Agonists for TLR4 are already clinically approved for use as immune adjuvants in vaccine therapy, as exemplified by the use of monophosphoryl lipid A (MPL), a detoxified version of the well-known TLR4 agonist LPS, in the human papillomavirus (HPV) vaccine, CervarixTM [124,125]. Preclinical studies in mice have shown that intratumoural administration of LPS can result in antitumour effects, with more consistent tumour control observed compared to subcutaneous administration at a contralateral site or intraperitoneal administration [36,126,127]. Assessment of tumours after intratumoural treatment with LPS, or with a synthetic lipid A derivative (formulated glucopyranosyl lipid A; G100), revealed an inflamed tumour milieu, with an influx of effector T cells and NK cells and increased expression of activation markers on tumour-resident cDC1s [30,128]. Interestingly, as seen with CpG-ODN, in a model of B cell lymphoma, G100 induced a protective CD8+ T cell response that was dependent on tumour cell expression of TLR4 [129].

It is widely known that exposure to LPS can polarise macrophages to an M1 phenotype [130–132]. Others have shown that TLR4 signalling in the TME can promote the recruitment of macrophages from the systemic circulation into the tumour environment [133–135]. However, these effects have not been consistently seen with intratumoural treatment. A minor increase in macrophage infiltrate within the TME was observed following G100 administration, but not with LPS [30,128]. Interestingly, low dose LPS resulted in increased infiltration of M1-type macrophages and elevated type I IFN, but failed to control tumour growth in a model of 4T1 breast cancer [136]. Instead, this treatment enhanced tumour growth, resulting in larger primary tumour volume and greater metastasis to the lungs. As intratumourally LPS treated mice displayed elevated systemic inflammatory cytokines, including IL-1α, IL-1β, and TNF, it was suggested that systemic inflammation hindered the antitumour response. Indeed, in a separate study, intratumoural LPS was found to enhance the antitumour response of an oncolytic virus targeting OVA in a model of B16.OVA melanoma, but this combination actually resulted in rapid morbidity and mortality in the majority of mice [137]. Together these findings suggest that strategies to improve sequestration in the tumour with limited leakage are needed, or perhaps the evaluation of TLR4 agonists with less potent activity.

Combination treatments may be required to extract benefit from intratumoural treatment with TLR4 agonists. The combination of intratumoural MPL and agonistic anti-CD40, which is known to activate macrophages, enhanced macrophage-induced killing of tumour cells, resulting in a significant increase in anti-tumour activity in mice compared to either treatment alone [36]. The alarmin, HMGN1, has been identified as a TLR4 ligand, and when injected intratumourally with cyclophosphamide and the TLR7/8 ligand, resiquimod (R848), caused the elimination of established CT26 colon carcinomas [138]. This response correlated with an increase in tumour infiltrating T cells and increased activation and homing of tumour DCs to the draining lymph node. Systemic administration of G100 has been reported to enhance the antitumour response when combined with either anti-PD1 or anti-PDL1 in a model of B16.F10 melanoma [37]. However, the impact of intratumoural delivery has not to our knowledge been reported. Despite this, numerous clinical trials are currently investigating the intratumoural effects of G100 as a monotherapy or in combination with checkpoint inhibitors, radiotherapy, and antigen (NCT02035657, NCT02180698, NCT02320305, NCT02501473).

#### 2.1.5. TLR7/8

The intracellular TLR7/TLR8 receptors recognise single-stranded RNA and RNA viruses, and induce signalling via the MyD88 pathway. The activation of TLR7/8 on cDCs leads to their maturation and improves antigen cross-presentation via increased formation of MHC-peptide complexes, up-regulation of costimulatory molecules (CD80/86, CD40), and release of IL-12, while ligation by TLR7/8 agonists on pDCs induces the production of type I IFNs, further amplifying antigen cross-presentation [139].

The two dual TLR7/8 agonists, resiquimod and telratolimod (also known as MEDI9197 and 3M-052), have been observed to delay tumour growth when administered intratumourally in models of squamous cell carcinoma [140], melanoma [39], colon adenocarcinoma [29], breast cancer [38], and glioma [141]. Both promote polarisation of the TME towards a Th1 phenotype, with increases in effector T cells and M1-type macrophages and the production of IFN-α by pDCs alongside DC maturation. Intratumoural PD-1/PD-L1 expression was increased following telratolimod administration and consequently, the combination of telratolimod and systemic anti-PD-L1 led to increases in median survival in a B16.OVA model and induced tumour regression [29]. Furthermore, combination with anti-PD1 increased survival and prevented metastases in the triple negative 4T1 breast cancer model [38]. Combining telratolimod with both anti-CTLA-4 and anti-PD-L1 resulted in superior activity against treated and untreated tumours than single agents in B16.F10 melanomas, with both tumours displaying an increase in antigen-specific CD8+ T cells [39]. Significant tumour growth inhibition was achieved when combining intratumoural telratolimod with systemic agonistic anti-OX40 compared to either agent given alone, which may be due to observed increases in OX40 receptor expression in treated tumours as a result of TLR7/8 agonism [29]. As observed with TLR9 agonists, attempts to improve the retention of TLR7/8 agonists in the tumour by formulating resiquimod into thermosensitive liposomes have been investigated [142].

Telratolimod has been evaluated in a phase I trial in patients with advanced solid tumours either as a monotherapy or in combination with anti-PD-L1 therapy and/or palliative radiation therapy. However, no patient responses were observed, despite both systemic and intratumoural immune activation, suggesting further investigation into the patient populations or combinations that best work with TLR7/8 agonists is needed [143].

#### *2.2. STING, RIG-I, or NLR Agonists*

#### 2.2.1. RIG-I

RIG-I is a cytosolic PRR that plays a prominent role in antiviral defence by detecting viral and endogenous RNA and triggering binding to the mitochondrial antiviral signalling protein MAVS [144]. Its activation induces apoptosis preferentially in tumour cells and simultaneously activates the innate immune system via type I IFN signalling [145,146]. Intratumoural administration of the RIG-I agonist, MK461, resulted in a rapid antitumour response dependent on NK cells followed by long-term tumour control, most likely mediated by T cells [74,147]. In support, the RIG-I agonist stem loop RNA 14 (SLR14) was found to induce a strong anti-tumour response at both treated and untreated tumour sites, with residual antitumour activity observed in RAG−/− mice [49]. Therefore, both T cells and non-T cells appear to be involved in the response triggered by intratumoural RIG-I signalling. Interestingly, fluorescence-labelled SLR14 is mainly taken up by CD11b+ myeloid cells in the TME, however, further investigation into what subtype of myeloid cells and how this enhanced the inflammatory profile in the tumour is required.

An identified advantage of therapy utilising RLR signalling is that tumour cells are highly sensitive to RLR-induced apoptosis, whereas non-malignant cells are protected by endogenous B-cell lymphoma-extra large (Bcl-xL) expression [145,148]. Therefore, RIG-I agonists have been combined with checkpoint inhibitors, with the addition of the agonist overcoming tumour resistance to anti-PD-1 and anti-CTLA4 by enhancing tumour cell death and cross-priming by cDC1s to potentiate systemic cytotoxic T lymphocyte (CTL) antitumour responses [49,149].

#### 2.2.2. STING

STING is a transmembrane receptor that responds to cyclic dinucleotide (CDN) binding, resulting in downstream signalling involving TBK1 activation, IRF-3 phosphorylation, and the production primarily of IFN-β, as well as IFN-α, TNF, and IL-1β [150–153]. Intratumoural administration of a mouse-selective STING agonist, 5,6-dimethylxanthenone-4acetic acid (DMXAA), was shown to generate potent T cell responses in multiple tumour models, including B16.SIY and methylcholanthrene (MCA)-induced sarcomas, resulting in tumour rejection in the majority of mice [154,155]. A significant portion of the anti-tumour response induced by DMXAA was lost in IFNAR-deficient mice and Batf3−/− mice, supporting the role of cDC1s and type I IFN signalling in STING agonist activity [155]. The response was abrogated in Rag−/− mice, indicating a crucial antitumour T cell response [52].

STING activation increases expression of OX40 and CD27 in the TME, while also upregulating inhibitory immune checkpoints such as the PD-1/PD-L1 axis and CTLA-4 [52], suggesting STING agonists should be used in combination with immune checkpoint inhibitors. Indeed, one study found that the agonist, cyclic di-GMP (CDG), potentiated the effects of a triple combination including anti-CTLA-4, anti-PD-1, and agonistic anti-CD137, in a model of prostate cancer [50]. Effective therapy was correlated with increased Teff/Treg ratio and increased M1-type macrophages. A separate study found that STING agonism in a combination with chemotherapeutic carboplatin and anti-PD1 significantly increased survival in an ovarian carcinoma model relative to the carboplatin and anti-PD-1 double combination [51].

The synthetic CDNs, 2 3 -c-di-AM(PS)2 (ADU-S100, also called MIW815 or RR-CDA) and 3 3 -cGAMP (cGAMP) and GSK532, and CDN-unrelated STING agonists, TTI-10001 and CRD5500 (also called LB-061), activate human STING alleles and have exhibited antitumour efficacy when administered intratumourally in several mouse tumour models including CT26 colon [53,156,157], MC-38 colon [158], Lewis lung carcinoma [52], and 4T1 breast carcinomas [159]. Similar to studies with murine agonists, induction of a strong type I IFN signal within the TME was central to the response alongside an increase in the Teff/Treg ratio. The dosing regimen was found to important in one study, with repetitive low doses of ADU-S100 shown to be more effective than a high dose regimen at inducing a tumour-specific CD8+ T cell response and durable anti-tumour immunity [53]. When used in combination therapy, STING agonists were found to help remodel the tumour vasculature, with the triple combination of ADU-S100 with vascular endothelial growth factor receptor 2 (VEGFR2) blockade and either anti-PD1 or anti-CTLA4 resulting in improved antitumour activity and overall survival [52]. These strong antitumour effects have provided the rationale for clinical assessment of intratumoural STING agonists alone (NCT03937141; NCT04220866; NCT03843359) or in combination with anti-PD1 and anti-CTLA4 (NCT03172936; NCT02675439; NCT03010176).

#### *2.3. Available Local Antigen Dose—The Missing Ingredient?*

The key threshold in determining the efficacy of intratumoural treatment with PRR agonists is the initial priming (or boosting) of sufficient numbers of T cells to elicit an effective response. Efficacy may, therefore, be down to the antigen dose released within the immunostimulatory environment that is created by treatment. As already noted, antigen dose can be enhanced with the use of agents that induce ICD, and this is likely to be the reason intratumoural PRR agonists often work in synergy with conventional treatments, such as chemotherapy or radiation therapy. This also likely explains the success in preclinical models in combining intratumoural PRR agonists with antibodies that cause antibody-dependent cellular cytotoxicity, such as anti-CD20 and anti-Mucin-1 [160–162]. In exploring the concept of raising local antigen dose, an early study showed enhanced antitumour activity with intratumoural CpG-ODN in combination with tumour lysate-pulsed DCs [163]. In another study, CpG-ODNs were combined with a recombinant adenovirus encoding tumour necrosis factor-related apoptosis-inducing ligand (TRAIL) to induce tumour cell apoptosis and thereby release antigens [164]. Absorption of LPS onto a GM-CSF-secreting whole tumour cell vaccine (GVAX) enhanced the therapeutic efficacy when injected intratumourally compared to GVAX alone in a CT26 colon carcinoma model [165]. Where tumour antigens are known, it may be possible to inject specific tumour antigens to increase the local dose. A recombinant adenovirus encoding a tumour-associated antigen was used successfully with CpG-ODN to increase survival times [166], and CpG-ODN also supported induction of antigen-specific responses following injection of a fusion protein containing a viral oncoprotein antigen [167]. Studies incorporating CpG-ODN and antigen into an injectable hydrogel were recently followed up with the additional incorporation of immune cells, which enhanced the antitumour effect [168]. In another study of note, tumours were injected with CpG-ODN and antigen that was

not even expressed in the tumour [169]. In this case, the choice of a particularly immunodominant epitope may have generated memory CD8+ T cells that performed immunologic helper functions. The authors speculated that other irrelevant antigens may be used similarly, and had previously shown that injecting the pan MHC class II-binding peptide (pan HLA-DR reactive epitope; PADRE) helped control tumours through the generation of CD4+ T helper cells [170]. As there have been significant advances in using bioinformatics to quickly develop personalised vaccines against patient-specific neoantigens [171], it is possible that in the future these vaccines could be repurposed for intratumoural delivery with PRR agonists.

With adoptive T cell therapies finding clinical application, current protocols may also be increased by timely intratumoural administration of PRR agonists. In mouse models, the efficacy of transfer of melanoma-specific CTL therapy was increased in this manner, with the PRR agonists activating host cells to enhance the IFN-γ production and killing by adoptively transferred T cells [35]. Intratumoural injection may also be used to help increase tumour-specific T cell numbers in vivo for subsequent ex vivo expansion and transfer [172].

We note that anti-tumour responses to PRR agonists alone have tended to be observed in tumour models known to respond to checkpoint inhibitors, such as CT26, MC38, and 4T1 [29,147,173], suggesting that some level of pre-existing immunity is advantageous. However, as noted earlier, combining checkpoint blockade with PRR agonists can often result in more animals responding, suggesting that the PRR agonists can to some extent turn a "cold" tumour, with limited immune infiltration, into a "hot" one with increased infiltration. While this is could be a major benefit of the combined approach, a potential side effect could be increased toxicity, which is not insignificant with checkpoint blockade alone. However, increased toxicity has not been noted in the clinical application of intratumoural CpG-ODN and anti-PD1 [174], suggesting that the localised effect of intratumoural administration may limit deleterious effects. Nonetheless, the dose of PRR agonists used in the clinic should be considered carefully, especially where repeated dosing in the context of combined treatment could potentially amplify risks.

#### **3. Concluding Statement**

Preclinical studies have highlighted the unique added benefit of delivering PRR agonists directly into the tumour bed. However, full regression is rarely seen with this treatment alone, which has also been seen in clinical studies. The future of this form of treatment is therefore likely to be in combination with other cancer therapies, whether this is chemotherapy, radiation therapy, or recently developed immunotherapies based on T cell modulation. The major advantage of such "in situ vaccination" with PRR agonists is that the tumour antigens targeted do not have to be defined. Nonetheless, it may be possible to incorporate this untargeted element of the treatment with targeted therapies, be they antigen-specific or personalised vaccines, or adoptive T cell therapies.

**Author Contributions:** Conceptualisation, writing, and editing: O.K.B., K.K.P., and I.F.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** O.K.B., K.K.P., and I.F.H. were financially supported by the Malaghan Institute of Medical Research and Maurice Wilkins Centre.

**Acknowledgments:** The authors were supported by an Independent Research Organisation grant from the Health Research Council of New Zealand to the Malaghan Institute (grant number HRC14/1003), and the New Zealand Ministry of Business Innovation and Employment (RTVU1603). Figure created with BioRender.com.

**Conflicts of Interest:** I.F.H. is CSO of biotech start-up Avalia Immunotherapies Limited. The other authors declare no conflict of interest.

#### **References**

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## *Review* **The Origin and Immune Recognition of Tumor-Specific Antigens**

#### **Anca Apavaloaei, Marie-Pierre Hardy, Pierre Thibault \* and Claude Perreault \***

Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, QC H3T 1J4, Canada<EMAIL_ADDRESS>(A.A<EMAIL_ADDRESS>(M.-P.H.)

Received: 27 August 2020; Accepted: 11 September 2020; Published: 12 September 2020

**Simple Summary:** Cancer immunology is a rapidly evolving field. In this context, this review article has three objectives. First, to explain the genomic origin of tumor antigens and to emphasize that many of them are encoded by unconventional RNAs. Second, to discuss the inherent limitations of all strategies aimed at discovering tumor antigens, and to highlight the importance of using mass spectrometry validation for each antigen considered for clinical trials. Third, to explain that many tumor antigens are not spontaneously detected by the immune system, because they are not presented adequately by dendritic cells. Concepts presented in this article must be taken into account in the design of cancer immunotherapies and of cancer vaccines in particular.

**Abstract:** The dominant paradigm holds that spontaneous and therapeutically induced anti-tumor responses are mediated mainly by CD8 T cells and directed against tumor-specific antigens (TSAs). The presence of specific TSAs on cancer cells can only be proven by mass spectrometry analyses. Bioinformatic predictions and reverse immunology studies cannot provide this type of conclusive evidence. Most TSAs are coded by unmutated non-canonical transcripts that arise from cancer-specific epigenetic and splicing aberrations. When searching for TSAs, it is therefore important to perform mass spectrometry analyses that interrogate not only the canonical reading frame of annotated exome but all reading frames of the entire translatome. The majority of aberrantly expressed TSAs (aeTSAs) derive from unstable short-lived proteins that are good substrates for direct major histocompatibility complex (MHC) I presentation but poor substrates for cross-presentation. This is an important caveat, because cancer cells are poor antigen-presenting cells, and the immune system, therefore, depends on cross-presentation by dendritic cells (DCs) to detect the presence of TSAs. We, therefore, postulate that, in the untreated host, most aeTSAs are undetected by the immune system. We present evidence suggesting that vaccines inducing direct aeTSA presentation by DCs may represent an attractive strategy for cancer treatment.

**Keywords:** antigen processing and presentation; cancer immunotherapy; cross-priming; immunogenicity; major histocompatibility complex; T lymphocyte; tumor-infiltrating lymphocytes; tumor microenvironment; tumor-specific antigen

#### **1. Introduction**

The introduction of immune checkpoint therapy in the treatment of several cancer types has dramatically changed the landscape of oncology [1,2]. The success of this approach is based on the paradigm that T lymphocytes, and in particular the CD8 subset [3], recognize tumor antigens that can elicit vigorous immune responses and tumor rejection [4]. Attention has focused on major histocompatibility complex (MHC)-associated peptides (MAPs), which are the ligands recognized by classic T cells. However, the precise nature of the MAPs capable of causing tumor rejection remains

**\*** Correspondence<EMAIL_ADDRESS>(P.T<EMAIL_ADDRESS>(C.P.); Tel.: +1-514-343-6910 (P.T.); +1-514-343-6126 (C.P.)

unclear. Seen as promising for decades, tumor-associated antigens (TAAs) have recently fallen into disfavor. TAAs are MAPs that are not cancer-specific but are overexpressed in cancer cells (Table 1). Since they are part of the normal immune self, TAAs are not highly immunogenic and TAA vaccines have yielded disappointing results [5,6]. Strong evidence suggests that anti-tumor immune responses potentiated by immune checkpoint therapy are directed against tumor-specific antigens (TSAs); that is, MAPs found only on cancer cells [4,7,8]. Nonetheless the molecular landscape of actionable TSAs remains largely elusive.

**Table 1.** Features of tumor antigens.

aeTSA, aberrantly expressed tumor-specific antigen; MS, mass spectrometry; mTSA, mutated tumor-specific antigen; TAA, tumor-associated antigen.

#### **2. Misconceptions about TSAs**

#### *2.1. Neoantigens and the Fallacy of the Converse*

Efforts seeking to discover TSAs initially focused on MAPs coded by mutated exons. This makes sense, because the cancer specificity of mutated MAPs is unquestionable, and exons have long been considered the sole protein-coding genomic sequences. These efforts led to the discovery of mutated TSAs (mTSAs), only a few of which were validated by mass spectrometry [4,20]. Furthermore, in several cases, tumor-infiltrating lymphocytes specific for mTSAs were shown to have the ability to mediate tumor regression [21,22]. Unfortunately, excitement over the discovery of exonic mTSAs led to a misconception with major implications. As exonic mTSAs were, in selected cases, sufficient to elicit anti-tumor responses, it was assumed that they were necessary to elicit anti-tumor responses (fallacy of the converse). In other words, it was postulated that exonic mTSAs were the sole actionable TSAs. This reasoning was incorrect, because the term TSA should designate any cancer-specific MAP, whatever its genomic origin (exonic or not) and irrespective of its mutational status (i.e., mutated or not). This concept has important implications. First, annotated exons represent only 2% of the genome and many allegedly non-protein coding (non exonic) sequences are coding for proteins and do generate MAPs [23–26]. Second, epigenetic and splicing aberrations as well as frameshift translation in cancer cells lead to the appearance of numerous proteins and MAPs that are not found in normal cells. Cancer-specific MAPs resulting from translation of any open reading frames not expressed in normal adult cells are referred to as aberrantly expressed TSAs (aeTSAs) (Table 1). aeTSAs can derive from i) canonical (annotated) onco-fetal genes that are normally repressed in the adult organism (e.g., MAGEA3) or ii) from non-canonical transcripts that arise from cancer-specific epigenetic changes, frameshift translation, or splicing aberrations. Notably, translation of a whole cancer-specific transcript can yield more numerous TSAs than a single base pair substitution [27]. When compared to mTSAs, aeTSAs display two advantageous features. First, they are more numerous [18]. Indeed, in a recent study of 23 ovarian cancers, 103 TSAs were identified of which only three were exonic mTSAs [19]. Second, whereas mTSAs are generally unique to individual patients, aeTSAs are shared by many tumors. In ovarian cancer, 78% of transcripts coding for individual aeTSAs were found in at least 10% of tumors and 18% in at least 80% of tumors [19].

When exonic mTSAs were discovered, they were frequently labeled as neoantigens. In fact, the terms TSA and neoantigens should be synonymous. Accordingly, we would have no objection to talk of neoantigens and to classify them into mutated and aberrantly expressed neoantigens. However, many scientists still believe that neoantigens means exonic mTSAs. Therefore, as recommended by Haen et al. [5], we will refrain from using the term neoantigen in order to avoid any ambiguity.

#### *2.2. Can mTSAs Be Identified without Mass Spectrometry Analyses?*

Mass spectrometry remains the only method that allows direct and definitive identification of the amino acid sequence of MAPs and TSAs [5,28–30]. However, mass spectrometry analyses require (i) large tumor samples and (ii) specialized equipment and expertise, which are not widely available. Hence, research teams have tried to identify TSAs using genomic data (exome and/or transcriptome sequencing) and algorithms to predict MHC-binding affinity. Unfortunately, two types of evidence suggest that most "predicted TSAs" are false discoveries: one type is based on mass spectrometry validation of predicted TSAs, the second on in-depth genomic analyses. Almost all studies have focused exclusively on exonic mTSAs.

#### 2.2.1. Mass Spectrometry Validation of Predicted mTSAs

In 16 primary human hepatocellular carcinomas, Löffler et al. predicted the occurrence of 1888 exonic mTSAs (a mean of 118 per tumor), none of which was validated by mass spectrometry [13]. In colorectal carcinomas, Newey et al. predicted the occurrence of 304 mTSAs, of which only three were validated by mass spectrometry. In smaller scale studies, not a single mTSA was validated by mass spectrometry analyses of four acute lymphoblastic leukemias [18] and three pancreatic adenocarcinomas [16].

#### 2.2.2. In-Depth Genomic Analyses

According to the tenets of immunoediting [31], exonic mTSAs should be under negative selection pressure (enforced by TSA-responsive T cells). This negative selection should decrease the ratio of non-synonymous over synonymous mutations in mTSA-coding sequences. However, no such decrease was found in analyses of genomic data from 8,683 tumor samples [32]. Likewise, comprehensive analyses of over 1,000 melanoma exomes revealed no evidence of HLA-restricted negative selection against exonic mutations [33]. Furthermore, response to immune checkpoint therapy in patients with lung cancer did not correlate more with predicted mTSAs than with the global mutation load [34]. These data mean that the number of exonic mTSAs has been grossly overestimated in many studies and that the mTSA repertoire of a tumor cannot be predicted with current algorithms. The reason for this is that while these algorithms can accurately predict with reasonable accuracy the MHC-binding affinity of a peptide, they fail to take into account the numerous translational and post-translational events that regulate MAP biogenesis and presentation [35–37].

#### 2.2.3. Can Reverse Immunology Eliminate False Positive TSA Predictions?

Testing the immunogenicity of validated TSAs using various in vitro methods (MHC-MAP multimers binding, cytokine production, etc.) provides useful information. It shows which TSAs are more likely to stimulate anti-tumor responses in vivo. However, it is commonly assumed that if a predicted mTSA (not validated by mass spectrometry) can elicit T-cell responses from peripheral blood mononuclear cells, it is more likely to be a genuine TSA. We disagree with this assumption. The fact that a predicted TSA is immunogenic simply means that it can be recognized by some T cells; this does not increase the likelihood that this predicted TSA is a genuine TSA (present on cancer cells). Two "peptide stories" illustrate this point: those of E*L*AGIGILTV and RIAECILGM. The E*L*AGIGILTV peptide is an in vitro modified version of the wild-type E*A*AGIGILTV MART-1/Melan-A26-35 decamer. Hence, for the immune system, E*L*AGIGILTV is akin to an mTSA. While this peptide is not found on cancer cells, it is so immunogenic that it is commonly used as a positive control in ex vivo immunogenicity assays [38,39]. The TEL-AML1 fusion protein results from a 12:21 chromosomal translocation and is frequently found in B-cell precursor acute lymphoblastic leukemia. A peptide resulting from this fusion protein, RIAECILGM, was predicted to be presented by HLA-A\* 02:01, and priming of T cells against this

peptide generated cytotoxic T cells that killed autologous leukemic cells [40]. Further in-depth studies showed that this epitope is not presented by leukemic cells; it is not endogenously processed, because it is cleaved by proteasomes [41]. Killing of leukemic cells by T cells primed against RIAECILGM was most likely due to the inherent cross-reactivity of T cells, which is further amplified in T-cell lines [42]. Indeed, positive selection in the thymus preferentially rescues cross-reactive T cells [43], and a single T-cell receptor may recognize more than a million different MAPs [44].

#### **3. Strategies for Mass Spectrometry-Based Identification of aeTSAs**

aeTSAs present several attractive features. They are more common than mTSAs, and they are shared by many tumors of a given type (Table 1). Furthermore, in pre-clinical models, they were shown to elicit curative anti-tumor responses [18,45]. Since aeTSAs can be coded by any reading frame of the entire genome, their search space is greater than that of mTSAs [8,46,47]. Therefore, it is currently impossible to rely on available bioinformatic tools to predict the aeTSA landscape of a tumor, and mass spectrometry analyses are mandatory for aeTSA identification. The key question here is: once a putative aeTSA is identified, how do we demonstrate its cancer-specificity? In other words, how can we prove that an unmutated MAP is not expressed by any normal cell type? Three approaches have been developed, each with pros and cons.

The first approach postulates that a MAP is a TSA if it is found in cancer cells but not in an atlas of MAPs identified in normal tissue extracts [9,10]. The problem here is that this atlas does not contain the MAP repertoire of all cell types. Thus, since most epithelial cells express lower levels of MHC molecules than hematopoietic cells [48,49], whole tissue extracts are enriched in hematopoietic relative to epithelial MAPs. Furthermore, several cell types in the organism are not present in numbers sufficient for mass spectrometry analyses. Hence, some TSAs identified with this approach may be false positives.

In the second approach, normal adjacent tissue (not tumor-infiltrated) is used as a negative control [50]. Once again, the absence of a MAP in the normal adjacent tissue does not guarantee that it is not present in other cell types in the organism. We speculate that this approach can also lead to dismissal of genuine TSAs. Our assumption is based on the notion that normal tumor-adjacent tissue may in fact not be normal but rather pre-neoplastic and, therefore, share TSAs with the tumor [51]. Indeed, as we age, physiologically healthy tissues such as skin [52,53], colon [54,55], esophagus [56,57], and blood [58–65] acquire mutations in cancer-associated genes. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. A notable example is ovarian adenocarcinoma, which appears to have a median latency of more than 10 years [66].

The third approach is based on the assumption that a TSA cannot be present in cells that do not express TSA-coding transcripts. In contrast to mass spectrometry analyses, transcriptomic analyses have been performed in many subjects on practically all cell types. Hence, when we identify aeTSA candidates, we evaluate whether its coding transcript is found in normal tissues from the GTEx database (https://gtexportal.org/home/) or in our datasets of medullary thymic epithelial cells [26]. We believe that inclusion of medullary thymic epithelial cells in the "negative controls" is important for three reasons: (i) they orchestrate central immune tolerance [67], (ii) they express much higher levels of MHC I molecules than other epithelial cell types [49], and (iii) they promiscuously express more genes than other types of somatic cells [68,69]. Promiscuous gene expression in medullary thymic epithelial cells involves not only classic genes, but also other genomic regions such as endogenous retroelements [26]. The downside of this approach is that it can lead to the dismissal of genuine aeTSAs. Indeed, expression of a transcript in some normal cell does not necessarily lead to expression processing and presentation of the corresponding TSA.

It must nonetheless be acknowledged that mass spectrometry studies come with intrinsic challenges and limitations [5]. First and foremost, in discovery mode, "shotgun mass spectrometry" has limited sensitivity and, therefore, requires large amounts of starting material for in-depth coverage of the immunopeptidome (e.g., 1 g of tumor tissue). Second, relative to transcriptome sequencing,

mass spectrometry has a relatively low throughput and is not quantitative. Finally, mass spectrometry fails to differentiate between isobaric amino acids (Leucine vs. Isoleucine) and is relatively costly in terms of reagents and resources. Several technical innovations are being developed in order to overcome these limitations [30,70].

#### **4. Immune Recognition of TSAs**

#### *4.1. Cancer Cells Are Poor T-Cell Activators*

The general rules of T-cell priming also apply to cancer cells. Indeed, T-cell recognition of tumors requires both signal 1 (TCR ligands such as TSAs) and signal 2 (co-stimulation) [71]. The most critical positive co-stimulatory signal is provided by CD28 upon interaction with its ligands of the B7 family (CD80/86) on antigen-presenting cells (APCs) [72]. Tumor cells are poor APCs: they express no/low levels of CD28 ligands, and carcinomas (90% of cancers) derive from epithelial cells expressing 10 to 100-fold less MHC I molecules than DCs [49]. As a result, tumor cells are inefficient at directly priming naïve CD8 T cells, and activation of T cells against tumor antigens depends on cross-presentation by professional APCs [73]. Accordingly, anti-tumor responses, either spontaneous or induced with immune checkpoint therapy, correlate with intratumoral infiltration and maturation of cross-presenting CD8α+CD103+ dendritic cells (DCs) [20,74,75]. These specialized DCs internalize and cross-present tumor antigens to T cells and induce a CD28-dependent proliferation of tumor-specific T cells, which regulates the strength of the immune response [76–79].

#### *4.2. Cross-Presentation Yields a Biased Representation of the TSA Repertoire*

The rules governing direct presentation and cross-presentation are different. Direct presentation favors short-lived and rapidly degraded proteins, many of which represent unstable defective ribosomal products that may derive from specialized ribosomes (immunoribosomes) [80–82]. In contrast, cross-presentation of exogenous antigens preferentially samples long-lived, stable proteins [83,84]. Thus, direct presentation correlates with the rate of protein translation and proteasomal degradation, whereas cross-presentation correlates with steady-state protein amounts [85]. APCs acquire proteins from donor cells (e.g., cancer cells) through endocytic mechanisms of which the most efficient is phagocytosis [86]. Internalized proteins can then be degraded by proteasomes, either in endocytic organelles or in the cytosol [86,87]. A key implication is that cross-presentation can only display a fraction of TSAs; that is, TSAs derived from highly abundant and stable proteins. Hence, the immune system remains ignorant of TSAs found in unstable and rapidly degraded proteins.

#### *4.3. The Strength of E*ff*ector T-Cell Responses*

The amplitude of anti-tumor T-cell responses depends on two factors: (i) epitope density on APCs and cancer cells, and (ii) the frequency of antigen-responsive T cells in the pre-immune repertoire. Epitope density (number of MAPs per cell) on APCs during initial priming regulates not only the magnitude but also the avidity and functionality of the effector T-cell population [88,89]. For most—though not all—antigens, cross-presentation yields a lower epitope density than direct presentation [90]. In addition, epitope density on tumor cells dictates their susceptibility to CD8 T-cell cytotoxicity. At this point, intratumoral heterogeneity has to be taken into consideration, because it is a hallmark of all cancers. For immuno-oncologists, this means that individual tumor cells may display different levels of TSA expression. Both in mice and humans, the proportion of TSA-positive tumor cells positively regulates the outcome of interactions between CD8 T cells and the tumor [91–93]. The presence of some TSA-negative tumor cells does not necessarily lead to immunotherapy failure. Indeed, TSA-negative tumor cells can be eradicated by T-cell targeting of the tumor stroma and, in particular, endothelial cells [94]. Intratumoral T cells can damage the tumor vasculature via two mechanisms: (i) killing of endothelial cells that cross-present TSAs and (ii) via the potent angiostatic effect of IFN-γ and TNF-α [89,95,96]. Nonetheless, these data suggest that chances of success of

immunotherapy should be improved by selecting clonal TSAs (present on most/all cancer cells) and targeting multiple TSAs.

#### *4.4. Vaccination-Induced T-Cell Priming*

DC-based vaccines can elicit strong anti-tumor responses against TSAs that are ignored when presented solely by cancer cells (Figure 1). This is illustrated well by the aeTSA VNYLHRNV. While this peptide is expressed at high levels on EL4 cells (908 copies per cell), immunization with irradiated EL4 cells does not prolong the survival of mice upon subsequent injection of unirradiated EL4 cells. However, 100% of mice immunized with DCs coated with VNYLHRNV survive when injected with EL4 cells [18]. Moreover, immunization generates TSA-responsive memory CD8 T cells, since mice survive a novel injection of EL4 cells 100 days later. Hence, VNYLHRNV is highly immunogenic when presented by DCs, but in the absence of therapeutic vaccination, this EL4 TSA is not cross-presented by DCs in vivo.

Properly designed nanoparticulate liposomal RNA vaccines are efficiently taken up by DCs in secondary lymphoid organs in vivo [97]. In these conditions, since the DC-targeted RNAs drive synthesis of antigenic peptides inside DCs, their processing follows the rule of direct presentation as opposed to cross-presentation. A phase I trial evaluating a nanoparticulate liposomal RNA vaccine in immune checkpoint therapy-experienced melanoma patients (stage III B, C and stage IV) recently provided suggestive evidence that, when presented by DCs, aeTSAs can elicit anti-tumor responses [98]. This vaccine contained four antigens that were originally labeled as TAAs. However, while one of these antigens is clearly a TAA (*TYR*), the other three are probably aeTSAs coded by conventional annotated genes: *MAGEA3*, *CTAG1B* (*NY-ESO-1*), and *TPTE*. Indeed, while *TYR* is expressed in the normal skin, the three putative aeTSAs are not expressed in normal extra-thymic tissues (except for the testis). However, *MAGEA3*, *CTAG1B,* and *TPTE* RNAs are expressed at very low levels in medullary thymic epithelial cells (<0.5 transcripts per kilobase million [26]). The probability that transcripts expressed at such levels might generate MAPs is very low [35,99] but cannot be formally excluded in the absence of immunopeptidomic studies on medullary thymic epithelial cells. We, therefore, consider that these three antigens are probably, but not certainly, aeTSAs. Notably, objective anti-tumor response correlated more closely with the expansion of T cells recognizing MAGEA3 and CTAG1B [98]. This study, therefore, (i) supports the immunogenicity of aeTSAs in humans and (ii) suggests that direct aeTSA presentation by DCs activates and expands a pool of complementary aeTSA-specific T cells that were insensitive to immune checkpoint therapy and likely tumor-naïve.

#### *4.5. Combining Vaccines and Immune Checkpoint Therapy*

In general, a high density of tumor-infiltrating lymphocytes positively correlates response to immune checkpoint therapy [100]. This suggests that immune checkpoint therapy works at least in part by invigorating T cells responding to cross-presented TSAs. Likewise, preliminary evidence suggests that immune checkpoint therapy may potentiate T-cell response to aeTSAs directly presented by DCs [98]. The idea of combining vaccines and immune checkpoint therapy, therefore, appears very attractive.

**Figure 1.** Priming of anti-tumor CD8+ T cells by dendritic cells (DCs). (**A**) Most cancer cells are poor antigen-presenting cells (APCs) that are not efficient at direct antigen presentation. DCs are potent APCs, but under basal conditions, they can cross-present only a fraction of the tumor-specific antigens (TSA) repertoire generated by cancer cells. TSAs derived from unstable rapidly degraded proteins (the most common of TSAs) are not cross-presented by DCs and are, therefore, ignored by the immune system. (**B**) Therapeutic mRNA vaccines can deliver any TSA-coding transcripts to DCs for direct presentation to CD8 T cells. In this way, TSAs derived from both short-lived proteins and stable proteins can be detected by CD8 T cells.

#### **5. Conclusions**

Therapeutic vaccines can induce durable regressions of premalignant oncogenic human papilloma virus type 16-induced anogenital lesions [101]. To the best of our knowledge, this vaccine containing viral peptides remains the sole therapeutic TSA-based vaccine that has shown reliable efficacy. The first non-viral TSA vaccines tested in clinical trials were based on predicted mTSAs not validated by mass spectrometry [102–105]. Evidence that most predicted mTSAs not validated by mass spectrometry may be false discoveries does not bode well for the success of these studies. aeTSAs present attractive features and give encouraging results in pre-clinical models. However, evidence supporting their value or superiority over TAAs in humans remains anecdotal and has yet to be formally assessed. For cancer immunologists wishing to develop therapeutic vaccines, the time is not for celebrations but rather to develop innovative research strategies in a climate tinged with both optimism and critical thinking. We also propose that TSAs should be validated by mass spectrometry analyses of primary human

tumors before they are tested in clinical trials. Finally, we strongly encourage the sharing of mass spectrometry datasets via the SysteMHC Atlas whose primary objective is to provide a systems-level definition of MAP and TSA repertoires presented by normal and neoplastic cells [30,106].

**Author Contributions:** Conceptualization, writing, and editing: A.A., M.-P.H., P.T., C.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by The Canadian Cancer Society, grant numbers 705604 and 705714, and The Oncopole (EMC2 grant).

**Acknowledgments:** The authors also thank the staff of the following core facilities at the Institute for Research in Immunology and Cancer: Proteomics, Genomics and Bioinformatics.

**Conflicts of Interest:** M.P.H., P.T. and C.P. are named inventors on patents related to tumor-specific antigens filed by Université de Montréal. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Commentary* **Immune Checkpoint Blockade in HER2-Positive Breast Cancer: What Role in Early Disease Setting?**

**Cinzia Solinas 1,\*, Debora Fumagalli 2 and Maria Vittoria Dieci 3**

**Simple Summary:** This work aims to discuss how an anti- or pro-tumor immune response could be manipulated through immune checkpoint blockade in patients with early stage HER2-positive breast cancer. By summarizing previously published evidence in the field, authors present their personal view on how immune checkpoint blockade could be implemented in the neoadjuvant setting in this patient population. The hypothesis being presented is that an appropriate and effective administration of immune checkpoint blockade could assure a lasting control of the disease, by preventing relapses. One of the research priorities should be the identification of the patients who could benefit more by this strategy.

**Abstract:** The present commentary synthesizes the current evidence on the role of the immune response in HER2-positive breast cancer. It points out the strengths and weaknesses of the findings observed so far, particularly in the early setting, including the clinical significance of scoring tumorinfiltrating lymphocytes. A figure proposing research hypotheses for the implementation of immune checkpoint blockade use for patient candidates to neoadjuvant treatment is presented.

**Keywords:** HER2-positive; breast cancer; immune checkpoint blockade; immune checkpoint molecules; PD-1/PD-L1; immunotherapy

#### **1. Introduction**

Immune checkpoint blockade represents a successful immunotherapy strategy aimed at boosting a pre-existing adaptive anti-tumor immune response with a potential lasting control of the disease [1]. The latter could be achieved because adaptive immunity is characterized by memory and by the possibility to simultaneously target the tumor-associated antigens that can be generated as cancer develops. This innovative treatment approach is revolutionary for the durable responses it can induce. Further, stimulating the immune system could potentially target multiple neo-antigens over time. All these peculiar aspects have generated a great interest in identifying the patient subsets that benefit more from immune checkpoint blockade, particularly in early settings, where the chances of cure are higher for cancer patients.

After the introduction of effective anti-human epidermal growth factor receptor-2 (HER2) agents, early-stage HER2-positive breast cancer patients have experienced improved survival [2]. Some of these patients achieve excellent outcomes and are ideal candidates for de-escalation treatment strategies aimed at sparing toxicities from chemotherapy, as explored in studies like CompassHER2-pCR (NCT04266249) and Decrescendo (NCT04675827) [3]. However, up to 20% of early stage HER2-positive breast cancer patients still relapse and develop an advanced disease [2]. For these patients, innovative add-on strategies should be developed. Upfront use of immune checkpoint blockade

**Citation:** Solinas, C.; Fumagalli, D.; Dieci, M.V. Immune Checkpoint Blockade in HER2-Positive Breast Cancer: What Role in Early Disease Setting?*Cancers* **2021**, *13*, 1655. https://doi.org/10.3390/ cancers13071655

Academic Editors: Michael Kershaw and Clare Slaney

Received: 8 March 2021 Accepted: 22 March 2021 Published: 1 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

could be useful in this context, but it is not known which patients could benefit more from this approach.

The aim of this commentary is to briefly summarize evidence on the relationship between a spontaneous immune response (i.e., the one present in inflamed tumors that are characterized by a detectable immune infiltration) and outcome and/or responses to standard treatments in HER2-positive breast cancer. In addition, some research hypotheses for the experimental use of immune checkpoint blockade in the neoadjuvant setting are proposed, which take into account the implementation of immune-related biomarkers and add-on or de-escalation treatment strategies. Further, research priorities are identified.

#### **2. HER2-Positive Breast Cancer Patients and Tumor-Infiltrating Lymphocytes: Clinical Significance**

The HER2-positive subtype represents up to 20% of breast cancer diagnoses [4]. Its main oncogenic driver is represented by the amplification and over expression of HER2, whose activity is inhibited by a variety of anti-HER2 agents that are nowadays being used in the clinic, with relevant clinical benefit in all the settings of the disease [2]. This subtype is characterized by heterogeneity, mostly driven by the presence or absence of the hormone receptors' expression, which identifies distinct subgroups with different prognoses and responses to treatments [5].

Gene-expression data reveal that up to 50–60% of HER2-positive breast cancers have a HER2-enriched PAM50 molecular profile, particularly hormone receptor-negative (75%) compared to hormone receptor-positive (30%) cancers [6–9]. This molecular subtype is characterized by high activity in HER2 signaling, rendering these tumors HER2 hypersensitive, thus particularly responsive to anti-HER2 targeted agents [10]. Other predictors of benefit from these drugs are: high expression of the *ERBB2* gene [10] and of immune gene signatures [11], the latter being able to predict a higher likelihood of pathological complete response after neoadjuvant treatments (an intermediate endpoint for survival [12]), and a longer event-free survival [13]. In addition, a high extent of tumor-infiltrating lymphocytes (TIL) evaluated on hematoxylin and eosin (HE)-stained slides has been associated with better outcomes (pathological complete response, event-free survival and disease-free survival) in several trials, confirming the relevant role played by the host immunity during the course of this disease [14,15].

TIL represent a well recognized biomarker in the cancer field, particularly in this new era of cancer immunotherapy. TIL are easily, reproducibly and cheaply scored by pathologists on HE slides used in clinical routine [16]. In the HER2-positive breast cancer subtype, higher TIL levels were observed in the hormone receptor-negative with respect to the hormone receptor-positive subgroup [17,18].

Immunohistochemical (IHC) analysis allows to identify the various immune cell subsets that are globally scored as TIL on HE slides (based on morphological features). These include: Helper T cells (Th, CD4+), cytotoxic T cells (CTL, CD8+), B cells (CD20+) from the adaptive immunity; macrophages, neutrophils, myeloid derived suppressor cells, natural killer (NK) from the innate immunity [19]. Among the cells of the adaptive immunity (characterized by immunological memory), Th lymphocytes contribute to the development of a response by activating tumor antigen-specific effector lymphocytes (CTL) and through the recruitment of various cells of the innate immunity.  CTL kill tumor cells, and B cells are able to secrete antibodies and cytokines, assuring an effective anti-tumor immune response in breast cancer [20].

Of interest, the prognostic relevance of TIL scored on HE slides in neoadjuvant and adjuvant trials of HER2-positive breast cancer has been shown to be independent both of other clinicopathological characteristics [21,22] and of the administered treatment [15]. The various anti-HER2 compounds exert in fact different immune effects [5] through complex interactions between the various cells of the tumor microenvironment (particularly those

from innate immunity) and the treatments (including chemotherapy) co-administered. Specifically, (1) the anti-HER2 monoclonal antibody trastuzumab increases the antibodydependent cell-mediated cytotoxicity (ADCC) since its Fc portion (the tail region of an antibody that does not recognize the antigen) interacts with the Fcγ receptors expressed on the effector cells of the innate immunity (NK cells, neutrophils and γδT-cells) and with some proteins of the complement system [5]. Similar effects are seen with (2) the anti-HER2 monoclonal antibody pertuzumab that induces ADCC and augments the density of Fcγ receptor binding sites on HER2-positive cells, potentiating NK cell activity [5], and with (3) the antibody drug conjugate trastuzumab emtasine (T-DM1), which increases ADCC [5]. The (4) tyrosine kinase inhibitor lapatinib raises the density of HER2 receptors on the surface of tumor cells, potentiating the ADCC associated with trastuzumab [5]. Anthracyclines, taxanes, cyclophosphamide and other cytotoxic agents employed in breast cancer can also mediate immune effects [23].

Thus, it is expected that stratification of patients on the basis of the level of immune infiltration of their tumors (reflected by the extent of baseline TIL on HE slides) should allow to identify patients that are more likely to benefit from standard treatments and/or experience better outcomes. Further, it could be helpful in identifying those patients at higher risk of relapse (those having lower TIL) who might have the opportunity to be treated with immune checkpoint blockade as an add-on strategy.

#### **3. Potential Role of Immune Checkpoint Blockade in HER2-Positive Breast Cancer**

A growing interest in manipulating the patient's own immune response against cancer has followed the introduction of immune checkpoint blockade strategies. One of the crucial steps for further exploration of this treatment strategy is represented by the identification of the target patient population.

Evidence from studies in triple negative breast cancer, the breast cancer subtype showing the highest levels of TIL infiltration, reveals that programmed cell death-1 or its ligand (PD-(L)1) immune checkpoint blockade is more active in early settings, independent of PD-L1 expression on tumor cells, lymphocytes and macrophages [24]. In contrast, in the advanced disease the presence of PD-L1 expression by immune cells identifies the subgroup that benefits more from the association of an anti-PD-L1 plus chemotherapy [25].

In HER2-positive breast cancer limited efficacy of immune checkpoint blockade combined with T-DM1 or trastuzumab has been observed in the advanced setting in patients previously treated with trastuzumab and expressing PD-L1 on immune cells and/or having higher levels of TIL [26,27]. However, these results derive from hypothesis-generating trials and deserve further confirmation, also considering that early and late diseases appear to have a different biological profile. For example, the levels of TIL on metastatic HER2 positive samples may be lower than matched primary tumors [28] and the infiltration by CTL may be lower in metastatic samples from patients pre-treated with chemotherapy and anti-HER2 therapy [29].

So far, not much is known about the role of immune checkpoint blockade in early settings in this subtype, also considering the variety and efficacy of the anti-HER2 drugs now available (i.e., trastuzumab +/− pertuzumab as part of the (neo)adjuvant treatment and T-DM1 as part of the post surgical treatment in patients with a residual disease after neoadjuvant treatment).

Biologically, a mouse model revealed that the efficacy of immune checkpoint blockade in early settings was higher in the presence of tumor antigens (i.e., as in the case of a neoadjuvant approach), rather than in their absence [30]. Ideally, administration of immune checkpoint blockade would be more efficacious as part of the upfront neoadjuvant treatment space, which in HER2-positive breast cancer currently looks crowded with several treatment strategies being explored [31].

However, some studies have shown that tumors with high TIL (the most infiltrated that traditionally benefit most from immune checkpoint blockade) are also characterized by excellent responses to neoadjuvant treatments with anti-HER2 agents plus taxane +/− anthracycline-based chemotherapy regimens and by excellent prognosis (event-free survival) [15,17].

Whether patients with highly infiltrated tumors really need a boost to their immunity with immune checkpoint blockade, considering that standard treatments are very efficacious, is still a crucial point that needs to be addressed.

#### **4. Potential Use of Immune Checkpoint Blockade in the Neoadjuvant Setting for HER2-Positive Breast Cancer Patients**

The neoadjuvant setting represents an ideal scenario to test drug sensitivity, characterize the biology of the disease, identify reliable predictive biomarkers and to guide treatment decisions based on the presence of a residual disease [32]. Current guidelines suggest that neoadjuvant treatment in HER2-positive breast cancer patients should be administered for clinical ≥ T2 or ≥N1 (Figure 1) [33]. Figure 1 summarizes various research hypotheses that could be tested upfront (panel A) or in the presence of a residual disease after neoadjuvant therapy (panel B) in HER2-positive breast cancer patients stratified based on the extent of baseline TIL assessed on HE slides from pre-treatment biopsies. Of note, frequencies of pathological complete response achievement based on baseline TIL levels refer to HER2-positive breast cancer patients treated with trastuzumab plus anthracyclines and taxanes in the pre-pertuzumab era [15].

**Figure 1.** Figure 1 represents proposed research hypotheses for the use of immune checkpoint blockade (ICB) in early-stage HER2-positive breast cancer patients who are candidates for neoadjuvant therapy (≥cT2 or ≥cN1 and M0) with the employment of tumor-infiltrating lymphocytes (TIL) assessed on diagnostic biopsies as an additional biomarker to guide treatment decisions. Panel (**A**) shows upfront treatment strategies whereas panel (**B**) proposes the treatment strategies to be administered in the presence of a residual disease after neoadjuvant therapy (panel (**B**)). References: [15,34]. Legend: CT, chemotherapy; HE, hematoxylin and eosin; HR, hormone receptor; HT, hormone therapy; ICB, immune checkpoint blockade; IHC, immunohistochemistry; M, metastasis; N, nodal status; pCR, pathologic complete response; T, tumor size; T-DM1, trastuzumab emtansine; TIL, tumor-infiltrating lymphocytes; TILhi, ≥60% tumor-infiltrating lymphocytes; TILint, ≥10% <60% tumor-infiltrating lymphocytes; TILlo, <10% tumor-infiltrating lymphocytes.

Baseline high TIL infiltration (with a TIL cut-off of ≥60% from previously published randomized controlled trials [15]) is found in around 20% of patients with early stage HER2-positive breast cancers and has been associated with a higher likelihood of pathological complete response (Figure 1, left side). Previous studies showed in fact that up to 60% of these patients achieve a pathological complete response (Figure 1) [15]. The remaining 40% present a residual disease, and become candidates to receive standard T-DM1 [25] +/− hormone therapy (according to the baseline hormone receptors status). In this patient population, upfront anti-PD-(L)1-based immune checkpoint blockade could be administered in association with dual anti-HER2 blockade (trastuzumab plus pertuzumab) as a chemotherapy de-escalation strategy (Figure 1, panel A, left side).

The remaining 80% of HER2-positive breast cancer patients treated in the neoadjuvant setting (Figure 1, right side) represent a highly heterogeneous group including tumors with either a very low (TILlo usually below 10%) or with an intermediate TIL (TILint ranging from 10% up to 60%) infiltration. Around half of these patients achieve a pathological complete response after standard anti-HER2 therapy, whereas the other half have a residual disease [15] and will be candidates for T-DM1 +/− hormonotherapy. Upfront anti-PD-(L)1 based immune checkpoint blockade could be associated with chemotherapy and standard anti-HER2 treatments (pertuzumab plus trastuzumab), particularly in the group of patients with baseline intermediate TIL, as an add-on strategy (Figure 1, panel A, right side). The group of patients with low TIL should theoretically be ideal candidates for strategies aimed at increasing immune infiltration, such as vaccines [35] or adoptive T cell transfer [36] though these treatments need further investigations in breast cancer.

Patients from both groups (Figure 1, panel A, left and right side) who have achieved a pathological complete response could be candidates to receive immune checkpoint blockade plus standard anti-HER2 treatment if immunotherapy was previously administered (Figure 1, panel A).

#### **5. Potential Use of Immune Checkpoint Blockade in the Presence of a Residual Disease after Neoadjuvant Therapy**

Even though HER2-positive breast cancer patients with baseline high TIL achieve a better event-free survival [17], it is not clear whether this also applies to patients who have a residual disease after neoadjuvant therapy.

For patients with a residual disease after standard neoadjuvant treatment (chemotherapy plus anti-HER2 agents), nodal involvement and estrogen receptor negativity have been associated with shorter event-free survival [37]. The presence of high TIL (≥25%) in the residual disease was associated with worse event-free survival in a retrospective study, questioning whether in this context the use of immune checkpoint blockade might have a role in rescuing the activity of potentially dysfunctional immune cells [34]. Interestingly, the expression of Immunoglobulin (Ig)-G in the residual disease was associated with longer event-free survival [13], signifying that the presence of a well-organized adaptive immunity, including B (that produce Ig) and T lymphocytes, assures a lasting control of the disease. Hence, this highlights the need to identify the subsets of immune cells that constitute the immune infiltrate (i.e., Th, CTL, B lymphocytes, macrophages, etc.), in addition to scoring them only on HE slides.

Further, the presence of a residual disease might represent an opportunity to screen patients for anti PD-(L)1-based combinations of immune checkpoint blockade through the evaluation of the expression of PD-L1, and of other inhibitory immune checkpoint molecules, such as: Lymphocyte activation gene 3 (LAG3), T cell immunoglobulin and mucin domain 3 (TIM3) which are heterogeneously expressed in early-stage HER2-positive breast cancer [38,39] and which tightly regulate the immune response through inhibitory, non-redundant pathways.

Patients at high risk of relapse for clinicopathological factors, but with baseline high TIL and a residual disease after neoadjuvant therapy (Figure 1, panel B, left side) could be eligible to receive a rescue treatment with immune checkpoint blockade, after assessment of various immune biomarkers on the surgical specimen.

Concerning those patients with baseline intermediate TIL and a residual disease after neoadjuvant therapy (Figure 1, panel B, right side) they could all be evaluated for TIL and other immune biomarkers on the surgical specimen in order to characterize their eligibility for an immune checkpoint blockade, as an add-on treatment strategy besides standard treatments.

Whether this strategy should be given sequentially or in concomitance with standard treatments in patients with a residual disease might need to be addressed by dedicated studies.

#### a. Additional multiple immune biomarkers

Beside TIL, PD-L1, LAG3 and TIM3, evaluation of other markers in the residual disease, such as CD73 and IDO, might be important, particularly in patients previously treated with PD-(L)1 blockade (i.e., administered as an upfront strategy) [40,41] (Figure 1, panel B).

So far, PD-L1 expression by IHC is performed as a biomarker for patient selection in trials of anti-PD-1/PD-L1-based immune checkpoint blockade. This also applies to LAG3 expression, which was shown to be associated with an improved benefit from PD-1 plus anti-LAG3 combinations [42]. Evaluation of the expression of multiple immune checkpoint molecules (starting from those for which drug compounds have been developed) might be achieved with the use of multiplex-IHC, a technique that allows the identification of several markers in one tissue-stain, or with the use of in situ hybridization techniques allowing the localization of the expression of genes, at both mRNA and protein levels within a histological section of a single tissue or tissue microarrays [43]. Cut-off values for positivity (usually set at >1% of positive cells, as for PD-L1 assessment) and standardization of the techniques need to be confirmed and validated.

Indeed, apart from PD-(L)1, it is not known which of these molecules is the most promising in breast cancer, considering that they can be heterogeneously expressed in tumors with intermediate or high TIL [38]. Thus, an entire panel of immune checkpoint molecules and other markers should be analyzed, also considering that they regulate non-redundant inhibitory pathways. Remarkably, by flow cytometry PD-1 and CTLA-4 are usually always expressed in inflamed breast tumors, whereas LAG3 and TIM3 are rarely highly expressed at the protein level in untreated early-stage breast tumors [38]. Ideally, the identification of different markers might represent a guide indicating which inhibitory pathway should be worthwhile as a target for immune checkpoint blockade in each patient.

#### **6. Research Priorities in the Immunotherapy Field**

To support the implementation of immune blockade strategies, some aspects need to be further investigated:

(8) Identification of reliable novel immune (and not) biomarkers for patient selection. Future studies will hopefully help address these research priorities.

#### **7. Conclusions**

The immune revolution challenges researchers in various ways, and the selection of patients that most likely derive lasting benefits from novel treatments represents one of the most important aspects. Diverse biomarkers have been proposed, and, among them, TIL represent the most standardized and cheaply assessed immune-related biomarker so far employed in breast cancer. However, future approaches will benefit from a more in depth characterization of the tumor microenvironment, including evaluation of the composition of TIL, of their functional profiles, of the expression of immune checkpoint molecules and from answers to open research questions as summarized above.

With this commentary, we provide an overview of the current knowledge in the field and propose an opportunity for exploitation of immune checkpoint blockade to further improve the outcome of patients with HER2-positive breast cancer.

**Author Contributions:** Conceptualization, C.S.; methodology, C.S., D.F., M.V.D.; validation, C.S., D.F., M.V.D.; writing—original draft preparation, C.S.; writing—review and editing, C.S., D.F., M.V.D.; supervision, M.V.D.; funding acquisition, M.V.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was not supported by any funding source. Maria Vittoria Dieci receives fundings from Celgene, Genomic Health, Eli Lilly and Novartis outside the submitted work.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest. The institution of Debora Fumagalli receives funding from AstraZeneca, Novartis, Roche/Genentech, Pfizer, Tesaro for the conduct of clinical trials.

#### **References**

## *Perspective* **The Influence of Chimeric Antigen Receptor Structural Domains on Clinical Outcomes and Associated Toxicities**

**Ashleigh S. Davey 1,2,\*, Matthew E. Call 1,2 and Melissa J. Call 1,2,\***

**Simple Summary:** The development and refinement chimeric antigen receptor (CAR)-T cell immunotherapy has significantly improved the prognosis of patients with B cell malignancies. Severe treatment related toxicities however remain a significant challenge for the field. This perspective reviews 17 clinical trials of the most widely used anti-CD19 (FMC63) CAR-T cell therapies, with the aim of dissecting the contribution of the structural and costimulatory domains of the CAR to clinical outcomes and toxicities. The CD28 structural hinge and transmembrane CAR domains are highlighted as strongly associated with both clinical efficacy and severe toxicity. This perspective supports further investigation into the structural CAR domains for improved CAR design and safer CAR-T cell therapies.

**Abstract:** Chimeric antigen receptor (CAR)-T cell therapy has transformed the treatment of B cell malignancies, improving patient survival and long-term remission. Nonetheless, over 50% of patients experience severe treatment-related toxicities including cytokine release syndrome (CRS) and neurotoxicity. Differences in severity of toxic side-effects among anti-CD19 CARs suggest that the choice of costimulatory domain makes a significant contribution to toxicity, but comparisons are complicated by additional differences in the hinge and transmembrane (TM) domains of the most commonly used CARs in the clinic, segments that have long been considered to perform purely structural roles. In this perspective, we examine clinical and preclinical data for anti-CD19 CARs with identical antigen-binding (FMC63) and signalling (CD3ζ) domains to unravel the contributions of different hinge-TM and costimulatory domains. Analysis of clinical trials highlights an association of the CD28 hinge-TM with higher incidence of CRS and neurotoxicity than the corresponding sequences from CD8, regardless of whether the CD28 or the 4-1BB costimulatory domain is used. The few preclinical studies that have systematically varied these domains similarly support a strong and independent role for the CD28 hinge-TM sequence in high cytokine production. These observations highlight the value that a comprehensive and systematic interrogation of each of these structural domains could provide toward developing fundamental principles for rational design of safer CAR-T cell therapies.

**Keywords:** CAR-T cell therapy; CD19; clinical trials; CRS; toxicity

#### **1. Introduction**

Recent decades have seen the development of groundbreaking new treatments for cancer. The emergence of adoptive cellular therapies, including chimeric antigen receptor (CAR)-T cells, represents an advance that has dramatically improved upon classical cytotoxic chemotherapies and led to significant improvements in patient outcomes for advanced and refractory B cell malignancies. CAR-T cell therapy utilises engineered receptors (CARs) to redirect a patient's cytotoxic T cells to a targeted cancer antigen of choice. This therapy is a multi-step process involving leukapheresis of a patient's blood, the genetic modification of their T cells to express a tumour-antigen-specific CAR, followed by ex vivo

**Citation:** Davey, A.S.; Call, M.E.; Call, M.J. The Influence of Chimeric Antigen Receptor Structural Domains on Clinical Outcomes and Associated Toxicities. *Cancers* **2021**, *13*, 38. https://dx.doi.org/10.3390/ cancers13010038

Received: 30 November 2020 Accepted: 22 December 2020 Published: 25 December 2020

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expansion and re-infusion of the CAR-T cells back to the patient where they selectively seek out and kill target tumour cells. Following high remission rates in clinical trials, two CD19 CAR-T cell products were approved by the FDA in late 2017 for the treatment of refractory non-Hodgkin lymphoma (NHL) and for patients with relapsed or refractory B cell acute lymphoblastic leukemia (B-ALL) up to the age of 25 [1]. There are now over 200 CAR-T cell clinical trials underway for a range of cancer antigens (clinicaltrials.gov). However, safety concerns and general applicability to many tumour types are issues that still need to be resolved. A great deal of research is now underway to modulate specific properties of CAR-T cells with the aim of improving their safety and efficacy for a wider range of cancers. In this perspective, we examine the influence of CAR domain structure on the clinical outcomes and toxicities observed in the most extensively studied CAR-T cell therapies, the anti-CD19 CAR-T cells using the antibody single-chain variable fragment (scFv) FMC63. While differences in efficacy and safety profiles between the two FDA-approved anti-CD19 therapies may reasonably be ascribed primarily to their use of different costimulatory signalling domains in the CAR constructs, we highlight clinical and preclinical observations suggesting that the hinge and transmembrane (TM) domains, long considered to be functionally inert structural features, make surprisingly strong contributions to CAR-T cell toxicity. Understanding the mechanisms underlying these effects will be a key step in improving rational CAR design.

#### *1.1. Components of the Chimeric Antigen Receptor (CAR)*

Naïve T cells require dual signalling interactions for activation, first between the multi-chain T cell receptor (TCR) complex and peptides presented by an interacting cell's MHC proteins (signal 1), and additionally from costimulatory receptor:ligand interactions (signal 2). The first generation of CARs generated by Esshar and colleagues utilised the antigen specificity of an scFv fused to the entire CD3ζ protein to mimic signal 1 in T cell activation [2]. The resulting CAR was expressed well at the cell surface and was successful in redirecting T cells to target tumour antigens. However, CAR-T cell persistence was poor, and cells showed signs of exhaustion prior to tumour clearance [3–6]. This prompted the development of second-generation CARs which also contain an scFv antigen recognition domain and intracellular CD3ζ signalling tail, but additionally incorporated a flexible hinge domain for improved antigen reach and a costimulatory domain to provide signal 2 required for strong and durable T cell activation [7]. The intracellular costimulatory and signalling domains are connected to the extracellular scFv and hinge via a TM domain, which is often an extension of the costimulatory or hinge sequence. Most CAR constructs exist as dimers on the cell surface, stabilized by disulfide bonds between hinge domains and inter-monomer TM interactions [8]. Inclusion of the additional domains provided second-generation CAR-T cells with the requisite signals for activation and significantly improved persistence and clinical outcomes in treated patients [9–11]. Further iterations of CARs continue to be developed, including those with two costimulatory domains (thirdgeneration CARs) as well as CARs with inducible expression of a transgene product such as cytokines (fourth-generation CARs/TRuCs) [12–14].

#### *1.2. Limitations of CAR-T Cell Therapy*

Since the development and initial success of second-generation CAR-T cell therapies in the clinic, many groups have now shifted focus to manipulating specific domains of the CAR with the aim of improving target cell recognition and therapeutic efficacy, and limiting CAR-associated toxicities. A significant leap in the field came with the introduction of a costimulatory domain into the CAR which significantly improved the persistence and efficacy of clinical CAR-T cell products [7]. The choice of which specific costimulatory domain is more clinically effective though is not yet unanimous. Extensive work is underway to further improve upon these current second-generation CAR-T cell constructs by altering specific CAR domains including the hinge, TM and costimulatory domains, particularly for the treatment of more difficult immunosuppressive solid tumours [8,15–22].

Clinical trials of CAR-T cell therapies all report similar treatment-related toxicities, including cytokine release syndrome (CRS) and neurotoxicity. CRS is triggered by the rapid systemic release of inflammatory cytokines including IFNγ, IL-6 and IL-1 into the blood by activated CAR-T cells and endogenous myeloid cells [23–25]. The symptoms of CRS in response to CAR-T cell therapy range from mild flu-like symptoms to severe symptoms such as hypotension, organ toxicity and acute respiratory distress, which can collectively be life threatening and often result in patients spending extended periods of time in the intensive care unit (ICU) [26]. The second major toxicity reported following CD19-targeted CAR-T cell therapy is neurotoxicity. Similar to CRS, neurotoxicity symptoms also range in severity and type, including headaches, delirium, dysphasia, ataxia, dysmetria, decrease in level of consciousness, seizures and acute cerebral edema [27–29]. The incidence of neurotoxicity among CAR-T cell-treated patients is quite variable, with some neurological events occurring in combination with CRS symptoms and others at different times or in the absence of CRS, suggesting that at least in some cases the mechanism of CAR-T cell induced neurotoxicity is different to that of CRS [30,31]. Comprehensive and timely management plans are also in place for the treatment of neurological toxicities, often involving the administration of systemic corticosteroids including dexamethasone, which has the unfortunate consequence of interfering with CAR-T cell function [32]. While both CRS and neurotoxicity symptoms can be treated in the clinic, many patients require expensive ICU administration and support, thus prevention of such severe toxicities with CAR-T cell treatment is ideal for further progression of this promising therapeutic.

It has generally been presumed that the costimulatory domains are mostly responsible for the severe toxicities observed with CAR-T cell therapies due to the increased potency and persistence observed in second-generation CARs compared to first-generation CARs [3–6]. Considering many of the costimulatory domains used clinically extend from the intracellular domain though the TM to the extracellular hinge and scFv, it is difficult to discern the contribution of the TM and hinge structural domains individually to clinical efficacy and toxicity development. We therefore examined clinical trials of the most extensively published anti-CD19 FMC63 scFv CARs, all of which contain a CD3ζ signalling tail, to understand whether CAR-related toxicities are defined by the costimulatory domain alone or whether structural features such as the hinge and TM domains of the CAR also play a role.

#### **2. Clinical Trials of Anti-CD19 CAR-T Cell Therapy**

CD19 is a cell-surface glycoprotein uniformly expressed during all stages of B cell differentiation and is present in more than 95% of B cell malignancies [33]. Under normal physiological conditions, CD19 acts as a co-receptor in complex with the B cell receptor and other surface markers to modulate downstream signalling pathways that induce B cell proliferation and activation [34]. Due to its homogenous and high expression in many B cell malignancies and the fact that bystander B cell depletion can be managed clinically, CD19 has become an attractive target for the initial testing and translation of CAR-T cell immunotherapy, evident by over half of all CAR-T cell clinical trials targeting the CD19 antigen (clinicaltrials.gov). To dissect the clinical implications of individual hinge, TM and costimulatory domains, we collected and summarised data from anti-CD19 CAR-T cell clinical trials in which the CAR construct contained the same FMC63 scFv [35] and CD3ζ signalling tail (Table 1). Clinical trials were included if they used a secondgeneration CAR containing these domains and provided details of clinical response rate, CRS and neurotoxicity.

*Cancers* **2021**, *13*, 38

**1.**SummaryofFMC63(CD19)CARTcellsinclinicaltrialswithpublishedclinical

474

PMBCL, primary mediastinal large B cell lymphoma.

The majority of FMC63 CAR-T cell clinical trials have been conducted on patients with B-ALL and NHLs including diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL) and marginal zone lymphoma (MZL), due to their generally high and stable expression of CD19 (Table 1). These CD19 CARs generally adopt one of two multi-domain structures: 1. FMC63 scFv, CD8α hinge and TM, 41BB costimulatory domain and CD3ζ signalling tail (8-8-41BB CAR; Figure 1a), or 2. FMC63 scFv, CD28 hinge, TM and costimulatory domain, and CD3ζ signalling tail (28-28-28 CAR; Figure 1b). There is also one published clinical trial utilising a CAR with a CD28 hinge and TM but a 41BB costimulatory domain (28-28-41BB; Figure 1c) [36], and another with an IgG4 hinge, CD28 TM and 41BB costimulatory domain (IgG4-28-41BB; Figure 1d) [37,38]. In the clinical trials reviewed here, all CD28 costimulatory domain-containing CAR-T cells were transduced using gamma retrovirus, while all 41BB-containing CAR-T cells were transduced using lentivirus (Table 1). The dosage of CAR-T cells within and between clinical trials ranges significantly from 3 × 104 cells to 3.7 × 108 cells per kg (Table 1). By directly comparing the clinical trials containing each of these four iterations of the FMC63 CAR, trends towards particular structural domains that influence specific outcomes such as response rate and toxicity may be exposed.

**Figure 1.** Structural domains of our main FMC63 anti-CD19 CARs. The structural design of the (**a**) 8-8-41BB CARs, (**b**) 28-28-28 CARs, (**c**) 28-28-41BB CARs and (**d**) IgG4-28-41BB CARs reviewed here. The individual scFv, hinge, transmembrane domain, costimulatory domain and signalling tails are labelled. Horizontal lines between hinge domains indicate disulfide bonds.

#### *2.1. The Effect of CAR Domains on Clinical Response*

Multiple factors have been reported to impact CD19 CAR-T cell therapy clinical outcome in patients, including whether a patient has received conditioning lymphodepletion therapy prior to CAR-T cell infusion, whether CAR-T cells were cultured with IL-2 prior to infusion and the extent of CAR-T cell persistence in patients [52,53]. For the studies shown in Table 1, we saw no obvious effect of age, conditioning lymphodepletion treatment or CAR-T cell dosage on patient complete response rate (Table 2). Not surprisingly, although the tumour target antigen was identical in all FMC63 CAR-T cell treatments,

patient complete response rate does appear to vary depending on tumour type, with B-ALL patients gaining the highest response rates, followed by FL, DLBCL and lastly MCL (Table 2). Considering the limited number of FMC63 anti-CD19 CAR-T cell clinical trials with published results, all clinical trials in Table 1, including those with MCL patients, were included for response and toxicity CAR comparisons. The complete response (CR) rate among patients treated with the various CD19 CAR-T cell therapies was calculated as the number of patients who achieved a CR at any stage post CAR-T cell infusion as a percentage of the total number of patients treated with the CAR-T cell therapy in a single clinical trial (Figure 2). Details of the individual trial patient numbers and other reported patient responses for each clinical trial are detailed in Table S1.

**Table 2.** Effect of prognostic factors on complete response rate.

**Figure 2.** Effect of CAR composition on complete response in patients treated with CD19 CAR-T cell therapy. CAR type separated based on hinge (H), transmembrane (TM) and costimulatory (Costim) domain composition. All CAR constructs use the FMC63 anti-CD19 scFv and the CD3ζ-chain activation domain. Each circle represents an individual clinical trial listed in Table 1. Complete response (CR) rate calculated from the number of patients who reached a CR at any stage post CAR-T cell infusion (n) as a percentage of all patients treated with the CAR-T cell therapy (N) (Table S1). Middle line represents the mean CR and error bars represent the SEM.

Overall, 28-28-28 CARs and 8-8-41BB CARs have very similar CR rates (Figure 2). Interestingly, in a single study where B-ALL patients were treated with a 41BB CAR that used the CD28 hinge-TM domain instead of CD8α (28-28-41BB CAR), a higher CR rate was observed in comparison to the average for studies using 28-28-28 and 8-8-41BB CARs (Figure 2). Although this is only a single clinical trial of 10 patients, it highlights the 41BB costimulatory domain may be associated with improved clinical response [36]. Furthermore, comparison of this CAR with one in which only the hinge was altered, using IgG4 instead of CD28 (IgG4-28-41BB CAR), a much lower CR rate was observed [37,38]. While the IgG4-28-41BB CAR clinical trial includes a number of participants with the poorest responding MCL tumour type, it highlights the possibility that inclusion of the CD28 hinge-TM structural domains in clinical CAR constructs may also be correlated with higher complete response rate in patients. Further preclinical comparisons and clinical studies are required to investigate the full impact of the individual hinge-TM and costimulatory domain substitutions in isolation and their correlation with response rates.

#### *2.2. CRS Toxicity Correlates with CAR Domain Design*

There is currently no clear consensus on a CRS grading system for CAR-T cell therapies among institutions worldwide, making CRS severity analysis between CAR-T cell products and trials exceedingly difficult. This is largely due to the fact that the timing and definition of CRS symptoms observed following cellular therapies such as CAR-T cells differs significantly to previously described CRS responses to antibody and drug therapies [54–56]. CRS following CAR-T cell treatment was initially graded using the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) grading scale for adverse event reporting [57,58]. However, this scale was developed before cell therapy-induced CRS was well understood. CTCAE was more consistent with an acute cytokine storm typically observed within minutes to hours of antibody or drug treatment but did not take into consideration the more likely delayed onset of CRS symptoms up to a few days post CAR-T cell infusion [57–59]. This grading system was modified by Lee and colleagues in 2014 to more accurately define mild, moderate, severe, and life-threatening CRS symptoms regardless of the inciting agent [60]. This guideline also included treatment recommendations for each of the CRS grades described and has been consequently used by many clinical trials conducted after this date. An alternative CRS grading system was developed and used for CTL-019 CAR T cell trials from the University of Pennsylvania (Penn Grading Scale), which had a similar grading structure to the 2014 Lee et al. scale but did not use absolute cut-off values for vital signs such as % oxygen requirement to define CRS grade, as such values can vary in severity depending on the individual patient [41]. The Penn Grading System was also designed in a way that allowed for CRS symptoms to be reproducibly graded among different institutions, CAR-T cell target antigens and disease settings. While we do not provide a detailed comparison of the various CRS grading methods here, it is clear that a consensus on an accurate CRS grading program and prompt management plan for CAR-T cell induced CRS symptoms that is used worldwide in all clinical trials would be more beneficial for inter-trial analysis [61].

Despite the above limitations, we felt that the CRS grading systems used in many of the FMC63 CAR-T cell clinical trials were similar enough to yield informative comparisons among the different CAR structural designs (Table 3). To assess the effect of domain structure on CRS development, patient CRS grade was collected for each clinical trial presented in Table 1 and taken as a percentage of total patients within each clinical trial (Table S2). The percentage CRS development for each grade and clinical trial was subsequently plotted by CAR domain configuration to examine correlations between CRS diagnosis and CAR domain structure. Overall, the incidence of CRS in patients treated with CAR-T cell therapy is high, with 60–80% of patients developing some level of identifiable symptoms. While the mean percentage of patients in the highest category of CRS (grade 3+) does not differ between CAR-T cell treatment products, a clear correlation between CRS development and CAR domain configuration is observed for grade 1–2 CRS (Figure 3). The 28-28-28

CAR-T cell treatment resulted in a higher proportion of patients developing CRS compared to 8-8-41BB CAR-T cell treatment (Figure 3). It is difficult to discern the contribution of the CD28 costimulatory domain to this increased CRS toxicity, as 28-28-28 and 28-28-41BB CAR-T cell-treated patients show similar levels of grade 1–2 CRS development. However, the 28-28-41BB CAR-T cell therapy does emerge with a higher incidence of more severe grade 3+ CRS in comparison (Figure 3). Interestingly, the use of CD28 hinge and TM domains in a 41BB CAR (28-28-41BB) was associated with an increased proportion of patients developing CRS, rising to a level similar to that observed in the 28-28-28 CAR (Figure 3) [36]. While this is only a single clinical trial, it suggests that CRS severity is at least in part attributable to the CD28 hinge and TM domains independently of which costimulatory domain is used. The same degree of increase in CRS was not observed in the trial using an IgG4-CD28 hinge-TM domain configuration (IgG4-28-41BB), further underscoring that the identity and/or combination of hinge-TM structural domains can exert significant influence on manifestation of CRS toxicity even when the scFv, costimulatory and activation domains are the same (Figure 3) [37,38]. Additional research and clinical trials that make direct comparisons among CAR configurations will be required to identify the relative contributions of each individual CAR domain in developing CRS.

**Figure 3.** CAR domain structure influences CRS severity in patients treated with CD19 CAR-T cell therapies. Both CAR type, based on hinge (H), transmembrane (TM) and costimulatory (Costim) domain composition, and CRS grade, separated into none, grade 1–2 and grade 3+, are represented on the left and right *y* axis, respectively. Each circle represents the percentage of patients in a single clinical trial listed in Table 1 that developed the corresponding grade of CRS (none, grade 1–2 and grade 3+). Middle line represents the mean percentage of patients with the corresponding grade of CRS and error bars represent the SEM (Table S2).

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Regulatory Authorities.

#### *2.3. Relationship between CAR Domain Structure and Neurotoxicity Development*

Neurotoxicity has been reported to occur both concurrently and independently of CRS toxicity in anti-CD19 CAR-T cell clinical trials. In general, neurotoxicity events that occur concurrently with CRS are of a shorter duration and severity (grade 1–2), while delayed neurotoxicity occurring post-CRS can arise up to 3-4 weeks after CAR-T cell therapy and is more commonly associated with grade 3+ neurotoxicity [37,38,51,62]. Neurotoxicity severity has been reported to fluctuate rapidly once diagnosed, demonstrating the requirement for close patient monitoring and precise management plans to prevent rare fatal events. Similar to CRS toxicity, there are multiple grading scales that can be used to assess the level of neurotoxicity in patients receiving CAR-T cell therapy. The most common grading protocol used in the clinical trials reviewed here is the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE), with CTCAE v3.0 used prior to 2009 [58] and CTCAE v4.0 after 2009 [57] (Table 3). The CTCAE neurotoxicity grading systems have limitations due to the unique characteristics and timing of neurotoxicity symptoms following CAR-T cell treatment compared to antibody and drug treatments. Consequently, a unique condition titled CAR-T cell-related encephalopathy syndrome (CRES) was introduced by the multi-institutional CAR-T cell therapy associated toxicity (CARTOX) working group. The CRES grading system encompasses a 10-point patient questionnaire to capture cognitive and attentive dysfunction combined with clinical tests to assess intracranial pressure and severity of seizures [32]. More recently, the American Society for Transplantation and Cellular Therapy (ASTCT; previously ASBMT) coined the term immune effector cell-associated neurotoxicity syndrome (ICANS) for CAR-T cell related neurotoxicity, using a modified version of the CARTOX screening grading system to also take into account patient consciousness, motor symptoms and cerebral edema symptoms [63]. A retrospective study using these three methods (CTCAE v4.03, CRES and ASTCT) to grade neurotoxicity events post CAR-T cell treatment in the JULIET trial uncovered the overdiagnosis of neurotoxicity using the CTCAE v4.03 grading system (45% of patients) in comparison to both CRES and ASTCT grading systems (both 17.1% of patients). Many of the patients only diagnosed with neurotoxicity using the CTCAE v4.03 and not CRES or ASTCT grading systems had mild symptoms such as headaches which were thought to be non-specific to the CAR-T cell treatment [64]. Clearly there is a disparity in the published grading systems for neurotoxicities seen after CAR-T cell therapy. A refined grading system proposed by 49 CAR-T cell experts and supported by the ASTCT was published in 2018 in the hope that future CAR-T cell clinical trials can use a consensus grading system for more accurate toxicity reporting and inter-trial analysis [63]. Use of such a universal grading system would enable better comparison of clinical trial products and outcomes.

Neurotoxicity symptoms were reported and graded in 16 of the 17 clinical trials listed in Table 1 (Table 3). To assess the effect of domain structure on neurotoxicity development, the percentage of patients that developed each grade of neurotoxicity was calculated for each clinical trial presented in Table 1 (Table S3). The percentage neurotoxicity development for each grade and clinical trial was subsequently plotted by CAR domain configuration to examine correlations between neurotoxicity diagnosis and CAR domain structure. Overall a clear increase in severe (grade 3+) neurotoxicity was observed in patients treated with the 28-28-28 CAR compared to the 8-8-41BB CAR (Figure 4). While the 8-8-41BB CAR showed on average the lowest level of severe grade 3+ neurotoxicity, use of the CD28 hinge-TM or IgG4 hinge and CD28 TM in a 41BB CAR (28-28-41BB and IgG4-28-41BB, respectively) was associated with increased incidence of grade 3+ neurotoxicity that was more in line with the 28-28-28 trials (Figure 4) [36,38]. As for CRS, these observations suggest that use of part or all of the CD28 structural domains is associated with higher toxicity. While the most relevant direct comparisons required to confirm these associations have not been made in any single large clinical trial, as outlined below, there is significant preclinical data that

offer strong support for the independent contributions of hinge and TM domains to CAR function and toxicity.

**Figure 4.** Grade of neurotoxicity development in patients treated with CD19 CAR T cell therapies is related to CAR domain structure composition. CAR domain structure is separated based on hinge (H), transmembrane (TM) and costimulatory (Costim) domain composition on the left *y* axis, while neurotoxicity grade is separated into grade 0–2 or grade 3+ on the right *y* axis. Each circle represents the percentage of patients in a single clinical trial listed in Table 1 that developed the corresponding grade of neurotoxicity (grade 0–2 and grade 3+). Middle line represents the mean percentage of patients with the corresponding grade of neurotoxicity and error bars represent the SEM (Table S3).

#### **3. Preclinical Evidence of CAR Domain Design Influencing CAR-Related Toxicities**

CRS symptoms are caused by a significant elevation of blood cytokine levels as a result of excessive cytokine secretion by both CAR-T cells and other immune cells. Consequently, CAR T cells which exhibit reduced cytokine secretion to levels that induce less CRS toxicity but still promote effective T cell proliferation and cytotoxicity are required. Optimisation of CAR domain configuration towards this end is a crucial goal, but unravelling the contributions of different domains is complicated by comparisons in which hinge, TM and costimulatory domains are all varied at the same time. A small number of carefully controlled preclinical studies have recently begun to address this problem systematically.

In one recent study focused on modifications to the hinge-TM domains alone, Ying and colleagues [15] generated CTL-019 variant CARs (FMC63 scFv with CD8α hinge and TM domains, 4-1BB costimulatory domain and CD3ζ tail) with small extensions to the CD8α hinge and TM domains. One of the longer hinge-TM constructs (named CD19-41BBz(86) in their study) showed a significant reduction in IFNγ, TNFα, IL-2 and IL-4 production in vitro when co-cultured with human CD19 target cells. Treatment of B cell lymphoma patients in a small phase I clinical trial with the CD19-41BBz(86) CAR-T cell therapy yielded good response rates, low circulating cytokines and little to no development of CRS or neurotoxicity symptoms post-treatment [15]. This study demonstrates that even small changes to the hinge-TM domains of a CAR can significantly impact cytokine production

and CRS development. The mechanism by which this extension of only a few amino acids on each end of the hinge-TM yields such significant effects is unknown.

Another study by Alabanza and colleagues [65] directly compared FMC63 anti-CD19 CARs containing the same CD28 costimulatory domains and CD3ζ tail with either CD28 or CD8 hinge-TM regions (named FMC63-28Z and FMC63-CD828Z in their study, respectively). Both CARs showed similar cell-surface expression and induced degranulation upon target cell recognition, however secretion of IFNγ and TNFα was significantly lower in FMC63-CD828Z CAR-T cells compared to FMC63-28Z CAR-T cells. The same hinge-TM substitutions in fully human anti-CD19 (HuCD19) and human VEGFR2 (hVEGFR2) CD28 costimulatory domain-containing CARs also demonstrated reduced cytokine secretion associated with the CD8 hinge-TM region compared to the CD28 hinge-TM region [8,66]. In a separate study examining independent variations of structural and costimulatory domains, Majzner and colleagues [20] showed that both the CD28 hinge-TM and CD28 costimulatory domains contribute to the high cytokine production associated with the FMC63-CD28z configuration compared to the FMC63-CD8-41BBz configuration. Importantly, the CD28 hinge-TM domains conferred improved cytotoxic potency against low-antigen target cells, regardless of the identity of the costimulatory domain, and was associated with both higher potency and increased cytokine production even in first-generation CAR constructs containing no costimulatory domains [20]. Emerging data suggest that such effects of the CD28 hinge-TM domains are attributed to its high propensity to form heterodimers with endogenous CD28 through specific TM sequences within the membrane, thus hijacking native CD28 receptor signalling in addition to CAR-mediated signalling [17,67]. These preclinical observations support the suggestion from our review of clinical trial outcomes that the higher incidence of intermediate CRS and high-grade neurotoxicity observed in trials using FMC63 CARs with CD28-derived hinge-TM and costimulatory sequences may be more closely associated with the structural domains than the costimulatory domain.

#### **4. Conclusions**

Here we examined FMC63 anti-CD19 CAR-T cell clinical trials in an attempt to unravel the impact of the hinge, TM and costimulatory domains of the CAR protein on patient clinical outcome and toxicities. CD28 hinge-TM containing CARs were associated with a slightly higher average clinical response rate but were also associated with more severe toxicity compared to CD8 hinge-TM containing CARs. While the number of patients and clinical trials contributing to this observation are limited, several recent preclinical studies highlighted above support this association, with CD8 hinge-TM CAR-T cells consistently shown to produce lower levels of cytokines than otherwise identical CD28 hinge-TM CAR-T cells. These studies contribute to an increasing recognition within the field that the hinge-TM structural domains are not functionally inert, and work to unravel the contribution of the hinge and TM domains independently is ongoing. It remains unclear whether one or both of these structural domains contributes directly to these effects, and the mechanisms underlying these functional outcomes are still not well understood. We suggest that a broad systematic interrogation of hinge and TM domain effects on CAR structure, stability, expression levels, signalling outputs and interactions with other cell-surface signalling molecules will yield fundamental new insights into how we may rationally design safer CAR-T cell therapies that do not compromise clinical efficacy.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/2072-669 4/13/1/38/s1, Table S1: Number and best clinical response of patients in each anti-CD19 CAR-T cell clinical trial, Table S2: Number and grade of patients diagnosed with CRS in each anti-CD19 CAR-T cell clinical trial, Table S3: Number and grade of patients diagnosed with neurotoxicity in each anti-CD19 CAR-T cell clinical trial.

**Author Contributions:** Conceptualization, A.S.D. and M.J.C.; writing-original draft preparation, A.S.D.; Writing-review and editing, A.S.D., M.E.C. and M.J.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** We acknowledge NHMRC project grant 1158249 for funding and article processing charges.

**Conflicts of Interest:** M.E.C. and M.J.C. are inventors on a patent application related to CAR structural modifications.

#### **References**

67. Muller, Y.D.; Nguyen, D.P.; Ferreira, L.M.R.; Ho, P.; Raffin, C.; Valencia, R.B.; Congrave-Wilson, Z.; Roth, T.; Eyquem, J.; Van Gool, F.; et al. The CD28-transmembrane domain mediates chimeric antigen receptor heterodimerization with CD28. *bioRxiv* **2020**. [CrossRef]

## *Editorial* **Challenges and Opportunities for E**ff**ective Cancer Immunotherapies**

**Clare Y. Slaney 1,2,\* and Michael H. Kershaw 1,2,\***

Received: 19 October 2020; Accepted: 24 October 2020; Published: 28 October 2020

**Keywords:** immunotherapy; PD-1; microenvironment; CAR T cells; immune checkpoint inhibitors

Using immunotherapy to treat cancers can be traced back to the 1890s, where a New York physician William Coley used heat-killed bacteria to treat cancer patients, which became known as "Coley's toxin". Of the almost 900 cancer patients he treated, some tumours regressed and some patients were free from recurrence for a number of years [1]. However, the toxin component was inconsistent, patients' reactions were unpredictable and the anti-cancer mechanism was not known. With the advancement of radiation therapy and chemotherapy in the 20th century, Coley's toxin was not used anymore.

In the past ten years, we have witnessed many revolutionary immunotherapies being approved to use in the clinic for treating cancer patients. These immunotherapies include the first cancer vaccine, Sipuleucel-T for advanced prostate cancer; checkpoint inhibitors such as ipilimumab, pembrolizumab and nivolumab for the treatment of advanced melanoma and other solid cancers; oncolytic virus T-Vec for melanoma; a bispecific cancer-directed T-cell engager, blinatumomab, for the treatment of acute lymphoblastic leukemia, and chimeric antigen receptor (CAR) T cells for treating certain lymphoma and leukemias [2,3]. Together, these immunotherapies have had a remarkable impact on clinical outcomes.

This year, although the majority of the world is locked down in response to the coronavirus disease (COVID-19) pandemic, a number of cancer immunotherapies were approved by the US Food and Drug Administration (FDA). The newly approved treatments include atezolizumab for advanced melanoma, brexucabtagene autoleucel (Tecartus) for mantle cell lymphoma, which is the third FDA-approved CAR T-cell therapy, pembrolizumab as the first line of treatment for colorectal cancer and pembrolizumab for cutaneous squamous cell carcinoma.

Although more and more treatment options are becoming available, challenges still remain. Immune checkpoint inhibitors (ICIs) work for certain cancer types such as melanoma, but not all cancer types respond. Even in melanoma, half of the patients do not achieve a significant beneficial response, and a substantial number of responding patients experience cancer relapse after the initial response [4]. Unfortunately, these ICI therapeutics are also often associated with a high rate of toxicity, with severe toxicities occurring in approximately 20–50% of patients [5]. Other immunotherapies can have similar problems. Certain cancers such as pancreatic cancer have proven to be difficult to treat using all the current available immunotherapies [6].

Building on the success of ICIs, numerous immunotherapies have been tested to be used in combination with other immunotherapies or with some already existing treatments. For example, anti-PD1 has been tested in combination with CAR T-cell therapy [7], oncolytic virus treatment [8,9], cyclin-dependent kinase inhibitors [10]. Given the potency of the treatment components as monotherapies, it is not surprising that a number of these combinations led to synergistic efficacy. With an abundance of combination immunotherapy trials ongoing, more and more factors that influence

the therapeutic success have been revealed and synergistic design of different combination therapies may provide optimal benefit to the patients with different types of cancers.

Although yet to demonstrate efficacy in solid tumours, enormous efforts have been made in CAR T-cell research. These include the discovery of new tumour antigen targets [11], more options for combination therapy [7,12], creating T-cell products with a more desirable phenotype [13], improved manufacturing protocols [14], and novel methods for enhancing in vivo expansion of the CAR T cells [15–17]. Although most of the current immunotherapies have focused on T cells, other cellular therapies such as those utilising NK cell cytotoxicity [18,19], dendritic cells [20] and macrophages [21] are also under investigation.

Another extensively explored area lies in the understanding of immunosuppression of the tumour microenvironment (TME) [22]. The TME consists of tumour cells, immune cells, stroma, extracellular matrix and some soluble factors. This complex environment plays a fundamental role in tumour progression, shapes the tumour immune response and eventually determines the efficacy of immunotherapies [23–25]. A number of strategies have been developed in the past few years to shift the TME to favour anti-tumour immunity, and clinical studies have validated several biomarkers of the TME predicting tumour responsiveness to immunotherapies [26,27].

Much knowledge has accumulated in the past ten years, and the cancer immunotherapy field is moving forward at a fast pace. Many current obstacles will likely be overcome through improved knowledge, more advances in treatment technologies [28] and the identification of new cancer targets. In addition, new combination treatments incorporating immunotherapies, and the identification of predictive biomarkers for cancer immunotherapies, may lead to further effective treatments utilizing the immune system for a wide range of cancers.

**Funding:** The authors are supported by grants from the National Health and Medical Research Council and the National Breast Cancer Foundation (NBCF) of Australia.

**Acknowledgments:** We are grateful for all the contributing authors for their contribution to this Special Issue and the support from the *Cancers* editorial staff.

**Conflicts of Interest:** The authors declare no potential conflicts of interest.

#### **References**

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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