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Review

Trial watch: chemotherapy-induced immunogenic cell death in oncology

Jenny Sprootena Cell Stress & Immunity (CSI) Lab, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, BelgiumView further author information
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Raquel S. Laureanoa Cell Stress & Immunity (CSI) Lab, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, BelgiumView further author information
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Isaure Vanmeerbeeka Cell Stress & Immunity (CSI) Lab, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, BelgiumView further author information
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Jannes Govaertsa Cell Stress & Immunity (CSI) Lab, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, BelgiumView further author information
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Stefan Naulaertsa Cell Stress & Immunity (CSI) Lab, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, BelgiumView further author information
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Daniel M. Borrasa Cell Stress & Immunity (CSI) Lab, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, BelgiumView further author information
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Lisa Kingetb Laboratory of Experimental Oncology, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, BelgiumView further author information
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Jitka Fucíkovác Department of Immunology, Charles University, 2Faculty of Medicine and University Hospital Motol, Prague, Czech Republic;d Sotio Biotech, Prague, Czech RepublicView further author information
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Radek Špíšekc Department of Immunology, Charles University, 2Faculty of Medicine and University Hospital Motol, Prague, Czech Republic;d Sotio Biotech, Prague, Czech RepublicView further author information
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Lenka Palová Jelínkovác Department of Immunology, Charles University, 2Faculty of Medicine and University Hospital Motol, Prague, Czech Republic;d Sotio Biotech, Prague, Czech RepublicView further author information
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Oliver Keppe Metabolomics and Cell Biology Platforms, Institut Gustave Roussy Cancer Center, Université Paris Saclay, Villejuif, France;f Centre de Recherche des Cordeliers, Equipe Labellisée Par la Liguecontre le Cancer, Université de Paris, sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, FranceView further author information
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Guido Kroemere Metabolomics and Cell Biology Platforms, Institut Gustave Roussy Cancer Center, Université Paris Saclay, Villejuif, France;f Centre de Recherche des Cordeliers, Equipe Labellisée Par la Liguecontre le Cancer, Université de Paris, sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France;g Department of Biology, Hôpital Européen Georges Pompidou, AP-HP, Institut du Cancer Paris CARPEM, Paris, FranceView further author information
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Dmitri V. Kryskoh Cell Death Investigation and Therapy (CDIT) Laboratory, Department of Human Structure and Repair, Ghent University, Ghent, Belgium;i Cancer Research Insitute Ghent, Ghent University, Ghent, BelgiumView further author information
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An Coosemansj Laboratory of Tumor Immunology and Immunotherapy, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, BelgiumView further author information
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Rianne D.W. Vaesk Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, The NetherlandsView further author information
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Dirk De Ruysscherk Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, The Netherlands;l Department of Radiotherapy, Erasmus University Medical Center, Rotterdam, The NetherlandsView further author information
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Steven De Vleeschouwerm Department Neurosurgery, University Hospitals Leuven, Leuven, Belgium;n Department Neuroscience, Laboratory for Experimental Neurosurgery and Neuroanatomy, KU Leuven, Leuven, Belgium;o Leuven Brain Institute (LBI), KU Leuven, Leuven, BelgiumView further author information
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Els Wautersp Laboratory of Respiratory Diseases and Thoracic Surgery (Breathe), Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, BelgiumView further author information
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Evelien Smitsq Center for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, Antwerp, Belgium;r Center for Cell Therapy and Regenerative Medicine, Antwerp University Hospital, Antwerp, BelgiumView further author information
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Sabine Tejpars Molecular Digestive Oncology, Department of Oncology, Katholiek Universiteit Leuven, Leuven, Belgium;t Cell Death and Inflammation Unit, VIB-Ugent Center for Inflammation Research (IRC), Ghent, BelgiumView further author information
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Benoit Beuselinckb Laboratory of Experimental Oncology, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, BelgiumView further author information
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Sigrid Hatseb Laboratory of Experimental Oncology, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, BelgiumView further author information
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Hans Wildiersb Laboratory of Experimental Oncology, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, BelgiumView further author information
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Paul M. Clementb Laboratory of Experimental Oncology, Department of Oncology, Leuven Cancer Institute, KU Leuven, Leuven, BelgiumView further author information
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Peter Vandenabeelet Cell Death and Inflammation Unit, VIB-Ugent Center for Inflammation Research (IRC), Ghent, Belgium;u Molecular Signaling and Cell Death Unit, Department of Biomedical Molecular Biology, Ghent University, Ghent, BelgiumView further author information
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Laurence Zitvogelv Tumour Immunology and Immunotherapy of Cancer, European Academy of Tumor Immunology, Gustave Roussy Cancer Center, Inserm, Villejuif, FranceView further author information
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Abhishek D. Garga Cell Stress & Immunity (CSI) Lab, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, BelgiumCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-9976-9922View further author information
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Article: 2219591 | Received 22 Dec 2022, Accepted 25 May 2023, Published online: 03 Jun 2023

ABSTRACT

Immunogenic cell death (ICD) refers to an immunologically distinct process of regulated cell death that activates, rather than suppresses, innate and adaptive immune responses. Such responses culminate into T cell-driven immunity against antigens derived from dying cancer cells. The potency of ICD is dependent on the immunogenicity of dying cells as defined by the antigenicity of these cells and their ability to expose immunostimulatory molecules like damage-associated molecular patterns (DAMPs) and cytokines like type I interferons (IFNs). Moreover, it is crucial that the host’s immune system can adequately detect the antigenicity and adjuvanticity of these dying cells. Over the years, several well-known chemotherapies have been validated as potent ICD inducers, including (but not limited to) anthracyclines, paclitaxels, and oxaliplatin. Such ICD-inducing chemotherapeutic drugs can serve as important combinatorial partners for anti-cancer immunotherapies against highly immuno-resistant tumors. In this Trial Watch, we describe current trends in the preclinical and clinical integration of ICD-inducing chemotherapy in the existing immuno-oncological paradigms.

Introduction

It has been two decades since the concept of apoptosis being solely immunologically quiet and hence unable to activate the immune system has been overthrown. A large number of studies have substantiated an immunogenic variant of regulated cell death (RCD) programs like apoptosis, called immunogenic cell death (ICD)Citation1–4. Since then, this concept of ICD has been extended to other RCDs, a term that refers to cell death programs that have a known intricate signaling cascade, such as necroptosis, pyroptosis, or ferroptosisCitation5–15.

The most well-known form is Apoptosis. Apoptosis is morphologically defined by the shrinking of cells, fragmentation of the DNA, and blebbing of the cell membrane. In contrast, necroptosis, pyroptosis, and ferroptosis are regulated forms of molecularly defined necrosis. They resemble accidental necrosis in terms of its final morphology (e.g., organelle swelling, plasma membrane rupture, cell lysis, and leakage of intracellular components) but utilize a distinct molecular machinery.

Nevertheless, some degree of caution is required with the ICD-like profile subscribed to some recently discovered RCD pathways, since a full consensus on their immunological impact is still pendingCitation16,Citation17. For instance, ferroptosis, a pathway first described in 2012Citation18, has been shown to be immuno-modulatory in multiple disease modelsCitation19–22 including cancerCitation13,Citation22–27. Depending on the temporal stage of ferroptosis (i.e., early vs. late), differences in modulating experimental anti-tumor immunity have been reportedCitation13. However, several immunosuppressive mechanisms have also been associated with ferroptosis such as formation of lipid bodiesCitation28, oxidized phospholipids (oxPLs)Citation29–33 formation, and cyclo-oxygenase 2 (MT-CO2; also known as COX2)Citation34,Citation35 activationCitation36. Some of the immunosuppressive mechanisms relevant for anti-tumor immunityCitation5 were also attributed to ferroptotic tumor-associated neutrophils resulting even in enhanced tumor growthCitation37. Clinical and immuno-oncology implications of such findings are still pending.

Owing to a lot of research studies published over the last few decades, the molecular and cellular mechanisms behind ICD have been largely deciphered. Organelle and cellular stress, most particularly endoplasmic reticulum (ER) stress induced by reactive oxygen species (ROS) production, is an essential early trigger for the initiation of ICDCitation38–42. Sequentially, ICD enables a time- and space-dependent organized exposure as well as release of damage-associated patterns (DAMPs) or alarmins from dying cancer cells. The main DAMPs associated with ICD include calreticulin exposure on the cell membraneCitation43–49, heat-shock proteins (HSPs), exposure on the cell membrane and/or passively releasedCitation50,Citation51, passively released high-mobility group box 1 (HMGB1)Citation52–56, surface exposure of annexin A1 (ANXA1)Citation57–60, and adenosine triphosphate (ATP), which can be actively or passively releasedCitation44,Citation61,Citation62. The binding of these DAMPs to their cognate pattern recognition-receptors (PRRs), present on antigen-presenting cells such as dendritic cells (DCs), eventually leads to the activation of both the innate and the adaptive immune system via a series of cytokine and chemokine networksCitation5,Citation63–66. Dying cancer cells undergoing ICD can autonomously release cytokines as well as induce cytokine production from neighboring immune or stromal cellsCitation67–72. Additionally, ICD can also cause the secretion of immunostimulatory and chemotactic cytokinesCitation73–75 including, but not limited to, type I interferons (IFNs)Citation76–80 and chemokine (C-X-C) ligand 9 and 10 (CXCL9/10)Citation73,Citation81,Citation82. In the correct context, ICD can also facilitate T cell expansion in a manner that leads to diversification of TCR repertoire,Citation83–91 which can help regress distant (metastatic) tumor lesions via abscopal effect-like immune responsesCitation92–96. This abscopal effect is driven by DAMPs (i.e., adjuvanticity) and tumor-associated antigen (TAA) (i.e., antigenicity),Citation97–99 thereby highlighting the importance of ICDCitation100–102. The ability of ICD to initiate an immune response is highly dependent on antigenicity and adjuvanticityCitation103–105. Without antigens, ICD can only induce an antigen-irrelevant inflammatory response without engagement of the adaptive immune systemCitation106. Conversely, the presentation of antigens to T cells with poor adjuvanticity actively promotes toleranceCitation107–110.

In general, ICD inducers can be broadly divided into two groups depending on how they initiate ER stress-related pathways relevant for DAMP mobilizationCitation111–114. ER stress will lead to unfolded protein response (UPR) activation, leading to an upregulation of pathways including PERK – eukaryotic initiation factor 2 (eIF2a)Citation79. Downstream, this will cause lower amounts of IkB and more activation of nuclear factor-kB (NF-kB)Citation115. The first group includes therapies that induce ICD without directly inducing ER stress. Such type I ICD inducers include radiotherapy as well as chemotherapies like paclitaxel, oxaliplatin, and anthracyclinesCitation61,Citation116–120. The second group, i.e., type II ICD inducers, includes treatment modalities that induce ICD by specifically targeting the ER to induce ER stress-driven cell death, e.g., photodynamic therapy (PDT) or oncolytic virusesCitation121–124. Comprehensively, ICD inducers can be part of different treatment classes including not only microbial and chemical but also physical treatments such as irradiation and high hydrostatic pressure (HHP)Citation57.

Importantly, there is little correlation between the specific chemical features of an anti-cancer therapy and their ability to induce ICD. For example, while cisplatin and oxaliplatin are both platinum-based chemotherapies and induce cell death via DNA adduct-formation, yet only oxaliplatin treatment results in ICDCitation125–128. Over the years, at least two pre-clinical criteria have been established to classify an anti-cancer therapy as a potential ICD inducerCitation129,Citation130. First criterion is that a therapy should be able to induce tumor regression in an immuno-competent, but not immuno-deficient, mouse settingCitation131–133. Secondly, the cancer cells treated with an ICD inducer should serve as an anti-cancer vaccine in a prophylactic settingCitation5,Citation134–139. To rephrase, when tumor-naïve mice are injected with cancer cells undergoing ICD upon exposure to the anti-cancer drug, subsequent challenging with live cancer cells of the same type should not result in the formation of a tumor at the vaccination and challenging site. It is important to keep in mind that these approaches of classifying ICD inducers may have clinical translational issues, since they only allow exploration in a mouse setting. For this reason, in addition to these in vivo mice experiments, in vitro or ex vivo settings can also be utilized to assess the immunogenic potency of dying cancer cells treated with a potential ICD inducerCitation57,Citation140. Here, the presence of aforementioned ICD-associated DAMPs can serve as a surrogate marker to confirm ICD in vitro or ex vivo Citation141–144. Additionally, culturing the dying cancer cells with innate immune cells, such as DCs, to assess ICD-relevant DC functions, is also possible. Phenotypic markers like phagocytic activityCitation145–149, DC activation markersCitation150–154 (CD86 and major histocompatibility complex (CIITA, also known as MHC) Class II molecules) or the secretion of cytokines like interleukin 1 beta (IL-1β)Citation155–157, interleukin 6 (IL-6)Citation158–161, interleukin 12 (IL-12)Citation162,Citation163, and tumor necrosis factor (TNF)Citation164–167, and the ability of DCs to activate T cell proliferation and functional activationCitation168–172 are examples of possible features to assess.

Multiple chemotherapeutics, commonly used in the clinic, have been identified as ICD inducers. The most commonly used chemotherapies with ICD potential are anthracyclines (including doxorubicin, epirubicin, mitoxantrone, and idarubicin), cyclophosphamide, oxaliplatin, paclitaxel, docetaxel, 5-fluorouracil, and targeted therapies like bortezomibCitation126,Citation173–182. In fact, there is concrete evidence supporting the beneficial effects of ICD in cancer patients. For instance, patients with tumors displaying markers of ER stress (like tribbles pseudokinase 3 (TRB3) and DNA damage inducible transcript 3 (DDIT3; also known as CHOP)Citation183–188 or ICD-associated DAMPs, such as calreticulin or HMGB1, have a better prognosisCitation189–197. These findings support the pursuit for an ideal ICD-inducing treatment regimenCitation40. It is important to note that most clinical trials do not choose for specific chemotherapeutics based on their ability to induce ICD. Instead, they are based on their ability to induce tumor response and disease control, without specific knowledge about their potential to induce ICDCitation198,Citation199. In some cases, these design-related decisions can be counterproductive for immune-mediated tumor control, thereby limiting the clinical benefit for cancer patientsCitation200–203. Besides chemotherapeutics, there are multiple other treatment options that can induce ICD. Herein, radiation-based modalities are particularly proficient at inducing ICD and associated abscopal effect-like responsesCitation204–209. Additionally, some upcoming immunotherapies like oncolytic viruses also operate via ICD inductionCitation210–214.

In this Trial Watch edition, we will be focusing on the most recent preclinical and clinical advances around ICD induction by anti-cancer chemotherapy.

Preclinical advances

Since the publication of the previous Trial Watch dealing with chemotherapeutic ICD inducers, several novel preclinical studies on this topic have been publishedCitation215. Here, we highlight the ones that are of particular importance and/or capture the general trends in this field.

Some papers further contributed to our mechanistic understanding of ICD. Mandula et al. (H. Lee Moffitt Cancer center, Tampa, USA.) established the role of protein kinase R-like reticulum kinase (EIF2AK3; also known as PERK), a well-known ER stress sensor, in mediating ICD via a new RCD sub-routine, i.e., paraptosis. They found that PERK inhibition resulted in an increased T cell activation followed by a reduction in tumor growth via type I IFN responses. These findings encourage the use of PERK-targeting therapies for cancer immunotherapyCitation216. Furthermore, Hayashi et al. (Cedars-Sinai Medical Center, Los Angeles, CA, USA) reported that although gemcitabine stimulates the release of immunostimulatory DAMPs, it also triggers prostaglandin E2 (PGE2) release, which counteracts ICD-relevant immune responses. However, when they combined gemcitabine and PGE2 blockade, an effective DC and T cell activation was induced, which led to tumor regressionCitation36. Oresta et al. (Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy) found that mitochondrial metabolic reprogramming is important for ICD occurrenceCitation217. This is accompanied by an increased oxidative phosphorylation. Moreover, tumors with low amounts of complex 1 of the respiratory chain expression had a higher chance of recurrence after chemotherapy. Lucarini et al. (Bambino Gesù Children’s Hospital, Rome, Italy) investigated the combination strategy of mitoxantrone and anti-transforming growth factor beta (TGFB1) with programmed cell death-1 (PD-1, also known as PDCD1) blockade in neuroblastoma mouse models. They found that the low dose of mitoxantrone by itself was already able to increase IFNγ and granzyme B (GZMB) in CD8+ T cells, which were further increased upon combination with anti-TGFβ and anti-PD-1 blockadesCitation218. Several papers also revealed a novel connection between anti-cancer agents and the ICD pathwayCitation125. Humea et al. (Centre de Recherche des Cordeliers, Université de Paris, Paris, France) reported about the induction of immunogenic cell stress and ICD via dactinomycinCitation219, an inhibitor of DNA and RNA transcriptionCitation220. Marin et al. (Barcelona institute of Science and Technology, Barcelona, Spain) found that senescent cells have a greater immunologic potential that could initiate CD8+ T cell responses. These senescent cells are able to release alarmins and PRR agonists and increase MHCI exposure. Even more so, immunization with these cancer cells caused protection superior to the standard ICD inducersCitation221.

Several papers also focused on increasing tumor-directed drug delivery while decreasing toxicity using nanoparticlesCitation222. For example, Zhou et al. (Department of Pharmaceutics, China Pharmaceutical University, Nanjing, China) published that direct delivery of therapeutic proteins, including RNase A, PD-1 antibodies, and photothermal agents, via hydrogels forming membrane pores, increased lactate dehydrogenase (LDHA), HMGB1, and ATP release in multiple murine cancer modelsCitation223. In due course, the intratumoral hydrogel injections resulted in more CD8+ T cell tumor infiltration and a lower tumor growth compared to the saline treated tumors. Another example is the study by Yang et al. (Wuhan University, Wuhan, China). They focused on decreasing the toxicity of small-molecule inhibitors by creating a prodrug nanomicelle that integrates both a phosphoinositide 3-kinase (PIK3CG; also known as PI3K)/mammalian target of rapamycin (mTOR) inhibitor and a cyclin-dependent kinase (CDK) inhibitor, flavopiridol. With this treatment, they were able to decrease tumor growth via the induction of ICD accompanied by HMGB1 and Gasdermin E (GSDME) release as well as ATP secretion in a breast cancer cell lineCitation224. Song et al. (Shanghai Jiaotong University, Shanghai, China) created a porphyrin-cisplatin conjugate (NP@Pt-1) that can be triggered by lightCitation225. NP@Pt-1 treatment resulted in an increased ROS production leading to ATP and HMGB1 in murine colon cancer cells release compared to untreated cells. Additionally, NP@Pt-1 treatment resulted in a decreased tumor growth compared to PBS-treated tumors.

Besides nanoparticles, other innovative strategies have been implemented to optimize ICD inducing treatment regimes. Tatarova et al. (OHSU Center for Spatial Systems Biomedicine, Portland, USA) developed a microdevice that could assist with microtargeting-specific regions of the tumor. This implantable chip is able to contain 18 different treatments that can be released in separate regionsCitation226. Zawilska et al. (Univeristy of Wroclaw, Wroclaw, Poland) developed a liposomal docetaxel therapy that could overcome the problems of toxicity and poor pharmacokinetics. They saw that the cell growth decreased using their treatment compared to docetaxel alone. Additionally, the half life of docetaxel was significantly increased when liposomal-pegylatedCitation227.

A series of studies are also trying to use ICD and its markers as a biomarker modalityCitation175. Use of an ICD-associated genetic signature is one of the approaches proposed to exploit ICD as a predicting marker for patient outcome. In high-grade glioma (HGG), such a signature was able to predict responsiveness to immune checkpoint blocker (ICB) therapy including anti-PD-1 and anti-cytotoxic T-lymphocyte-associated antigen 4 (CTLA4)Citation160. This signature, composed of FOXP3, IL6 LY96, MYD88, and PDIA3, was able to distinguish patients with increased immune modulation and immune escape and high expression of human leukocyte antigen (HLA)-related genes. On top of that, multiple papers have reported that even micro-RNAs linked to ICD-relevant DAMPs are able to predict treatment outcomeCitation228. Several microRNAs relevant for modulating expression of calreticulin, e.g., miR-27a-3pCitation229,Citation230, or HMGB1Citation231–234 have been characterized.

Finally, optimizing the detection of ICD in response to chemotherapy has also been investigated further since the last Trial Watch publicationCitation235. Zhang et al. (Chonnam National University Medical School, Hwasun, Korea) engineered calreticulin-targeting monobodies to detect ICD more accuratelyCitation236. Via this method, they were able to detect surface expression in multiple cancer cell lines and in mice treated with ICD inducers. Similar to this, Kim et al. (Gyeongsang National University, Jinju, Korea) created a synthetic 18F-labeled peptide that specifically binds calreticulinCitation237. Via this method, they were able to detect calreticulin surface exposure in mouse colon cancer tumors via a small-animal positron emission tomography (PET) scan. This staining was visible in tumors treated with multiple ICD inducers including doxorubicin, oxaliplatin, and radiation.

Finalized clinical studies

All finalized clinical studies published after the previous Trial Watch (June 2019) were gathered using PubMed (http://www.ncbi.nlm.nih.gov/pubmed) with the following search string taking into account to most established ICD inducers (cancer OR tumor OR tumor OR neoplasm) AND (oxaliplatin OR cyclophosphamide OR bortezomib OR doxorubicin OR epirubicin OR idarubicin OR mitoxantrone OR paclitaxel) AND (“danger signal” OR “damage associated molecular pattern” OR “immunogenic cell death” OR “immunogenic cancer cell death” OR immunogenic OR immunogenicity), together with the clinical trial filter. Additionally, articles were filtered manually based on relevance as well as on the presence of measurements of immunological parameters. On November 15, 2022, this query with PubMed resulted in 268 published clinical trial studies. From these papers, 67 studies were investigating immune responses using biomarkers. Of note, many studies reported the blood cell counts of patients during treatment. This kind of publication was not considered for this Trial Watch, since such counts are not valid ICD biomarkers. In this section, we will give a general overview of these published clinical studies () and highlight a few of them.

Figure 1. Recently published clinical studies testing immunogenic cell death (ICD)-inducing chemotherapy in oncology that investigate the immunogenic response. Clinical studies were classified on: (a) cancer type, (b) ICD-inducing drug, (c) combinatorial immunotherapy. (d) immunomonitoring approach, CAR, chimeric antigen receptor; CRC, colorectal carcinoma; ICB, immune checkpoint blocker; IHC, immunohistochemistry; PD-L1, programmed death-ligand; TIL, tumor-infiltrating lymphocyte.

Figure 1. Recently published clinical studies testing immunogenic cell death (ICD)-inducing chemotherapy in oncology that investigate the immunogenic response. Clinical studies were classified on: (a) cancer type, (b) ICD-inducing drug, (c) combinatorial immunotherapy. (d) immunomonitoring approach, CAR, chimeric antigen receptor; CRC, colorectal carcinoma; ICB, immune checkpoint blocker; IHC, immunohistochemistry; PD-L1, programmed death-ligand; TIL, tumor-infiltrating lymphocyte.

Among these studies, we found that there were 15 individual cancer types investigated. A large portion (29.9%) of the studies focused on breast cancer ()Citation238–241. Page et al. (Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, Oregon) reported on the efficiency of cyclophosphamide in combination with a cytokine cocktail including TNF, IL-2, IL-1, IFN-γ, IL-6, IL-8, granulocyte-macrophage colony-stimulating factor (CSF2; also known as GM-CSF), and granulocyte colony stimulating factor (CSF3; also known as G-CSF) with the product name IRX-2 in breast cancer. In this phase 1b study, they demonstrated that after treatment there was a higher T-cell activation profile, based on GZMB, GZMA, IFNG, membrane cofactor protein-4 (CD46; also known as MCP-4), S100, CD184, CC-motif chemokine ligand 21 (CCL21) and perforin-1 (ZNF395; also known as PRF-1) compared to the baseline. Additionally, they found a cyclophosphamide-associated peripheral T-regulatory (Treg) cell depletionCitation242. However, upregulation of the immune-checkpoint ligand, programmed cell death ligand 1 (PD-L1, also known as CD274), assessed by immunohistochemistry (IHC) was also seen in these patients after treatment. Additionally, there were 3 “basket trials” (4.5% of all included published trials), consisting of multiple solid tumor typesCitation243–245. Haas et al. (University of Pennsylvania, Philadelphia, PA, USA) analyzed the effects of cyclophosphamide, with and without chimeric antigen receptor (CAR) T cells specific for mesothelin (meso), a protein highly expressed by many cancersCitation246–249. They found that patients pre-treated with cyclophosphamide increased the initial CAR T cell expansion but did not alter the persistence at day 28Citation244. Although both treatment arms were well tolerated, patients showed limited clinical benefit.

Most of the studies included into this Trial Watch, i.e., 41 out of 67, investigated the effect of co-treatment with more than one ICD inducer (). Chemotherapeutic regimes based on paclitaxel, together with other ICD inducers, were very prevalent in the published clinical trials. This was most likely because paclitaxel is regularly applied as part of a multi-modal chemotherapeutic regimen, especially against breast cancer and ovarian cancerCitation241,Citation250–252. In this sense, an immunotherapy-relevant example includes the KEYNOTE-355 trial, where the investigators tested paclitaxel or gemcitabine in combination with carboplatin and pembrolizumab, an anti-PD-1 ICB, in triple-negative breast cancerCitation253. They found that the addition of pembrolizumab resulted in a significant increase in patient overall survival (OS) compared to chemotherapy alone. It is important to note that the combination of ICD inducers together with ICBs is very prevalent (). In general, most studies find that the addition of ICBs increases the overall response rate (ORR) compared to chemotherapy aloneCitation254–256. Tumor-targeting passive immunotherapies, such as trastuzumab (anti-human epidermal growth factor receptor 2 (ERBB2; also known as HER2)), are also repeatedly combined with ICD inducers. Similarly, CAR T cells are often combined with ICD inducers like fludarabine and cyclophosphamideCitation257–260. The latter is not per se to promote an immune activation but rather to eliminate the circulating lymphocyte population.

Lastly, another parameter that we assessed in this Trial Watch was the measurement of immune response parameters. In the above studies, the most frequently used immune biomarkers were tumor infiltrating lymphocytes (TILs) analysis and the PD-L1 detection ()Citation239,Citation261–263. For instance, a phase II study for breast cancer investigating the addition of durvalumab, a monoclonal anti-PD-1 antibody, together with anthracycline-taxane-based neoadjuvant therapy included a broad biomarker analysis. The authors found that paclitaxel was able to increase the TILs in both treatments’ arms (with and without durvalumab). Additionally, they found that both arms showed a higher pathological complete response (pCR) rate in the PD-L1-positive tumors compared to PD-L1low tumorsCitation264. Finally, they concluded that durvalumab together with anthracycline-/taxane-based neoadjuvant chemotherapy (NACT) was the most optimal treatment regime for increased pCR rates. Additionally, more advanced molecular analysis such as RNA sequencing was also often used as biomarker discoveryCitation242,Citation265,Citation266. For instance, Pusztai et al. (Yale Cancer Center, New Haven, CT, USA) found while investigating paclitaxel in combination with durvalumab and the poly-ADP ribose polymerase (PARP) inhibitor olaparib in breast cancer that their dendritic cell signature had a positive correlation with pCR in the treatment armCitation267. Additionally, they found that their mast cell signature correlated negatively with pCR. However, they did not find any correlation between their T cell signatures and pCR in their clinical trial.

Altogether, these results highlight the promising perspectives and clinical trends in ICD research.

Ongoing clinical studies

In parallel, we also assessed the ClinicalTrials.gov database (http://www.clinicaltrials.gov/) for all the ongoing or active clinical trials using oxaliplatin, cyclophosphamide, bortezomib, doxorubicin, epirubicin, idarubicin, mitoxantrone, or paclitaxel in combination with cancer immunotherapy. With a relevant search string, we found not less than 84 clinical studies that matched the following criteria: (1) they involved at least one ICD-inducing chemotherapeutic agent and (2) they were initiated after June 2019 (when the latest Trial Watch on this topic was published).

In this context, multiple cancer types are being studied (). Like the finalized studies described above, the ongoing clinical trials are predominately focused on breast cancer. This is a trend that has been seen over multiple Trial Watch publicationsCitation215,Citation268. In contrast to the published clinical trials, recently enlisted clinical studies also have a considerable percentage focusing on hematopoietic cancer types such as lymphoma, leukemia, and multiple myeloma (). Most likely, this is due to a high increase in CAR T cell studies, a treatment that has been approved for these cancer types in combination with specific chemotherapies that can also induce ICDCitation86. For instance, a single arm clinical study with the aim of observing the tolerance and safety of Fludarabine in combination with CAR natural killer (NK)-CD19 cells in acute lymphoblastic leukemia (NCT05563545). Furthermore, clustering of several different solid tumor types in the same study is also something that is often noticed in these ongoing trials.

Table 1. Contemporary clinical studies assessing the therapeutic and immunological characteristics of chemotherapeutics.

Table 2. Contemporary clinical studies assessing the therapeutic and immunological characteristics of paclitaxel.

Most of the clinical trials selected for this Trial Watch analysis applied paclitaxel, cyclophosphamide, oxaliplatin, and doxorubicin, not only as a monotherapy but also in combination with other anti-cancer treatments. The most prevalent ICD inducer is paclitaxel as shown in . However, only a small percentage of the studies assesses ICD as the main study objective. Most of the time, studies include treatment arms of the ICD inducer with and without combinatorial therapies. In general, ICD inducers are mostly combined with (1) other cell death inducers including other chemotherapeutics, such as carboplatin, or radiation therapy; (2) ICBs targeting molecules such as anti-PD-1 or anti-PD-L1 antibodies; (3) tumor-targeting passive immunotherapies such as agents against epidermal growth factor receptor (EGFR) and HER2; (4) T cells that are adoptively transferred or T cells expressing engineered TAA-specific transgenic TCRs; (5) cytokines that further stimulate the immune responses including not only interferon alpha (IFNA) but also mixtures like IRX-2 (IL-1β, IL-2, IL-6, IL-8, IL-10, IL-12, TNF, and IFNγ). These trends are very similar to the previous Trial WatchCitation215 as well as the published clinical trials that we summarized above.

In these selected clinical trials, there are multiple immunological assessment methodologies that were examined with the aim of acquiring a predictive marker for treatment response as or assessing the immune response during treatment. This includes T cell analysis, either via IHC, e.g., to estimate the quantity and phenotypes of tumor-infiltrating T cells or via flow cytometry, e.g., for detailed profiling of the peripheral blood immune cell subsets. For example, clinical trial NCT05033769 is going to assess KI-67 on peripheral T cells. Additionally, in some ongoing clinical trials, such as NCT04868708, PD-L1 expression of the tumor tissue samples is being determined by IHC. In addition, in this clinical trial they will also investigate the development of anti-drug antibodies over 30 days after the last treatment. This latter practice is being performed in many other clinical trials (e.g., NCT04282070, NCT04093596, and NCT04888312). Another biomarker being utilized is antigen-specific immunogenicity especially in CAR T cell or adoptive T cell clinical trials (e.g., NCT04596033). Evaluating the presence of specific or several cytokines is also prioritized in some clinical trials. For example, NCT04895761 aims to assess the IFNγ in breast cancer patients treated with cyclophosphamide or radiotherapy in combination with a neoadjuvant aromatase inhibitor. In each treatment arm, an ELISPOT will be performed in PBMCs. Lastly, multiple ongoing clinical studies are evaluating immunological markers on a larger scale via omics technologies. Omics approaches like RNA sequencing are creating many variables that can give functional patterns associated with T cell status shifting after chemotherapy. So far, a long list of ways to create some sort of immunological output is being practiced in these clinical trials. Yet, there are still many clinical trials that are not planning on assessing any markers for immune response (e.g., NCT04637763, NCT05504252).

Conclusion

Currently, various chemotherapeutics linked to ICD are approved worldwide for use as anti-cancer treatment in patients with multiple cancer subtypes. Approval of most of these chemotherapies was largely preceded by pre-clinical investigations with tumor xenograftsCitation269–272 in immunodeficient mice and therefore these were often translated to the clinic without any validation for immune-modulationCitation273–275. Therefore, most of these chemotherapies are currently utilized at doses and treatment schedules that are meant to achieve tumor reduction via maximal tolerable doseCitation276–279 rather than a balance between short-term (tumor reduction) and long-term (anti-cancer immunity) effects. In this sense, overcoming some common side effects of chemotherapies that can also counteract ICD, i.e., neutropenia,Citation280–284 lymphopeniaCitation285–289 and intestinal mucositisCitation290–292, should be prioritized via more tumor-targeted delivery of chemotherapies through nanoparticles or other controlled delivery methodsCitation293,Citation294.

The ‘first generation’ of anti-cancer immunotherapy has nearly passed. Despite the stunning success of immuno-oncology drugs through a broad spectrum of distinct malignant diseases, it turned out that a large majority of patients do not respond to currently approved immunotherapies, or if they do, responses are mostly transient. Currently, the field of immuno-oncology aims to tackle these immunotherapy-resistant contexts via multi-modal anti-cancer therapies integrating anti-cancer agents, falling into different treatment modalities (radiotherapy, chemotherapy, targeted therapy, and immunotherapy). However, systematically designed as well as multi-arm comparative clinical studies coupled with proper immune biomarkers aimed at identifying correct dosing and treatment scheduling/ordering are pending. Such studies are crucial to maximize the immune-activation relevant synergism between active immunotherapies and ICD-inducing chemotherapies. Simultaneously, pre-selection of patients via specific biomarkers is also necessary to tailor these multi-modal immunotherapies to specific patient sub-groups rather than giving it in a nonspecific fashion thereby contributing to socio-economic healthcare burden.

Acknowledgments

This study was supported by Research Foundation Flanders (FWO) (Fundamental Research Grant, G0B4620N to ADG; Excellence of Science/EOS grant, 30837538, for ‘DECODE’ consortium, for ADG), KU Leuven (C1 grant, C14/19/098; C3 grant, C3/21/037; and POR award funds, POR/16/040 to ADG), Kom op Tegen Kanker (KOTK/2018/11509/1 to ADG; and KOTK/2019/11955/1 to ADG), and VLIR-UOS (iBOF grant, iBOF/21/048, for ‘MIMICRY’ consortium to ADG). I.V. was supported by FWO-SB PhD Fellowship (1S06821N). J.S. was funded by Kom op tegen Kanker (Stand up to Cancer), the Flemish cancer society via Emmanuel van der Schueren (EvDS) PhD fellowship (projectID: 12699). D.M.B. was supported by KU Leuven’s Postdoctoral FWO fellowship (1279223N). R.S. was supported by FWO-SB PhD Fellowship (1S44123N); E.S. was supported by the KOTK (KOTK/2019/7878); O.K. was supported by Institut National du Cancer (INCa) and the DIM ELICIT; D.V.K. was supported by FWO-Flanders (G016221N) and Excellence of Science/EOS grant, 40007488. Research in the Vandenabeele unit was supported by the FWO (research grants G.0C76.18N, G.0B71.18N, G.0B96.20N, G.0A93.22N, EOS MODEL-IDI Grant (30826052), and EOS CD-INFLADIS (40007512)), grants from the Special Research Fund UGent (Methusalem grant BOF16/MET_V/007, iBOF ATLANTIS grant 20/IBF/039), grants from the Foundation against Cancer (F/2016/865, F/2020/1505), CRIG and GIGG consortia, and VIB.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study is supported by Research Foundation Flanders (FWO) (Fundamental Research Grant, G0B4620N to ADG; Excellence of Science/EOS grant, 30837538, for ‘DECODE’ consortium, for ADG), KU Leuven (C1 grant, C14/19/098; C3 grant, C3/21/037; and POR award funds, POR/16/040 to ADG), Kom op Tegen Kanker (KOTK/2018/11509/1 and KOTK/2019/11955/1 to ADG) and VLIR-UOS (iBOF grant, iBOF/21/048, for ‘MIMICRY’ consortium to ADG and ST). IV and RSL are supported by FWO-SB PhD Fellowship (1S06821N and 1S44123N). JS is funded by Kom op tegen Kanker (Stand up to Cancer), the Flemish cancer society through the Emmanuel van der Schueren (EvDS) PhD fellowship (projectID: 12699). DMB got support from Senior Postdoctoral FWO fellowship (1279223N) and KU Leuven’s Postdoctoral mandate grant (PDMT1/21/032).

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