consisting of 312 genes (of the 404 CRGs, 312 are present in the microarray data) with overall survival. Of 10,000 gene sets, only 5.93% were more strongly associated with overall survival than the CRGs (p = 6.8E-15, red line). Panel B: Scatter plot (upper panel) of the –log10 p-values of the association between MSigDB gene sets with overall survival (x-axis) and the number of genes in the set (y-axis) (larger signatures display inflated association). Signatures known to be related to breast cancer or chromatin regulation are labeled. The size of the CRG set the p-value for the association with overall survival are indicated by red lines in the density plot in the upper right corner: the association between the CRG set and overall survival is greater than would be expected for gene sets of similar size. Among all signatures, only 4.16% were more strongly associated with overall survival than the CRGs (red line in bottom panel histogram). Both panels A and B display p-values for the Cox Proportional Hazards model of the interaction between treatment and gene expression adjusted by clinical covariates in the anthracycline versus non-anthracycline cohort (N=760; see Methods).

Extended Data Fig. 6 Chromatin regulatory complexes are associated with anthracycline response.

Hazard ratio of overall survival (OS) for members of trithorax (Compass and SWI/SNF) and polycomb (PRC1, PRC2 and PR-DUB) complexes, and the summary statistic (summary p-value from the Cox Proportional Hazard of the interaction between drug and gene expression) for each of the combined complex among anthracycline versus non-anthracycline-treated patients (N=760 individuals). The forest plots show the log-2 hazard ratios (boxes) and error bars represent 95% confidence intervals.

Extended Data Fig. 7 Comparison of genes associated with anthracycline response in the clinical and in vitro analyses.

Panels A, B, C: Venn diagrams indicate the overlap between CRGs associated with anthracycline response in vivo based on the Cox Proportional Hazard model in the breast cancer patient metacohort for different treatment regimens, and in vitro based on VIPER (38 genes). Panel D: Correlation between Heiser microarray dataset Normalized Enriched Score (NES) for each CRG identified via VIPER and the log hazard ratio for overall survival for anthracyline-treated versus non-anthracycline-treated cases in the metacohort (N=760 individual samples). Labels correspond to significant CRGs based on VIPER. Here, r indicates the Pearson correlation across all CRGs and r 12 indicates the Pearson correlation across the 12 CRGs that were significant in both the clinical and VIPER analyses. Panel E: Venn diagram of the overlap between CRGs that were significant in comparisons of different treatment regimens, namely anthracycline versus non-anthracycline, anthracycline versus CMF, and anthracycline versus taxanes.

Extended Data Fig. 8 Chromatin regulatory genes associated with anthracycline response stratified by clinical subgroups.

Forest plots illustrate the difference in hazards of death (overall survival) among patients treated with anthracyclines versus those who were not treated with anthracyclines from the metacohort across the three clinical subgroups, namely ER+/HER2-, HER2+ and ER-/HER2- (triple-negative, TNBC) patients. P-values indicate the Cox Proportional Hazard interaction model among the two conditions and the expression of the gene, adjusted by age, size, lymph node positive, ER, PR and HER2 status (excluding ER and PR in ER+, HER2 in HER2+ and ER, PR and HER2 in TNBC). The forest plots show the log-2 hazard ratios (boxes) and error bars represent 95% confidence intervals.

Extended Data Fig. 9 Drug response, accessibility in controls, and transcription correlation in meta-cohort.

Panel A: Doxorubicin dose response in scrambled shRNA HCC1954 cells and non-induced HCC1954 cells normalized to DMSO vehicle. n=3 independent experiments. Center equals mean +/- s.e.m. Panel B: Etoposide dose response in scrambled shRNA HCC1954 cells and non-induced HCC1954 cells normalized to DMSO vehicle. n=3 independent experiments. Center equals mean +/- s.e.m. Panel C: Paclitaxel dose response in scrambled shRNA HCC1954 cells and non-induced HCC1954 cells normalized to DMSO vehicle. n=3 independent experiments. Center equals mean +/- s.e.m. Panel D: Western blot of input samples and resolubilized chromatin pellet after wash with 500mM NaCl from HCC1954 cells treated with etoposide for given time, +/- induction of shScramble control. Histone H3 is shown as a loading control. Experiments were repeated independently three times with similar results. See Source Data 2. Panel E: KDM4B expression correlations by subtype. Pearson correlation between expression of KDM4B and ABCB1 (top panels), TOP2A (center panel) and TOP2B (bottom panels) in the TCGA breast cancer cohort (left, N=1078 individuals) and the adjuvant breast cancer metacohort (right, N=760 individuals) reveals no significant trend.

Source Data

Extended Data Fig. 10 Functional evaluation of KAT6B as a mediator of anthracycline response in breast cancer cells.

Panel A: RT-qPCR on cDNA showing inducible shRNA knockdown of KAT6B with 2 unique shRNA sequences in the HCC1954 breast cancer cell line. n=3 independent experiments. Center value equals mean +/- s.e.m. Panel B: Western blot on whole cell extract showing non-induced or induced knockdown shRNA KAT6B with no concomitant loss of drug targets TOP2A and TOP2B or gain in the drug efflux pump ABCB1; RNF2 as a loading control. Experiments were repeated independently three times with similar results. See Source Data 3. Panel C: Doxorubicin dose–response curves for KAT6B induced shRNAs (shRNA 2, 3) in HCC1954 cells and non-induced HCC1954 cells normalized to DMSO vehicle. Panel D: Etoposide dose–response curve for KAT6B induced shRNAs (shRNA 2, 3) in HCC1954 cells and non-induced HCC1954 cells normalized to DMSO vehicle. Panel E: Paclitaxel dose–response curve for KAT6B induced shRNAs (shRNA 2, 3) in HCC1954 cells and non-induced HCC1954 cells normalized to DMSO vehicle. n=4; 2 independent experiments of 2 independent shRNAs each for panels c-e. Center value is mean +/- s.e.m.

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Seoane, J.A., Kirkland, J.G., Caswell-Jin, J.L. et al. Chromatin regulators mediate anthracycline sensitivity in breast cancer. Nat Med 25, 1721–1727 (2019). https://doi.org/10.1038/s41591-019-0638-5

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