T cells are specialized agents of the immune system that can detect and attack tumors. They spot their target by identifying small pieces of proteins – or antigens – at the surface of diseased cells. In particular, they can recognize the new and abnormal antigens that a cancer cell often displays. Yet, cells that become cancerous and start displaying suspicious antigens can manage to escape T cells and grow into full tumors. Why does the immune system not recognize and kill these early cancers before they get out of control?

One possibility is that T cells do not identify certain antigens carried by cancer cells. To test this, researchers have conducted experiments where they inject a mouse with cancer cells that display a single new antigen. If the animal develops a tumor, then this antigen does not trigger an immune response. However, this method is slow and laborious, because only one antigen can be tested at the time. Instead, Gejman, Chang et al. developed a new technique, PresentER, where a rodent gets injected with a mix of millions cancer cells that each displays a different antigen. This way, many thousands of new antigens can be studied in one go. The tumors are left to grow for several weeks before they are removed and analyzed to see which cells survived and which have been killed by the immune system.

Unexpectedly, the nature of the antigen did not make a big difference. Instead, cancer cells with new antigens could go undetected if they were rare and made up only a small proportion of all the different cancer cells in a tumor. However, the immune system would eliminate the exact same cancer cells when they were the major component of a cancerous lump.

Future research now has to explore exactly how rare cancer cells can hide amongst other cells, and remain invisible to the body. Armed with this knowledge, it might be possible to improve cancer therapy by prompting the immune system to target these emerging threats earlier.

Human cancers bear uniquely distinguishable features on the surface of their cells in the form of neoantigens, which are peptides derived from mutated, foreign or oncofetal proteins that are presented in complex with major histocompatibility complex I (MHC-I) molecules. These short, 8–11 amino acid fragments specifically mark cancer cells and activate potent immune responses that can lead to effective anti-cancer therapy (Tran et al., 2014; Rosenberg and Restifo, 2015; Zacharakis et al., 2018). Yet, the existence of T cells that recognize neoantigens is often not sufficient to eliminate tumors. Karl Hellström first described the coexistence of tumor-specific lymphocytes together with cancer cells in human solid tumors as a paradox 50 years ago (Hellström et al., 1968). In mice, an analogously enigmatic observation has been made that sporadic tumors occurring in aged or carcinogen-treated mice induce strong T cell responses only when transferred into new hosts, thereby preventing engraftment (Heike et al., 1994; Dubey et al., 1997; Shankaran et al., 2001). Thus, tumor-specific T cells can lead to tumor rejection in a new host or when used as a cancer therapy, but somehow immune surveillance is evaded during early tumorigenesis in the host that originally developed an immunogenic tumor. The increased rate of tumor formation in immunocompromised individuals has led to the hypothesis that the immune system can and does eliminate some tumors, particularly virally induced tumors, before they become clinically apparent (Schulz, 2009). We hypothesized that if the immune system can eliminate some early tumors, but not others, perhaps it is because some antigens are more potent at inducing effective T cell responses during early tumorigenesis. Identification and characterization of neoantigens that can induce an effective immune response and clear cancer cells is critical to understanding why and how immunogenic tumors develop in immunocompetent hosts.

Immunogenic peptides have been discovered in animal studies by injection of thousands or millions of tumor cells bearing neoantigens into animals and observing tumor rejection. However, the robust immune activation and tumor rejection in these cases is not analogous to the events of early tumorigenesis in humans, when the number of transformed cells is miniscule. Thus, even though some tumors are immunogenic, it is not clear why the host cannot eliminate them when the tumors first arise. If the biochemical features of neoantigens that lead to effective T cell responses were known, it might be possible to identify which tumors bear immunogenic antigens. Some reports have linked peptide immunogenicity to the biochemical characteristics of amino acid residues at certain positions along the MHC-I ligand (Calis et al., 2013), while others have focused on the difference between the affinity of a wild-type ligand and a mutated ligand (Duan et al., 2014). However, the absence of a large, unbiased data set of known immunogenic and non-immunogenic peptides has stymied the validation of these approaches. Indeed, most known immunogenic antigens are derived from viral proteins; few mutationally-derived neoantigens are confirmed as bonafide immunogenic peptides in mice or humans. Here, we have developed a novel genetic method to express libraries of precisely defined MHC-I ligands in mammalian tumor cells and have used this method to ask questions about MHC peptide immunogenicity in immunocompetent animals during early tumorigenesis.

Using libraries of genetically encoded MHC-I ligands, we tracked the dynamic growth and depletion of thousands of tumor subclones in vivo and noted a striking failure of cancer immunosurveillance that is potentially analogous to the failure of immune surveillance in humans during early tumorigenesis. We demonstrate for the first time that the ability of the naive immune system to surveille a nascent tumor and reject immunogenic subclones is limited by the fraction of cells expressing each unique antigen. Furthermore, we show that these rejection thresholds vary among antigens. Our data are consistent with the observation in humans that patients whose tumors have high numbers of subclones—and thus more subclonal neoantigens—have increased levels of relapse and worse survival than patients with more homogenous tumors (McGranahan et al., 2016; Reuben et al., 2017; Turajlic et al., 2018a). Thus, our data provide an antigen-specific rationale for the impact that tumor heterogeneity has on survival of human patients. According to our findings, antigen-specific immune effects are limited during early tumorigenesis, which has implications for the emergence and outgrowth of immunogenic tumors.

The immune system is capable of recognizing cancer cells as foreign based on the presentation of altered or unusual MHC-I ligands on the surface of tumor cells—a phenomenon that has been leveraged for cancer therapies such as immune checkpoint blockade and adoptive cell transfer (Tran et al., 2014; Rosenberg and Restifo, 2015; Zacharakis et al., 2018). Despite this, the immune system fails to clear immunogenic, clinically detected tumors on its own, as has been paradoxically noted for decades (Hellström et al., 1968). It is not clear at what point in tumorigenesis the immune system begins to initiate (ineffectively) a response to immunogenic MHC-I ligands presented on cancer cells. There is epidemiologic evidence that immunocompromised patients develop more tumors, suggesting that the immune system may prevent some tumors from ever manifesting clinically. However, recent data also suggests that mutations accumulate at high rates in sun-damaged, but otherwise normal, tissue at levels comparable to cancer cells (Martincorena et al., 2015) and that tumors face overall little negative selective pressure (Martincorena et al., 2017). Indeed, immune escape by loss of MHC (Masuda et al., 2007; Cabrera et al., 2000; Gudmundsdóttir et al., 2000; So et al., 2005; Shukla et al., 2015), when it occurs, is a late and subclonal event (McGranahan et al., 2017; Turajlic et al., 2018b). If T cell surveillance were highly effective during early tumor development, negative selective pressure would be notable in the evolutionary trajectories of tumors and HLA loss would be expected to occur early, frequently and in a clonal manner. We interpret the data from human and animal experiments to suggest that routine T cell immunosurveillance of nascent cancer cells does occur, but is limited by unknown factors, and that recognition of tumors as foreign occurs mostly later in tumor development.

If tumor immunosurveillance is sometimes effective in the early growth of a tumor, we hypothesized that it might be due to potent neoantigens expressed by cancer cells. Discovery of the biochemical characteristics of mutationally-derived neoantigens that are immunogenic would be important to clarify why some neoantigens are tolerated by the immune system and others are not. The significance of this question is underscored by the major challenge currently facing cancer immunotherapy: the identification of patients whose tumors bear immunogenic neoantigens—and are thus likely to respond well to immune checkpoint blockade or other immunotherapies—vs those patients who will be unnecessarily exposed to potentially toxic therapies without a chance for efficacy.

Here, we have developed a reductionist approach to determine which MHC-I ligands are immunogenic in immunocompetent mice. We demonstrate that tumors are rejected when an immunogenic pMHC is expressed on all or most injected cancer cells. To our surprise, we have discovered that effective T cell responses are not mounted against highly immunogenic peptides when cells expressing these peptides are a minor fraction of the tumor, which is the case in early developing tumors in humans. Furthermore, the threshold percentage of tumor cells necessary to yield an antigen-specific immunogenic T cell response varies with the antigen. We propose that ineffective T cell responses may be a consequence of intratumoral (or intra-tissue) heterogeneity and that neoantigen clonal fraction is an important, overlooked aspect of MHC-I antigen immunogenicity.

There are many mechanisms by which immune mediated killing of immunogenic cancer cells can fail. Here we report the first evidence that tumor heterogeneity is an explicit factor leading to failure of effective immunity in a tumor and that the maximum level of heterogeneity tolerable before immune escape occurs is dependent on the antigen. Immune checkpoints or active tumor suppression do not explain the observed immune evasion because such mechanisms would be expected to suppress T cell killing irrespective of the fraction of tumor cells that expresses an immunogenic epitope. This is evidenced by robust depletion of the highly immunogenic peptides SIINFEKL and TAYRYHLL when present at ≥1% of the tumor. The mechanism by which T cell responses are restrained when immunogenic MHC-I antigens are present at low frequency is not yet clear. Low levels of antigen presentation may lead to ineffective cross-presentation of antigen in the tumor draining lymph node, thus limiting T cell activation (Spiotto et al., 2002). In animal models, low levels of immunogenic epitopes of oncogenic drivers presented on transformed cells early during tumorigenesis (at the premalignant stage) were shown to induce a program of cellular hypo-responsiveness in tumor-specific (oncogene-specific) CD8 T cells (Willimsky and Blankenstein, 2005; Schietinger et al., 2016). After prophylactic vaccination with irradiated library cells we observed smaller tumors and increased numbers of antigen-specific T cells in splenocytes and draining lymph nodes, suggesting that some antigen-specific T cell proliferation does occur. While we demonstrated sufficient levels of the high affinity, highly immunogenic antigens on the cell surface, tumor associated epitopes of lower affinity, immunogenicity and expression levels should produce an even more pronounced deficit in T cell recognition. We speculate that the mechanism of immune escape in this model is either ineffective T cell activation or failure of activated T cells to identify and kill antigen-positive cells present at low abundance within a sea of cells displaying irrelevant antigens.

The observation that animals prophylactically vaccinated with MCA205∆Tap2 cells are partially protected from outgrowth of RMA/S tumors may be due to several possibilities. For instance, the vaccinations may have activated T cells that recognize some immunogenic peptides in the library, or T cells that recognize mutated peptides expressed by both cell types, or, most intriguingly, T cells specific to T cell epitopes associated with impaired peptide processing (TEIPPs). TEIPPs are T cell epitopes derived from wild type proteins that are not normally presented, unless antigen presentation is altered by loss of Tap, which yields a relative deficit of peptides translocated into the ER. TEIPPs in RMA/S cells can be immunogenic (Van Hall et al., 2006; Doorduijn et al., 2016). Although RMA/S and MCA205∆Tap2 are unlikely to share neoantigens derived from somatic mutations, they may share TEIPPs that contribute to protection against RMA/S tumor growth. A caveat to the vaccination experiments is that differences in the antigen presentation machinery of MCA205∆Tap2 and RMA/S cells may lead to incomplete congruence in the MHC-I peptidome of these two cell lines. These differences may lead to some peptides encoded by the PresentER library (or by the endogenous genome of the cell) to be poorly presented in one cell line but well presented in another cell line.

Although we have focused our efforts on understanding the role of CD8 T cells, other infiltrating cell types are also important for anti-tumor effects. In particular, CD4 T cells have been shown to play important roles in the CD8 T cell responses to tumors (Borst et al., 2018) and to target neoantigens (Ott et al., 2017; Sahin et al., 2017). However, our experimental design does not allow us to assess the role of CD4 T cells in CD8 T cell mediated anti-tumor responses. Thus, while it may be the case that the magnitude or quality of the immune response may differ when both CD8 and CD4 cells recognize a related immunogenic antigen, we are unable to study this with the tools we have developed to perform pooled library experiments because we cannot encode both a specific MHC II and MHC I ligand in a single minigene. Moreover, there is the confounding possibility that some CD4 help is coming from unrelated antigens, such as mutated proteins found in RMA/S or the exogenous proteins introduced into the cells by the PresentER vector. More broadly, the heterogeneity in actual human tumors is characterized by differential infiltration with antigen presenting cells, which can change the sensitivity of the immune system to detect immunogenic antigens present at low clonal fractions, and thus could impact our findings. Although we could not modulate the infiltrating immune cells in our experimental tumor models, we appreciate that this may influence the potency of a subpopulation of immunogenic cells. It would be interesting to understand how various levels of antigen presenting cells contribute to dynamic thresholds of T cell immunogenicity.

The PresentER system we have employed does have some potential biochemical caveats that may impact the interpretation of the data. While we have not observed alternative cleavage patterns in individual peptides we have studied (Gejman, 2018b), in a library setting we cannot test if every minigene is properly yielding its encoded MHC-I ligand. For instance, some encoded peptides may not bind well to MHC-I or may only bind in the presence of an MHC loading chaperone. Some peptides may lead to improper or no cleavage of the signal peptide whereas other peptides may be shortened (e.g. by ERAP1, an ER associated endopeptidase), altered or destroyed in the endoplasmic reticulum.

New therapeutic modalities such as immune checkpoint blockade have shown clinical efficacy in the treatment of human tumors, but the toxicity of the drug regimens has led to many efforts to find biomarkers that predict patient response. Tumors with high mutation burdens—which are more likely to have immunogenic neoantigens (Van Allen et al., 2015; Yarchoan et al., 2017; Snyder et al., 2014)—and tumors that have mismatch repair (MMR) deficiency or microsatellite instability (MSI-H) respond well to checkpoint blockade (Le et al., 2015; Le et al., 2017). In mouse models, syngeneic tumors with MMR gene knock outs accumulate mutations over time and grow more slowly in wild type mice than do tumors without MMR deficiency. Cell lines derived by sub-cloning of MMR deficient lines—thus increasing the clonal fractions of each neoantigen—grow more slowly or are rejected entirely. In all cases, MMR inactivation in tumors leads to better responses to checkpoint blockade than the parental tumors (Germano et al., 2017). Moreover, survival is inversely related to tumor neoantigen clonality in human patients with lung adenocarcinoma (McGranahan et al., 2016; Reuben et al., 2017). Patients whose tumors bear high numbers of subclonal (or branched) neoantigens have increased levels of relapse and worse survival than those patients with more homogenous tumors (McGranahan et al., 2016; Reuben et al., 2017). Response to checkpoint blockade in lung and skin tumors is also associated with lower levels of intratumoral heterogeneity (McGranahan et al., 2016). The combination of these data are highly suggestive that while total neoantigen burden is important for long term survival and response to checkpoint blockade, neoantigen clonality is an additional important factor in mediating tumor regression.

The discovery that intratumoral heterogeneity prevents effective T cell responses has implications for the development of therapeutic cancer vaccines, checkpoint blockade, adoptive T cell therapies, and studies of tumor immunogenicity. In addition, our data may provide a new understanding of mechanisms of immune surveillance and its failure that allows growth and evolution of tumors and subclones. For instance, recent data shows that subclones with very low clonal fraction yet distinct intratumoral functions may be important to tumor survival and growth (Vinci et al., 2018)—suggesting that even low abundance tumor subclones may be important targets for immunotherapy. In general, we may speculate on one mechanism for cancer escape and progression, in which small early cancers generally do not bear immunogenic epitopes derived from their limited number of driver oncogenic proteins and few passenger mutations. Then as the tumors evolve, the neoantigens appearing in subclones do not reach a clonal fraction high enough to breach their antigen-specific immunogenicity thresholds, thereby allowing escape of these otherwise immunogenic clones. This model may help to explain why sun-damaged and aged healthy tissue is replete with mutations, sometimes reaching frequencies seen in human cancers (Martincorena et al., 2015; Risques and Kennedy, 2018). Cumulatively, these findings suggest that effective T cell immunity is restrained in the context of healthy tissue and growing tumors and paint a picture of T cell immunogenicity that is poorly captured by existing models of tissue immunosurveillance.

PresentER plasmids are available from Addgene (#102942, #102943, #102945, #102946, #102944). Data are available in the following repositories: DOI:10.5281/zenodo.1310902, DOI:10.5281/zenodo.1309836 and DOI:10.5281/zenodo.1308909.

1. 1. Gejman RS
   2. Scheinberg DA

   (2018) Zenodo

   Outgrowth of transferred tumors expressing libraries of PresentER minigenes in immunocompetent and immunodeficient mice.

   https://doi.org/10.5281/zenodo.1310902
2. 1. Gejman RS
   2. Scheinberg DA

   (2018) Zenodo

   Outgrowth of tumors expressing libraries of PresentER minigenes in vaccinated or unvaccinated immunocompetent mice.

   https://doi.org/10.5281/zenodo.1309836
3. 1. Gejman RS
   2. Scheinberg DA

   (2018) Zenodo

   Outgrowth of tumors in immunocompetent mice expressing libraries of PresentER minigenes.

   https://doi.org/10.5281/zenodo.1308909

1. 1. Borst J
   2. Ahrends T
   3. Bąbała N
   4. Melief CJM
   5. Kastenmüller W

   (2018) CD4⁺ T cell help in Cancer immunology and immunotherapy

   Nature Reviews Immunology 18:635–647.

   https://doi.org/10.1038/s41577-018-0044-0

   • PubMed
   • Google Scholar
2. 1. Cabrera T
   2. Salinero J
   3. Fernandez MA
   4. Garrido A
   5. Esquivias J
   6. Garrido F

   (2000) High frequency of altered HLA class I phenotypes in laryngeal carcinomas

   Human Immunology 61:499–506.

   https://doi.org/10.1016/S0198-8859(00)00097-5

   • PubMed
   • Google Scholar
3. 1. Calis JJ
   2. Maybeno M
   3. Greenbaum JA
   4. Weiskopf D
   5. De Silva AD
   6. Sette A
   7. Keşmir C
   8. Peters B

   (2013) Properties of MHC class I presented peptides that enhance immunogenicity

   PLOS Computational Biology 9:e1003266.

   https://doi.org/10.1371/journal.pcbi.1003266

   • PubMed
   • Google Scholar
4. 1. Chang AY
   2. Dao T
   3. Gejman RS
   4. Jarvis CA
   5. Scott A
   6. Dubrovsky L
   7. Mathias MD
   8. Korontsvit T
   9. Zakhaleva V
   10. Curcio M
   11. Hendrickson RC
   12. Liu C
   13. Scheinberg DA

   (2017) A therapeutic T cell receptor mimic antibody targets tumor-associated PRAME peptide/HLA-I antigens

   Journal of Clinical Investigation 127:2705–2718.

   https://doi.org/10.1172/JCI92335

   • PubMed
   • Google Scholar
5. 1. Dao T
   2. Yan S
   3. Veomett N
   4. Pankov D
   5. Zhou L
   6. Korontsvit T
   7. Scott A
   8. Whitten J
   9. Maslak P
   10. Casey E
   11. Tan T
   12. Liu H
   13. Zakhaleva V
   14. Curcio M
   15. Doubrovina E
   16. O'Reilly RJ
   17. Liu C
   18. Scheinberg DA

   (2013) Targeting the intracellular WT1 oncogene product with a therapeutic human antibody

   Science Translational Medicine 5:ra33.

   https://doi.org/10.1126/scitranslmed.3005661

   • Google Scholar
6. 1. Doorduijn EM
   2. Sluijter M
   3. Querido BJ
   4. Oliveira CC
   5. Achour A
   6. Ossendorp F
   7. van der Burg SH
   8. van Hall T

   (2016) TAP-independent self-peptides enhance T cell recognition of immune-escaped tumors

   Journal of Clinical Investigation 126:784–794.

   https://doi.org/10.1172/JCI83671

   • PubMed
   • Google Scholar
7. 1. Duan F
   2. Duitama J
   3. Al Seesi S
   4. Ayres CM
   5. Corcelli SA
   6. Pawashe AP
   7. Blanchard T
   8. McMahon D
   9. Sidney J
   10. Sette A
   11. Baker BM
   12. Mandoiu II
   13. Srivastava PK

   (2014) Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity

   The Journal of Experimental Medicine 211:2231–2248.

   https://doi.org/10.1084/jem.20141308

   • PubMed
   • Google Scholar
8. 1. Dubey P
   2. Hendrickson RC
   3. Meredith SC
   4. Siegel CT
   5. Shabanowitz J
   6. Skipper JC
   7. Engelhard VH
   8. Hunt DF
   9. Schreiber H

   (1997) The immunodominant antigen of an ultraviolet-induced regressor tumor is generated by a somatic point mutation in the DEAD box helicase p68

   The Journal of Experimental Medicine 185:695–706.

   https://doi.org/10.1084/jem.185.4.695

   • PubMed
   • Google Scholar
9. 1. DuPage M
   2. Cheung AF
   3. Mazumdar C
   4. Winslow MM
   5. Bronson R
   6. Schmidt LM
   7. Crowley D
   8. Chen J
   9. Jacks T

   (2011) Endogenous T cell responses to antigens expressed in lung adenocarcinomas delay malignant tumor progression

   Cancer Cell 19:72–85.

   https://doi.org/10.1016/j.ccr.2010.11.011

   • PubMed
   • Google Scholar
10. 1. DuPage M
    2. Mazumdar C
    3. Schmidt LM
    4. Cheung AF
    5. Jacks T

    (2012) Expression of tumour-specific antigens underlies cancer immunoediting

    Nature 482:405–409.

    https://doi.org/10.1038/nature10803

    • PubMed
    • Google Scholar
11. 1. Dyall R
    2. Bowne WB
    3. Weber LW
    4. LeMaoult J
    5. Szabo P
    6. Moroi Y
    7. Piskun G
    8. Lewis JJ
    9. Houghton AN
    10. Nikolić-Zugić J

    (1998) Heteroclitic immunization induces tumor immunity

    The Journal of Experimental Medicine 188:1553–1561.

    https://doi.org/10.1084/jem.188.9.1553

    • PubMed
    • Google Scholar
12. 1. Engelhorn ME
    2. Guevara-Patiño JA
    3. Noffz G
    4. Hooper AT
    5. Lou O
    6. Gold JS
    7. Kappel BJ
    8. Houghton AN

    (2006) Autoimmunity and tumor immunity induced by immune responses to mutations in self

    Nature Medicine 12:198–206.

    https://doi.org/10.1038/nm1363

    • PubMed
    • Google Scholar
13. Preprint

    1. Gejman RS

    (2018a) Identification of the targets of T cell receptor therapeutic agents and cells by use of a high throughput genetic platform

    bioRxiv.

    https://doi.org/10.1101/267047

    • Google Scholar
14. 1. Gejman RS

    (2018b) Identification of the targets of T cell receptor therapeutic agents and cells by use of a high throughput genetic platform

    bioRxiv.

    https://doi.org/10.1101/267047

    • Google Scholar
15. 1. Germano G
    2. Lamba S
    3. Rospo G
    4. Barault L
    5. Magrì A
    6. Maione F
    7. Russo M
    8. Crisafulli G
    9. Bartolini A
    10. Lerda G
    11. Siravegna G
    12. Mussolin B
    13. Frapolli R
    14. Montone M
    15. Morano F
    16. de Braud F
    17. Amirouchene-Angelozzi N
    18. Marsoni S
    19. D'Incalci M
    20. Orlandi A
    21. Giraudo E
    22. Sartore-Bianchi A
    23. Siena S
    24. Pietrantonio F
    25. Di Nicolantonio F
    26. Bardelli A

    (2017) Inactivation of DNA repair triggers neoantigen generation and impairs tumour growth

    Nature 552:116–120.

    https://doi.org/10.1038/nature24673

    • PubMed
    • Google Scholar
16. 1. Gudmundsdóttir I
    2. Gunnlaugur Jónasson J
    3. Sigurdsson H
    4. Olafsdóttir K
    5. Tryggvadóttir L
    6. Ogmundsdóttir HM

    (2000) Altered expression of HLA class I antigens in breast Cancer: association with prognosis

    International Journal of Cancer 89:500–505.

    https://doi.org/10.1002/1097-0215(20001120)89:6<500::AID-IJC6>3.0.CO;2-#

    • PubMed
    • Google Scholar
17. 1. Heike M
    2. Blachere NE
    3. Srivastava PK

    (1994) Protective cellular immunity against a spontaneous mammary carcinoma from ras transgenic mice

    Immunobiology 190:411–423.

    https://doi.org/10.1016/S0171-2985(11)80612-1

    • PubMed
    • Google Scholar
18. 1. Hellström I
    2. Hellström KE
    3. Pierce GE
    4. Yang JP

    (1968) Cellular and humoral immunity to different types of human neoplasms

    Nature 220:1352–1354.

    https://doi.org/10.1038/2201352a0

    • PubMed
    • Google Scholar
19. 1. Holler PD
    2. Chlewicki LK
    3. Kranz DM

    (2003) TCRs with high affinity for foreign pMHC show self-reactivity

    Nature Immunology 4:55–62.

    https://doi.org/10.1038/ni863

    • PubMed
    • Google Scholar
20. 1. Le DT
    2. Uram JN
    3. Wang H
    4. Bartlett BR
    5. Kemberling H
    6. Eyring AD
    7. Skora AD
    8. Luber BS
    9. Azad NS
    10. Laheru D
    11. Biedrzycki B
    12. Donehower RC
    13. Zaheer A
    14. Fisher GA
    15. Crocenzi TS
    16. Lee JJ
    17. Duffy SM
    18. Goldberg RM
    19. de la Chapelle A
    20. Koshiji M
    21. Bhaijee F
    22. Huebner T
    23. Hruban RH
    24. Wood LD
    25. Cuka N
    26. Pardoll DM
    27. Papadopoulos N
    28. Kinzler KW
    29. Zhou S
    30. Cornish TC
    31. Taube JM
    32. Anders RA
    33. Eshleman JR
    34. Vogelstein B
    35. Diaz LA

    (2015) PD-1 blockade in tumors with mismatch-repair deficiency

    The New England Journal of Medicine 372:2509–2520.

    https://doi.org/10.1056/NEJMoa1500596

    • PubMed
    • Google Scholar
21. 1. Le DT
    2. Durham JN
    3. Smith KN
    4. Wang H
    5. Bartlett BR
    6. Aulakh LK
    7. Lu S
    8. Kemberling H
    9. Wilt C
    10. Luber BS
    11. Wong F
    12. Azad NS
    13. Rucki AA
    14. Laheru D
    15. Donehower R
    16. Zaheer A
    17. Fisher GA
    18. Crocenzi TS
    19. Lee JJ
    20. Greten TF
    21. Duffy AG
    22. Ciombor KK
    23. Eyring AD
    24. Lam BH
    25. Joe A
    26. Kang SP
    27. Holdhoff M
    28. Danilova L
    29. Cope L
    30. Meyer C
    31. Zhou S
    32. Goldberg RM
    33. Armstrong DK
    34. Bever KM
    35. Fader AN
    36. Taube J
    37. Housseau F
    38. Spetzler D
    39. Xiao N
    40. Pardoll DM
    41. Papadopoulos N
    42. Kinzler KW
    43. Eshleman JR
    44. Vogelstein B
    45. Anders RA
    46. Diaz LA

    (2017) Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade

    Science 357:409–413.

    https://doi.org/10.1126/science.aan6733

    • PubMed
    • Google Scholar
22. 1. Martincorena I
    2. Roshan A
    3. Gerstung M
    4. Ellis P
    5. Van Loo P
    6. McLaren S
    7. Wedge DC
    8. Fullam A
    9. Alexandrov LB
    10. Tubio JM
    11. Stebbings L
    12. Menzies A
    13. Widaa S
    14. Stratton MR
    15. Jones PH
    16. Campbell PJ

    (2015) Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin

    Science 348:880–886.

    https://doi.org/10.1126/science.aaa6806

    • PubMed
    • Google Scholar
23. 1. Martincorena I
    2. Raine KM
    3. Gerstung M
    4. Dawson KJ
    5. Haase K
    6. Van Loo P
    7. Davies H
    8. Stratton MR
    9. Campbell PJ

    (2017) Universal patterns of selection in cancer and somatic tissues

    Cell 171:1029–1041.

    https://doi.org/10.1016/j.cell.2017.09.042

    • PubMed
    • Google Scholar
24. 1. Masuda K
    2. Hiraki A
    3. Fujii N
    4. Watanabe T
    5. Tanaka M
    6. Matsue K
    7. Ogama Y
    8. Ouchida M
    9. Shimizu K
    10. Ikeda K
    11. Tanimoto M

    (2007) Loss or down-regulation of HLA class I expression at the allelic level in freshly isolated leukemic blasts

    Cancer Science 98:102–108.

    https://doi.org/10.1111/j.1349-7006.2006.00356.x

    • PubMed
    • Google Scholar
25. 1. Mathias MD
    2. Sockolosky JT
    3. Chang AY
    4. Tan KS
    5. Liu C
    6. Garcia KC
    7. Scheinberg DA

    (2017) CD47 blockade enhances therapeutic activity of TCR mimic antibodies to ultra-low density cancer epitopes

    Leukemia 31:2254–2257.

    https://doi.org/10.1038/leu.2017.223

    • PubMed
    • Google Scholar
26. 1. Matsushita H
    2. Vesely MD
    3. Koboldt DC
    4. Rickert CG
    5. Uppaluri R
    6. Magrini VJ
    7. Arthur CD
    8. White JM
    9. Chen YS
    10. Shea LK
    11. Hundal J
    12. Wendl MC
    13. Demeter R
    14. Wylie T
    15. Allison JP
    16. Smyth MJ
    17. Old LJ
    18. Mardis ER
    19. Schreiber RD

    (2012) Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting

    Nature 482:400–404.

    https://doi.org/10.1038/nature10755

    • PubMed
    • Google Scholar
27. 1. McGranahan N
    2. Furness AJ
    3. Rosenthal R
    4. Ramskov S
    5. Lyngaa R
    6. Saini SK
    7. Jamal-Hanjani M
    8. Wilson GA
    9. Birkbak NJ
    10. Hiley CT
    11. Watkins TB
    12. Shafi S
    13. Murugaesu N
    14. Mitter R
    15. Akarca AU
    16. Linares J
    17. Marafioti T
    18. Henry JY
    19. Van Allen EM
    20. Miao D
    21. Schilling B
    22. Schadendorf D
    23. Garraway LA
    24. Makarov V
    25. Rizvi NA
    26. Snyder A
    27. Hellmann MD
    28. Merghoub T
    29. Wolchok JD
    30. Shukla SA
    31. Wu CJ
    32. Peggs KS
    33. Chan TA
    34. Hadrup SR
    35. Quezada SA
    36. Swanton C

    (2016) Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade

    Science 351:1463–1469.

    https://doi.org/10.1126/science.aaf1490

    • PubMed
    • Google Scholar
28. 1. McGranahan N
    2. Rosenthal R
    3. Hiley CT
    4. Rowan AJ
    5. Watkins TBK
    6. Wilson GA
    7. Birkbak NJ
    8. Veeriah S
    9. Van Loo P
    10. Herrero J
    11. Swanton C
    12. TRACERx Consortium

    (2017) Allele-Specific HLA loss and immune escape in lung Cancer evolution

    Cell 171:1259–1271.

    https://doi.org/10.1016/j.cell.2017.10.001

    • PubMed
    • Google Scholar
29. 1. Ott PA
    2. Hu Z
    3. Keskin DB
    4. Shukla SA
    5. Sun J
    6. Bozym DJ
    7. Zhang W
    8. Luoma A
    9. Giobbie-Hurder A
    10. Peter L
    11. Chen C
    12. Olive O
    13. Carter TA
    14. Li S
    15. Lieb DJ
    16. Eisenhaure T
    17. Gjini E
    18. Stevens J
    19. Lane WJ
    20. Javeri I
    21. Nellaiappan K
    22. Salazar AM
    23. Daley H
    24. Seaman M
    25. Buchbinder EI
    26. Yoon CH
    27. Harden M
    28. Lennon N
    29. Gabriel S
    30. Rodig SJ
    31. Barouch DH
    32. Aster JC
    33. Getz G
    34. Wucherpfennig K
    35. Neuberg D
    36. Ritz J
    37. Lander ES
    38. Fritsch EF
    39. Hacohen N
    40. Wu CJ

    (2017) An immunogenic personal neoantigen vaccine for patients with melanoma

    Nature 547:217–221.

    https://doi.org/10.1038/nature22991

    • PubMed
    • Google Scholar
30. 1. Reuben A
    2. Gittelman R
    3. Gao J
    4. Zhang J
    5. Yusko EC
    6. Wu CJ
    7. Emerson R
    8. Zhang J
    9. Tipton C
    10. Li J
    11. Quek K
    12. Gopalakrishnan V
    13. Chen R
    14. Vence LM
    15. Cascone T
    16. Vignali M
    17. Fujimoto J
    18. Rodriguez-Canales J
    19. Parra ER
    20. Little LD
    21. Gumbs C
    22. Forget MA
    23. Federico L
    24. Haymaker C
    25. Behrens C
    26. Benzeno S
    27. Bernatchez C
    28. Sepesi B
    29. Gibbons DL
    30. Wargo JA
    31. William WN
    32. Swisher S
    33. Heymach JV
    34. Robins H
    35. Lee JJ
    36. Sharma P
    37. Allison JP
    38. Futreal PA
    39. Wistuba II
    40. Zhang J

    (2017) TCR repertoire intratumor heterogeneity in localized lung adenocarcinomas: an association with predicted neoantigen heterogeneity and postsurgical recurrence

    Cancer Discovery 7:1088–1097.

    https://doi.org/10.1158/2159-8290.CD-17-0256

    • PubMed
    • Google Scholar
31. 1. Risques RA
    2. Kennedy SR

    (2018) Aging and the rise of somatic cancer-associated mutations in normal tissues

    PLOS Genetics 14:e1007108.

    https://doi.org/10.1371/journal.pgen.1007108

    • PubMed
    • Google Scholar
32. 1. Rosenberg SA
    2. Restifo NP

    (2015) Adoptive cell transfer as personalized immunotherapy for human cancer

    Science 348:62–68.

    https://doi.org/10.1126/science.aaa4967

    • PubMed
    • Google Scholar
33. 1. Sahin U
    2. Derhovanessian E
    3. Miller M
    4. Kloke BP
    5. Simon P
    6. Löwer M
    7. Bukur V
    8. Tadmor AD
    9. Luxemburger U
    10. Schrörs B
    11. Omokoko T
    12. Vormehr M
    13. Albrecht C
    14. Paruzynski A
    15. Kuhn AN
    16. Buck J
    17. Heesch S
    18. Schreeb KH
    19. Müller F
    20. Ortseifer I
    21. Vogler I
    22. Godehardt E
    23. Attig S
    24. Rae R
    25. Breitkreuz A
    26. Tolliver C
    27. Suchan M
    28. Martic G
    29. Hohberger A
    30. Sorn P
    31. Diekmann J
    32. Ciesla J
    33. Waksmann O
    34. Brück AK
    35. Witt M
    36. Zillgen M
    37. Rothermel A
    38. Kasemann B
    39. Langer D
    40. Bolte S
    41. Diken M
    42. Kreiter S
    43. Nemecek R
    44. Gebhardt C
    45. Grabbe S
    46. Höller C
    47. Utikal J
    48. Huber C
    49. Loquai C
    50. Türeci Ö

    (2017) Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer

    Nature 547:222–226.

    https://doi.org/10.1038/nature23003

    • PubMed
    • Google Scholar
34. 1. Sanjana NE
    2. Shalem O
    3. Zhang F

    (2014) Improved vectors and genome-wide libraries for CRISPR screening

    Nature Methods 11:783–784.

    https://doi.org/10.1038/nmeth.3047

    • PubMed
    • Google Scholar
35. 1. Schietinger A
    2. Philip M
    3. Krisnawan VE
    4. Chiu EY
    5. Delrow JJ
    6. Basom RS
    7. Lauer P
    8. Brockstedt DG
    9. Knoblaugh SE
    10. Hämmerling GJ
    11. Schell TD
    12. Garbi N
    13. Greenberg PD

    (2016) Tumor-Specific T cell dysfunction is a dynamic Antigen-Driven differentiation program initiated early during tumorigenesis

    Immunity 45:389–401.

    https://doi.org/10.1016/j.immuni.2016.07.011

    • PubMed
    • Google Scholar
36. 1. Schulz TF

    (2009) Cancer and viral infections in immunocompromised individuals

    International Journal of Cancer 125:1755–1763.

    https://doi.org/10.1002/ijc.24741

    • PubMed
    • Google Scholar
37. 1. Sergeeva A
    2. Alatrash G
    3. He H
    4. Ruisaard K
    5. Lu S
    6. Wygant J
    7. McIntyre BW
    8. Ma Q
    9. Li D
    10. St John L
    11. Clise-Dwyer K
    12. Molldrem JJ

    (2011) An anti-PR1/HLA-A2 T-cell receptor-like antibody mediates complement-dependent cytotoxicity against acute myeloid leukemia progenitor cells

    Blood 117:4262–4272.

    https://doi.org/10.1182/blood-2010-07-299248

    • PubMed
    • Google Scholar
38. 1. Shankaran V
    2. Ikeda H
    3. Bruce AT
    4. White JM
    5. Swanson PE
    6. Old LJ
    7. Schreiber RD

    (2001) IFNgamma and lymphocytes prevent primary tumour development and shape tumour immunogenicity

    Nature 410:1107–1111.

    https://doi.org/10.1038/35074122

    • PubMed
    • Google Scholar
39. 1. Shukla SA
    2. Rooney MS
    3. Rajasagi M
    4. Tiao G
    5. Dixon PM
    6. Lawrence MS
    7. Stevens J
    8. Lane WJ
    9. Dellagatta JL
    10. Steelman S
    11. Sougnez C
    12. Cibulskis K
    13. Kiezun A
    14. Hacohen N
    15. Brusic V
    16. Wu CJ
    17. Getz G

    (2015) Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes

    Nature Biotechnology 33:1152–1158.

    https://doi.org/10.1038/nbt.3344

    • PubMed
    • Google Scholar
40. 1. Snyder A
    2. Makarov V
    3. Merghoub T
    4. Yuan J
    5. Zaretsky JM
    6. Desrichard A
    7. Walsh LA
    8. Postow MA
    9. Wong P
    10. Ho TS
    11. Hollmann TJ
    12. Bruggeman C
    13. Kannan K
    14. Li Y
    15. Elipenahli C
    16. Liu C
    17. Harbison CT
    18. Wang L
    19. Ribas A
    20. Wolchok JD
    21. Chan TA

    (2014) Genetic basis for clinical response to CTLA-4 blockade in melanoma

    New England Journal of Medicine 371:2189–2199.

    https://doi.org/10.1056/NEJMoa1406498

    • PubMed
    • Google Scholar
41. 1. So T
    2. Takenoyama M
    3. Mizukami M
    4. Ichiki Y
    5. Sugaya M
    6. Hanagiri T
    7. Sugio K
    8. Yasumoto K

    (2005) Haplotype loss of HLA class I antigen as an escape mechanism from immune attack in lung cancer

    Cancer Research 65:5945–5952.

    https://doi.org/10.1158/0008-5472.CAN-04-3787

    • PubMed
    • Google Scholar
42. 1. Spiotto MT
    2. Yu P
    3. Rowley DA
    4. Nishimura MI
    5. Meredith SC
    6. Gajewski TF
    7. Fu YX
    8. Schreiber H

    (2002) Increasing tumor antigen expression overcomes "ignorance" to solid tumors via crosspresentation by bone marrow-derived stromal cells

    Immunity 17:737–747.

    https://doi.org/10.1016/S1074-7613(02)00480-6

    • PubMed
    • Google Scholar
43. 1. Tran E
    2. Turcotte S
    3. Gros A
    4. Robbins PF
    5. Lu YC
    6. Dudley ME
    7. Wunderlich JR
    8. Somerville RP
    9. Hogan K
    10. Hinrichs CS
    11. Parkhurst MR
    12. Yang JC
    13. Rosenberg SA

    (2014) Cancer immunotherapy based on mutation-specific CD4+ T cells in a patient with epithelial Cancer

    Science 344:641–645.

    https://doi.org/10.1126/science.1251102

    • PubMed
    • Google Scholar
44. 1. Turajlic S
    2. Xu H
    3. Litchfield K
    4. Rowan A
    5. Horswell S
    6. Chambers T
    7. O'Brien T
    8. Lopez JI
    9. Watkins TBK
    10. Nicol D
    11. Stares M
    12. Challacombe B
    13. Hazell S
    14. Chandra A
    15. Mitchell TJ
    16. Au L
    17. Eichler-Jonsson C
    18. Jabbar F
    19. Soultati A
    20. Chowdhury S
    21. Rudman S
    22. Lynch J
    23. Fernando A
    24. Stamp G
    25. Nye E
    26. Stewart A
    27. Xing W
    28. Smith JC
    29. Escudero M
    30. Huffman A
    31. Matthews N
    32. Elgar G
    33. Phillimore B
    34. Costa M
    35. Begum S
    36. Ward S
    37. Salm M
    38. Boeing S
    39. Fisher R
    40. Spain L
    41. Navas C
    42. Grönroos E
    43. Hobor S
    44. Sharma S
    45. Aurangzeb I
    46. Lall S
    47. Polson A
    48. Varia M
    49. Horsfield C
    50. Fotiadis N
    51. Pickering L
    52. Schwarz RF
    53. Silva B
    54. Herrero J
    55. Luscombe NM
    56. Jamal-Hanjani M
    57. Rosenthal R
    58. Birkbak NJ
    59. Wilson GA
    60. Pipek O
    61. Ribli D
    62. Krzystanek M
    63. Csabai I
    64. Szallasi Z
    65. Gore M
    66. McGranahan N
    67. Van Loo P
    68. Campbell P
    69. Larkin J
    70. Swanton C
    71. TRACERx Renal Consortium

    (2018a) Deterministic evolutionary trajectories influence primary tumor growth: TRACERx renal

    Cell 173:595–610.

    https://doi.org/10.1016/j.cell.2018.03.043

    • PubMed
    • Google Scholar
45. 1. Turajlic S
    2. Xu H
    3. Litchfield K
    4. Rowan A
    5. Chambers T
    6. Lopez JI
    7. Nicol D
    8. O'Brien T
    9. Larkin J
    10. Horswell S
    11. Stares M
    12. Au L
    13. Jamal-Hanjani M
    14. Challacombe B
    15. Chandra A
    16. Hazell S
    17. Eichler-Jonsson C
    18. Soultati A
    19. Chowdhury S
    20. Rudman S
    21. Lynch J
    22. Fernando A
    23. Stamp G
    24. Nye E
    25. Jabbar F
    26. Spain L
    27. Lall S
    28. Guarch R
    29. Falzon M
    30. Proctor I
    31. Pickering L
    32. Gore M
    33. Watkins TBK
    34. Ward S
    35. Stewart A
    36. DiNatale R
    37. Becerra MF
    38. Reznik E
    39. Hsieh JJ
    40. Richmond TA
    41. Mayhew GF
    42. Hill SM
    43. McNally CD
    44. Jones C
    45. Rosenbaum H
    46. Stanislaw S
    47. Burgess DL
    48. Alexander NR
    49. Swanton C
    50. PEACE
    51. TRACERx Renal Consortium

    (2018b) Tracking cancer evolution reveals constrained routes to metastases: tracerx renal

    Cell 173:581–594.

    https://doi.org/10.1016/j.cell.2018.03.057

    • PubMed
    • Google Scholar
46. 1. Udaka K
    2. Wiesmüller KH
    3. Jung G
    4. Walden P

    (1996)

    Self-MHC-restricted peptides recognized by an alloreactive T lymphocyte clone

    Journal of Immunology 157:670–678.

    • Google Scholar
47. 1. Van Allen EM
    2. Miao D
    3. Schilling B
    4. Shukla SA
    5. Blank C
    6. Zimmer L
    7. Sucker A
    8. Hillen U
    9. Foppen MHG
    10. Goldinger SM
    11. Utikal J
    12. Hassel JC
    13. Weide B
    14. Kaehler KC
    15. Loquai C
    16. Mohr P
    17. Gutzmer R
    18. Dummer R
    19. Gabriel S
    20. Wu CJ
    21. Schadendorf D
    22. Garraway LA

    (2015) Genomic correlates of response to CTLA-4 blockade in metastatic melanoma

    Science 350:207–211.

    https://doi.org/10.1126/science.aad0095

    • PubMed
    • Google Scholar
48. 1. Van Hall T
    2. Wolpert EZ
    3. van Veelen P
    4. Laban S
    5. van der Veer M
    6. Roseboom M
    7. Bres S
    8. Grufman P
    9. de Ru A
    10. Meiring H
    11. de Jong A
    12. Franken K
    13. Teixeira A
    14. Valentijn R
    15. Drijfhout JW
    16. Koning F
    17. Camps M
    18. Ossendorp F
    19. Kärre K
    20. Ljunggren HG
    21. Melief CJ
    22. Offringa R

    (2006) Selective cytotoxic T-lymphocyte targeting of tumor immune escape variants

    Nature Medicine 12:417–424.

    https://doi.org/10.1038/nm1381

    • PubMed
    • Google Scholar
49. 1. Vinci M
    2. Burford A
    3. Molinari V
    4. Kessler K
    5. Popov S
    6. Clarke M
    7. Taylor KR
    8. Pemberton HN
    9. Lord CJ
    10. Gutteridge A
    11. Forshew T
    12. Carvalho D
    13. Marshall LV
    14. Qin EY
    15. Ingram WJ
    16. Moore AS
    17. Ng HK
    18. Trabelsi S
    19. H'mida-Ben Brahim D
    20. Entz-Werle N
    21. Zacharoulis S
    22. Vaidya S
    23. Mandeville HC
    24. Bridges LR
    25. Martin AJ
    26. Al-Sarraj S
    27. Chandler C
    28. Sunol M
    29. Mora J
    30. de Torres C
    31. Cruz O
    32. Carcaboso AM
    33. Monje M
    34. Mackay A
    35. Jones C

    (2018) Functional diversity and cooperativity between subclonal populations of pediatric glioblastoma and diffuse intrinsic pontine glioma cells

    Nature Medicine 24:1204–1215.

    https://doi.org/10.1038/s41591-018-0086-7

    • PubMed
    • Google Scholar
50. 1. Wang M
    2. Bai F
    3. Pries M
    4. Buus S
    5. Prause JU
    6. Nissen MH

    (2006) Identification of MHC class I H-2 kb/Db-restricted immunogenic peptides derived from retinal proteins

    Investigative Opthalmology & Visual Science 47:3939–3945.

    https://doi.org/10.1167/iovs.06-0133

    • Google Scholar
51. 1. Willimsky G
    2. Blankenstein T

    (2005) Sporadic immunogenic tumours avoid destruction by inducing T-cell tolerance

    Nature 437:141–146.

    https://doi.org/10.1038/nature03954

    • PubMed
    • Google Scholar
52. 1. Yarchoan M
    2. Hopkins A
    3. Jaffee EM

    (2017) Tumor mutational burden and response rate to PD-1 inhibition

    New England Journal of Medicine 377:2500–2501.

    https://doi.org/10.1056/NEJMc1713444

    • PubMed
    • Google Scholar
53. 1. Zacharakis N
    2. Chinnasamy H
    3. Black M
    4. Xu H
    5. Lu YC
    6. Zheng Z
    7. Pasetto A
    8. Langhan M
    9. Shelton T
    10. Prickett T
    11. Gartner J
    12. Jia L
    13. Trebska-McGowan K
    14. Somerville RP
    15. Robbins PF
    16. Rosenberg SA
    17. Goff SL
    18. Feldman SA

    (2018) Immune recognition of somatic mutations leading to complete durable regression in metastatic breast Cancer

    Nature Medicine 24:724–730.

    https://doi.org/10.1038/s41591-018-0040-8

    • PubMed
    • Google Scholar

Author details

1. Ron S Gejman

   1. Tri-Institutional MD-PhD Program, Memorial Sloan-Kettering Cancer Center, Rockefeller University, Weill Cornell Medical College, New York, United States
   2. Weill Cornell Medicine, New York, United States
   3. Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Contributed equally with

   Aaron Y Chang

   Competing interests

   Memorial Sloan Kettering Cancer Center has filed for intellectual property protection for the inventions of this author in relation to the PresentER method.

   "This ORCID iD identifies the author of this article:" 0000-0001-5138-2772

2. Aaron Y Chang

   1. Weill Cornell Medicine, New York, United States
   2. Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Ron S Gejman

   Competing interests

   Memorial Sloan Kettering Cancer Center has filed for intellectual property protection for the inventions of this author in relation to TCR mimic antibodies.

   "This ORCID iD identifies the author of this article:" 0000-0002-7770-7405

3. Heather F Jones

   1. Weill Cornell Medicine, New York, United States
   2. Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9459-377X

4. Krysta DiKun

   1. Weill Cornell Medicine, New York, United States
   2. Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Conceptualization, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Abraham Ari Hakimi

   1. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, United States
   2. Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Conceptualization, Funding acquisition, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0930-8824

6. Andrea Schietinger

   1. Weill Cornell Medicine, New York, United States
   2. Immunology Program, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

7. David A Scheinberg

   1. Weill Cornell Medicine, New York, United States
   2. Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Resources, Supervision, Funding acquisition, Writing—original draft, Writing—review and editing

   For correspondence

   scheinbd@mskcc.org

   Competing interests

   Memorial Sloan Kettering Cancer Center has filed for intellectual property protection for the inventions of this author in relation to the PresentER method and TCR mimic antibodies. DAS is also a board member of, or consultant to, and/or owns equity in SLS, IOVA, PFE, and Eureka therapeutics that work in the immunotherapy field.

   "This ORCID iD identifies the author of this article:" 0000-0002-4160-923X

• 10,066

  views

• 1,396

  downloads

• 100

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.