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. Author manuscript; available in PMC: 2022 Jan 17.
Published in final edited form as: J Invest Dermatol. 2021 Apr 13;141(8):1897–1905. doi: 10.1016/j.jid.2020.12.037

Quality Is King: Fundamental Insights into Tumor Antigenicity from Virus-Associated Merkel Cell Carcinoma

Miranda C Lahman 1,2, Kelly G Paulson 1,2,3,4, Paul T Nghiem 1,2,5, Aude G Chapuis 1,2,5
PMCID: PMC8763020  NIHMSID: NIHMS1768715  PMID: 33863500

Abstract

Merkel cell carcinoma (MCC) is a rare skin malignancy that is a paradigm cancer for solid tumor immunotherapy. MCCs associated with Merkel cell polyomavirus (virus-positive MCC [VP-MCC]) or chronic UV exposure (virus-negative MCC [VN-MCC]) are anti–PD(L)1 responsive, despite VP-MCC’s low mutational burden. This suggests that antigen quality, not merely mutation quantity, dictates immunotherapy responsiveness, and cell-based therapies targeting optimal antigens may be effective. Despite VP-MCC’s antigenic homogeneity, diverse T-cell infiltration patterns are observed, implying microenvironment plasticity and multifactorial contributions to immune recognition. Moreover, VP-MCC exemplifies how antitumor adaptive immunity can provide tumor burden biomarkers for early detection and disease monitoring.

Introduction

Merkel cell carcinoma (MCC) is a rare skin cancer with increasing incidence. It disproportionally affects elderly and immunocompromised persons, indicating that immune suppression favors disease progression (Albores-Saavedra et al., 2010; Penn and First, 1999). Risk factors include UV light exposure and fair skin, similar to other skin malignancies and consistent with UV-derived mutations driving oncogenesis (Albores-Saavedra et al., 2010; Heath et al., 2008; Wong et al., 2015). In 2008, clonal integration of the Merkel cell polyomavirus (MCPyV) revealed a second oncogenic driver (Feng et al., 2008), subdividing MCC into virus-positive MCC (VP-MCC) (~80% of cases in the United States), and UV-associated MCCs (virus-negative MCC [VN-MCC]) (Figure 1) (Becker et al., 2009; Feng et al., 2008; Kassem et al., 2008). VP-MCCs are driven by obligate expression of conserved MCPyV-derived oncoproteins and large and small T antigen, with few additional genomic mutations (Goh et al., 2016; Houben et al., 2010; Knepper et al., 2019). This makes VP-MCC genetically quite similar across patients and considerably more homogeneous than most cancers. Uniform (VP-MCC) and diverse (VN-MCC) variants of the same cancer make MCC a natural model for studying cancere–immune interactions.

Figure 1. MCC pathogenetic pathways and immune infiltrate patterns.

Figure 1.

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(a) Schematic of MCC dual pathways to pathogenesis. MCPyV drives oncogenesis in VP-MCC (left, blue). VP-MCC has a low median TMB of 1.2 mutations per MB (Knepper et al., 2019). Many UV mutations are present in VN-MCC (right, green), corresponding to a very high median TMB of 63.1 mutations per MB (Knepper et al., 2019). (b) Illustrations of the three major cancer immune infiltrate patterns, all observed in MCC and partially predictive of anti–PD-1 response (Chen and Mellman, 2017): T cell Desert, no observed T cells in the tumor; T cell Excluded, T cells restricted to the tumor periphery; and T cell Inflamed, T cells infiltrate the tumor. MB, Megabase; MCC, Merkel cell carcinoma; MCPyV, Merkel cell polyomavirus; TMB, tumor mutational burden; VN-MCC, virus-negative Merkel cell carcinoma; VP-MCC, virus-positive Merkel cell carcinoma.

Immune checkpoint blockade (ICB) (Ishida et al., 1992; Leach et al., 1996) of the PD-1 axis has shown widespread clinical benefit across cancer types (Brahmer et al., 2012; Galanina et al., 2018; Herbst et al., 2014; Nghiem et al., 2016; Yarchoan et al., 2017). ICB improves outcomes for approximately 50% of patients with MCC—one of the highest solid tumor response rates (Becker et al., 2017; Paulson et al., 2019)—and National Comprehensive Cancer Network guidelines now recommend ICB therapy as first-line treatment for metastatic MCC (Bichakjian et al., 2018). MCC has a relatively even distribution of ICB responders (30–50%), nonresponders (25–30%), and escapees (25–30%) (Kaufman et al., 2016; Nghiem et al., 2016) across both pathogenic pathways, again rendering MCC a prototype cancer.

In this review, we use MCC as a paradigm cancer for understanding ICB response and resistance. We first explore how MCC—with dual pathogenic pathways and even distribution of responders, nonresponders, and escapees—can be used to model molecular mechanisms by assessing epitope quality and immune cell infiltrate patterns. We then discuss how cell-based immunotherapies for MCC provide a pertinent model for refining immunotherapy development. Finally, with MCC as an example, we consider strategies utilizing immune cells for disease detection.

Highly immunogenic tumor-specific epitopes facilitate ICB response

Although ICB has revolutionized cancer therapy, outcomes vary and, currently, cannot be accurately predicted. For example, higher expression of the PD-1 ligand CD274, commonly denoted PD-L1, on tumor or tumor-associated immune cells correlates with improved response but does not reliably separate responders from nonresponders (Herbst et al., 2014). For VP-MCC, VN-MCC, and other immunogenic solid tumors (e.g., HIV-associated Kaposi Sarcoma [HIV-KS]), post-ICB remissions are observed even with undetectable PD-L1 expression. A more widely used predictor of ICB response is median tumor mutational burden (TMB), which positively correlates with ICB response (Yarchoan et al., 2017). Not all cancers follow this correlation, emphasizing the need to precisely understand mechanisms governing response and resistance so that accurate predictors can be identified (Galanina et al., 2018; Lipson et al., 2013). Deciphering immune interactions is challenging, because tumor mutations vary between tumors and different T-cell clones can recognize identical mutations when presented by HLA. Uniform (VP-MCC) and diverse (VN-MCC) variants provide a uniquely suitable platform in which to investigate this heterogeneity in the context of both high and low TMB.

Tumor-specific antigen quality is associated with response to PD-1 axis blockade.

The positive correlation between TMB and ICB response implies that heavily mutated tumors are more immunogenic and subsequently more likely to respond (Cristescu et al., 2018; Le et al., 2017, 2015; Rizvi et al., 2015; Yarchoan et al., 2017). The molecular basis of this observation remains unclear, particularly with regard to a tumor’s immunopeptidome, that is, the sum of presented epitopes (intracellularly derived peptides presented on HLA) recognized by cytotoxic T cells that subsequently mount an immune response. Is this correlation between TMB and ICB response driven by TMB-high tumors having numerous, minimally immunogenic epitopes or a few, highly immunogenic epitopes? MCC’s dual etiology in conjunction with other virus-associated cancers provides an excellent opportunity to address this question and suggest that epitope quality is consequential.

VN-MCC has a high TMB (median, 63.1 mutations per Megabase [MB]; range, 31–133), consistent with other UV-associated skin cancers (Knepper et al., 2019). Conversely, VP-MCC has an exceptionally low TMB (median, 1.2 mutations per MB; range, 0.0–2.6) and few other genomic aberrations (Goh et al., 2016; Knepper et al., 2019; Paulson et al., 2009). VN-MCC and VP-MCC have similar overall response rates (ORRs) (44–54%) to anti-PD-1 blockade. For VN-MCC, this high ORR is predicted by median TMB (Goh et al., 2016; Goodman et al., 2019, 2017; Harms et al., 2015; Yarchoan et al., 2019, 2017). VP-MCC has a substantially better ORR than TMB alone would predict (Goh et al., 2016; Knepper et al., 2019; Nghiem et al., 2019, 2016). Although VP-MCC has low TMB and subsequently few predicted neoepitopes, it does contain MCPyV viral proteins (Berry et al., 2019; Iyer et al., 2011) that generate highly immunogenic epitopes. Its viral protein sequence is foreign, and thus MCPyV-specific T cells recognize its HLA-presented epitopes generally with higher avidity or ability to efficiently kill VP-MCC cells, because these T cells have not undergone thymic-negative selection (Croft et al., 2019). Furthermore, the MCPyV T antigen locus codes for proteins under 900 amino acids in length (Spurgeon and Lambert, 2013). This small, finite, viral epitope repertoire and high ORR suggests that limited but highly immunogenic epitopes may be sufficient for favorable responses.

To explore if tumor-specific antigen quality influenced ICB ORRs, we analyzed virus- and nonvirus-associated cancers separately. As a starting point, Dr. Mark Yarchoan kindly shared his published data set comparing PD-1 ORR and TMB across several cancers (Yarchoan et al., 2017). Five virus-associated cancers were identified—VP-MCC, HIV-KS (HHV-8 driven), virus-positive head and neck (VP-H&N), cervical, and anal cancers (latter three, human papillomavirus driven). The specific etiology was not available for HIV-KS, cervical, or anal cancers; these are >90% virus-driven (Chang et al., 1994; Joseph et al., 2008; Walboomers et al., 1999). However, a skewing of the associated response rates could not be formally excluded. Because virus-associated etiology was not available for VP-MCC and VP-H&N in the Yarchaon dataset, these data were removed and replaced with data from studies that explicitly divided patients based on cancer etiology (Chow et al., 2016; Harms et al., 2015; Nghiem et al., 2019; Seiwert et al., 2015; Yarchoan et al., 2017). Log transformations, linear regressions, and tests of linear combinations of regression coefficients were performed using R.

When virus-associated cancers were removed from the data set, leaving only nonvirus-associated cancers, the correlation between TMB and ORR increased (from 0.488 to 0.849; n = 25). In contrast, the virus-associated cancers (n = 5) showed a negative correlation (R2 = −0.619), suggesting that TMB may not be an adequate predictor for all cancers (Figure 2). Thus, separating virus-positive and virus-negative cancers will be critical moving forward in deepening our understanding of TMB and ORR.

Figure 2. Correlation between ORR to ICB therapy and median TMB for nonvirus- and virus-associated cancers.

Figure 2.

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High TMB positively correlates with ICB ORR in nonvirus-associated cancers (black dots, bold line) (R2 = 0.849; n = 25) (Yarchoan et al., 2017) but negatively correlates in virus-associated cancers (red squares, dotted line) (R2 = −0.619; n = 5). A difference of 17.8 between these two slopes (95% CI = 10.2–25.6, P < 0.001) indicates that the association between ORR and TMB is significantly different in virus-associated cancers compared with nonvirus cancers. Note that all virus-associated cancers have higher ORRs than TMB would predict and generally higher ORRs than the nonvirus cancer counterparts with the same TMB. ACC, adrenocortical carcinoma; CI, confidence interval; CSCC, cutaneous squamous cell carcinoma; HCC, hepatocellular carcinoma; HIV-KS, HIV-associated Kaposi Sarcoma; ICB, immune checkpoint blockade; MMRd, mismatch repair deficient; MMRp, mismatch repair proficient; NSCLC, non-small cell lung cancer; ORR, overall response rate; SCLC, small cell lung cancer; TMB, tumor mutational burden; UM, uveal melanoma; VN-H&N, virus-negative head and neck; VP-H&N, virus-positive head and neck; VN-MCC, virus-negative Merkel cell carcinoma; VP-MCC, virus-positive Merkel cell carcinoma.

All virus-associated cancers analyzed exhibited higher ORR than TMB alone might predict. For the same TMB, virus-associated cancers have an average of 25.3% higher ORR than nonvirus cancers (95% confidence interval = 2.4–48.2; P = 0.03). Nonvirus-associated cancers require 30 times greater TMB to elicit responses similar to those in virus-associated cancers (Chow et al., 2016; Goh et al., 2016; Harms et al., 2015; Nghiem et al., 2019; Seiwert et al., 2015; Yarchoan et al., 2017). On average, nonvirus-associated cancers (n = 25) had a TMB of 11.6 and ORR of 17.4, whereas virus-associated cancers (n = 5) had a TMB of 5.7 and ORR of 39.4. Although it cannot be deduced that multiple less immunogenic neoepitopes could also be effective in tumors with high TMB, these data suggest that a small number of highly immunogenic epitopes can elicit ICB responses relative to a large number of less immunogenic epitopes.

Oligoclonal expanded tumor-infiltrating lymphocytes suggest limited quantity of immunogenic epitopes.

In melanoma, a virus-negative cancer with high TMB, very few tumor-derived epitopes are neoantigens (<0.1%) or unique tumor-associated antigens (1.7–3%). Analysis of tumor-infiltrating lymphocytes (TILs) from these same melanomas showed expansion of T-cell clones specific to the few neoepitopes and unique tumor-associated epitopes, again implying that limited but potent epitopes can drive T-cell responses (Kalaora et al., 2018). Furthermore, high-throughput genomic and proteomic methods have shown that relatively few of the total possible neoepitopes can mediate tumor rejection (Ebrahimi-Nik et al., 2019; Yadav et al., 2014). Identifying these needle in the haystack epitopes driving immunogenic tumor rejection will be critical for developing broadly applicable vaccination or engineered cell therapies. VP-MCC offers a unique advantage for piloting such therapies owing to a small antigenic space (MCPyV T antigens) to test approaches, and shared, tumor-specific antigens across patients assure broad applicability.

Tumor-specific T-cell response patterns are plastic and independent of tumor-associated antigen expression.

Similar to TMB, TIL patterns can partially predict ICB response.(Chen and Mellman, 2017; Cristescu et al., 2018; Gruosso et al., 2019) Three distinct patterns described by Mellman and others are observed in MCC (Paulson et al., 2011) and other solid tumors (Chen and Mellman, 2017) (Figure 1b): (i) T cell desert—no T cells detected in or near tumor. These tumors rarely respond to ICB and are common in relapsed metastases (Chen and Mellman, 2017; Knepper et al., 2019; Paulson et al., 2018); (ii) T cell exclusion— T cells concentrate on edges but fail to infiltrate the tumor because of a physical and/or chemical barrier. These tumors sometimes respond to ICB therapy (Giraldo et al., 2018; Knepper et al., 2019; Paulson et al., 2018); and (iii) T cell inflamed—T cells observed throughout the tumor. This phenotype is frequently seen during response and is typically associated with improved prognosis (Chen and Mellman, 2017; Gruosso et al., 2019; Herbst et al., 2014; Paulson et al., 2018).

Determining whether TIL patterns are a consequence of the microenvironment or variations of tumor epitope immunogenicity is exceptionally challenging. Indeed, large-scale investigations of these features have been unfruitful (Tamborero et al., 2018; Thorsson et al., 2018). Similarly, allelic diversity in presentation molecules, particularly class I HLA, can influence immunogenicity, further complicating studies (Crux and Elahi, 2017; Goulder and Watkins, 2008; Kiepiela et al., 2004). The field does recognize that immunogenic antigens (e.g., viral proteins) play a critical role in activating lymphocytes and recruiting T cells to tumors (Gameiro et al., 2018; Miller et al., 2018). Here, VP-MCC provides an informative natural system in which obligate viral oncoproteins and low TMB imply relatively homogeneous immunogenicity. Therefore, in this system, TIL pattern differences are not directly related to the tumor antigen itself (MCPyV) but instead associated with microenvironment fluctuations or HLA polymorphisms rendering the same antigen (MCPyV) more or less immunogenic. Surprisingly, all three TIL patterns have been recorded in the same patient with VP-MCC at different stages of response and resistance to T-cell immunotherapy (Paulson et al., 2018), suggesting that complex microenvironment factors dictate T-cell infiltration. Moving forward, sophisticated systems analyses will be necessary to precisely identify which factors drive or inhibit T-cell infiltration.

MCC serves as a model cancer in which to understand factors mediating immunotherapy response and resistance

Many patients’ tumors progress after starting ICB treatment (primary resistance) or after a period of initial response (acquired resistance) (Sharma et al., 2017). For MCC, ~30% of initial responders progress by 2 years after ICB initiation (Nghiem et al., 2019).

Primary resistance is generally a direct consequence of a patient’s T cells being unable to respond to tumor, rendering ICB ineffective (Pos et al., 2014; Sharma et al., 2017). This may be driven by a number of factors: tumor lacking target antigen(s) (Gubin et al., 2014), dysfunctional antigen presentation (Marincola et al., 2000; Sucker et al., 2014), advanced or irreversible tumor-specific T-cell dysfunction, and/or lack of tumor-specific T cells. Primary-resistant tumors presumably experience little immunogenic pressure from ICB, making the tumor unlikely to undergo major shifts; therefore, primary-resistant tumors are not often utilized in studying escape mechanisms. Tumors with initial response followed by acquired resistance offer an opportunity to rigorously examine mechanisms of resistance. Identifying the root cause of acquired resistance remains challenging, as multiple contributors can produce the same clinical phenotype.

Immune escape mechanisms fit into two broad categories, immune cell intrinsic or tumor intrinsic. Immune cell intrinsic mechanisms include changes to innate immune cells, such as reduced neutrophil chemotaxis (Akhbari et al., 2018) and toll-like receptor 9 downregulation (Shahzad et al., 2013), and changes to adaptive immune cells, such as T-cell exhaustion (PD-1, CTLA-4, TIM3) (Afanasiev et al., 2013b) and T-cell migration and infiltration patterns (Afanasiev et al., 2013a; Clark et al., 2008; Dowlatshahi et al., 2013; Feldmeyer et al., 2016; Sihto et al., 2012). Tumor intrinsic changes include HLA loss (Paulson et al., 2018, 2014b; Ritter et al., 2016) and proliferation of a preexisting, resistant clone (Sharma et al., 2017).

Precisely deciphering if resistance is immune cell intrinsic or tumor intrinsic is complex (Sharma et al., 2017). It requires simultaneously knowing which T cells can see tumor (immune cell intrinsic factor, TCR clonotypes) and what is recognized by T cells (tumor intrinsic factor, antigen and/or epitope). Often it is difficult to know both, given that discovery is codependent, that is, identifying a tumor-specific T-cell clone requires knowing which HLA-presented epitope is recognized, and vice versa. Again, VP-MCC homogeneity provides an exceptional model system.

Shared oncoprotein in VP-MCC facilitates tracking of tumor-specific T cells.

In VP-MCC, continuous expression of a known, shared viral oncoprotein MCPyV large-T antigen (LT-Ag) facilitates identification of LT-Ag–specific T cells, using fluorophore-labeled epitope:HLA tetramers (Burrows et al., 2000; Chapuis et al., 2013; Greiner et al., 2010). Tetramers tiled across LT-Ag discern MCC-specific T cells from other, quiescent T cells and can reveal epitope spread; that is, nascent immune responses against epitopes not targeted by initial immunotherapy (Paulson et al., 2017). The phenotype or TIL pattern of tumor-specific T cells can be further examined to inform molecular drivers of tumor-immune interactions and, ultimately, immunotherapy resistance.

Clinical trials using adoptively transferred MCPyV-specific T cells in VP-MCC (Chapuis et al., 2014; Paulson et al., 2018) provide an opportunity to directly study cancere–immune interactions with easily identifiable T cells recognizing a defined epitope. Briefly, T cells are isolated from patients’ peripheral blood; MCC-specific cells are expanded ex vivo against a known epitope (e.g., LT-Ag92–101), purified by specific tetramer-binding, and reinfused into the patient (Chapuis et al., 2014; Paulson et al., 2018). Transferred, polyclonal MCC-specific T cells all recognize the same epitope (e.g., LT-Ag92–101). Persistence is monitored with LT-Ag92–101 tetramers and additional markers, including PD-1, can identify functional phenotypes. Similarly, tetramers tiled across LT-Ag can elucidate antigen spread. Responding patients who received MCC-specific T cells and avelumab (anti–PD-L1) showed expansion of both MCC-specific T cells and T cells recognizing other LT-Ag epitopes (Paulson et al., 2017). Adoptive T-cell trials provide valuable details characterizing tumor-specific T cells throughout ICB therapy, insights into T celleintrinsic or tumor-specific resistance mechanisms (Paulson et al., 2018), and novel epitopes for adoptive T-cell therapy (Cheever et al., 2009; Gulley et al., 2017; Paulson et al., 2017).

MCC cell-based immunotherapies offer insight to cancer–immune interactions.

Cell-based therapies facilitate identifying, tracking, and profiling of tumor-specific immune cells. Similar to adoptive T-cell transfer, TCR-transduced T cells (TCR-T) uniformly recognize a defined epitope:HLA complex. Multiple MCPyV epitopes have been identified, and many are being considered as TCR-T targets for patients with VP-MCC, including in combination therapy trials (NCT03747484) (Gavvovidis et al., 2018; Iyer et al., 2011). Unfortunately, therapies targeting MCPyV oncoproteins are only applicable to patients with VP-MCC. To broaden the clinical scope, alternate targets are being considered, including cancer testes antigens (Dasgeb et al., 2019) and aberrantly glycosylated proteins (e.g., MUC-1) (Kurzen et al., 2003). Targeting surface proteins with chimeric antigen receptor T cells recognizing glypican-3 is also being pursued in a phase I clinical trial. Other immune cells can likewise be therapeutic. An ongoing phase 2 clinical trial uses activated NK cells and ALT-803 (IL-15) to treat patients with MCC (NCT02465957). Cell-based therapies offer naturally controlled systems with unprecedented opportunities to observe response and resistance mechanisms, cancere–immune interactions, and tumor microenvironments in vivo. Lessons learned will likely be broadly applicable, offering insights for next-generation immunotherapies.

Acquired resistance mechanisms provide rationale for therapeutic combinations.

Tumor cells can adapt to escape immune pressure. In MCC, loss or reduction of class I HLA correlates with worse prognosis, independent of immunotherapy intervention (Chowell et al., 2018; Paulson et al., 2018). Increased prevalence of cytotoxic CD8+ T cells that recognize specific epitope:HLA complexes (Rock et al., 2016) is a positive prognostic indicator (Naito et al., 1998; Paulson et al., 2014a; Shimizu et al., 2019), suggesting that CD8+ T cells play a prominent role in disease clearance. Of note, many viruses evolved specific mechanisms to evade immune recognition, including downregulation of class I HLAs (Fletcher et al., 1998; Koutsakos et al., 2019). Evasion mechanisms employed by virus-associated cancers could differ, and some may not apply to all cancer types, but understanding these evasion mechanisms will undoubtedly inform cancere–immune interactions more broadly. Helper CD4+ T cells are also emerging as integral to coordinating effective immune responses, including by producing cytokines that upregulate class I HLAs (Bhat et al., 2017; Drew et al., 1993; Phares et al., 2012; Shankaran et al., 2001). Indeed, engineered CD4+ TCR-T cells are being considered as therapeutics (Longino et al., 2019). MCC can effectively dampen CD8+ T-cell recognition by decreasing class I diversity or expression, making CD4+ T-cell employment a reasonable strategy to drive immune response in tumors with limited class I (Chowell et al., 2018; Kreiter et al., 2015; Paulson et al., 2018).

PD-1 or PD-L1 ICB-resistant tumors often show increased expression of other negative immune regulators, including TIM3, LAG3, CTLA-4, and TIGIT, suggesting that inhibiting more than one immune regulator could improve responses and/or expand the patient population benefiting from ICB (Anderson et al., 2016; Golden-Mason et al., 2009; Koyama et al., 2016; McMahan et al., 2010). Indeed, combination ICB therapy can improve clinical response (Chapuis et al., 2014; Curran et al., 2010; Paulson et al., 2017). Another combination therapy trial (NCT02584829) is testing if dual or triple ICB treatment regimens combined with cell-based immunotherapies are synergistic in MCC.

ICB therapies place selective pressure on cancers with preexisting genomic instability and heterogeneity, which can lead to resistance and/or relapse. Understanding these escape mechanisms is essential to broadening immunotherapy benefits (Mehta et al., 2018; Pai et al., 2019; Paulson et al., 2018). Targeting multiple resistance mechanisms will likely be necessary to improve outcomes and minimize late immunotherapy escape (Sharma et al., 2017). Engineered cell therapies in VP-MCC offer remarkable cancer and immune cell homogeneity. Together, they provide an unprecedented opportunity to clarify underlying mechanisms and to validate advanced therapeutic approaches.

Utilizing the immune system for more than therapy

Our immune system amplifies signals, a characteristic that can be used to monitor disease (Paulson et al., 2017, 2010). For example, antibody titers are commonly used in clinics to determine immune status (Spradling et al., 2013) and could be extended to oncology for tracking tumor burden or B-cell and T-cell clonal expansion.

MCC demonstrates tumor antigenicity that can be exploited for disease monitoring.

Foreign proteins derived from MCPyV and certain other cancer-associated antigens can induce antibody production that is measurable in peripheral blood (Kikuchi et al., 1995; Paulson et al., 2010). Circulating MCPyV T antigen-specific antibodies are rare in healthy individuals but present in most patients with VP-MCC. Oncoprotein-specific antibody titers fall during clinical response and spike before relapse, offering an easy, affordable, noninvasive, and predictive method for monitoring disease that is now included in MCC management guidelines (Bichakjian et al., 2018; Paulson et al., 2017). Similarly, in non-small cell lung cancer, circulating tumor-specific antibodies noninvasively inform disease state by differentiating benign and malignant lung nodules (Lastwika et al., 2019). Changes in antibody titers alert physicians before clinical relapse, guiding early intervention for optimal patient outcomes (Miller et al., 2018; Paulson et al., 2017).

T cells and B cells can likewise be employed to detect tumor burden and direct treatment (Chapuis et al., 2019, 2017; Grupp et al., 2013; Logan et al., 2011; Robins, 2013; Wu et al., 2012). Variable regions of B-cell receptors (BCRs) or TCRs serve as a natural barcode generated in B-cell and T-cell development. In B-cell and T-cell malignancies, BCR or TCR clonotype sequencing is used to monitor tumor progression via expansion or contraction of a cancerous lymphocytic clone (Grupp et al., 2013; Wu et al., 2012) and to detect minimal residual disease (Logan et al., 2011; Wu et al., 2012). These same sequencing methods track TCRs for monitoring persistence and efficacy of engineered T-cell therapies (Chapuis et al., 2019, 2017).

Developing antibody arrays or immune cell monitoring for additional cancers could promote early detection, direct therapy, and improve patient outcomes. Employing these methodologies to evaluate hard-to-screen cancers could replace current, highly invasive procedures and costly imaging.

Conclusions

ICB therapy success has greatly improved outcomes for patients with many previously lethal malignancies, yet many obstacles remain. Analyses revealed an unmet need to accurately predict response, which requires a deeper understanding of mechanisms governing response, resistance, and relapse. The MCC model suggests that cancer epitope quality is associated with successful ICB response. Diverse immune infiltrate patterns demonstrate microenvironment plasticity in MCC and underscore a need for predictive diagnostics. Broadening the range of validated immunotherapeutic targets will be imperative to reducing late therapy escape, as demonstrated by improved response with combination regimens in MCC. Using MCC as a paradigm cancer not only broadly informs molecular immunology and cancer immunotherapy but also supports using endogenous immune responses to monitor disease and direct treatment.

ACKNOWLEDGMENTS

This work was supported by the SITC-Merck Fellowship (KGP), Immunotherapy Integrated Research Center at Fred Hutchinson Cancer Research Center (KGP, AGC), NIH T32CA009515 (KGP), NIH T32GM095421 and NIH T32CA080416 (MCL), NIH P01 CA225517 (PTN and AGC), and Cancer Center Support Grant P30 CA015704.

CONFLICT OF INTEREST

KGP receives research funding to her previous institution from EMD Serono, Bluebird Biosciences, and SITC-Merck. AGC receives research funding to her institution from EMD Serono, Bluebird Biosciences, Juno Therapeutics, SignalOne, and Amazon. KGP and AGC hold intellectual property related to TCRs for therapy of cancer and other diseases. PTN receives research funding to his institution from EMD Serono and Bristol-Myers Squibb and has served as a paid consultant for EMD Serono, Pfizer, 4SC, and Merck Sharp & Dohme. The remaining authors state no conflict of interest.

Abbreviations:

BCR

B-cell receptor

HIV-KS

HIV-associated Kaposi Sarcoma

ICB

Immune checkpoint blockade

LT-Ag

large-T antigen

MB

Megabase

MCC

Merkel cell carcinoma

MCPyV

Merkel cell polyomavirus

ORR

overall response rate

TCR-T

TCR-transduced T cell

TIL

tumor-infiltrating lymphocyte

TMB

tumor mutational burden

VN-MCC

virus-negative Merkel cell carcinoma

VP-H&N

virus-positive head and neck

VP-MCC

virus-positive Merkel cell carcinoma

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