Abstract
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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
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).
•BCs treated only with paclitaxel GSE25066. Source data are provided with this paper.
Code availability
The codes for the HDAC6 score calculation and other analyses are freely available at https://github.com/jyyulab/HDAC6-score
References
Luo, J., Solimini, N. L. & Elledge, S. J. Principles of cancer therapy: oncogene and non-oncogene addiction. Cell 136, 823–837 (2009).
McClure, J. J., Li, X. & Chou, C. J. Advances and challenges of HDAC inhibitors in cancer therapeutics. Adv. Cancer Res. 138, 183–211 (2018).
Santo, L. et al. Preclinical activity, pharmacodynamic, and pharmacokinetic properties of a selective HDAC6 inhibitor, ACY-1215, in combination with bortezomib in multiple myeloma. Blood 119, 2579–2589 (2012).
Huang, P. et al. Selective HDAC inhibition by ACY-241 enhances the activity of paclitaxel in solid tumor models. Oncotarget 8, 2694–2707 (2017).
Cao, J. et al. Ricolinostat (ACY-1215) suppresses proliferation and promotes apoptosis in esophageal squamous cell carcinoma via miR-30d/PI3K/AKT/mTOR and ERK pathways. Cell Death Dis. 9, 817 (2018).
Wang, F., Zhong, B. W. & Zhao, Z. R. ACY 1215, a histone deacetylase 6 inhibitor, inhibits cancer cell growth in melanoma. J. Biol. Regul. Homeost. Agents 32, 851–858 (2018).
Cosenza, M., Civallero, M., Marcheselli, L., Sacchi, S. & Pozzi, S. Ricolinostat, a selective HDAC6 inhibitor, shows anti-lymphoma cell activity alone and in combination with bendamustine. Apoptosis 22, 827–840 (2017).
van Uden, D. J., van Laarhoven, H. W., Westenberg, A. H., de Wilt, J. H. & Blanken-Peeters, C. F. Inflammatory breast cancer: an overview. Crit.Rev. Oncol. Hematol. 93, 116–126 (2015).
Putcha, P. et al. HDAC6 activity is a non-oncogene addiction hub for inflammatory breast cancers. Breast Cancer Res. 17, 149 (2015).
Margolin, A. A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinf. 7, S7 (2006).
Cancer Genome Atlas, N. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).
Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).
Parry, M. Introducing the Metastatic Breast Cancer Project: a novel patient-partnered initiative to accelerate understanding of MBC. ESMO Open 3, e000452 (2018).
Kawaguchi, Y. et al. The deacetylase HDAC6 regulates aggresome formation and cell viability in response to misfolded protein stress. Cell 115, 727–738 (2003).
Matthias, P., Yoshida, M. & Khochbin, S. HDAC6 a new cellular stress surveillance factor. Cell Cycle 7, 7–10 (2008).
Hubbert, C. et al. HDAC6 is a microtubule-associated deacetylase. Nature 417, 455–458 (2002).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Khatamian, A., Paull, E. O., Califano, A. & Yu, J. SJARACNe: a scalable software tool for gene network reverse engineering from big data. Bioinformatics 35, 2165–2166 (2019).
Du, X. et al. Hippo/Mst signalling couples metabolic state and immune function of CD8α+ dendritic cells. Nature 558, 141–145 (2018).
Neve, R. M. et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell 10, 515–527 (2006).
Majid, T., Griffin, D., Criss, Z. 2nd, Jarpe, M. & Pautler, R. G. Pharmocologic treatment with histone deacetylase 6 inhibitor (ACY-738) recovers Alzheimer’s disease phenotype in amyloid precursor protein/presenilin 1 (APP/PS1) mice. Alzheimers Dement. (N Y) 1, 170–181 (2015).
Ma, X. J. et al. HDAC-selective inhibitor Cay10603 has single anti-tumour effect in Burkitt’s lymphoma cells by impeding the cell cycle. Curr. Med. Sci. 39, 228–236 (2019).
Dawood, S., Ueno, N. T. & Cristofanilli, M. The medical treatment of inflammatory breast cancer. Semin. Oncol. 35, 64–71 (2008).
Matro, J. M. et al. Inflammatory breast cancer management in the national comprehensive cancer network: The disease, recurrence pattern, and outcome.Clin. Breast Cancer 15, 1–7 (2015).
Tallarida, R. J. Quantitative methods for assessing drug synergism. Genes Cancer 2, 1003–1008 (2011).
Chou, T. C. Drug combination studies and their synergy quantification using the Chou-Talalay method. Cancer Res. 70, 440–446 (2010).
Pfefferle, A. D. et al. Transcriptomic classification of genetically engineered mouse models of breast cancer identifies human subtype counterparts. Genome Biol. 14, R125 (2013).
Cheung, Y. K. & Chappell, R. Sequential designs for phase I clinical trials with late-onset toxicities. Biometrics 56, 1177–1182 (2000).
Obuchowski, N. A. & Bullen, J. A. Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine. Phys. Med. Biol. 63, 07TR01 (2018).
Alvarez, M. J. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet. 48, 838–847 (2016).
Alvarez, M. J. & Califano, A. Darwin OncoTarget/OncoTreat: NY CLIA certified tests to identify effective drugs on an individual cancer patient basis from RNASeq profiles https://www.pathology.columbia.edu/diagnostic-specialties/personalized-genomic-medicine/oncology-testing/darwin-oncotarget-tm-oncotreat (2018).
Hatzis, C. et al. A genomic predictor of response and survival following taxane-anthracycline chemotherapy for invasive breast cancer. JAMA 305, 1873–1881 (2011).
Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).
Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).
Yan, J. Interplay between HDAC6 and its interacting partners: essential roles in the aggresome-autophagy pathway and neurodegenerative diseases. DNA Cell Biol. 33, 567–580 (2014).
Gregory, M. A. & Hann, S. R. c-Myc proteolysis by the ubiquitin-proteasome pathway: stabilization of c-Myc in Burkitt’s lymphoma cells. Mol. Cell. Biol. 20, 2423–2435 (2000).
Chakraborty, A. A. et al. A common functional consequence of tumor-derived mutations within c-MYC. Oncogene 34, 2406–2409 (2015).
Hai, Y. & Christianson, D. W. Histone deacetylase 6 structure and molecular basis of catalysis and inhibition. Nat. Chem. Biol. 12, 741–747 (2016).
Lynch, J. T., Somerville, T. D., Spencer, G. J., Huang, X. & Somervaille, T. C. TTC5 is required to prevent apoptosis of acute myeloid leukemia stem cells. Cell Death Dis. 4, e573 (2013).
Faiola, F. et al. Dual regulation of c-Myc by p300 via acetylation-dependent control of Myc protein turnover and coactivation of Myc-induced transcription. Mol. Cell. Biol. 25, 10220–10234 (2005).
Farrell, A. S. & Sears, R. C. MYC degradation.Cold Spring Harb. Perspect. Med. 4, a014365 (2014).
Lee, E. K. et al. Results of an abbreviated Phase Ib study of the HDAC6 inhibitor ricolinostat and paclitaxel in recurrent ovarian, fallopian tube, or primary peritoneal cancer. Gynecol. Oncol. Rep. 29, 118–122 (2019).
Vogl, D. T. et al. Ricolinostat, the first selective histone deacetylase 6 inhibitor, in combination with bortezomib and dexamethasone for relapsed or refractory multiple myeloma. Clin. Cancer Res. 23, 3307–3315 (2017).
Yee, A. J. et al. Ricolinostat plus lenalidomide, and dexamethasone in relapsed or refractory multiple myeloma: a multicentre phase 1b trial. Lancet Oncol. 17, 1569–1578 (2016).
Twomey, J. D., Brahme, N. N. & Zhang, B. Drug-biomarker co-development in oncology: 20 years and counting. Drug Resist. Updat. 30, 48–62 (2017).
Hackanson, B. et al. HDAC6 as a target for antileukemic drugs in acute myeloid leukemia. Leuk. Res. 36, 1055–1062 (2012).
Shouksmith, A. E. et al. Class I/IIb-selective HDAC inhibitor exhibits oral bioavailability and therapeutic efficacy in acute myeloid leukemia. ACS Med. Chem. Lett. 11, 56–64 (2020).
Gabay, M. et al. MYC activation is a hallmark of cancer initiation and maintenance.Cold Spring Harb. Perspect. Med. 4, a014241 (2014).
Chen, H., Liu, H. & Qing, G. Targeting oncogenic Myc as a strategy for cancer treatment. Signal Transduct. Target Ther. 3, 5 (2018).
Vervoorts, J., Luscher-Firzlaff, J. & Luscher, B. The ins and outs of MYC regulation by posttranslational mechanisms. J. Biol. Chem. 281, 34725–34729 (2006).
Sears, R. C. The life cycle of C-myc: from synthesis to degradation. Cell Cycle 3, 1133–1137 (2004).
Boyault, C. et al. HDAC6 controls major cell response pathways to cytotoxic accumulation of protein aggregates. Genes Dev. 21, 2172–2181 (2007).
Boyault, C., Sadoul, K., Pabion, M. & Khochbin, S. HDAC6, at the crossroads between cytoskeleton and cell signaling by acetylation and ubiquitination. Oncogene 26, 5468–5476 (2007).
Lee, J. Y., Nagano, Y., Taylor, J. P., Lim, K. L. & Yao, T. P. Disease-causing mutations in parkin impair mitochondrial ubiquitination, aggregation, and HDAC6-dependent mitophagy. J. Cell Biol. 189, 671–679 (2010).
Martinus, R. D. et al. Selective induction of mitochondrial chaperones in response to loss of the mitochondrial genome. Eur. J. Biochem. 240, 98–103 (1996).
Hu, F. & Liu, F. Mitochondrial stress: a bridge between mitochondrial dysfunction and metabolic diseases? Cell Signal. 23, 1528–1533 (2011).
Vives-Bauza, C., de Vries, R. L., Tocilescu, M. & Przedborski, S. PINK1/Parkin direct mitochondria to autophagy. Autophagy 6, 315–316 (2010).
Narendra, D., Tanaka, A., Suen, D. F. & Youle, R. J. Parkin is recruited selectively to impaired mitochondria and promotes their autophagy. J. Cell Biol. 183, 795–803 (2008).
Haynes, C. M. & Ron, D. The mitochondrial UPR - protecting organelle protein homeostasis. J. Cell Sci. 123, 3849–3855 (2010).
Carroll, R. G., Hollville, E. & Martin, S. J. Parkin sensitizes toward apoptosis induced by mitochondrial depolarization through promoting degradation of Mcl-1. Cell Rep. 9, 1538–1553 (2014).
Thompson, P. R. et al. Regulation of the p300 HAT domain via a novel activation loop. Nat. Struct. Mol. Biol. 11, 308–315 (2004).
Banik, D. et al. HDAC6 Plays a noncanonical role in the regulation of antitumor immune responses, dissemination, and invasiveness of breast cancer. Cancer Res. 80, 3649–3662 (2020).
Lee, S. M. & Ying Kuen, C. Model calibration in the continual reassessment method. Clin. Trials 6, 227–238 (2009).
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
Bai, B. et al. Deep profiling of proteome and phosphoproteome by isobaric labeling, extensive liquid chromatography, and mass spectrometry. Methods Enzymol. 585, 377–395 (2017).
Pagala, V. R. et al. Quantitative protein analysis by mass spectrometry. Methods Mol. Biol. 1278, 281–305 (2015).
Wang, X. et al. JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy. Mol. Cell Proteomics 13, 3663–3673 (2014).
Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass. Spectrom. 5, 976–989 (1994).
Niu, M. et al. Extensive peptide fractionation and y1 ion-based interference detection method for enabling accurate quantification by isobaric labeling and mass spectrometry. Anal. Chem. 89, 2956–2963 (2017).
Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinf. 12, 77 (2011).
Acknowledgements
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.).
Author information
Authors and Affiliations
Contributions
J.S., J.Y. and K.K. designed and coordinated the research and wrote the manuscript (J.S. coordinated the experimental preclinical studies, J.Y. coordinated the computational analysis and K.K. coordinated the clinical trial). A.C. coordinated the VIPER studies and wrote the manuscript. T.Z.Z. performed the preclinical experimental studies. Q.P. performed the computational studies. C.C. performed the statistical analysis of the clinical trial. Y.L., H.T., A.H. and J.P. performed proteomics studies. M.A. performed the VIPER studies. M.Y. and S.J. performed the dose–response studies with ricolinostat. P.C. and P.M. collaborated with T.Z.Z. to perform animal studies. M.O., M.T., M.A., S.K., E.H., R.W., K.F., K.C., D.H., M.M. and K.K. performed the clinical trial.
Corresponding authors
Ethics declarations
Competing interests
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.
Peer review
Peer review information
Nature Cancer thanks Christina Yap and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 HDAC6 score in BC.
(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.
Extended Data Fig. 2 Anticancer activity of HDAC6 inhibitors.
(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.
Extended Data Fig. 3
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.
Extended Data Fig. 6 HDAC6 scores in other human cancers.
(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.
Extended Data Fig. 7 The correlation of HDAC6 score with immune infiltrates.
(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.
Extended Data Fig. 9 Acetylation of c-Myc in Lys148 after inhibition of HDAC6.
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.
Supplementary information
Supplementary Table 1
Supplementary Tables 1–14.
Source data
Source Data Fig. 1
Unprocessed western blots.
Source Data Fig. 2
Unprocessed western blots.
Source Data Fig. 5
Unprocessed western blots.
Source Data Fig. 6
Unprocessed western blots.
Source Data Extended Data Fig. 2
Unprocessed western blots.
Source Data Extended Data Fig. 8
Unprocessed western blots.
Source Data Extended Data Fig. 9
Unprocessed western blots.
Source Data Extended Data Fig. 10
Unprocessed western blots.
Statistical source data
Statistical source data for all figures, extended figures and supplementary tables.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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 4, 257–275 (2023). https://doi.org/10.1038/s43018-022-00489-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43018-022-00489-5
This article is cited by
-
Multifunctional CaCO3@Cur@QTX125@HA nanoparticles for effectively inhibiting growth of colorectal cancer cells
Journal of Nanobiotechnology (2023)
-
Ricolinostat is not a highly selective HDAC6 inhibitor
Nature Cancer (2023)
-
NetBID2 provides comprehensive hidden driver analysis
Nature Communications (2023)
-
Reply to: Ricolinostat is not a highly selective HDAC6 inhibitor
Nature Cancer (2023)
-
HDAC6 score: to treat or not to treat?
Nature Cancer (2022)
