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Inhibition of transcription by dactinomycin reveals a new characteristic of immunogenic cell stress

2020; Springer Nature; Volume: 12; Issue: 5 Linguagem: Inglês

10.15252/emmm.201911622

1757-4684

Juliette Humeau, Allan Sauvat, Giulia Cerrato, Wei Xie, Friedemann Loos, Francesca Iannantuoni, Lucillia Bezu, Sarah Lévesque, Juliette Paillet, Jonathan Pol, Marion Leduc, Laurence Zitvogel, Hugues de Thé, Oliver Kepp, Guido Kroemer,

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Article23 April 2020Open Access Transparent process Inhibition of transcription by dactinomycin reveals a new characteristic of immunogenic cell stress Juliette Humeau Juliette Humeau Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France Search for more papers by this author Allan Sauvat Allan Sauvat Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Search for more papers by this author Giulia Cerrato Giulia Cerrato Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France Search for more papers by this author Wei Xie Wei Xie Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France Search for more papers by this author Friedemann Loos Friedemann Loos orcid.org/0000-0002-5976-5978 Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Search for more papers by this author Francesca Iannantuoni Francesca Iannantuoni Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Hospital Doctor Peset - FISABIO, Valencia, Spain Search for more papers by this author Lucillia Bezu Lucillia Bezu Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Hospital Doctor Peset - FISABIO, Valencia, Spain Search for more papers by this author Sarah Lévesque Sarah Lévesque Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France Search for more papers by this author Juliette Paillet Juliette Paillet Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France Search for more papers by this author Jonathan Pol Jonathan Pol orcid.org/0000-0002-8355-7562 Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Search for more papers by this author Marion Leduc Marion Leduc Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Search for more papers by this author Laurence Zitvogel Laurence Zitvogel Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France Gustave Roussy Cancer Campus (GRCC), Villejuif, France INSERM U1015, Villejuif, France Center of Clinical Investigations in Biotherapies of Cancer (CICBT), Villejuif, France Search for more papers by this author Hugues de Thé Hugues de Thé College de France, INSERM UMR 1050, CNRS UMR 7241, PSL University, Paris, France INSERM UMR 944, CNRS UMR 7212, Equipe labellisée par la Ligue contre le Cancer, IRSL, Hopital St. Louis, Université de Paris, Paris, France Search for more papers by this author Oliver Kepp Corresponding Author Oliver Kepp [email protected] orcid.org/0000-0002-6081-9558 Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Search for more papers by this author Guido Kroemer Corresponding Author Guido Kroemer [email protected] orcid.org/0000-0002-9334-4405 Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Suzhou Institute for Systems Medicine, Chinese Academy of Medical Sciences, Suzhou, China Pôle de Biologie, Hôpital Européen Georges Pompidou, AP-HP, Paris, France Department of Women's and Children's Health, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden Search for more papers by this author Juliette Humeau Juliette Humeau Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France Search for more papers by this author Allan Sauvat Allan Sauvat Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Search for more papers by this author Giulia Cerrato Giulia Cerrato Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France Search for more papers by this author Wei Xie Wei Xie Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France Search for more papers by this author Friedemann Loos Friedemann Loos orcid.org/0000-0002-5976-5978 Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Search for more papers by this author Francesca Iannantuoni Francesca Iannantuoni Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Hospital Doctor Peset - FISABIO, Valencia, Spain Search for more papers by this author Lucillia Bezu Lucillia Bezu Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Hospital Doctor Peset - FISABIO, Valencia, Spain Search for more papers by this author Sarah Lévesque Sarah Lévesque Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France Search for more papers by this author Juliette Paillet Juliette Paillet Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France Search for more papers by this author Jonathan Pol Jonathan Pol orcid.org/0000-0002-8355-7562 Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Search for more papers by this author Marion Leduc Marion Leduc Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Search for more papers by this author Laurence Zitvogel Laurence Zitvogel Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France Gustave Roussy Cancer Campus (GRCC), Villejuif, France INSERM U1015, Villejuif, France Center of Clinical Investigations in Biotherapies of Cancer (CICBT), Villejuif, France Search for more papers by this author Hugues de Thé Hugues de Thé College de France, INSERM UMR 1050, CNRS UMR 7241, PSL University, Paris, France INSERM UMR 944, CNRS UMR 7212, Equipe labellisée par la Ligue contre le Cancer, IRSL, Hopital St. Louis, Université de Paris, Paris, France Search for more papers by this author Oliver Kepp Corresponding Author Oliver Kepp [email protected] orcid.org/0000-0002-6081-9558 Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Search for more papers by this author Guido Kroemer Corresponding Author Guido Kroemer [email protected] orcid.org/0000-0002-9334-4405 Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France Suzhou Institute for Systems Medicine, Chinese Academy of Medical Sciences, Suzhou, China Pôle de Biologie, Hôpital Européen Georges Pompidou, AP-HP, Paris, France Department of Women's and Children's Health, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden Search for more papers by this author Author Information Juliette Humeau1,2,3, Allan Sauvat1,2, Giulia Cerrato1,2,3, Wei Xie1,2,3, Friedemann Loos1,2, Francesca Iannantuoni1,2,4, Lucillia Bezu1,2,4, Sarah Lévesque1,2,3, Juliette Paillet1,2,3, Jonathan Pol1,2, Marion Leduc1,2, Laurence Zitvogel3,5,6,7, Hugues de Thé8,9, Oliver Kepp *,1,2 and Guido Kroemer *,1,2,10,11,12 1Equipe labellisée par la Ligue contre le Cancer, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Université de Paris, Paris, France 2Metabolomics and Cell Biology Platforms, Gustave Roussy, Villejuif, France 3Faculty of Medicine Kremlin Bicêtre, Université Paris Sud, Paris Saclay, Paris, France 4Hospital Doctor Peset - FISABIO, Valencia, Spain 5Gustave Roussy Cancer Campus (GRCC), Villejuif, France 6INSERM U1015, Villejuif, France 7Center of Clinical Investigations in Biotherapies of Cancer (CICBT), Villejuif, France 8College de France, INSERM UMR 1050, CNRS UMR 7241, PSL University, Paris, France 9INSERM UMR 944, CNRS UMR 7212, Equipe labellisée par la Ligue contre le Cancer, IRSL, Hopital St. Louis, Université de Paris, Paris, France 10Suzhou Institute for Systems Medicine, Chinese Academy of Medical Sciences, Suzhou, China 11Pôle de Biologie, Hôpital Européen Georges Pompidou, AP-HP, Paris, France 12Department of Women's and Children's Health, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden *Corresponding author. Tel: +33 1 42 11 45 16; E-mail: [email protected] *Corresponding author. Tel: +33 1 44 27 76 67; E-mail: [email protected] EMBO Mol Med (2020)12:e11622https://doi.org/10.15252/emmm.201911622 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Chemotherapy still constitutes the standard of care for the treatment of most neoplastic diseases. Certain chemotherapeutics from the oncological armamentarium are able to trigger pre-mortem stress signals that lead to immunogenic cell death (ICD), thus inducing an antitumor immune response and mediating long-term tumor growth reduction. Here, we used an established model, built on artificial intelligence to identify, among a library of 50,000 compounds, anticancer agents that, based on their molecular descriptors, were predicted to induce ICD. This algorithm led us to the identification of dactinomycin (DACT, best known as actinomycin D), a highly potent cytotoxicant and ICD inducer that mediates immune-dependent anticancer effects in vivo. Since DACT is commonly used as an inhibitor of DNA to RNA transcription, we investigated whether other experimentally established or algorithm-selected, clinically employed ICD inducers would share this characteristic. As a common leitmotif, a panel of pharmacological ICD stimulators inhibited transcription and secondarily translation. These results establish the inhibition of RNA synthesis as an initial event for ICD induction. Synopsis Anticancer drugs that trigger immunogenic cell death (ICD) are particularly efficient because they mobilize the host immune system against malignant cells expressing tumor-associated antigens. Dactinomycin is identified as an ICD inducer and showed to stimulate anticancer immune responses in vivo. An algorithm designed to discover cancer cell-killing drugs with immunostimulatory properties led to the identification of dactinomycin (DACT, also known as actinomycin D), as an ICD inducer. DACT induces all hallmarks ICD including phosphorylation of eukaryotic initiation factor 2a (eIF2a) in the context of a partial endoplasmic reticulum stress response, as well as cell surface exposure of calreticulin. DACT stimulated anticancer immune responses in vivo with an improvement of the ratio of cytotoxic T lymphocytes over regulatory T cells in tumor-infiltrating lymphocytes, while sensitizing tumors to subsequent immunotherapy with a PD-1-blocking antibody. DACT is a known transcriptional inhibitor, and a range of distinct ICD inducers were found to potently inhibit transcription and translation, as documented for anthracyclines, crizotinib, lurbinectedin and oxaliplatin. Artificial intelligence applied to a library of 50,000 drugs corroborated that transcription and translation inhibitors have a higher probability to induce ICD than other classes of antineoplastics. The paper explained Problem Chemotherapy still constitutes the standard treatment for most cancers. Yet, some chemotherapeutics are able to trigger stress signals in cancer cells, which activate an antitumor immune response and thereby confer long-term protection. We first investigated the immunogenic potential of chemotherapeutics which are already tested in clinics. Then, we further elucidated the mechanisms underlying this effect. Results A machine learning approach was used for the prediction of novel inducers of immunogenic cell death (ICD) within a library of 50,000 compounds. This approach led to the identification of dactinomycin (DACT) that induces ICD in vitro and mediates anticancer immunity in vivo. DACT is commonly used as an inhibitor of DNA to RNA transcription. An analysis of established and predicted ICD inducers revealed the inhibition of RNA transcription (and secondarily protein translation) as an initial event for ICD induction. Impact These findings may improve the application of dactinomycin in clinics and offer new combination strategies for the treatment of childhood sarcoma. In addition, the discovery of transcription as a characteristic of ICD may facilitate the development of immunotherapies. Introduction The last decade has witnessed the clinical implementation of anticancer immunotherapies (Nishino et al, 2017), as well as the realization that the long-term success of other antineoplastic therapies (i.e., with cytotoxicants, irradiation or targeted agents) beyond therapy discontinuation depends on the reinstatement of immunosurveillance (Vesely et al, 2011). Indeed the density, composition, and functional state of the tumor immune infiltrate, the "immune contexture" has a decisive impact on the outcome of such non-immune targeted therapies (Fridman et al, 2017). Successful chemotherapeutic agents (Obeid et al, 2007b; Tesniere et al, 2010), radiotherapy (Golden et al, 2012), and some targeted agents (Menger et al, 2012; Liu et al, 2019) kill cancer cells in a way that they become recognizable by the immune system, hence causing "immunogenic cell death" (ICD) (Casares et al, 2005; Galluzzi et al, 2017). As a general rule, immunogenicity results from the combination of two phenomena, namely (i) antigenicity, implying that tumor cells must be antigenically distinct from their normal counterparts, and (ii) adjuvanticity, meaning that stressed and dying neoplastic cells must emit damage-associated molecular patterns (DAMPs) to activate innate immune effectors (Galluzzi et al, 2017). Although cancer therapies may affect the immunopeptidome presented by class I molecules at the cancer cell surface (Bloy et al, 2017), the most important effect of ICD concerns the release/exposure of DAMPs (Kroemer et al, 2013). At the molecular level, ICD is characterized by an autocrine stimulation of type-1 interferon (IFN) receptors (Sistigu et al, 2014), the pre-apoptotic exposure of calreticulin (CALR) on the cell surface (Obeid et al, 2007a,b; Panaretakis et al, 2008), the release of ATP during the blebbing phase of apoptosis (Martins et al, 2014), the post-apoptotic/post-necrotic exodus of annexin A1 (ANXA1) (Vacchelli et al, 2015; Baracco et al, 2016), and chromatin-binding protein high mobility group B1 (HMGB1) (Apetoh et al, 2007). Type-1 interferon secretion depends on the stimulation of several pattern recognition receptors (PRRs including TLR3 and cGAS/STING) (Sistigu et al, 2014), CALR exposure on a partial endoplasmic reticulum stress response (Panaretakis et al, 2009; Bezu et al, 2018), ATP release on pre-mortem autophagy (Michaud et al, 2011; Martins et al, 2012), and ANXA1/HMGB1 exodus on secondary necrosis (Apetoh et al, 2007; Vacchelli et al, 2015). ATP, ANXA1, CALR, and HMGB1 interact with four receptor types, namely, purinergic P2Y2 or P2X7 receptors (Ghiringhelli et al, 2009), formyl peptide receptor-1 (FPR1) (Vacchelli et al, 2015), CD91 (Garg et al, 2012), and toll-like receptor 4 (TLR4) (Apetoh et al, 2007; Yamazaki et al, 2014), respectively, that are present on the surface of dendritic cells (DCs) or their precursors. P2RY2/P2RX7, FPR1, CD91, and TLR4 promote chemotaxis, juxtaposition with dying cells (Vacchelli et al, 2015), subsequent engulfment of portions of dying cells (Obeid et al, 2007b) in addition to production of interleukin-1β (Sistigu et al, 2014), and cross-presentation of tumor antigens (Ma et al, 2013), by DCs, respectively. Of note, inhibition of the aforementioned ligand–receptor interactions abolishes the efficacy of anticancer ICD-inducing therapies in pre-clinical models (Apetoh et al, 2007; Ghiringhelli et al, 2009; Garg et al, 2012; Vacchelli et al, 2015). Moreover, there is an abundant literature suggesting that deficiencies in these ligand and/or receptors (and their downstream signals) have a negative impact on patient prognosis, predicting therapeutic failure (Apetoh et al, 2007; Ghiringhelli et al, 2009; Vacchelli et al, 2015). In patients, suboptimal therapeutic regimens (failing to induce ICD) (Tesniere et al, 2010; Pfirschke et al, 2016), selective alterations in cancer cells (preventing the emission of immunogenic signals during ICD), and inherited or acquired defects in immune effectors (abolishing the perception of ICD by the immune system) can contribute to therapeutic failure due to an insufficient immune recognition of malignant cells (Kroemer et al, 2013). Importantly, ICD induction can synergize with subsequent immune checkpoint blockade targeting PD-1/PD-L1 interaction (Pfirschke et al, 2016; Liu et al, 2019) and hundreds of clinical trials are investigating such combination effects (www.clinicaltrials.gov). Given the rising significance of ICD, it is important to understand the rules governing its induction at the cellular and molecular levels, especially considering that their comprehension may facilitate the identification of novel, effective ICD inducers. Here, we report the discovery of dactinomycin (DACT, commonly known as actinomycin D), a chemotherapeutic agent used to treat various sarcomas and an efficient inhibitor of transcription, as an ICD inducer. Based on this serendipitous finding, we developed the concept that inhibition of RNA synthesis is a close-to-common feature of ICD. Results Identification of dactinomycin as a bona fide ICD inducer We used an artificial intelligence machine learning approach (Bezu et al, 2018) to predict the probability of inducing ICD of 50,000 distinct compounds tested for their anticancer effects on the NCI-60 panel of human tumor cell lines (Shoemaker, 2006; Fig 1A), while plotting the ICD prediction score against their mean IC50, i.e., the dose that reduces cell proliferation by half (Fig 1B). The compounds that exhibited cytotoxicity and an ICD score higher than mitoxantrone (MTX), a standard ICD inducer (Obeid et al, 2007b; Ma et al, 2011), were considered as potential ICD inducers. Two compounds, among the ones that have entered clinical trials, stood out as drugs having a low IC50 and a high ICD score. Trabectedin is known for its capacity to selectively eliminate tumor-associated macrophages, which explains at least part of its anticancer activity (Germano et al, 2013). Dactinomycin (DACT, best known as actinomycin D, a product of Streptomyces parvulus), which is generally considered as a DNA intercalator that inhibits topoisomerases and RNA polymerases (Goldberg et al, 1962), is used for the treatment of childhood-associated sarcomas (Wilms, Ewing, rhabdomyosarcoma), gestational trophoblastic disease including hydatidiform moles and choriocarcinomas (Khatua et al, 2004; Turan et al, 2006), and some types of testicular cancers (Early & Albert, 1976). We therefore evaluated DACT for its capacity to induce ICD. Figure 1. Prediction of immunogenic cell death (ICD) A. The 50,000 potential anticancer agents from the NCI-60 human tumor cell lines screen were analyzed with an artificial intelligence model that can predict immunogenic cell death (ICD) based on molecular descriptors. B. The distribution of the drugs based on their IC50 and predicted ICD score is depicted as density plot. Based on the properties of the standard ICD inducer mitoxantrone (blue), we selected negative (green) and positive (red) hits: Agents that entered into clinical trials, having an IC50 < 1 μM (and therefore -log(IC50) > 6) and whose predicted ICD score is higher than the ICD score of mitoxantrone, are potential ICD inducers (positive hits). Some agents that entered into clinical trials, whose IC50 > 1 μM and which have an ICD prediction score lower than 1, are negative hits. Download figure Download PowerPoint When added to human osteosarcoma U2OS cells engineered to express a CALR-green fluorescent protein (GFP) fusion protein, DACT (used around the IC60 for these cells, i.e., at 0.5 and 1 μM, Appendix Fig S1) caused peripheralization of the green fluorescence to the same extent as the positive control, MTX, as determined by videomicroscopy (Fig 2A–C). Similarly, live-cell imaging revealed the decrease of HMGB1-GFP in the nuclei of DACT-treated cells (Fig 2D–F). DACT also reduced the ATP-dependent quinacrine fluorescence staining of cells (Fig 2G and H), and the supernatants of DACT-treated cells stimulated the expression of MX1, a type 1 interferon-related biosensor, with GFP under the control of its promoter (Fig 2I and J). Alternative methods were then used to measure the emission of endogenous DAMPs. Thus, the plasma membrane surface exposure of CALR on viable cells was detected by flow cytometry (Fig 2K and L); the release of endogenous HMGB1 into the culture medium was confirmed by ELISA (Fig 2M), and ATP release into the supernatant of DACT-treated cells was assessed by a luciferin conversion assay (Fig 2N). Figure 2. ICD hallmarks in human cancer cellsHuman osteosarcoma U2OS cells were treated with dactinomycin (DACT) at 0.5 or 1 μM, or with mitoxantrone (MTX) between 1 and 6 μM as positive control (A-N). A–C. Human osteosarcoma U2OS cells stably expressing CALR-GFP and H2B-RFP were treated as described above, and images were acquired once per hour for 12 h (A). For one representative experiment among three, the mean ± SEM of the average area of high CALR dots (normalized to the control at each time point) of quadruplicates is shown (B). Values are depicted as the area under the curve mean ± SD of triplicates (C). D–F. Treated U2OS cells stably expressing HMGB1-GFP and H2B-RFP images were acquired every hour for 24 h (D). For one representative experiment among three, the mean ± SEM of the green fluorescence intensity in the nucleus (normalized to the control at each time point) of quadruplicates is depicted (E). For each cell, the speed of nuclear release (difference of HMGB1 nuclear green fluorescence intensity between two time points) was calculated. Values are depicted as the average speed of the nuclear release mean ± SD of quadruplicates (F). G, H. U2OS cells were treated for 6, 12, or 24 h, and ATP was stained with quinacrine (G). The number of quinacrine negative cells was assessed based on the distribution of cellular green fluorescence intensity in MTX versus control conditions. For one representative experiment among three, the mean ± SD of quadruplicate assessments is shown (H). I, J. U2OS wild-type cells were treated with MTX or DACT as described above for 6 h. Then, medium was refreshed and 24 h later, type I interferon response was assessed by transferring the supernatant on HT29 MX1-GFP reporter cells lines cells for additional 48 h. Human type 1α interferon (IFNα1) was also added on the cells as an additional positive control. Images were acquired by fluorescence microscopy, and the number of positive cells was assessed based on the distribution of cellular green fluorescence intensity in IFNα1 versus control conditions (I). The percentage of MX1-positive cells was calculated, and the mean ± SEM of five independent experiments is depicted (J). K, L. U2OS wild-type cells were treated as mentioned above for 6 h, and then, medium was refreshed. Twenty-four hours later, cells were collected and surface-exposed calreticulin (CALR) was stained with an antibody specific for CALR. DAPI was used as an exclusion dye, and cells were acquired by flow cytometry (K). The percentage of CALR+ cells among viable (DAPI−) ones is depicted. The mean ± SEM of six independent experiments is depicted (L). M. U2OS cells were treated as described above for 24 h, and the concentration of HMGB1 released in the supernatant was quantified with an ELISA kit and then normalized to control. The mean ± SEM of four independent experiments is shown. N. U2OS were treated as described above for 24 h. Concentration of secreted ATP in the supernatant was quantified with a luciferase-based bioluminescence kit. The mean ± SD of quadruplicates from one representative among three experiments is depicted. Data information: Scale bars represent 20 μm. All P-values showing significances of treatments compared to control (Ctr) were calculated with Student's t-test: *P < 0.05, **P < 0.01, ***P < 0.001. Download figure Download PowerPoint One of the pathognomonic features of ICD is a partial endoplasmic reticulum (ER) stress response that involves phosphorylation of eukaryotic initiation factor 2α (eIF2α) without activation of its downstream factor ATF4, and without the ATF6 and the IRE1/XBP1 arms of the unfolded protein response (Panaretakis et al, 2009; Pozzi et al, 2016; Bezu et al, 2018). Accordingly, DACT caused eIF2α phosphorylation (measured by immunofluorescence, Fig 3A and B), but neither significant downstream ATF4 activation (expressed as a GFP fusion protein, Fig 3C and D), nor ATF6 translocation from the cytosol to the Golgi and to nuclei (detected as a GFP fusion protein, Fig 3E and F), nor expression of an XBP1-(DBD-venus fusion protein that is only in-frame for venus (a variant of GFP) when XBP1 has been spliced by IRE1 (Fig 3G and H). We knocked out each of the four eIF2α kinases (EIF2AK1 to 4) in U2OS cel