Inhibiting individual histone deacetylase (HDAC) is emerging as well-tolerated anticancer strategy compared with pan-HDAC inhibitors. Through preclinical studies, we demonstrated that the sensitivity to the leading HDAC6 inhibitor (HDAC6i) ricolinstat can be predicted by a computational network-based algorithm (HDAC6 score). Analysis of ~3,000 human breast cancers (BCs) showed that ~30% of them could benefice from HDAC6i therapy. Thus, we designed a phase 1b dose-escalation clinical trial to evaluate the activity of ricolinostat plus nab-paclitaxel in patients with metastatic BC (MBC) (NCT02632071). Study results showed that the two agents can be safely combined, that clinical activity is identified in patients with HR+/HER2− disease and that the HDAC6 score has potential as predictive biomarker. Analysis of other tumor types also identified multiple cohorts with predicted sensitivity to HDAC6i’s. Mechanistically, we have linked the anticancer activity of HDAC6i’s to their ability to induce c-Myc hyperacetylation (ac-K148) promoting its proteasome-mediated degradation in sensitive cancer cells.
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The gene expression profile data are available at GEO under accession number GSE180607. The data include two subseries, one for the RNA-seq data of clinical trial samples and four BC cell lines (GSE128623), and another for the microarray data of three cell lines and one mouse model (GSE180606). The acetylomics data, including raw files and pepXML files for each sample, can be accessed at PRIDE under accession number PXD026010. Source data have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author on reasonable request.
Previously published transcriptomic data that were reanalyzed here are available:
•Cancer cell lines. The RNA-seq transcripts per million data of 1,165 cell lines representing 29 cancer types from the CCLE project, together with cell line annotations and gene dependency scores, were downloaded from the portal of the Dependency Map (DepMap) project (https://depmap.org/portal, release: Public 19Q1).
•TCGA. The TCGA RNA-seq data at both isoform and gene levels for 32 human primary cancer types including BC were extracted from the QIAGEN OncoLand release TCGA_B38 2020v1.
•METABRIC. The human BC microarray data (Illumina HT 12 arrays, N = 1,981) were downloaded from Synapse (https://www.synapse.org/#!Synapse:syn1757063, version syn1757063).
The codes for the HDAC6 score calculation and other analyses are freely available at https://github.com/jyyulab/HDAC6-score
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We thank members of the Silva and Yu laboratories for advice generating this manuscript and for technical assistance, including D. Alsina-Beauchamp and R. Werner (Silva lab) and X. Dong, K.-K. Yan and L. Ding (Yu lab). We also thank S.-W. Lee from Mount Sinai’s CCMS core for technical assistance with mouse xenograft experiments. We also thank K. A. Laycock for scientific editing of the manuscript. This research was partially funded through the DOD Breakthrough award 151500 (J.S.); ALSAC (J.Y.); National Institutes of Health grants R01 CA153233 (J.S.), R01 CA153233–supplement (T.Z.Z.), R01 GM134382 (J.Y)., U01 CA217858 (A.C.), S10 OD012351 (A.C.) and S10 OD021764 (A.C.); and the Irving Scholar program (K.K.).
M.Y. and S.J. were Acetylon employees when this project was initiated. This research has been partially supported by a sponsor research agreement with Acetylon. J.Y. was a consultant of the computational analysis for the phase 1b trial 2015–2017. M.J.A. is Chief Scientific Officer and equity holder at DarwinHealth, a company that has licensed some of the algorithms used in this paper from Columbia University. A.C. is the founder, equity holder, consultant and director of DarwinHealth, a company that has licensed some of the algorithms used in this paper from Columbia University. Columbia University is also an equity holder in DarwinHealth. The remaining authors declare no competing interests.
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(a) The number of samples and dataset of origin used to evaluate the HDAC6 regulon. (b) Overlap of the original and updated HDAC6 regulons. P value was estimated using two-tailed Fisher’s exact test. N was determined by the total number of genes for network inference. (c) The network plot of HDAC6 and updated HDAC6 regulon. Edge width is corresponding to the correlation strength measured by mutual information. Red and blue edges indicate positive and negative correlations between HDAC6 and each of the regulon genes. The table below shows the pathway enrichment of the genes in the regulon, showing its association with unfolded protein response. P value was estimated using two-tailed Fisher’s exact test. (d) New HDAC6 score comparing IBCs vs non-IBCs. (e) HDAC6 scores of all BCs from TCGA and METABRIC are divided into molecular subtypes. (f) HDAC6 scores in 45 ductal metastatic breast cancer samples from the MBC Project divided into histological molecular subtypes. In d, e and f, the center line indicates the median value. The lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5x interquartile range; the red line represents the median of the HDAC6 scores in IBC samples and the numbers over each whisker plot indicate the percentage of samples over this value in each clinical subtype. Sample size (n = number of samples) of each group was indicated in the axis labels. P value was estimated using two-tailed t test.
(a) The western blot shows the titration of ricolinostat to identify an effective dose (accumulation of Ac-α-Tubulin) without off-target effects in class-I HDACs (accumulation of Ac-H3K27). SAHA is used as a control Pan-HDAC inhibitor. WT-blot results were reproduced n = 3 times from independent experiments. (b) Chemical structure of the different HDAC6 inhibitors used in Fig. 1e. (c) Normalized cell number 6 days after transfection with individualized siRNAs targeting HDAC6 or non-targeting control (NTC) (n = 3 independent independent experiments per siRNA). The WT-blots show the silencing efficiency. WT-blot results were reproduced n = 3 times from independent experiments. All error bars represent Mean±SD. P value was estimated by two-tailed t test. (d) The graphic shows the lack of synergistic activity between ricolinostat and commonly used chemotherapy (paclitaxel and doxorubicin) in MDA-MB-436 cells. In contrast, cells sensitive to ricolinostat MDA-MB-453, SK-BR-3 and MDA-MB-474 show synergistic activity between ricolinostat and chemotherapy. R and S indicate ricolinostat resistance and sensitivity respectively. N = 3 independent replicate experiments per drug combination and concentration. (e) Histological intratumoral evaluation of H&E, Caspase-3, and Ki-67 in tumor samples from Fig. 1b. Quantification is also shown in bar graphs. Notice that the combo treatment (Pac+Ric) is not shown because all tumors regressed with this treatment. The white asterisks indicate necrotic areas and the white arrows indicate Caspase-3 positive stained cells. All error bars represent Mean±SD. P value was estimated by two-tailed t test. N = 6 samples from individual tumors. (f) The list shows all the transgenic mouse models evaluated by the HDAC6 score in Fig. 2c, d and indicates their correlation with human BCs. (g) Kaplan–Meier graphic showing the survival of the MMTV tumors in Fig. 2e. Control n = 7; ricolinostat (Ric) n = 8; paclitaxel (Pac) n = 8; Ric+Pac n = 8. P value was estimated using two-tailed Log-Rank test.
Characteristics of the evaluable patients enrolled in the clinical trial.
Extended Data Fig. 4 Biomarker evaluation of HDAC6 score calculated by using the NetBID and VIPER algorithms in the phase Ib trial.
The Supplementary Extended Data Fig. shows the similarities between the HDAC6 scores inferred by NetBID (a) and VIPER (b) in responders and non-responders, and (c) ROC curve plot of HDAC6 score inferred by VIPER (similar plot for NetBID is in Fig. 3d). In a and b, one dot represents one patient sample and the center line indicates the median value. The lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5x interquartile range. P value was estimated by two-tailed t test. For all panels n = 3 non-responders and n = 7 responders.
Extended Data Fig. 5 The HDAC6 score does not correlate with the response in paclitaxel-only treated patients.
The Supplementary Extended Data Fig. shows the HDAC6 scores of all BCs from Hatzis et al. (JAMA, 2011) divided into clinical (a) and molecular (b) subtypes. (c) HDAC6 scores of patients divided by response to paclitaxel, Residual Disease (CD) and Pathological Complete Response (pCR), showing the lack of correlation between the HDAC6 score and the response to paclitaxel. In a, b and c the number of patient samples is indicate as (n). d, Kaplan–Meier graphic showing the survival of breast cancer patients treated exclusively with paclitaxel in the neoadjuvant setting separated by HDAC6 score (high/low=higher and lower −0.36, based on the ROC analysis in our clinical trial). N = 35 patient samples for HDAC6 score-low and N = 71 patient samples for HDAC6 score-high group. P value was estimated using two-tailed Log-Rank test. The 95% confidence interval of the regression lines were displayed. In a, b and c, the center line indicates the median value. The lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5x interquartile range; the red line represents the median of the HDAC6 scores in IBC samples and the numbers under each whisker plot indicate the percentage of samples over this value in each clinical subtype. Sample size of each group was indicated in the axis labels. P value was estimated using two-tailed t test.
(a) Correlation between HDAC6 regulon in different tumor types. The tumors sets are the same as in Fig. 4c. The number of patient sample for each set is indicated there. LAML: Acute Myeloid Leukemia; LIHC: Liver Hepatocellular Carcinoma; UVM: Uveal Melanoma; KIRP: Kidney Renal Papillary Cell Carcinoma; PCPG: Pheochromocytoma and Paraganglioma; DLBC: Diffuse Large B-Cell Lymphoma; ACC: Adrenocortical Carcinoma; UCS: Uterine Carcinosarcoma; KIRC: Kidney Renal Clear Cell Carcinoma; THYM: Thymoma; CHOL: Cholangiocarcinoma; SKCM: Skin Cutaneous Melanoma; CRC: Colorectal Carcinoma; LGG: Brain Lower Grade Glioma; UCEC: Uterine Corpus Endometrial Carcinoma; KICH: Kidney Chromophobe; MESO: Mesothelioma; TGCT: Testicular Germ Cell Tumors; GBM: Glioblastoma; PRAD: Prostate Adenocarcinoma; CESC: Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma; BLCA: Bladder Urothelial Carcinoma; SARC: Sarcoma; OV: Ovarian Serous Cystadenocarcinoma; THCA: Thyroid Carcinoma; ESCA: Esophageal Carcinoma; BRCA: BC; LUAD: Lung Adenocarcinoma; HNSC: Head and Neck Squamous Cell Carcinoma; PAAD: Pancreatic Adenocarcinoma; STAD: Stomach Adenocarcinoma; LUSC: Lung Squamous Cell Carcinoma. P value was estimated by two-tailed Fisher’s exact test. (b) List with all the cell lines evaluated by dose–response to ricolinostat and HDAC6 scores. The correlation (R) and P value between the response to ricolinostat and HDAC6 scores were estimated by two-tailed Spearman correlation test. (c) Graphic showing the correlation between the HDAC6 score and the response to ricolinostat in individual cell types (only tumor types with more than 6 cell lines are shown). N = 8 individual independent experiments for each ricolinostat dose. The curve was fitted by stat_smooth algorithsm using lm smoothing method and y~log2(x) formula.
(a) Scatter plot showing the correlation between HDAC6 score and immune score by ESTIMATE in TCGA-BRCA primary patient samples (n = 1109). The correlation coefficient (R) and P value were estimated using Spearman correlation test. (b), Violin plot showing the distribution of immune score across IHC-based breast cancer subtypes. Sample size of each group was indicated in the axis labels. P value was estimated using two-tailed t test. The center line indicates the median value. The lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5x interquartile range.
Extended Data Fig. 8 Ricolinostat treatment reduces the expression of c-MYC in sensitive cell lines.
(a) Heatmap representing GSEA analysis of hallmark signatures during ricolinostat exposure in sensitive breast cancer cell lines and TgMMTV-Neu model. P value was estimated by two-tailed t test. The Z-scores were transformed from these P values and further combined using Stouffer’s method. Only significant (combined Z > 1.96 or <−1.96) sets are shown. (b) The graphic shows summarized z-scores in cell lines and TgMMTV-Neu sensitive to ricolinostat during a time curse treatment (6, 12, 24 hours and 4 weeks). For a and b N = 2 individual independent experiments for cell lines and N = 3 individual tumors for TgMMTV-Neu. P value was estimated using two-tailed t test. (c) Bubble plot representing GSEA analysis of multiple MYC-associated signatures after ricolinostat in sensitive and resistant cells. N = 3 independent experiments per cell line. P value was estimated by two-tailed t test. The Z-score was transformed from the P values and further combined by Stouffer’s method. (d) QRT-PCR of c-Myc mRNA expression after 6 hours of exposure to ricolinostat. N = 3 independent experiments for each time point All effort bars represent Mean±SD. P value was estimated by two-tailed t test. (e) The WT-blot shows the changes in the protein expression of c-Myc and ac-Tubulin in SUM-149 cells after ricolinostat is added to the culture media. WT-blot results were reproduced n = 3 times from independent experiments. (f) Graphic showing an efficient reduction in the HDAC6 score after treatment with ricolinostat in multiple cell lines, n ≥ 2 independent experiments per time point. The center line indicates the median value. The lower and upper hinges represent the 25th and 75th percentiles, respectively, and whiskers denote 1.5x interquartile range.
a) MA plots showing the peptides upregulated upon HDAC6 knockout (above) and HDAC6 catalytic domain 2 mutants (below). In the MA plots, each dot represents a peptide. The significantly upregulated peptides were identified by fold change > 1.5 and p-value < 0.05 and highlighted in red. P value was estimated by two-tailed t test. (b) Scatter plot showing the correlation of the Z-score of the comparison between HDAC6 KO and wild type with that of the comparison between HDAC6 mutant and wild type. The curve was fitted by stat_smooth algorithm using lm smoothing method and y~x formula. The correlation coefficient (R) and P value were estimated using two-tailed Spearman correlation test. Each dot represents a peptide. For a and b the N = 2 independent proteomic replica studies per cell line. c) The western blot shows the accumulation of ac-K148-c-Myc after HDAC6 is inhibited by ricolinostat in MDA-MB-453 and SK-BR-3 BC lines. WT-blot results were reproduced n = 3 times from independent experiments.
Extended Data Fig. 10 Acetylation of c-Myc in Lys148 after inhibition of HDAC6 in sensitive and resistant BC cancer cells.
The western blot shows the accumulation of ac-K148-c-Myc after HDAC6 is inhibited by ricolinostat in MDA-MB-453 (sensitive) and MDA-MB-436 (resistant) lines. WT-blot results were reproduced n = 3 times from independent experiments.
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Zeleke, T.Z., Pan, Q., Chiuzan, C. et al. Network-based assessment of HDAC6 activity predicts preclinical and clinical responses to the HDAC6 inhibitor ricolinostat in breast cancer. Nat Cancer (2022). https://doi.org/10.1038/s43018-022-00489-5