Identification of Antigenic Targets

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Feature Review

Identification of Antigenic Targets Hans-Peter Gerber,1,* Leah V. Sibener,1 Luke J. Lee,1 and Marvin H. Gee1 The ideal cancer target antigen (Ag) is expressed at high copy numbers on neoplastic cells, absent on normal tissues, and contributes to the survival of cancer cells. Despite significant investments in the identification of cell surface Ags, there is a paucity of targets that meet such ideal cancer target criteria. Recent clinical trials in patients with cancer treated with immune checkpoint inhibitors (ICIs) indicate that cluster of differentiation (CD)8+ T cells, by means of their T cell receptors (TCRs) recognizing intracellular targets presented as peptides in the context of human leukocyte antigen (peptide–human leukocyte antigen complex; pHLA) molecules on tumor cells, can mediate deep and longlasting antitumor responses in patients with solid tumors. Therefore, pHLA-target Ags may represent the long sought-after, ideal targets for solid tumor targeting by high-potency oncology compounds.

Highlights Cell surface Ags with high expression across tumors are often expressed on normal tissues, resulting in on-target, off-tumor toxicities when targeted by high-potency oncology compounds. Clinical studies with patients with solid tumors that were treated with ICIs demonstrated that CD8+ T cells and their TCRs binding to tumor-specific pHLA targets, represent key mediators of deep and durable responses in solid tumors in the absence of normal tissue toxicity. Challenges facing the development of TCR-based therapeutics include the enormous diversity of the target Ag space (1015), the various genetically encoded HLA alleles, and the ability to monitor the cross-reactivity of a therapeutic TCR.

Immune Checkpoint Inhibitor Therapies Are Guiding the Identification of Novel Cancer Targets and the Development of Novel Therapeutics The utility of T cells for cancer immunotherapy has been propelled by the recent success of ICI therapies in a subset of patients with solid tumors [1]. Trials conducted with antibodies blocking immune checkpoints, known as ICIs, revealed durable responses in a variety of solid tumor indications, leading to US FDA approvals in melanoma, non-small-cell lung cancer (NSCLC), renal cell carcinoma, bladder cancer, Hodgkin lymphoma, head and neck cancer, Merkel cell carcinoma, microsatellite instable (MSI) high tumors, hepatocellular carcinoma, and gastroesophageal junction cancer [1,2]. The mechanisms by which ICI compounds induce antitumor responses include the reactivation of exhausted CD8+ T cells in tumors that have been historically difficult to treat with conventional cytotoxic therapies [1]. However, this notion was recently challenged by the observation that pre-existing tumor-specific T cells located in tumors may have limited reinvigoration capacity, and that the T cell response to ICI compounds may be derived from a distinct repertoire of T cell clones that may have entered the tumor post ICI treatment [3]. Despite a lack of validation showing these new TCR clones to be tumor reactive, it suggested that T cells outside the tumor mass also express antitumor reactive TCRs and, thus, these cells should be included in efforts to identify the most antitumor reactive T cells. Detailed immune monitoring studies identified CD8+ T cells as the primary effectors responsible for clinical responses and that their antitumor activities are mediated by engagement of TCRs targeting pHLA complexes presented on tumors cells [4–9]. However, identifying the TCR–pHLA targets that mediate complete responses in ICI-treated solid tumors has proven to be a challenge. Numerous innovative efforts have been recently described to determine these targets, which, if successful, have the potential to ignite the development of therapies inducing deep and durable responses in broader patient populations with solid tumors [10]. The identification of pHLA targets recognized by TCRs is also relevant for the treatment of patients with solid tumors using preselected tumor-infiltrating lymphocyte (TIL) products [11]. This approach is based on ex vivo enrichment of TILs, followed by adoptive transfer of T cells selected for specific Ag reactivities [12]. Adoptive cell therapy trials conducted with in vitro Trends in Cancer, Month 2020, Vol. xx, No. xx

Multiple platforms have been developed to identify pHLA targets for TCRs and will likely result in the identification of therapeutics with unique selectivity for solid tumor targeting.

1

3T Biosciences, 1455 Adams Drive, Menlo Park, CA 94025, USA

*Correspondence: [email protected] (H.-P. Gerber).

https://doi.org/10.1016/j.trecan.2020.01.002 © 2020 Elsevier Inc. All rights reserved.

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expanded TILs induced promising antitumor responses in patients with metastatic melanoma. Importantly, the antitumor responses happened in the absence of toxicities associated with the cell product, providing important proof-of-concept evidence for the therapeutic potential of tumor-specific TILs administered to patients with solid tumors. Concomitantly with the success of ICI treatment in solid tumors, the use of single cell sequencing has now become feasible in translational oncology research in the clinic, enabling identification of TCRs expressed on TILs and determination of their relative abundance in ICI-responding tumors [3,13]. The frequency of clonal TIL populations within tumors is an important predictor of their antitumor activities, because it demonstrates T cell responsiveness in the tumor microenvironment. T cell proliferation, cytokine secretion, and tumor cell killing are ultimately dependent on engagement of a TCR and cognate target pHLAs. The results from ICI trials in patients with melanoma revealed that a convergence of TCR clonality is indicative of clinical responses to anti-programmed cell death-1 (PD-1) treatment, of which some have been mapped to responses against neoantigens, or variants of naturally processed peptides [14,15]. However, in patients with anticytotoxic T-lymphocyte-associated protein 4 (CTLA-4)-treated melanoma, or patients with pancreatic cancer treated with an antiPD-1 antibody, the association of TCR repertoire metrics with clinical outcomes yielded mixed results [16–18]. Most importantly, there remains a dearth of knowledge regarding the specificities of the clonal repertoire of TCRs. Multiple studies have recognized the possibility of shared immune responses across patients, implying the potential for discovery of additional, novel broadly expressed and shared target Ags [13,19,20]. Combining technologies to determine the most abundant and shared TCRs across patients with technologies identifying the pHLA targets recognized by these TCRs, creates a unique opportunity to not only discover novel, solid tumor targets that are shared across tumor indications and patients, but also to identify TCRs targeting them with the potential to induce complete antitumor responses.

Challenges Faced When Targeting pHLA Complexes with TCRs in the Clinic Previous attempts with engineered, patient-derived lymphocytes expressing TCRs that were engineered to bind to cancer targets with higher affinities for the purpose of inducing increased antitumor responses, had limited success in the clinic [21–23]. In one case, affinity maturing a TCR specific for melanoma antigen family A3 (MAGE-A3) introduced a novel specificity to a peptide expressed in heart muscle derived from titin (an unrelated protein). These changes caused significant levels of off-target, off-tumor toxicity in the heart, ultimately resulting in the trial being halted after the first two patients were treated [22]. Another TCR-transduced T cell (TCR-T) study used an affinity-enhanced TCR, recognizing a naturally processed peptide shared by the cancer-testis Ags New York esophageal squamous cell carcinoma-1 (NY-ESO-1) and L antigen family member 1 (LAGE-1). In general, the adoptive transfer was well tolerated, and TCR-T cells expanded and trafficked to tumors, where they persisted and displayed durable, target-specific antitumor activities. Despite the 20% partial response rates reported in 20 patients with multiple myeloma, there were also seven serious adverse events and 17 adverse events that were likely treatment related [23,24]. Combined, these studies not only highlighted the potential of TCR-T cell therapies as an emerging therapeutic modality for the treatment of solid tumors, but also revealed a need for technology improvement to monitor off-target, off-tumor reactivities of TCRs introduced during TCR engineering to optimize their antitumor activities before clinical testing [23]. 2

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Glossary MCR: novel pMHC–TCR hybrid molecules used to identify Class II peptide epitopes. MHCflurry: an open-source software package for pHLA class I binding prediction. NetMHCPan: prediction of peptideMHC class I binding using artificial neural networks. T-scan: a screening approaching using 293 cells transduced with a function pHLA chimeric receptor and a tandem minigene library to identify TCR ligands. X-scan: systematic substitution using all-natural amino acids at each position of an antigenic peptide sequence.

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An alternative path to avoid alterations in TCR specificity is to identify endogenous TCRs that are tumor reactive in patients with responses to ICI treatment. The use of endogenous TCR sequences has the potential to avoid or reduce the off-target toxicities that have impacted the clinical development of first-generation, engineered TCR-T programs. This may particularly apply to TCRs derived from patients with ICI-treated cancer who did not display any signs of autoimmune-related toxicities, while experiencing complete regressions of their tumors.

Improving TCR Specificity for Therapeutic Development TCRs can be cross-reactive to varying degrees toward peptides presented by HLA molecules [19,25–27]. The Ag specificity of T cells is conferred by the highly variable complementaritydetermining region (CDR) loops of the TCR, which interact with both the peptide and the HLA [28–31] (Figure 1). During T cell development in the thymus, a theoretically large TCR repertoire is generated, comprising up to ~1015 different TCRs [28,108]. However, a recent deep-sequencing analysis indicated that the actual repertoire is more likely to be ~1011 different TCRs [32]. From this repertoire, TCRs that bind to self-peptide HLA complexes with low to intermediate affinities, are positively selected [33,34], whereas TCRs with high affinity to self-Ags are deleted during negative selection. Thymic selection of T cells is known as central tolerance and prevents the peripheral TCR repertoire from recognizing the human ‘peptidome’ [35,36].

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Figure 1. Interactions between Target Peptides and Human Leukocyte Antigen (HLA) Complexes That Define T Cell Receptor (TCR) Specificity. (A) Structural snapshot of a TCR binding to peptide (p)HLA. Shown is the human antimelanoma antigen recognized by T cells 1 (MART-1) TCR DMF5 bound to HLA A2:MART-1 [Protein Data Bank (PDB) ID: 3qdj]. (B) Example of peptide residues that face toward the TCR (mustard), and toward the HLA (magenta). Shown is MART-1 [27–35]. (C) Heatmap of the most polymorphic residues on HLA A: 8-9 different amino acids encoded and 6-7 residues encoded colored in red and orange, respectively. Data taken from version 3.2.0 of the IPD-IMGT/HLA Database and HLA.allels.org. (D) Polymorphic residues on the HLA that primarily interact with the peptide colored in magenta. (E) Polymorphic residues on the HLA that primarily interact with the TCR colored in hot pink.

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Given that the human T cell repertoire in adults (106–108 unique TCRs) is many orders of magnitude lower than the theoretical target peptide repertoire of 1015 [144], it has been hypothesized that TCRs must be capable of recognizing more than one Ag to have sufficient coverage of the foreign ‘peptidome’ [37–41] (Figure 2). More recent studies suggested that TCRs recognize on the order of thousands to tens of thousands of peptides; however, individual TCRs vary significantly in the range of their crossreactivity profiles [19,26]. Cross-reactivity is further constrained by structural considerations, where the ‘up-facing’ TCR-contact residues of the peptide tend to be more energetically important and specific for TCR binding, whereas the peptide residues that do not directly interact with the TCR can exhibit a higher degree of sequence diversity while maintaining the ability to bind to HLA (Figure 1B) [26]. Nonetheless, the tendency of TCRs to exhibit off-cancer target reactivity has stalled the progress of many TCR-based therapeutics relative to other cellular therapies, such as chimeric antigen receptor-transduced T cells (CAR-T). The difficulty in finding the most widely shared Ags combined with the lack of technologies to comprehensively monitor off-target, off-tumor interactions of TCRs poses a significant risk for clinical development of TCR-based therapeutics. A reminder of such risk associated with TCR-based therapeutics is the off-target, off-tumor toxicity of some TCR-T therapies tested in the clinic that induced off-target-related fatalities [21,22]. Most importantly, this earlier generation of TCR-based therapeutics often used affinitymatured TCRs with the purpose of improving their potency and ability to bind low-density pHLA targets.

Advantages of Intracellular Targets TCRs recognize extra- and intracellular tumor-specific Ag (TSA)-derived peptides presented on the cell surface by HLA molecules. Traditionally due to their location, intracellular proteins were considered ‘untargetable’ by conventional IgG-based modalities. However, all proteins expressed in a given cell are degraded via the proteasome or immunoproteasome into peptides, which are subsequently loaded onto HLA molecules and, therefore, can be targeted by T cells. Ideal intracellular targets for immunotherapy lack expression in normal, adult tissues but display increased expression in tumors. Theoretically, TCRs can recognize an exponentially larger number of targets compared with antibodies, which traditionally recognize canonical cell surface Ags, representing only ~1% of all human proteins [42]. In addition, another advantage is that TCRs can be exquisitely specific and can recognize single amino acid variants between peptides. This was demonstrated for neoantigens, whereby a TCR can be specific for a mutated epitope but not the wild-type peptide [7,43–45]. However, finding suitable intracellular targets that are presented in a tumor-specific manner and with sufficiently high frequencies across patients with cancer and tumor indications is critical for the success of TCR-guided therapeutics. Another challenge facing the development of TCR-based therapeutics is to be able to discern and control the specificity of the modality itself. In one case, the dose-limiting toxicities observed in the clinic resulted from the introduction of additional target specificities by the engineered TCR (off-target, off-tumor toxicity) [22]. Therefore, it is critical to develop novel technologies to comprehensively determine cross-reactivities of therapeutic TCRs. Perhaps equally important is to find TCRs targeting suitable, TSAs that are present across patients and tumor indications. 4

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Figure 2. Molecular Diversity of T Cell Receptor–Peptide human Leukocyte Antigen (TCR–pHLA) Interactions. The central interaction in cell-mediated adaptive immunity is between the αβ T cell antigen receptor (αβ-TCR, depicted in orange and yellow), and an antigen-presenting molecule (HLA, in blue) loaded with a given antigen (Ag) (peptide, in green). The diversity of the TCR repertoire is generated by both germline-encoded diversity (variable regions), and the somatically recombined complementarity-determining region 3 (CDR3) loop. The TCR heterodimer comprises an α and β chain, each of which consists of a variable (46 alpha and 48 beta segments), joining (eight alpha and 12 beta segments and constant region (one alpha and two beta segments), while the β chain has an additional diversity region (two segments). The most diverse region, the CDR3 loop, is formed at the intersection of the variable region with additional nucleotide additions and/or deletions. The diversity generated through this process is estimated to be 1015 unique TCRs (reviewed in [28]). Antigenic cross-reactivity of T cells results from a discrepancy between the TCR repertoire and the functional diversity (i.e., the number of antigens recognized by the T cell repertoire), which is estimated to be 1015 [144]. Peptides presented by class I HLA typically range between 8- and 11-mers. Finally, the diversity of the HLA alleles in human is on the order of 10 4. With N7000 alleles, HLA is the most polymorphic region of the human genome (reviewed in [109]). The diversity inherent to the TCR, peptide, and HLA molecules makes identifying the specificity of any one TCR complex.

Technologies to Identify pHLA Targets of TCRs Identifying the cognate ligands of TCRs has been a challenge due to the enormous diversity within the TCR repertoire, the various genetically encoded HLA subtypes, and the subsequent peptides that are processed and presented at the tumor cell surface (Figures 1 and 2). Additionally, because many TCR–pHLA interactions are of low affinity and short-lived, their identification has required technically challenging experimental procedures and has been historically slow (Boxes 1–3). Therefore, there is a need to develop high-throughput, diversity-oriented and highly sensitive approaches to uncover TCR specificities [46] (Table 1 and Figure 3). Trends in Cancer, Month 2020, Vol. xx, No. xx

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Positional Peptide Scanning to Assess TCR Cross-Reactivity A common technology to profile the cross-reactivity of a TCR is substitution positional scanning within the known target Ag peptide sequence. To identify binding hotspots of the TCR on the peptide, every position of the peptide can be substituted by all 20 natural amino acids to systematically test peptides and generate a profile of TCR specificity (e.g., ‘X-scan’; see Glossary) [47] and, thus, potential cross-reactive TCRs can be identified. This method helps to predict binding of TCRs to peptides with high sequence homology (Figure 3). For example, the likelihood of off-target reactivity was investigated by searching the human proteome for sequences that match an X-scan profile and were tested against a panel of primary cell lines. This facilitated the selection of specific and potent candidate TCRs for further preclinical and clinical development [48]. However, structurally related but sequence-unrelated peptides can be recognized by a TCR, which is outside of the scope of detection of a position-based scanning method [19,49]. Thus, it is likely that single amino acid substitutions are insufficient to comprehensively capture the complexity of TCR cross-reactivities. Tandem Minigene Libraries to Identify Targets of TCRs Minigene or tandem minigene (TMG) libraries transfected into Ag-presenting cells (APCs) were used to induce the proliferation of T cells and to isolate T cells with a TCR specifically binding to Box 1. Cellular and Molecular Biology-Based Approaches to Identify pHLA Complexes Bound by TCRs Some of the earliest experimental approaches to determine TCR specificities in tumors were based on expanding TILs or PBMCs in the presence of autologous tumor cells, leading to the accumulation of tumor-specific cytotoxic lymphocytes (CTLs) [110]. Such CTL clones have the ability to kill autologous tumor cells while maintaining inactivity against a range of normal cells. The TCRs from these tumor-specific CTLs were subsequently cloned and their ligands were identified by MS [96,111]. By testing immortalized CTLs against cDNA libraries generated from a patient’s tumor, the first human gene that encoded a tumor-specific Ag (TSA) was identified [97]. The identified gene was named MAGE-A1, which is uniquely expressed in many human tumors but not in normal tissues. MAGE-A1 was the first member of a family of genes, the germline tumor Ags, later renamed as the cancer testis Ags (CTA), which are uniquely expressed in cancer but with limited expression in normal tissues, mostly on male or female germline cells [112]. Since these cell types do not express HLAs, the likelihood of presentation of CTA Ags on normal tissues is very low [113]. The most commonly used and readily available approach to determine the specificity of TCRs are fluorescently labeled pHLA multimers, including tetramers or dextramers used in flow cytometry and cell-sorting experiments [99,100,114,115] (see Table 1 in the main text). This can be useful in cases where a tumor-derived exome provides a small subset of target Ags for a specific T cell repertoire, but with a clearly defined and limited target landscape scope. As a consequence, this candidate approach requires prior knowledge of the antigenic landscape (i.e., designated pHLA complexes), and the designation of individual pHLA complexes limits the assay feasibility to ~1000 Ags [116]. A variation of this approach, called tetramer-associated T cell receptor sequencing (TetTCR-seq), uses DNA-barcoded tetramers to isolate paired TCR sequences via pHLA binding on single T cells [117] (see Table 1 and Figure 3A in the main text). Tetramer and other multimer-based assays are used to identify Ag-specific T cell clones recognizing known pHLA targets. However, tetramer pull downs work optimally within a narrow affinity window for receptor–ligand interactions and are somewhat prone to false negative results, potentially missing important, low-affinity TCR clones. In addition, not all tetramer-positive T cells are functional and the pHLA targets do not induce TCR signaling in T cells [71,118]. Essentially, all methods of TCR identification based on TCR-pMHC binding require further testing and validation, which poses a significant limitation. Similarly, multimer-based technologies that rely on in silico prediction models, also require additional functional validation to determine their therapeutic potential to induce tumor cell killing [71,119–121] (see Figure 3 in the main text). In conclusion, the main limitations of tetramer and multimer-based assays to determine the specificity of TCRs are their relatively low sensitivity, the reliance on known targets to develop tetramers, and a lack of a functional output following a successful TCR–pHLA interaction. However, when using tetramers to enrich for T cells with specific TCRs among human peripheral blood cells grown in vitro, it is possible to detect and amplify T cells at a frequency of 1 in 106 [122], and all classes and types of target Ag can be detected (see Table 1 and Figure 3 in the main text).

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Box 2. Biochemical Approaches to Identify pHLA Complexes on the Surface of Tumors Tandem MS was used to identify peptides eluted from HLA molecules and isolated from the surface of tumor cells. The peptides identified were subsequently tested for HLA binding and the efficiency of Ag processing and cell surface presentation to the TCR repertoire, identifying their immunogenic capacity and potential therapeutic value. Among the thousands of peptides presented on melanoma tumors, several were found to be recognized with high affinity by melanoma-specific cytotoxic T lymphocytes (CTLs). Recognition by multiple CTL lines suggested that these peptide targets are shared across patients and, therefore, represent promising candidate Ags for solid tumor targeting [98]. An additional advantage of using MS to identify peptides is the potential to identify peptides derived from aberrantly expressed Ags. While MS has the ability to directly identify peptides presented by HLAs on solid tumors, there are limitations in both assay sensitivity and throughput. Typically, 107–108 cells are required to yield 103–104 peptides [123]. However, since there are ~105 pHLA complexes per cell, there may be substantial undersampling of the less abundant peptides [124]. Moreover, there remain challenges in recovering highly hydrophobic peptides using MS-based approaches [125]). As a result of the continuous improvement in the sensitivity of peptide detection, many tumor Ags have been identified, including the melanocytic differentiation protein gp100, also known as premelanosome protein (PMEL) and PMEL17 [98,126,127]. More recently, MS-based technologies have enabled the identification of patient-specific neoantigens as well as shared Ags [128–131] and provided promising leads for personalized cancer vaccine development [132,133]). However, MS analysis of MHC peptides does not directly provide information regarding the nature of the TCRs recognizing them. A key advantage of using MS to identify tumor Ags is the ability to detect post-translational modifications of peptides. For instance, the deamidation of asparagine into an aspartic acid residue was identified in a tyrosinase peptide, which was shown to be essential for a T cell clone’s tumor specific recognition and activation [134]. MS-based technologies have become the method of choice to identify post-translationally modified peptides but fall short of deciphering the full repertoire of HLA peptide ligands encoded by the genes of a pathogen or tumor-specific Ags. This is due to the limited coverage of peptides identified by MS relative to the diversity and quantity of peptides that are present on the surface of tumor cells. For a more comprehensive detection of peptides presented by HLAs on tumor cells by MS, the development of rapid and efficient sample processing techniques will be critical for the robust analysis of immunopeptidomes and throughput that is necessary to advance MS to routine clinical application [125]. In summary, MS-based target identification approaches have been highly successful for the development of personalized cancer therapies and enabled a large variety of peptide-based cancer vaccines, many of which are currently in clinical trials [133,135].

known neoantigens or pathogenic ligands. APCs are transfected or transduced with a pool of DNA cassettes that encode 20–50 amino acid protein tiles. The APCs then process and present pHLA at the cell surface. The pool of transfected APCs is subsequently co-cultured with T cells of interest to either identify tumor-reactive T cells or immunogenic epitopes. Using this method, it is Box 3. In silico Methods to Predict Peptide Binding to HLA MS-validated HLA-bound peptides have provided a wealth of information used to generate predictive models for peptide binding to HLA, such as MHCflurry [104,136] and NetMHCPan [137] (see Table 1 in the main text). These tools have been used to identify somatic mutations that correlate with clinical responses to treatment with ICIs [138] and enabled the identification of the most immunogenic neoantigens for the development of personalized cancer vaccines [132]. Recent algorithms have helped to improve the accuracy of determining Ag presentation by patient HLAs and to triage testing for immune responses against predicted, immunogenic pHLA complexes. Candidate peptides encoding specific mutations that bind to a given HLA molecule are identified by exome or RNA sequencing of an individual patient’s tumor. These data provide a rich source of peptides covering private and public somatic mutations that can be tested in silico for binding to relevant HLAs [139]. Importantly, when using in silico approaches, it is essential to verify that the peptides identified are presented via the endogenous Ag-processing machinery of tumor cells. This method has successfully identified several antigenic peptides from mutated targets that were recognized by CD4+ and CD8+ T lymphocytes [140,141]. By using genomic sequencing analysis as opposed to MS analysis of tumor cells, several mutated Ags expressed on autologous tumor cells were identified as targets of tumor infiltrating lymphocytes (TILs) [5]. Among the targets identified, a handful of tumor-specific Ags where found to be present across many cancer types, such as KRAS, TP53, or BCR-ABL19, that are not present in normal tissues [8,142,143]. In conclusion, combining MS data with high-throughput sequencing analysis has helped to improve conventional [119] bioinformatics approaches to more accurately predict immunogenic Ags, such as applied in the Epitope Discovery in Cancer Genomes (EDGE) platform described recently [105].

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Method

Molecular biology (see Box 1 in the main text)

Biochemical: MS and tetramer based (see Box 2 in the main text)

In silico (see Box 3 in the main text)

High-throughput panning of pMHC libraries

Technology

MS of Ags bound by TCRs of cytotoxic T cell clones

MS of Ags bound by CTLs

FACS (tetramer and dextramer)

MS:CyToF (tetramer and dextramer (TetTCR-seq)

Deep sequencing (MHC flurry and NetMHC)

Deep sequencing and learning (EDGE)

Site-directed mutagenesis (X-SCAN and Ala scan)

Mammalian pMHC-Ag library

Synthetic pMHC-Ag library

MHC restriction

Class I+II

Class I

Class I

Class I

Class INNII

Class INNII

Class I

Depends on system

Class I+II

Assay read out

Cell killing

Binding

Binding

Binding

Probability of binding

Binding

Binding

Binding+Signaling

Binding

Unknown pMHCs

Yes (low throughput)

No

No

No

No

No

No

No

Yes

Ag or library diversity

1

1–5

1–5

≤103

N/A

N/A

≤106

≤106

≤109 baculovirus b1010 yeast

Assay species

N/A

N/A

N/A

N/A

N/A

N/A

Mammalian

Mammalian co-culture

Yeast, baculovirus

Ag classes identifiable

All (technical limits)

All (technical limits)

All (technical limits)

All (technical limits)

Non-self: neoantigens, viral

Non-self: neoantigens, viral

All

Dependent on library input

All

Posttranslational modifications

Yes

Yes

N/A

Yes

No

No

No

Not directly from assay

No

Refs

[97,98]

[99]

[100,101]

[44,102,103]

[104,105]

[106]

[48,107]

[53,55,56,57,59,61,107,108]

[20,27,72]

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8 Table 1. Overview of Technologies to Identify pMHC Interactions with their Respective TCRs

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(See figure legend at the bottom of the next page.)

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not feasible to identify both reactive TCRs and immunogenic Ags simultaneously and must be deconvoluted subsequently with further experimentation. These libraries had most success when covering a limited Ag space, such as patient-specific exomes, and several programs using TILs expanded by using such APC-minigene libraries are currently being tested in the clinic (Figure 3). For personalized cancer treatment approaches, the input Ags for the TMG screen are individually tailored based on the exome sequencing of patient tumor samples. This technique can be used for identifying common shared neoantigens, as in the recurrent mutations in p53 [50,51]. Similarly, an approach combining mass spectrometry (MS) with deep sequencing to identify neoantigens in individual patient tumors has provided initial proof-of-concept efficacy in Phase I clinical trials [52,53]. When expressing Ags that resulted from this approach, T cellspecific responses were observed leading to tumor regression in a fraction of patients with melanoma.

Functional Selections of TCR–pHLA Interactions in Mammalian Cells To overcome the need to deconvolute TCR and Ag identification, several functional screens based on co-culturing APCs and T cell lines have been reported to identify TCR–ligand pairs. To generate a functional readout for TCR recognition, the HLA was engineered to incorporate an intracellular signaling domain driving expression of a reporter gene. Upon T cell recognition of a specific pHLA target, activation of the reporter in the APC was used to isolate cells expressing the targets of interest. The cells containing the pHLAs of interest were subjected to deep sequencing to recover the encoded peptide Ag [52,54–58] (Table 1). One of these cell-based panning methods uses a novel class of chimeric receptors called ‘signaling and antigen-presenting bifunctional receptors’ (SABRs), which was generated to screen a library of pHLA complexes against TCRs with known specificities [55]. The SABR is constructed similarly to a chimeric Ag receptor: the extracellular domain is a covalently linked class I pHLA single-chain trimer (or dimer without the linked peptide), fused to an intracellular domain (ICD) comprising CD3ζ with a CD28-ICD co-stimulatory domain. The SABR library is then transfected into a Jurkat nuclear factor of activated T-cells (NFAT)-GFP reporter cell line, where upon TCR engagement with SABRs expressing agonist pHLA complexes can be identified via fluorescence. The SABR construct was also shown to present noncovalently linked peptides via introduction of soluble peptides or peptide minigene cassettes. In an elegant study, individual SABR libraries were developed to identify known viral, tumor, and

Figure 3. Cellular, Biochemical, and Molecular Biology Approaches to Identify T Cell Receptor–Peptide human Leukocyte Antigen (TCR–pHLA) Complexes. (A) Biochemical approaches: sample size is 102–105 cells. Target antigen peptides can be identified by elution of surface-presented peptides from tumor cells, which are then identified via tandem mass spectrometry (MS) (i). T cells expressing TCRs of interest can be isolated by using pHLA multimers comprising known tumor antigens, and subsequently sequenced (ii) to identify cognate TCR–pHLA pairs. (B) Mammalian libraries and functional selections: maximum diversity is 105–106. Many high-throughput approaches take advantage of the ability of T cell lines to recapitulate TCR signaling and to perform functional selections. One approach, signaling and antigen-presenting bifunctional receptors (SABR) (i), uses a chimeric antigen receptor (CAR)-like construct with a single-chain library expressed on the surface of a NFAT-GFP-expressing T cell line, with a signaling competent intracellular domain (ICD). Upon binding a TCR, the ‘activated cells’ can be isolated and the activating peptide ligands are identified by sequencing. Another approach uses the same selection criteria but a different antigen library. The MCR technology is based on a TCR–HLA chimeric molecule that induces a T cell signal using the native TCR signaling machinery (cluster of differentiation 3; CD3) (ii). A membrane-swapping event, trogocyosis, has also been used to identify TCR–pMHC pairs (iii). (C) Synthetic libraries and selections: diversity is 108–1012. To increase the diversity of the target libraries, non-mammalian libraries were created to express pHLA complexes. Using multiple rounds of selections with multimerized or nanoparticle-coated recombinant TCRs allows for the selection of pHLA molecules specific for a TCR of interest. Yeast display libraries are able to enrich 100–10000 unique peptides for each TCR. By using yeast, which displays high copy numbers of pHLA molecules, low-affinity TCR–pMHC interactions can be profiled and cross-reactivity can be assessed (i). Phage display pHLA libraries have the largest diversity, but present very low copy number of pHLA on the surface, making it difficult to use them for lowaffinity selections (ii). Using phage display pHLA library selection methods, a significantly lower average number of peptides were identified compared with yeast display-based methods.

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mutated neoantigens as well as mutated neoantigens. The library comprised peptides derived from the immune epitope database (IEDB) and varied in size from 103 to 104 peptides. Interestingly, signaling derived from the SABR construct was shown to be weaker compared with a native TCR recognizing a pHLA complex, indicating that the sensitivity of SABR libraries can be improved, and that the screen may favor detection of higher affinity TCR–pHLA interactions (Figure 3B). An additional approach was described by the same group taking advantage of a membraneswapping event called trogocytosis, to reveal TCR target specificity [59]. In this setting, K562 cells were transduced with signal chain trimer libraries that were made from peptide ligands derived from the immune epitope database (IEDB) or a personalized peptide library derived from a patient exome (Figure 3B). The platform was used to identify ligands of known receptors, as well as identify new TCRs for a specific pHLA of interest. The size of these libraries again ranged from 103 to 104 unique peptides. A related approach uses functional library selection for an HLA class II peptide identification. A novel chimeric receptor, termed MCR, was generated by grafting the extracellular domain of the MHC class II and β chain on the transmembrane and intracellular domain of the TCR and β chain, respectively, with a peptide linked to the MCR β chain [56]. Upon TCR binding to the MCR, an NFAT-GFP reporter was used to detect TCR-like signaling, which could be used to sort out cells carrying MCRs of interest (Figure 3B). Using this approach, known viral Ags, tumor neoantigens as well as Ags present on murine tumor models (Table 1) have been identified. Interestingly, administration of these peptides inhibited the growth of tumors in mice, demonstrating their therapeutic potential as components of the cancer vaccine approach. An advantage to the construction of the MCR library is the ability to use naturally processed peptides coupled with a functional readout leading to direct Ag identification. A similar approach, T-scan, uses lentiviral delivery of TMG Ag libraries (detailed earlier) into engineered 293 cells, for endogenous processing and presentation on MHC molecules, which can be used as target cells for T cell killing and can subsequently be used to identify cognate class I pHLA targets [60]. Using this approach, 293 cells were engineered to express a fluorescent protein Granzyme-B-specific reporter that is activated in cells presenting pHLA complexes that are recognized by T cells. The ‘activated target cells’ can then be sorted and subsequently sequenced to identify the TMG expressed in the selected pool. This screening method was validated by incubating T cells expressing known cytomegalovirus (CMV) (pp65)-specific TCRs as well as bulk memory T cells from CMV+ donors and screening against a TMG library of the CMV proteome, with successful identification of the cognate Ag or known immunodominant epitopes. This method was further applied to a MAGE-A3-specific TCR that was screened against a library that tiled the human proteome. The MAGE-A3 Ag and the related MAGE-A6 epitope were identified, but, interestingly, additional off-target peptides from unrelated proteins were also identified. While the assay identified additional targets, their low number is strongly against the dogma that TCRs have the ability to recognize hundreds to thousands of potential targets, in some cases reports of upwards of a million peptides [61]. A limitation of using mammalian cell-based panning methods is that, when target tumor Ags are co-expressed in 293 cells or APCs, they cannot be screened due to high background signals. However, the ability of these multiple mammalian platforms to characterize the reactivity of candidate TCRs against a select library of target Ags provides a rationale for future exploration to screen target libraries against populations of T cells isolated from patients with cancer and antitumor responses to ICI treatment (albeit a complex libraryon-library screening approach).

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Microfluidic Approaches to Identify TCR–Ligand Pairs Single cell encapsulation via microfluidic devices has been successfully applied to TCR sequencing [62]. In an extension of this method, T cells expressing a known TCR of interest and an activation reporter were co-encapsulated into droplets with cells expressing pHLA targets of interest [63]. These droplets were then sorted into microwells and monitored for T cell activation. In the initial proof-of-concept study, this was only shown for a model TCR–ligand pair, but could likely be extended to include a library of ligands. While this approach was able to identify T cells and activating ligand pairs based on a TCR activation endpoint, the throughput of the assay (~105) compared with the diversity of potential TCR–ligand pairs will need further improvements to pursue novel target identification campaigns. Additionally, this approach, similar to the multimer-based approaches mentioned earlier, is limited by the designation of pHLA complexes used to isolate individual T cells in the microfluidic droplets. Therefore, the most successful applications for this technology are candidate TCR-based approaches screened against a clearly defined set of target Ags.

TCR Deorphanizing and TCR Cross-Reactivity Screens Using Yeast- or Baculovirus-Based Library Display Technologies Diversity in both the immune receptor and ligand repertoire poses major barriers to understanding TCR specificities. Therefore, library-based approaches providing maximal diversity confer significant advantages for comprehensive identification of TCR–pHLA interactions. Previous work utilized baculovirus-based pMHC libraries with diversities of 107 to identify ligands for mouse-specific TCRs; however, this methodology required significant amounts of both material and time to identify TCR ligands [64]. Recent work has shown that the diversity of yeast-display libraries is in the range of 108–109. Given that these libraries display randomly generated, synthetic peptides, sophisticated statistical and machine-learning algorithms were developed to enable the prediction of human peptides from the library selection data (Figure 3C). A major surprise from the initial studies of yeast pMHC libraries was that known peptide Ags could be recovered from an unbiased screen of the libraries [19,26,65]. Several reports using unbiased pMHC/pHLA display libraries reported crossreactivity of TCRs previously characterized in patients with multiple sclerosis, neoantigenspecific TCRs, and TIL-derived TCRs of unknown specificity (orphan TCRs) [19,25,26]. Using this approach, a novel shared Ag, U2AF2, was identified based on an overlap in target Ags bound by two TCRs isolated from two different patients with colorectal cancer [19]. In a more recent study, the yeast display screen was applied to profile a MAGE-A3-specific TCR previously tested in clinical trials, which ultimately showed fatal off-target cross-reactivity for titin, a heart-related peptide [22]. The yeast display technology was able to predict binding of the MAGE-A3 TCR to both the target Ag (MAGE-A3) and the off-target Ag (titin) [65]. Therefore, such methodologies could have utility to guide TCR-based therapeutics during lead optimization to minimize the cross-reactivity of TCRs with off-target Ags expressed on normal tissues. However, the yeast display technology is not without its drawbacks. Yeast display incorporates expression of pHLAs that contain yeast glycosylation patterns that do not necessarily mimic those of mammalian expression. Additionally, expression of mammalian peptides in yeast does not incorporate all post-translational modifications that exist in the human system. Finally, the theoretical peptide space cannot be completely encompassed within the limits of yeast-display library sizes (~109), limiting the ability to comprehensively survey the peptide landscape. A potential work-around is provided by including computational algorithms in the data analysis of consensus peptide sequences generated with synthetic peptide libraries to predict potential human targets that may not exist in the library composition. Additionally, there lacks the 12

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connectivity between TCRs and their HLA restriction, requiring screening against all patient HLA libraries (Figure 2). Furthermore, there is no direct functional TCR signal used for selection of matching TCR–pHLA pairs, because target Ags are selected based on their ability to bind to the TCR, but not by their potential to induce TCR signaling. Finally, the existence of peptides identified from yeast display libraries needs to be verified independently in peptide-processing studies. Despite the often strong binding to, and activation of, TCRs by peptides identified via yeast display, there is the potential that some of the initial hits are false positive, because they may fail to be processed by the proteasome complex in tumor cells or to be inefficiently loaded on the HLA complex in the endoplasmic reticulum (ER).

Phage-Display Libraries for TCR Target Discovery Before yeast- and baculovirus-based pHLA libraries, there were attempts to generate systems to display pHLA on phage. Despite these libraries being of considerably larger diversity (~1012), phage-display systems had limited success, as reviewed in [46]. Previous work showed that correctly folded pMHC molecules can be displayed on phage using covalently linked peptide with β2M and heavy chain; however, the complex was not able to activate T cells [66]. Furthermore, the numbers of pHLAs presented on the surface of phage via gpIII is approximately three to five copies [66], which is significantly less than the number of HLA complexes presented on APCs, yeast, or tumor cells (in the range of 1000–10 000). This results in substantially lower levels of avidity between TCRs and HLAs expressed on bacteria and a higher probability of missing low-affinity interactions between cognate TCR–pHLA pairs, which are typically in the micromolar range (Figure 3C).

Outstanding Questions What are the best strategies to identify antigenic and tumor-specific target Ags presented as peptide–HLA complexes on the cell surface of solid tumors that are most widely shared among patients and across solid tumor indications? How can we identify the most promising TCRs or TCR mimic compounds with the potential to induce complete tumor regressions when administered to patients with solid tumors? How do we select the most promising TCRs or TCR mimic compounds lacking cross-reactivities to Ags present on normal tissues? How can we develop diagnostics to identify patients with cancer who are most likely to respond to TCR-guided therapeutics? How do we select for TCR or TCR mimic compounds that can overcome treatment resistance?

Previous work determined that ~1–2% of phage express the murine MHC H2-Kd from a pool of 1013 phage [67]; interestingly, this was sufficient to activate a T cell hybridoma line. While T cell activation can be achieved one to ten molecules of pMHC binding, additional functional copy numbers are necessary at the surface to identify low-affinity TCR–pMHC interactions. To address the issue of poor functional expression, researchers constructed a library comprising a covalently linked peptide and β2M with exogenously added MHC [68]. By expressing this library, the OT-I TCR was screened, ultimately identifying two peptides resembling the wild-type ovalbumin (OVA) peptide presented by H-2Kb. A more successful case of panning a vesicular stomatitis virus (VSV)-specific TCR against the H-2Kb library resulted in 30 peptides; however, these peptides were of low potency for the TCRs they were selected against. In conclusion, the use of phage-display pMHC libraries to identify TCR ligands is suboptimal in that it is a low-avidity display technique prone to false negatives and the peptide ligands identified are few in number and many do not induce physiological T cell activation, pointing toward a lack of functional pMHC complexes isolated from phage-display screening.

Concluding Remarks For the successful treatment of solid tumors, identification of novel, truly tumor-specific targets is critical for high-potency oncology compounds. The lack of extracellular, cell surface Ags that are uniquely expressed on tumors, but not on normal tissues, has limited the progress of highpotency oncology compounds in solid tumors. The sites of on-target, off-tumor toxicities encountered during dose escalation studies with high-potency compounds targeting cell surface Ags frequently coincided with low-level expression of the target Ag on normal tissue cells at the affected sites. Importantly, the dose-limiting toxicities appeared at or below the drug exposure levels required for antitumor activity, indicative of a low therapeutic index. Thus, shifting the focus from extracellular toward intracellular targets, which are presented as pHLA complexes on the surface of tumor cells, carries the potential to correct the current imbalance between the Trends in Cancer, Month 2020, Vol. xx, No. xx

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rapid progress made in the development of high-potency therapeutic modalities and the paucity of targets that are selectively present on solid tumors. However, the future success of high-potency therapeutic modalities for the treatment of solid tumors is not only dependent on the discovery of novel, tumor-specific targets that are broadly shared among patients with cancer, but will also be defined by our ability to improve several key areas of oncology drug research and development, as discussed later. The utility of the emerging technologies reviewed here to improve each of these key areas of TCR based drug development, will be critical to advance next-generation immunotherapies. Identification of Novel, Widely Shared Intracellular Targets One of the most critical features of novel, tumor-specific intracellular targets is their degree of sharedness across patients and tumor indications to maximize the impact of future therapeutics targeting them. Traditionally, tumor-specific expression of target Ags was determined by using gene and/or protein expression-based methods to determine the frequency of target gene expression across patients and indications. However, these methods are not conducive to the identification of the most immunogenic epitopes derived from these proteins, leading to efficient T cell activation. By combining TCR repertoire profiling with library-based Ag identification approaches (Table 1), it is possible to identify the most immunogenic epitopes of intracellular targets. Several studies have demonstrated that TCRs with sequence similarities across patients and indications are likely to bind the same target Ag [69,70]. Such TCRs with shared alpha and/or beta chains can be readily identified by means of single cell sequencing of TILs or peripheral blood mononuclear cells (PBMCs). Thus, one potential selection criterion to select novel target Ags is to select the most highly expanded TILs expressing TCRs that share either the alpha and/or the beta chain across patients, because they are likely to recognize the most widely shared, immunogenic cancer target Ags. Once TCRs, or T cell pools, are chosen for screening on a given Ag identification platform, it is imperative to screen them against a target Ag identification library to deorphanize the TCRs. Libraries with functional readouts to directly measure TCR activation and/or proliferation enable the identification of Ags with highest immunogenicity. However, when using mammalian reporter libraries with smaller diversities and lower sensitivity, it is difficult to cover all potential target Ag classes in a comprehensive manner. Despite these limitations, mammalian cell-based libraries have utility to identify immunogenic Ags within a subclass of intracellular, tumor-specific targets that are limited in numbers, such as neo-antigens or viral antigens. However, to comprehensively profile TCR cross-reactivities, additional improvement in library diversity will be required to deorphanize TCRs with unknown target Ag specificities. Finally, most of the intracellular targets currently pursued in the clinic were originally identified by molecular biology-based approaches in combination with MS. By contrast, the target Ags identified by bioinformatics- or high-throughput panning methods, as reported recently, have not yet been validated in the clinic. Therefore, while the recent progress in the identification of novel intracellular targets by tumor sequencing or high throughput panning approaches has been impressive, in particular in the areas of personalized cancer vaccines, the impact of these new technologies on clinical trial outcomes of T cell therapies relative conventional approaches can only be appreciated in the years to come after their clinical response rates have been determined. Identification of Safe TCRs with Potent Antitumor Activities The most promising technologies to identify antitumor reactive TCRs are based on functional read outs of select TCRs, inducing a measurable signal following engagement of the TCRs with their 14

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cognate pHLA complexes. Therefore, TCR–pHLA panning approaches using a direct TCR signaling and/or functional activation readout are advantageous to select the most antitumor reactive TCRs. This is of particular importance because of the unique features of pHLA–TCR interactions, whereby binding affinities alone are not predictive of their antitumor activities [71]. However, the optimal tissues to identify the most potent, tumor-specific T cells and TCRs remain unclear. We posit that tumor samples or PBMCs from patients with strong antitumor responses during ICI treatment may be enriched in CD8+ T cells expressing TCRs that are highly antitumor reactive. By obtaining samples from these patients, it may be possible to identify T cell clones that are specific to the Ag of interest and that can be used as lead therapeutic compounds. Therefore, combining methods with functional readouts (TCR activation) or binding readouts (such as tetramer staining) may be most impactful to select for pHLA-specific T cells with highly antitumor reactive TCRs. This may be helpful to replace the strategy used in the past, to affinity maturate endogenous TCRs to enforce potent antitumor responses. Caveats associated with this approach were extensively discussed earlier. Identification of TCRs with Minimal Cross-Reactivities to Normal Tissue Antigens Comprehensive TCR profiling to map cross-reactivities of TCRs during the compound selection process is key to minimize off-tumor, off-target cross-reactivities of therapeutic TCRs. TCRs have different degrees of target selectivity and tend to recognize more than one target Ag. While some of the cross-reactive peptides are closely related to each other, others have entirely unrelated sequence compositions, apart from the conserved anchor amino acids. For the former, positional scanning methods across the peptide have been used successfully to screen TCRs in a systematic way to guide lead compound selection and to predict potential off-target, off-tumor toxicities. However, for low-sequence homology peptides, which cannot be identified by conventional positional scanning methods, additional methods need to be developed to account for covariation within the peptide space. Therefore, an important feature for a platform to enable comprehensive profiling of candidate TCR or TCR mimetics is the diversity of the library. The use of pHLA libraries with large diversities is critical for the derisking of clinical programs using TCRs or TCR mimic compounds (Table 1) and to avoid unexpected, off-target, off-tumor toxicities during early-stage clinical development, as observed for some of the first-generation TCR-based therapeutics [22]. While it has been proposed that mammalian cell-based TCR screening methods can be used to screen for cross-reactivity, there are certain limits to these technologies. First, they rely on APCs or dendritic cells (DC) to process and present peptides in a similar way as tumor cells do. Given the known differences in how immune and cancer cells process intracellular peptides, especially during MHC downregulation [72,73], there remains a risk that target Ags identified using mammalian cell-based screening approaches may not be processed and presented efficiently by primary human tumor cells [74]. An alternative and promising approach to identify TCRs with minimal cross-reactivities is the use of PBMCs or TILs from patients treated with ICI compounds who experienced complete antitumor responses without signs of autoimmunity. The clinical proof of concept was provided by the many patients with cancer who mounted complete antitumor responses when treated with ICI, reviewed in [75], or with autologous TIL preparations [76]. One important consideration is that adoptive T cell therapies using engineered T cells expressing exogenous TCRs can lead to massively increased numbers of antitumor reactive T cells following infusion of TCR-T into patients compared with endogenous T cell response toward tumor Ags. This can lead to nonphysiologically high exposure levels of one specific TCR-T clone adoptively Trends in Cancer, Month 2020, Vol. xx, No. xx

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transferred to patients with cancer compared with autologous TILs or anti-PD-1 treatment, which will have more diverse TCR repertoires. Therefore, small impurities in the binding specificities of the therapeutic TCRs present in TCR-T, which may not have manifested in toxicity during treatment with TILs or during anti PD-1 treatment because of the lower abundance of this specific clone, may become amplified significantly when using TCR-T. In conclusion, the identification of highly tumor reactive TCRs from patients with complete responses to ICI treatment and devoid of autoimmune toxicity in combination with technologies to comprehensively profile their pHLA specificity will enable the selection of the most tumor Agspecific and antitumor-reactive TCRs. Such a combination of novel technologies may be required to minimize the potential for novel, off-target off-tumor toxicity of TCR-T therapies that have previously significantly impacted their clinical success. Diagnostics for TCR-Guided Compounds Another challenge in the development of therapeutic compounds targeting pHLA complexes is the inherent difficulty of detecting pHLA complexes on the surface of tumor cells due to their low abundance and limited access to the unique epitope. However, the unmodified TCR cannot usually be used as a diagnostic compound due to its low binding affinity and low Ag density on the surface of cells. Interestingly, affinity-matured TCRs have been shown to circumvent these limitations and to bind to target pHLA complexes expressed at low levels on tumor cells [77,78]. However, affinity maturation of the diagnostic compound is likely to introduce novel cross-reactivities toward unknown targets, as reported for several affinityenhanced TCR therapeutics. However, it is conceivable that some of the high-throughput pHLA profiling methods developed to guide therapeutic development of TCRs or murine (m) TCR compounds will also be instrumental for the development of companion diagnostics. These pHLA-specific compounds recognizing the fraction of target peptides presented on tumor cells will enable the selection of patients with cancer who are most likely to respond to TCR-guided therapeutics. Finally, the most sensitive assay to detect pHLA complexes on tumor cells is TCR-T because of its ability to detect and kill tumor cells with a single copy of pHLA on the surface [79]. Therefore, incubation of freshly biopsied cancer cells with TCR-T is the most sensitive diagnostic assay. However, the complexities of acquiring fresh tumor biopsies combined with the difficulty of establishing a highly reproducible ex vivo tumor cell killing assay that can be used for clinical decision-making, have so far prevented the wider use of this assay in clinical practice. Previously, the most commonly used diagnostic assay in conjunction with TCR-T was conventional, IHC-based assays for whole protein using formalin-fixed tumor tissues combined with HLA typing. These well-established methods may be sufficient for target Ags that are proteolytically processed and presented as pHLA in a highly efficient manner. In the case of NY-ESO-1, patients enrolled using this diagnostic strategy displayed up to a 70% objective response (synovial carcinomas) and up to a 45% response in multiple myeloma [23,80], suggesting that IHC-based approaches can be sufficient to achieve meaningful antitumor responses. TCR-Guided Therapeutics to Overcome Resistance to Therapy Consistent with a central role for cytotoxic T cells as key effector cells in patients treated with ICI, mutations in genes encoding components of the MHC-I Ag processing pathway (APP) or the interferon (IFN)-γ response pathway have emerged as a frequent cause of both primary and acquired resistance [81–86]. Mutations in APP, including HLA and the beta-2 microglobulin gene (b2M), both of which are required for cell surface presentation of peptide Ags, were found 16

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to be increased in tumors resistant to immunotherapy [87–89]. However, resistance to cancer immunotherapies may occur through not only genomic mutations that inactivate the MHC-I and/or APP, but also nongenomic mechanisms that exploit the activity of repressive chromatin complexes, such as PRC2 [74]. The resulting downregulation of HLA facilitates evasion of immune surveillance of the mutated tumors. Conversely, augmenting the IFN-γ response and MHC-I expression enhances T cellmediated antitumor immunity [83–85]. Therefore, the most compelling and validated strategy to increase IFN-γ levels in tumors is to increase the number of activated CD8+ T cells in the tumor mass, where they constitute the major source of IFN-γ secretion. An efficient way to achieve this goal is by targeting tumor-specific targets in solid tumors, thereby avoiding the off-target, off-tumor toxicities that currently limit dose escalations of high-potency modalities. In addition to HLA upregulation, increased intratumoral IFN-γ levels can ultimately lead to epitope spreading and recruitment of endogenous T cells [90,91]. Epitope spreading was first observed in studies conducted with cancer vaccines, where a new wave of tumor-specific cytotoxic T lymphocyte (CTL) clones became detectable, recognizing tumor-specific, mutated Ags that were not present in the cancer vaccines. Epitope spreading was more broadly associated with stronger responses in patients with cancer receiving TILs [92,93], TCR-T [94], and following PD-1 treatment [91]. The key driver of epitope spreading is an increase in intratumoral levels of IFN-γ [95]. Therefore, the common denominator associated with the development of resistance toward immunotherapies is the inability to achieve sufficiently high threshold levels of IFN-γ within the tumor mass to induce epitope spreading. Such suboptimal drug exposure levels result in prolonged periods of tumor stasis or growth, allowing for immune editing and outgrowth of tumor cells as a consequence of low IFN-γ levels, ultimately leading to treatment resistance to immuno-oncology (IO) therapies. We posit that increasing the exposure levels of TCR-guided therapeutics may be sufficient to achieve threshold levels of intratumoral IFN-γ, triggering both HLA upregulation and epitope spreading, as observed in patients with PD-1-responding solid tumor [91] and patients with melanoma treated with a cancer vaccine targeting the cancer testis Ags MAGE-A3 and A1 [90]. Therefore, the future success of high-potency targeted therapeutics in solid tumors depends on the identification of novel, tumor-specific targets and our ability to select compounds targeting them selectively to unleash their full potential and to induce deep and durable antitumor responses.

Acknowledgments We would like to thank K. Christopher Garcia and Mark M. Davis for critical reading of the manuscript and their helpful comments and discussions.

References 1. 2.

3. 4.

5.

Darvin, P. et al. (2018) Immune checkpoint inhibitors: recent progress and potential biomarkers. Exp. Mol. Med. 50, 165 Spranger, S. and Gajewski, T.F. (2018) Impact of oncogenic pathways on evasion of antitumour immune responses. Nat. Rev. Cancer 18, 139–147 Yost, K.E. et al. (2019) Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 Rizvi, N.A. et al. (2015) Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in nonsmall cell lung cancer. Science 348, 124–128 Robbins, P.F. et al. (2013) Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat. Med. 19, 747–752

6.

7.

8.

9.

10.

Snyder, A. et al. (2014) Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 Stevanovic, S. et al. (2017) Landscape of immunogenic tumor antigens in successful immunotherapy of virally induced epithelial cancer. Science 356, 200–205 Tran, E. et al. (2016) T-cell transfer therapy targeting mutant KRAS in cancer. N. Engl. J. Med. 375, 2255–2262 Tran, T.L. et al. (2014) Dammarane triterpenes from the leaves of Panax ginseng enhance cellular immunity. Bioorg. Med. Chem. 22, 499–504 Shields, B.D. et al. (2017) Indicators of responsiveness to immune checkpoint inhibitors. Sci. Rep. 7, 807

Trends in Cancer, Month 2020, Vol. xx, No. xx

17

Trends in Cancer

11.

12. 13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26. 27.

28. 29.

30.

31.

32.

33.

34.

35.

18

Zacharakis, N. et al. (2018) Immune recognition of somatic mutations leading to complete durable regression in metastatic breast cancer. Nat. Med. 24, 724–730 Yang, J.C. and Rosenberg, S.A. (2016) Adoptive T-cell therapy for cancer. Adv. Immunol. 130, 279–294 Sade-Feldman, M. et al. (2019) Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 176, 404 Gubin, M.M. et al. (2014) Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 Tumeh, P.C. et al. (2014) PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 Cha, E. et al. (2014) Improved survival with T cell clonotype stability after anti-CTLA-4 treatment in cancer patients. Sci. Transl. Med. 6, 238ra70 Postow, M.A. et al. (2015) Peripheral T cell receptor diversity is associated with clinical outcomes following ipilimumab treatment in metastatic melanoma. J. Immunother. Cancer 3, 23 Robert, L. et al. (2014) CTLA4 blockade broadens the peripheral T-cell receptor repertoire. Clin. Cancer Res. 20, 2424–2432 Gee, M.H. et al. (2018) Antigen identification for orphan T cell receptors expressed on tumor-infiltrating lymphocytes. Cell 172, 549–563 Munson, D.J. et al. (2016) Identification of shared TCR sequences from T cells in human breast cancer using emulsion RT-PCR. Proc. Natl. Acad. Sci. U. S. A. 113, 8272–8277 Cameron, B.J. et al. (2013) Identification of a Titin-derived HLAA1-presented peptide as a cross-reactive target for engineered MAGE A3-directed T cells. Sci. Transl. Med. 5, 197ra103 Linette, G.P. et al. (2013) Cardiovascular toxicity and titin cross-reactivity of affinity-enhanced T cells in myeloma and melanoma. Blood 122, 863–871 Rapoport, A.P. et al. (2015) NY-ESO-1-specific TCRengineered T cells mediate sustained antigen-specific antitumor effects in myeloma. Nat. Med. 21, 914–921 Stadtmauer, E.A. et al. (2019) Autologous transplantation, consolidation, and maintenance therapy in multiple myeloma: results of the BMT CTN 0702 Trial. J. Clin. Oncol. 37, 589–597 Adams, J.J. et al. (2016) Structural interplay between germline interactions and adaptive recognition determines the bandwidth of TCR-peptide-MHC cross-reactivity. Nat. Immunol. 17, 87–94 Birnbaum, M.E. et al. (2014) Deconstructing the peptide-MHC specificity of T cell recognition. Cell 157, 1073–1087 Riley, T.P. and Baker, B.M. (2018) The intersection of affinity and specificity in the development and optimization of T cell receptor based therapeutics. Semin. Cell Dev. Biol. 84, 30–41 Davis, M.M. and Bjorkman, P.J. (1988) T-cell antigen receptor genes and T-cell recognition. Nature 334, 395–402 Garboczi, D.N. et al. (1996) Assembly, specific binding, and crystallization of a human TCR-alphabeta with an antigenic Tax peptide from human T lymphotropic virus type 1 and the class I MHC molecule HLA-A2. J. Immunol. 157, 5403–5410 Garboczi, D.N. et al. (1996) Structure of the complex between human T-cell receptor, viral peptide and HLA-A2. Nature 384, 134–141 Garcia, K.C. et al. (1996) An alphabeta T cell receptor structure at 2.5 A and its orientation in the TCR-MHC complex. Science 274, 209–219 Robins, H.S. et al. (2010) Overlap and effective size of the human CD8+ T cell receptor repertoire. Sci. Transl. Med. 2, 47ra64 Goldrath, A.W. and Bevan, M.J. (1999) Low-affinity ligands for the TCR drive proliferation of mature CD8+ T cells in lymphopenic hosts. Immunity 11, 183–190 Goldrath, A.W. and Bevan, M.J. (1999) Selecting and maintaining a diverse T-cell repertoire. Nature 402, 255–262 Sprent, J. and Kishimoto, H. (1998) T cell tolerance and the thymus. Ann. N. Y. Acad. Sci. 841, 236–245

Trends in Cancer, Month 2020, Vol. xx, No. xx

36.

37. 38.

39. 40.

41.

42. 43.

44.

45.

46.

47.

48.

49.

50.

51.

52.

53. 54.

55.

56.

57.

58.

59.

60. 61.

Stockinger, B. (1999) T lymphocyte tolerance: from thymic deletion to peripheral control mechanisms. Adv. Immunol. 71, 229–265 Arstila, T.P. et al. (1999) A direct estimate of the human alphabeta T cell receptor diversity. Science 286, 958–961 Brown, S.D. and Holt, R.A. (2019) Neoantigen characteristics in the context of the complete predicted MHC class I self– immunopeptidome. Oncoimmunology 8, 1556080 Lythe, G. et al. (2016) How many TCR clonotypes does a body maintain? J. Theor. Biol. 389, 214–224 Mason, L.H. et al. (1998) Characterization of an associated 16kDa tyrosine phosphoprotein required for Ly-49D signal transduction. J. Immunol. 160, 4148–4152 Robins, H.S. et al. (2009) Comprehensive assessment of T-cell receptor beta-chain diversity in alphabeta T cells. Blood 114, 4099–4107 Walseng, E. et al. (2017) A TCR-based chimeric antigen receptor. Sci. Rep. 7, 10713 Bentzen, A.K. et al. (2018) T cell receptor fingerprinting enables in-depth characterization of the interactions governing recognition of peptide–MHC complexes. Nat. Biotechnol. 36, 1191–1196 Ichiki, Y. et al. (2004) Simultaneous cellular and humoral immune response against mutated p53 in a patient with lung cancer. J. Immunol. 172, 4844–4850 Sharkey, M.S. et al. (2004) CD4(+) T-cell recognition of mutated B-RAF in melanoma patients harboring the V599E mutation. Cancer Res. 64, 1595–1599 Birnbaum, M.E. et al. (2012) Diversity-oriented approaches for interrogating T-cell receptor repertoire, ligand recognition, and function. Immunol. Rev. 250, 82–101 Border, E.C. et al. (2019) Affinity-enhanced T-cell receptors for adoptive T-cell therapy targeting MAGE-A10: strategy for selection of an optimal candidate. Oncoimmunology 8, e1532759 Harper, J. et al. (2018) An approved in vitro approach to preclinical safety and efficacy evaluation of engineered T cell receptor anti-CD3 bispecific (ImmTAC) molecules. PLoS ONE 13, e0205491 Riley, T.P. et al. (2018) T cell receptor cross-reactivity expanded by dramatic peptide-MHC adaptability. Nat. Chem. Biol. 14, 934–942 Malekzadeh, P. et al. (2019) Neoantigen screening identifies broad TP53 mutant immunogenicity in patients with epithelial cancers. J. Clin. Invest. 129, 1109–1114 Parkhurst, M. et al. (2017) Isolation of T-cell receptors specifically reactive with mutated tumor-associated antigens from tumor-infiltrating lymphocytes based on CD137 expression. Clin. Cancer Res. 23, 2491–2505 Hu, Z. et al. (2018) A cloning and expression system to probe T-cell receptor specificity and assess functional avidity to neoantigens. Blood 132, 1911–1921 Sahin, U. and Tureci, O. (2018) Personalized vaccines for cancer immunotherapy. Science 359, 1355–1360 Gejman, R.S. et al. (2018) Identification of the targets of T cell receptor therapeutic agents and cells by use of a high throughput genetic platform. bioRxiv. Published online September 22, 3018. https://doi.org/10.1101/267047 Joglekar, A.V. et al. (2019) T cell antigen discovery via signaling and antigen-presenting bifunctional receptors. Nat. Methods 16, 191–198 Kisielow, J. et al. (2019) Deciphering CD4(+) T cell specificity using novel MHC–TCR chimeric receptors. Nat. Immunol. 20, 652–662 Ruhl, G. et al. (2016) Multiple sclerosis: molecular mimicry of an antimyelin HLA class I restricted T-cell receptor. Neurol. Neuroimmunol. Neuroinflamm. 3, e241 Siewert, K. et al. (2012) Unbiased identification of target antigens of CD8+ T cells with combinatorial libraries coding for short peptides. Nat. Med. 18, 824–828 Li, D. et al. Early prenatal diagnosis of lysosomal storage disorders by enzymatic and molecular analysis. Prenat. Diagn. 38, 779–787. Kula, T. et al. (2019) T-scan: a genome-wide method for the systematic discovery of T cell epitopes. Cell 178, 1016–1028 Wooldridge, M. (2012) Evidence for the circulation of antimicrobial-resistant strains and genes in nature and

Trends in Cancer

62.

63.

64.

65.

66.

67.

68.

69. 70. 71.

72.

73.

74.

75. 76.

77.

78.

79.

80.

81.

82. 83.

84.

85.

especially between humans and animals. Rev. Sci. Tech. 31, 231–247 De Simone, M. et al. (2018) Single cell T cell receptor sequencing: techniques and future challenges. Front. Immunol. 9, 1638 Segaliny, A.I. et al. (2018) Functional TCR T cell screening using single-cell droplet microfluidics. Lab Chip 18, 3733–3749 Crawford, F. et al. (2006) Use of baculovirus MHC/peptide display libraries to characterize T-cell receptor ligands. Immunol. Rev. 210, 156–170 Gee, M.H. et al. (2018) Facile method for screening clinical T cell receptors for off-target peptide–HLA reactivity. bioRxiv. Published online November 22, 2018. https://doi.org/ 10.1101/472480 Ostergaard Pedersen, L. et al. (2001) Efficient assembly of recombinant major histocompatibility complex class I molecules with preformed disulfide bonds. Eur. J. Immunol. 31, 2986–2996 Le Doussal, J. et al. (2000) Phage display of peptide/major histocompatibility complex. J. Immunol. Methods 241, 147–158 Dogan, I. et al. (2004) Phage-displayed libraries of peptide/ major histocompatibility complexes. Eur. J. Immunol. 34, 598–607 Dash, P. et al. (2017) Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89–93 Glanville, J. et al. (2017) Identifying specificity groups in the T cell receptor repertoire. Nature 547, 94–98 Sibener, L.V. et al. (2018) Isolation of a structural mechanism for uncoupling T cell receptor signaling from peptide-MHC binding. Cell 174, 672–687 Marijt, K.A. et al. (2018) Identification of non-mutated neoantigens presented by TAP-deficient tumors. J. Exp. Med. 215, 2325–2337 Oliveira, C.C. et al. (2011) Peptide transporter TAP mediates between competing antigen sources generating distinct surface MHC class I peptide repertoires. Eur. J. Immunol. 41, 3114–3124 Burr, M.L. et al. (2019) An evolutionarily conserved function of polycomb silences the MHC class I antigen presentation pathway and enables immune evasion in cancer. Cancer Cell 36, 385–401 June, C.H. et al. (2017) Is autoimmunity the Achilles' heel of cancer immunotherapy? Nat. Med. 23, 540–547 Rosenberg, S.A. et al. (2011) Durable complete responses in heavily pretreated patients with metastatic melanoma using T-cell transfer immunotherapy. Clin. Cancer Res. 17, 4550–4557 Bossi, G. et al. (2013) Examining the presentation of tumorassociated antigens on peptide-pulsed T2 cells. Oncoimmunology 2, e26840 Purbhoo, M.A. et al. (2006) Quantifying and imaging NY-ESO1/LAGE-1-derived epitopes on tumor cells using high affinity T cell receptors. J. Immunol. 176, 7308–7316 Sykulev, Y. et al. (1996) Evidence that a single peptide-MHC complex on a target cell can elicit a cytolytic T cell response. Immunity 4, 565–571 Stadtmauer, E.A. et al. (2019) Long-term safety and activity of NY-ESO-1 SPEAR T cells after autologous stem cell transplant for myeloma. Blood Adv. 3, 2022–2034 Gao, J. et al. (2016) Loss of IFN-gamma pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell 167, 397–404 Kearney, C.J. et al. (2018) Tumor immune evasion arises through loss of TNF sensitivity. Sci. Immunol. 3, eaar3451 Manguso, R.T. et al. (2017) In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547, 413–418 Pan, C. et al. (2018) MiR-544 promotes immune escape through downregulation of NCR1/NKp46 via targeting RUNX3 in liver cancer. Cancer Cell Int. 18, 52 Patel, H. et al. (2017) Frequent inoculations with radiation attenuated sporozoite is essential for inducing sterile protection that correlates with a threshold level of Plasmodia liver-stage specific CD8(+) T cells. Cell. Immunol. 317, 48–54

86.

87.

88.

89.

90.

91.

92.

93.

94.

95. 96.

97.

98.

99. 100. 101.

102.

103.

104. 105.

106.

107. 108.

109.

110.

Zaretsky, J.M. et al. (2016) Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 Bernards, R. et al. (1986) N-myc amplification causes downmodulation of MHC class I antigen expression in neuroblastoma. Cell 47, 667–674 Paulson, K.G. et al. (2018) Acquired cancer resistance to combination immunotherapy from transcriptional loss of class I HLA. Nat. Commun. 9, 3868 Restifo, N.P. et al. (1993) Molecular mechanisms used by tumors to escape immune recognition: immunogenetherapy and the cell biology of major histocompatibility complex class I. J. Immunother. Emphasis Tumor Immunol. 14, 182–190 Corbiere, V. et al. (2011) Antigen spreading contributes to MAGE vaccination-induced regression of melanoma metastases. Cancer Res. 71, 1253–1262 Memarnejadian, A. et al. (2017) PD-1 blockade promotes epitope spreading in anticancer CD8(+) T cell responses by preventing fratricidal death of subdominant clones to relieve immunodomination. J. Immunol. 199, 3348–3359 Khammari, A. et al. (2009) Treatment of metastatic melanoma with autologous Melan-A/MART-1-specific cytotoxic T lymphocyte clones. J. Invest. Dermatol. 129, 2835–2842 Vignard, V. et al. (2005) Adoptive transfer of tumor-reactive Melan-A-specific CTL clones in melanoma patients is followed by increased frequencies of additional Melan-A-specific T cells. J. Immunol. 175, 4797–4805 Hunder, N.N. et al. (2008) Treatment of metastatic melanoma with autologous CD4+ T cells against NY-ESO-1. N. Engl. J. Med. 358, 2698–2703 Castro, F. et al. (2018) Interferon-gamma at the crossroads of tumor immune surveillance or evasion. Front. Immunol. 9, 847 Herin, M. et al. (1987) Production of stable cytolytic T-cell clones directed against autologous human melanoma. Int. J. Cancer 39, 390–396 van der Bruggen, P. et al. (1991) A gene encoding an antigen recognized by cytolytic T lymphocytes on a human melanoma. Science 254, 1643–1647 Cox, A.L. et al. (1994) Identification of a peptide recognized by five melanoma-specific human cytotoxic T cell lines. Science 264, 716–719 Castle, J.C. et al. (2012) Exploiting the mutanome for tumor vaccination. Cancer Res. 72, 1081–1091 Klenerman, P. et al. (2002) Tracking T cells with tetramers: new tales from new tools. Nat. Rev. Immunol. 2, 263–272 Bentzen, A.K. et al. (2016) Large-scale detection of antigenspecific T cells using peptide-MHC-I multimers labeled with DNA barcodes. Nat. Biotechnol. 34, 1037–1045 Zhang, S.Q. et al. (2018) High-throughput determination of the antigen specificities of T cell receptors in single cells. Nat. Biotechnol. 36, 1156–1159 Jurtz, V. et al. (2017) NetMHCpan-4.0: improved peptideMHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 Rammensee, H. et al. (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50, 213–219 Bulik-Sullivan, B. et al. (2018) Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nat. Biotechnol. 37, 55–63 Obenaus, M. et al. (2015) Identification of human T-cell receptors with optimal affinity to cancer antigens using antigen-negative humanized mice. Nat. Biotechnol. 33, 402–407 Li, G. et al. (2019) T cell antigen discovery via trogocytosis. Nat. Methods 16, 183–190 Laydon, D.J. et al. (2015) Estimating T-cell repertoire diversity: limitations of classical estimators and a new approach. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 370, 20140291 Markov, P.V. and Pybus, O.G. (2015) Evolution and diversity of the human leukocyte antigen (HLA). Evol. Med. Public Health 2015, 1 Rosenberg, S.A. (2011) Cell transfer immunotherapy for metastatic solid cancer–what clinicians need to know. Nat. Rev. Clin. Oncol. 8, 577–585

Trends in Cancer, Month 2020, Vol. xx, No. xx

19

Trends in Cancer

111. Van den Eynde, B. et al. (1989) Presence on a human melanoma of multiple antigens recognized by autologous CTL. Int. J. Cancer 44, 634–640 112. Simpson, P.M. et al. (2005) Ewing's sarcoma of the upper extremity: presenting symptoms, diagnostic delay and outcome. Sarcoma 9, 15–20 113. Haas Jr., G.G. et al. (1988) Distribution of human leukocyte antigen-ABC and -D/DR antigens in the unfixed human testis. Am. J. Reprod. Immunol. Microbiol. 18, 47–51 114. Altman, D.G. and Bland, J.M. (1996) Absence of evidence is not evidence of absence. Aust. Vet. J. 74, 311 115. Newell, E.W. and Davis, M.M. (2014) Beyond model antigens: high-dimensional methods for the analysis of antigen-specific T cells. Nat. Biotechnol. 32, 149–157 116. Bentzen, A.K. and Hadrup, S.R. (2017) Evolution of MHCbased technologies used for detection of antigen-responsive T cells. Cancer Immunol. Immunother. 66, 657–666 117. Zhang, A.W. et al. (2018) Interfaces of malignant and immunologic clonal dynamics in ovarian cancer. Cell 173, 1755–1769 118. Simon, P. et al. (2014) Functional TCR retrieval from single antigen-specific human T cells reveals multiple novel epitopes. Cancer Immunol. Res. 2, 1230–1244 119. Adams, E.J. and Lopez-Sagaseta, J. (2011) The immutable recognition of CD1d. Immunity 34, 281–283 120. Bentzen, P. and Bradbury, I.R. (2016) Don't bet against the natal homing abilities of marine fishes. Mol. Ecol. 25, 2691–2692 121. Yu, W. et al. (2015) Clonal deletion prunes but does not eliminate self-specific alphabeta CD8(+) T lymphocytes. Immunity 42, 929–941 122. Moon, J.J. et al. (2007) Naive CD4(+) T cell frequency varies for different epitopes and predicts repertoire diversity and response magnitude. Immunity 27, 203–213 123. Abelin, J.G. et al. (2017) Mass spectrometry profiling of HLAassociated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46, 315–326 124. Croft, N.P. et al. (2015) Quantifying epitope presentation using mass spectrometry. Mol. Immunol. 68, 77–80 125. Caron, E. et al. (2015) Analysis of major histocompatibility complex (MHC) immunopeptidomes using mass spectrometry. Mol. Cell. Proteomics 14, 3105–3117 126. Vigneron, N. (2015) Human tumor antigens and cancer immunotherapy. Biomed. Res. Int. 2015, 948501 127. Vigneron, N. et al. (2013) Database of T cell-defined human tumor antigens: the 2013 update. Cancer Immun. 13, 15 128. Bassani-Sternberg, M. et al. (2015) Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong

20

Trends in Cancer, Month 2020, Vol. xx, No. xx

129.

130.

131.

132. 133. 134.

135. 136.

137.

138.

139. 140.

141.

142.

143.

144.

effects of protein abundance and turnover on antigen presentation. Mol. Cell. Proteomics 14, 658–673 Creech, A.L. et al. (2018) The role of mass spectrometry and proteogenomics in the advancement of HLA epitope prediction. Proteomics 18, e1700259 Kalaora, S. et al. (2018) Combined analysis of antigen presentation and T-cell recognition reveals restricted immune responses in melanoma. Cancer Discov. 8, 1366–1375 Sahin, U. et al. (2017) Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature 547, 222–226 Lee, C.H. et al. (2018) Update on tumor neoantigens and their utility: why it is good to be different. Trends Immunol. 39, 536–548 Mullard, A. (2017) New cancer vaccines show clinical promise. Nat. Rev. Drug Discov. 16, 519 Skipper, J.C. et al. (1996) An HLA-A2-restricted tyrosinase antigen on melanoma cells results from posttranslational modification and suggests a novel pathway for processing of membrane proteins. J. Exp. Med. 183, 527–534 Hilf, N. et al. (2019) Actively personalized vaccination trial for newly diagnosed glioblastoma. Nature 565, 240–245 Parker, K.C. et al. (1994) Pocket mutations of HLA-B27 show that anchor residues act cumulatively to stabilize peptide binding. Biochemistry 33, 7736–7743 Jurtz, V.I. et al. (2017) An introduction to deep learning on biological sequence data: examples and solutions. Bioinformatics 33, 3685–3690 Samstein, R.M. et al. (2019) Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 Wirth, T.C. and Kuhnel, F. (2017) Neoantigen targeting–dawn of a new era in cancer immunotherapy? Front. Immunol. 8, 1848 Coulie, P.G. et al. (2014) Tumour antigens recognized by T lymphocytes: at the core of cancer immunotherapy. Nat. Rev. Cancer 14, 135–146 Van Der Bruggen, P. et al. (2002) Tumor-specific shared antigenic peptides recognized by human T cells. Immunol. Rev. 188, 51–64 Klebanoff, C.A. and Wolchok, J.D. (2018) Shared cancer neoantigens: making private matters public. J. Exp. Med. 215, 5–7 Horst, D. et al. (2009) The cancer stem cell marker CD133 has high prognostic impact but unknown functional relevance for the metastasis of human colon cancer. J. Pathol. 219, 427–434 Wooldridge, L. et al. (2012) A single autoimmune T cell receptor recognizes more than a million different peptides. J. Biol. Chem. 287, 1168–1177