Genetic Basis for Clinical Response
`to CTLA-4 Blockade in Melanoma
`Alexandra Snyder, M.D., Vladimir Makarov, M.D., Taha Merghoub, Ph.D.,
`Jianda Yuan, M.D., Ph.D., Jesse M. Zaretsky, B.S., Alexis Desrichard, Ph.D.,
`Logan A. Walsh, Ph.D., Michael A. Postow, M.D., Phillip Wong, Ph.D.,
`Teresa S. Ho, B.S., Travis J. Hollmann, M.D., Ph.D., Cameron Bruggeman, M.A.,
`Kasthuri Kannan, Ph.D., Yanyun Li, M.D., Ph.D., Ceyhan Elipenahli, B.S.,
`Cailian Liu, M.D., Christopher T. Harbison, Ph.D., Lisu Wang, M.D.,
`Antoni Ribas, M.D., Ph.D., Jedd D. Wolchok, M.D., Ph.D.,
`and Timothy A. Chan, M.D., Ph.D.
`
`ABSTR ACT
`
`Background
`Immune checkpoint inhibitors are effective cancer treatments, but molecular deter-
`minants of clinical benefit are unknown. Ipilimumab and tremelimumab are anti-
`bodies against cytotoxic T-lymphocyte antigen 4 (CTLA-4). Anti–CTLA-4 treatment
`prolongs overall survival in patients with melanoma. CTLA-4 blockade activates
`T cells and enables them to destroy tumor cells.
`
`Methods
`We obtained tumor tissue from patients with melanoma who were treated with
`ipilimumab or tremelimumab. Whole-exome sequencing was performed on tumors
`and matched blood samples. Somatic mutations and candidate neoantigens gener-
`ated from these mutations were characterized. Neoantigen peptides were tested for
`the ability to activate lymphocytes from ipilimumab-treated patients.
`
`Results
`Malignant melanoma exomes from 64 patients treated with CTLA-4 blockade were
`characterized with the use of massively parallel sequencing. A discovery set con-
`sisted of 11 patients who derived a long-term clinical benefit and 14 patients who
`derived a minimal benefit or no benefit. Mutational load was associated with the
`degree of clinical benefit (P = 0.01) but alone was not sufficient to predict benefit.
`Using genomewide somatic neoepitope analysis and patient-specific HLA typing,
`we identified candidate tumor neoantigens for each patient. We elucidated a neo-
`antigen landscape that is specifically present in tumors with a strong response to
`CTLA-4 blockade. We validated this signature in a second set of 39 patients with
`melanoma who were treated with anti–CTLA-4 antibodies. Predicted neoantigens
`activated T cells from the patients treated with ipilimumab.
`
`Conclusions
`These findings define a genetic basis for benefit from CTLA-4 blockade in melanoma
`and provide a rationale for examining exomes of patients for whom anti–CTLA-4
`agents are being considered. (Funded by the Frederick Adler Fund and others.)
`
`From the Department of Medicine (A.S.,
`T.M., M.A.P., J.D.W.), Human Oncology
`and Pathogenesis Program (A.S., V.M.,
`A.D., L.A.W., K.K., T.A.C.), Swim across
`America–Ludwig Collaborative Research
`Laboratory (T.M., Y.L., C.E., C.L., J.D.W.),
`Department of Radiation Oncology
`(T.A.C.), Department of Pathology
`(T.J.H.), and Immunology Program, Lud-
`wig Center for Cancer Immunotherapy
`(J.Y., P.W., T.S.H., J.D.W.), Memorial
`Sloan Kettering Cancer Center; Weill Cor-
`nell Medical College (A.S., M.A.P., J.D.W.,
`T.A.C.); and Department of Mathemat-
`ics, Columbia University (C.B.) — all in
`New York; the Department of Molecular
`and Medical Pharmacology (J.M.Z., A.R.)
`and the Department of Medicine, Divi-
`sion of Hematology–Oncology, Jonsson
`Comprehensive Cancer Center (A.R.),
`University of California, Los Angeles, Los
`Angeles; and Bristol-Myers Squibb,
`Princeton, NJ (C.T.H., L.W.). Address re-
`print requests to Dr. Chan at the Human
`Oncology and Pathogenesis Program,
`Memorial Sloan Kettering Cancer Center,
`1275 York Ave., Box 20, New York, NY
`10065, or at chant@mskcc.org; or to Dr.
`Wolchok at the Ludwig Center for Cancer
`Immunotherapy, Memorial Sloan Kettering
`Cancer Center, 1275 York Ave., New York,
`NY 10065, or at wolchokj@mskcc.org.
`
`Drs. Snyder, Makarov, Merghoub, and Yuan
`and Drs. Wolchok and Chan contributed
`equally to this article.
`
`This article was published on November
`19, 2014, and last updated on November
`12, 2015, at NEJM.org.
`
`N Engl J Med 2014;371:2189-99.
`DOI: 10.1056/NEJMoa1406498
`Copyright © 2014 Massachusetts Medical Society.
`
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`Immune checkpoint blockade has led
`
`to durable antitumor effects in patients with
`metastatic melanoma, non–small-cell lung
`cancer, and other tumor types, but the factors
`determining whether a patient will have a re-
`sponse remain elusive.1,2 The fully human mono-
`clonal antibodies ipilimumab and tremelimumab
`block cytotoxic T-lymphocyte antigen 4 (CTLA-4),
`resulting in T-cell activation. Some studies have
`established correlations between outcomes with
`ipilimumab and peripheral-blood lymphocyte
`count, markers of T-cell activation,3 an “inflam-
`matory” microenvironment,4,5 and maintenance
`of high-frequency T-cell receptor clonotypes.6
`The relationship among the genomic land-
`scape of the tumor, the mutational load, and the
`benefit from treatment remains obscure. The im-
`munogenicity resulting from nonsynonymous
`melanoma mutations has been shown in a mouse
`model,7 and the antigenic diversity of human
`melanoma tumors has been modeled in silico8
`and in melanoma-specific CD8 T-cell responses
`after treatment with ipilimumab.9 Effector and
`helper T-cell function and regulatory T-cell de-
`pletion are necessary for the efficacy of CTLA-4
`blockade,10 but there is not an association be-
`tween a specific HLA type and a clinical bene-
`fit.11 Melanomas have very high mutational
`burdens (0.5 to >100 mutations per megabase)
`as compared with other solid tumors.12 Elegant
`studies have shown that somatic mutations can
`give rise to neoepitopes13 and that these may
`serve as neoantigens.14-16 We conducted a study
`to determine whether the genetic landscape of a
`tumor affects the clinical benefit provided by
`CTLA-4 blocking agents.
`
`Methods
`
`Sample Acquisition and DNA Preparation
`For the discovery set, we conducted whole-exome
`sequencing of DNA from tumors and matched nor-
`mal blood from 25 ipilimumab-treated patients. A
`validation set included an additional 39 patients, of
`whom 5 were treated with tremelimumab. Pri-
`mary tumor samples and matched normal pe-
`ripheral-blood specimens were obtained after the
`patients had provided written informed consent.
`DNA was extracted, and exon capture was per-
`formed with the use of the SureSelect Human All
`Exon 50-Mb kit (Agilent Technologies). Enriched
`
`exome libraries were sequenced on the HiSeq 2000
`platform (Illumina) to provide a mean exome cov-
`erage of more than 100× (Memorial Sloan Ketter-
`ing Cancer Center Genomics Core and Broad In-
`stitute).
`
`Immunogenicity Analysis of Somatic
`Mutations
`We created a bioinformatic tool to translate all
`mutations in exomes and then evaluate binding
`with major histocompatibility complex (MHC)
`class I molecules. The neoantigen signature was
`generated from the nonamers containing four
`amino acid strings of peptides that are common
`to tumors from patients with a long-term benefit
`from therapy. Details are provided in the Supple-
`mentary Appendix, available with the full text of
`this article at NEJM.org.
`
`Intracellular Cytokine Staining
`Candidate neoantigen peptides were synthesized
`(GenScript), cultured with autologous peripheral-
`blood mononuclear cells (PBMCs), and then ana-
`lyzed by means of intracellular cytokine staining
`for interleukin-2, CD107a, macrophage inflam-
`matory protein 1β, tumor necrosis factor α, and
`interferon-γ on restimulation of cells with the
`candidate peptides.
`
`Statistical Analysis
`The Mann–Whitney test was used to compare
`mutational loads, and the log-rank test was used
`to compare Kaplan–Meier curves. The statistical
`methods used in the study are more fully described
`in the Supplementary Appendix.
`
`R esults
`
`Mutational landscape of Melanomas
`from the Study Patients
`Baseline patient characteristics are shown in Ta-
`ble 1 (for more detailed information, see Tables
`S1 and S2 in the Supplementary Appendix). The
`study involved patients with and those without a
`long-term clinical benefit from therapy (CTLA-4
`blockade alone or CTLA-4 blockade with resec-
`tion of an isolated stable or nonresponding lesion).
`A long-term clinical benefit was defined by radio-
`graphic evidence of freedom from disease or evi-
`dence of a stable or decreased volume of disease
`for more than 6 months. Lack of a long-term ben-
`
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`Table 1. Clinical Characteristics of the Patients in the Discovery and Validation Sets, According to Clinical Benefit
`from Therapy.
`
`Characteristic
`
`Discovery Set
`
`Validation Set
`
`Long-Term
`Benefit
`(N = 11)
`
`Minimal or No
`Benefit
`(N = 14)
`
`Long-Term
`Benefit
`(N = 25)
`
`Minimal or No
`Benefit
`(N = 14)
`
`Age at start of treatment — yr
`
`Median
`
`Range
`
`Sex — no. of patients (%)
`
`Female
`
`Male
`
`Disease origin — no. of patients (%)
`
`Acral
`
`Uveal
`
`Cutaneous
`
`Unknown primary
`
`Not available
`
`BRAF or NRAS mutation — no. of
`patients (%)
`
`No
`
`Yes
`
`Lactate dehydrogenase level at start of
`therapy — no. of patients (%)
`
`63
`
`39–70
`
`3 (27)
`
`8 (73)
`
`0
`
`0
`
`10 (91)
`
`1 (9)
`
`0
`
`1 (9)
`
`10 (91)
`
`60
`
`48–79
`
`8 (57)
`
`6 (43)
`
`3 (21)
`
`0
`
`8 (57)
`
`3 (21)
`
`0
`
`6 (43)
`
`8 (57)
`
`66
`
`33–90
`
`9 (36)
`
`16 (64)
`
`1 (4)
`
`1 (4)
`
`15 (60)
`
`3 (12)
`
`5 (20)
`
`17 (68)
`
`8 (32)
`
`57
`
`18–74
`
`5 (36)
`
`9 (64)
`
`1 (7)
`
`0
`
`11 (79)
`
`0
`
`2 (14)
`
`11 (79)
`
`3 (21)
`
`8 (32)
`
`9 (64)
`
`Normal
`
`Above normal
`
`Not available
`
`Duration of response to therapy
` — wk
`
`Median
`
`Range
`
`Previous therapies — no.*
`
`Median
`
`Range
`
`Melanoma stage at time of diagnosis
`— no. of patients (%)
`
`IIIC
`
`M1a
`
`M1b
`
`M1c
`
`Overall survival — yr†
`
`Median
`
`Range
`
`8 (73)
`
`2 (18)
`
`1 (9)
`
`59
`
`42–361
`
`1
`
`0–3
`
`0
`
`0
`
`5 (45)
`
`6 (55)
`
`4.4
`
`2.0–6.9
`
`8 (57)
`
`5 (36)
`
`1 (7)
`
`14
`
`11–23
`
`1
`
`0–2
`
`0
`
`1 (7)
`
`1 (7)
`
`12 (86)
`
`0.9
`
`0.4–2.7
`
`3 (12)
`
`14 (56)
`
`130
`
`64–376
`
`0
`
`0–2
`
`3 (12)
`
`4 (16)
`
`2 (8)
`
`16 (64)
`
`3.3
`
`1.6–7.2
`
`3 (21)
`
`2 (14)
`
`11
`
`3–29
`
`0
`
`0–3
`
`0
`
`1 (7)
`
`3 (21)
`
`10 (71)
`
`0.8
`
`0.2–2.1
`
`* Previous therapies included interleukin-2 and cytotoxic chemotherapy.
`† Overall survival was calculated from the date of the first dose of ipilimumab to the date of death or censoring of data.
`
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`the Supplementary Appendix). The wide ranges
`of mutational burdens (Fig. 2A, and Table S3 in
`the Supplementary Appendix) and recurrent and
`driver mutations (Fig. S2C and S2D and Table S4
`in the Supplementary Appendix) among samples
`were consistent with previously reported find-
`ings.17-19 The ratio of transitions to transversions
`(Fig. S2E in the Supplementary Appendix) and
`the frequency of nucleotide changes (Fig. S2F in
`the Supplementary Appendix) were similar in the
`discovery and validation sets.12 No gene was uni-
`versally mutated across patients with a sustained
`benefit.
`
`ASSOCIATION between Mutational Burden
`and Clinical Benefit
`We hypothesized that an increased mutational
`burden in metastatic melanoma samples would
`correlate with a benefit from CTLA-4 blockade.
`There was a significant difference in mutational
`load between patients with a long-term clinical
`benefit and those with a minimal benefit or no
`benefit, both in the discovery set (P = 0.01 by the
`Mann–Whitney test) and in the validation set
`(P = 0.009 by the Mann–Whitney test) (Fig. 2A, and
`Table S5 in the Supplementary Appendix). In the
`discovery set, a high mutational load was sig-
`nificantly correlated with improved overall sur-
`vival (P = 0.04 by the log-rank test) (Fig. 2B), and
`there was a trend toward improved survival in
`the validation set (Fig. S3A in the Supplementary
`Appendix). The latter set included eight patients
`with nonresponding tumors who otherwise had
`systemic disease control, which may confound
`the relationship between mutational load and
`survival. Further subdivision into four clinical
`categories was suggestive of a dose–response re-
`lationship in the discovery set (Fig. S3B in the
`Supplementary Appendix). These data indicate
`that a high mutational load correlates with a sus-
`tained clinical benefit from CTLA-4 blockade but
`that a high load alone is not sufficient to impart
`a clinical benefit, because there were tumors
`with a high mutational burden that did not re-
`spond to therapy.
`
`Somatic Neoepitopes in Responding Tumors
`and Efficacy of CTLA-4 Blockade
`MHC class I presentation and cytotoxic T-cell rec-
`ognition are required for ipilimumab activity.10
`Because mutational load alone did not explain a
`clinical benefit from CTLA-4 blockade, we hypoth-
`
`Figure 1. Paired Pretreatment and Post-Treatment Computed Tomographic
`Scans.
`In Panel A, the scans on the top were obtained on January 2, 2011, and Au-
`gust 26, 2013, and the scans on the bottom were obtained on September 6,
`2011, and January 14, 2013. In Panel B, the scans were obtained on August
`13, 2009, and January 9, 2010.
`
`efit was defined by tumor growth on every com-
`puted tomographic scan after the initiation of
`treatment (no benefit) or a clinical benefit lasting
`6 months or less (minimal benefit). Representa-
`tive scans are shown in Figure 1, and Figure S1
`in the Supplementary Appendix.
`To determine the genetic features associated
`with a sustained benefit from CTLA-4 blockade,
`we analyzed DNA in tumor and matched blood
`samples using whole-exome sequencing. In the
`discovery set, we generated 6.4 Gb of mapped
`sequence, with more than 99% of the target se-
`quence covered to at least 10× depth and a mean
`exome coverage of 103× (Table S3 and Fig. S2 in
`
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`A MutationalLoad
`
`P=0.009 by
`Mann–Whitney test
`
`Long-term
`benefit
`
`Minimal or
`no benefit
`
`1750
`
`1500
`
`1250
`
`1000
`
`750
`
`500
`
`250
`
`0
`
`P=0.01 by
`Mann–Whitney test
`
`Long-term
`benefit
`
`Minimal or
`no benefit
`
`1750
`
`1500
`
`1250
`
`1000
`
`750
`
`500
`
`250
`
`0
`
`No.ofExomicMissenseMutations
`
`DiscoverySet
`
`ValidationSet
`
`B SurvivalinDiscoverySet
`100
`
`>100 mutations (N=17)
`
`≤100 mutations (N=8)
`
`P=0.04 by
`log-rank test
`
`20
`
`40
`
`60
`
`80
`
`100
`
`Months
`
`80
`
`60
`
`40
`
`20
`
`0
`
`0
`
`Survival(%ofpatients)
`
`Figure 2. Mutational Landscape of Tumors According to Clinical Benefit
`from Ipilimumab Treatment.
`Panel A shows the mutational load (number of nonsynonymous mutations
`per exome) in the discovery and validation sets, according to status with re-
`spect to a clinical benefit from therapy. Panel B depicts the Kaplan–Meier
`curves for overall survival in the discovery set for patients with more than
`100 nonsynonymous coding mutations per exome and patients with 100 or
`fewer mutations.
`
`quency tetrapeptides in the combined exomes.
`Subsequent analysis is restricted to post-filtering
`peptides (see the Methods section in the Supple-
`
`2193
`
`esized that the presence of specific tumor neoan-
`tigens might explain the varied therapeutic ben-
`efit. To identify these neoepitopes, we developed
`a bioinformatic pipeline incorporating predic-
`tion of MHC class I binding, modeling of T-cell
`receptor binding, patient-specific HLA type, and
`epitope-homology analysis (see the Methods sec-
`tion and Fig. S4 in the Supplementary Appendix).
`We created a computational algorithm, called
`NAseek, to translate all nonsynonymous mis-
`sense mutations into mutant and nonmutant
`peptides (see the Methods section and Fig. S4 in
`the Supplementary Appendix). We examined
`whether a subgroup of somatic neoepitopes
`would alter the strength of peptide–MHC bind-
`ing, using patient-specific HLA types (Table S3
`in the Supplementary Appendix). We first com-
`pared the overall antigenicity trend of all mutant
`versus nonmutant peptides. In aggregate, the
`mutant peptides were predicted to bind MHC
`class I molecules with higher affinity than the
`corresponding nonmutant peptides (Fig. S5 in
`the Supplementary Appendix).
`Using only peptides predicted to bind to MHC
`class I molecules (binding affinity, ≤500 nM), we
`searched for conserved stretches of amino acids
`shared by multiple tumors. Using the methods
`described in the Methods section in the Supple-
`mentary Appendix, we identified shared, con-
`sensus sequences.20 We identified a number of
`tetrapeptide sequences that were shared by pa-
`tients with a long-term clinical benefit but com-
`pletely absent in patients with a minimal benefit
`or no benefit (Fig. 3A and 3B, and Table S6 in the
`Supplementary Appendix). It has been shown that
`short amino acid substrings comprise conserved
`regions across antigens recognized by a T-cell
`receptor.21 In these experiments, recognition of
`epitopes was driven by consensus tetrapeptides
`within the immunogenic peptides, and tetrapep-
`tides within cross-reacting T-cell receptor epit-
`opes were necessary and sufficient to drive T-cell
`proliferation, findings that are consistent with
`evidence that this polypeptide length can drive
`recognition by T-cell receptors.22 Tetrapeptides
`are used to model genome phylogeny because
`they occur relatively infrequently in proteins and
`typically reflect function.23
`We used the discovery set to generate a pep-
`tide signature from the candidate neoepitopes.
`This analysis initially pooled the aforementioned
`discovery and validation sets to remove low-fre-
`
`Genetic Basis for Response to CTLA-4 Blockade
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`Figure 3. Association of a Neoepitope Signature
`with a Clinical Benefit from CTLA-4 Blockade.
`Candidate neoepitopes were identified by means of
`mutational analysis, as described in the Methods sec-
`tion in the Supplementary Appendix. Panel A shows a
`heat map of candidate tetrapeptide neoantigens that
`were present in patients with a long-term clinical ben-
`efit but absent in patients with a minimal benefit or
`no benefit in the discovery set (comprising 25 patients).
`Each row represents a neoepitope; each column repre-
`sents a patient. The vertical red line indicates the tetra-
`peptide signature associated with a response to block-
`ade of cytotoxic T-lymphocyte antigen 4 (CTLA-4).
`The exact tetrapeptides, chromosomal loci, and non-
`mutant and mutant nonamers in which they occur are
`listed in Table S6 in the Supplementary Appendix.
`Panel B shows the same information for the validation
`set (comprising 39 patients). Panel C shows the Kap-
`lan–Meier curves for overall survival in the discovery
`set for patients with the signature and those without
`the signature. Panel D shows the same data for the
`validation set.
`
`mentary Appendix). We found that the tetrapep-
`tides common to each group (candidate neoepi-
`topes) included 101 shared exclusively among
`patients in the discovery set who had a long-
`term clinical benefit; this was also indepen-
`dently observed in the validation set (Fig. 3A and
`3B, and Tables S6 and S7 in the Supplementary
`Appendix). This set of neoepitopes defines a
`signature linked to a benefit from CTLA-4
`blockade. Because of the size of our discovery
`set, we cannot exclude the possibility that addi-
`tional biologically relevant epitopes exist and
`conversely that there are biologically relevant
`epitopes that were predicted bioinformatically
`but were not expressed or presented in patients
`with a minimal benefit or no benefit (Tables S7A
`and S7B in the Supplementary Appendix).
`Shared tetrapeptide neoepitopes did not sim-
`ply result from a high mutational load. For ex-
`ample, in the discovery set, the patient with a
`minimal benefit or no benefit who had the
`greatest number of mutations (Patient SD7357,
`who had 1028 mutations) did not share any of
`the tetrapeptide signatures. This concept was
`illustrated again in the validation set, in which
`even tumors from patients with more than 1000
`mutations (Patients NR9521 and NR4631) did
`not respond (Table S3 in the Supplementary Ap-
`pendix). Simulation testing with five different
`
`A
`
`NeoepitopesinDiscoverySet
`Minimal
`Long-
`orNo
`Term
`Benefit
`Benefit
`
`B
`
`NeoepitopesinValidationSet
`Minimal
`Long-
`orNo
`Term
`Benefit
`Benefit
`
`NeoepitopeSignature
`
`NeoepitopeSignature
`
`C SurvivalinDiscoverySet
`100
`
`With signature (N=10)
`
`Without signature (N=15)
`
`P<0.001
`by log-rank
`test
`
`20
`
`40
`
`60
`
`80
`
`100
`
`Months
`
`80
`
`60
`
`40
`
`20
`
`0
`
`0
`
`Survival(%ofpatients)
`
`With signature (N=16)
`
`P=0.002
`by log-rank
`test
`
`Without signature (N=23)
`
`D SurvivalinValidationSet
`100
`
`80
`
`60
`
`40
`
`20
`
`Survival(%ofpatients)
`
`0
`
`0
`
`20
`
`40
`
`60
`Months
`
`80
`
`100
`
`AUTHOR:
`
`Snyder (Chan)
`
`FIGURE:
`
`REVISED 3 of 4 (10/22/15)
`
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`ARTIST:
`
`ts
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`n engl j med 371;23 nejm.org december 4, 2014
`
`AUTHOR, PLEASE NOTE:
`
`Fig
`
`Iss
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`models showed that the association between the
`neoepitope signature and a long-term clinical
`benefit was highly significant and was unlikely
`to have resulted from chance alone (P<0.001 for
`four methods and P = 0.002 for a fifth method)
`(Fig. S6 in the Supplementary Appendix). A high
`mutational load appeared to increase the probabil-
`ity, but not guarantee formation, of a neoepitope
`signature associated with a benefit. Consensus
`analysis revealed that the neoepitopes were not
`random. The frequencies of amino acids that made
`up the tetrapeptides in the group of patients with
`a long-term clinical benefit were different from
`those observed in the group with a minimal ben-
`efit or no benefit (Fig. S7A in the Supplementary
`Appendix).
`Presence of the neoepitope signature peptides
`correlated strongly with survival in both the
`discovery set and the validation set (P<0.001 and
`P<0.002, respectively, by the log-rank test) (Fig.
`3C and 3D). The correlation between mutational
`load and survival was not as strong (Fig. 2B, and
`Fig. S3A in the Supplementary Appendix).
`The shared tetrapeptides were encoded by
`mutations in diverse genes across the genome
`(Fig. S7B and Table S6 in the Supplementary Ap-
`pendix). Using RNA-sequencing data from the
`Cancer Genome Atlas, we confirmed that the
`genes harboring our somatic neoepitopes were
`widely expressed in melanoma (Table S8 in the
`Supplementary Appendix). In some cases, the
`amino acid change resulting from the somatic
`mutation led to a change in the tetrapeptide it-
`self. In others, the mutant amino acid was sepa-
`rate from the tetrapeptide and altered MHC
`binding, as has been described previously.24-26
`In addition, candidate neoepitopes common
`to both clinical groups were analyzed with the
`use of the Immune Epitope Database (www.iedb
`.org). This is the most comprehensive database of
`experimentally validated, published, and curated
`antigens, and it has been used to develop algo-
`rithms to identify antigens with high accuracy.14
`The candidate neoepitopes common to patients
`with a long-term clinical benefit were homolo-
`gous to many more viral and bacterial antigens
`in the database than were the neoepitopes com-
`mon to patients with a minimal benefit or no
`benefit (Table S9 in the Supplementary Appen-
`dix). For example, the tetrapeptide substring
`ESSA was shared by patients with a long-term
`
`clinical benefit (Fig. 4A) and corresponds to the
`precise antigenic portion of human cytomegalo-
`virus immediate early epitope (MESSAKRKMDP-
`DNPD).27 These data suggest that the neoepi-
`topes in patients with strong clinical benefit from
`CTLA-4 blockade may resemble epitopes from
`pathogens that T cells are likely to recognize. The
`cross-reactive peptides defined by short peptide
`consensus sequences that were discovered by
`Birnbaum et al. with the use of an unbiased
`screen also had substantial homology to antigens
`in microbes.21 Although tantalizing, these obser-
`vations will require further study to confirm.
`Using a whole-exome sequencing approach,
`we characterized the predicted antigenic peptide
`space (see the Methods section in the Supple-
`mentary Appendix). As further validation of our
`study, we reidentified melanoma antigen recog-
`nized by T cells (MART-1, also known as MelanA),
`an experimentally validated melanocytic antigen
`(Fig. S8).28 EKLS, which comprises the core
`amino acids of the MART-1 MHC class II epitope,
`was shared by patients with a long-term clinical
`benefit, and the phosphoserine moiety is critical
`for T-cell receptor recognition.29 The frequency of
`leukocyte common antigen–positive cells and ra-
`tio of CD8-positive cells to FOXP3-positive cells
`were substantially different between patients
`with a long-term clinical benefit from ipilim-
`umab and those with a minimal benefit or no
`benefit (Fig. S9 in the Supplementary Appendix).
`
`In Vitro Validation of Predicted
`Immunogenic Peptides
`Translation of next-generation sequencing into
`in vitro validation of peptide predictions has
`proven challenging, even in expert hands, with
`very low published validation rates.15 In vitro as-
`says are hampered by the paucity of clinical sam-
`ples, the sensitivity of preserved cells to the
`freeze–thaw process, the low frequency of anti-
`neoantigen T cells in clinical samples, and the
`very low sensitivity of T cells in vitro in the ab-
`sence of the complex in vivo immunogenic mi-
`croenvironment.
`We attempted to optimize prediction by inte-
`grating multiple high-throughput approaches
`(Fig. S4 in the Supplementary Appendix). On the
`basis of our prediction algorithm, we generated
`pools of peptides and performed assays of T-cell
`activation for patients for whom we had suffi-
`
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`
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`

`T h e ne w e ngl a nd jou r na l o f m e dicine
`
`Ipilimumab
`
`Antigen-
`presenting
`cell
`
`Tcell
`
`CTLA-4
`8
`C D 2
`T C R
`
`B 7
`
`B 7
`
`M H C
`
`Peptide
`
`Blockage of
`CLTA-4 results
`in T-cell activation
`
`PatientNo.
`
`Nonmutant
`
`Mutant
`
`SD1494
`
`YLLGSSALT
`
`YLLESSALT
`
`CR9306
`
`VGSSADILY
`
`VESSADILY
`
`PR4092
`
`YFPEESSAL
`
`YSPEESSAL
`
`TESPFEQHI
`
`1
`
`7
`
`24
`
`36
`48
`Weeks
`
`60
`
`108
`
`156
`
`GLEREGFTF
`
`Baseline
`
`7
`
`24
`
`Weeks
`
`48
`
`120
`
`1.50
`
`1.25
`
`1.00
`
`0.75
`
`0.50
`
`0.25
`
`0.00
`
`0.10
`
`0.08
`
`0.06
`
`0.04
`
`0.02
`
`0.00
`
`IFN-γ–PositiveT-CellResponse(%)
`
`IFN-γ–PositiveT-CellResponse(%)
`
`TKSPFEQHI
`
`TESPFEQHI
`
`GLERGGFTF GLEREGFTF
`
`1.50
`
`1.25
`
`1.00
`
`0.75
`
`0.50
`
`0.25
`
`0.00
`
`0.08
`
`0.06
`
`0.04
`
`0.02
`
`0.00
`
`PolyfunctionalT-CellResponse(%)
`
`PolyfunctionalT-CellResponse(%)
`
`A
`
`B
`
`C
`
`Figure 4. Role of Neoantigens in Activation of T Cells from Patients Treated with CTLA-4 Blockade.
`Panel A shows an example of a tetrapeptide substring of human cytomegalovirus. In each case, the nonamer con-
`taining the mutation is predicted to bind and be presented by a patient-specific HLA. Panel B shows the dual posi-
`tive (interferon-γ [IFN-γ] and tumor necrosis factor α [TNF-α]) CD8+ T-cell response to TESPFEQHI and nonmutant
`peptide TKSPFEQHI and the increase in IFN-γ+ T cells over time. Data from Patient CR9306 are shown. T bars indi-
`cate the standard deviation. Panel C shows the dual positive (IFN-γ and TNF-α) CD8+ T-cell response to GLEREGFTF
`and nonmutant peptide GLERGGFTF and illustrates the increase in peptide-specific T cells 24 weeks after the initia-
`tion of treatment with ipilimumab relative to baseline. Data from Patient CR0095 are shown. MHC denotes major
`histocompatibility complex, and TCR T-cell receptor.
`
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`cient lymphocytes (see the Methods section in
`the Supplementary Appendix). Positives pools
`were observed for three of five patients (Fig.
`S10A, S10B, and S10C in the Supplementary Ap-
`pendix). We identified the exact peptides for pa-
`tients with adequate PBMCs. We found a polyfunc-
`tional T-cell response to the peptide TESPFEQHI
`in Patient CR9306 (Fig. S10D in the Supplementary
`Appendix) but not to its nonmutant counterpart,
`TKSPFEQHI. This response peaked at 60 weeks
`after the initiation of treatment (Fig. 4B). T-cell
`responses were absent in healthy donors (Fig.
`S10E in the Supplementary Appendix). TESPFEQHI
`had a predicted MHC class I affinity for B4402
`of 472 nM, as compared with 18323 nM for
`TKSPFEQHI. ESPF is a common tetrapeptide
`found in the response signature and is a sub-
`string (positions 176 through 179) of the hepati-
`tis D virus large delta epitope p27 (PESPFA and
`ESPFAR).30 TESPFEQHI results from a mutation
`in FAM3C (c.A577G;p.K193E), a gene highly ex-
`pressed in melanoma (Table S8 in the Supple-
`mentary Appendix).
`We also found that peptide GLEREGFTF elicited
`a polyfunctional T-cell response in Patient CR0095
`(Fig. 4C, and Fig. S10F in the Supplementary Ap-
`pendix), whereas nonmutant GLERGGFTF did not.
`This response peaked at 24 weeks after the ini-
`tiation of treatment (Fig. 4C). GLEREGFTF arises
`from a mutation in CSMD1 (c.G10337A;p.G3446E),
`which is also highly expressed in melanoma (Ta-
`ble S8 in the Supplementary Appendix), and the
`peptide has 80% homology to a known Burkhold-
`eria pseudomallei antigen (Immune Epitope Data-
`base Reference ID: 1027043). The lack of T-cell
`activation may not rule out a given neoantigen
`because in vitro assays are limited in sensitivity,
`as described above.
`
`Discussion
`
`Anti–CTLA-4 and anti–programmed cell death 1
`antibodies have resulted in long-term disease
`control in a subgroup of patients with melano-
`ma.1,2 Here, we have illustrated the importance
`of tumor genetics in defining the basis of the
`clinical benefit from CTLA-4 blockade.
`Our observations suggest a number of prin-
`ciples relevant to immunotherapy for cancer.
`Although a high mutational load is associated
`
`with a benefit from immune checkpoint abroga-
`tion, this factor alone is not sufficient to impart
`a clinical benefit. Rather, there are somatic neo-
`epitopes that are shared by patients with a pro-
`longed benefit and are absent in those without a
`prolonged benefit. Owing to somatic mutations,
`a subset of proteins present in the tumor becomes
`recognized by the immune system as nonself,
`given their novelty in the tumor context.8,14,31,32
`These concepts were formulated in the discovery
`set and confirmed in the validation set and will
`require further prospective study before use as a
`definitive biomarker.
`It is well known in the field of infectious
`diseases that an individual amino acid within a
`peptide can affect immunogenicity by altering
`peptide–MHC or peptide–T-cell receptor interac-
`tions.33,34 In cancers, the altered amino acid resi-
`due resulting from a single missense mutation
`can create a T-cell epitope from a previously self
`peptide.31,32,35 In the patients described here,
`altered amino acids resulting from tumor muta-
`tions caused the tumors to display somatic neoepi-
`topes that elicited an antitumor response aug-
`mented by CTLA-4 blockade.
`Our study has limitations. Although large
`for a genomic study (128 exomes), our sample
`size was limited, patients had received a variety
`of previous treatments, and tumor samples
`were obtained at various time points. Further-
`more, although the panel of somatic neoepi-
`topes (Fig. 3A and 3B, and Table S6 in the
`Supplementary Appendix) may constitute the
`most important ones, the in vivo relative immu-
`nologic contribution of each peptide is unclear.
`However, data showing that functionally im-
`portant immunogenic epitopes persisted after
`treatment with expanded tumor-infiltrating
`lymphocytes suggest that the response to mu-
`tations may persist over time.16 Although the
`recapitulation of the neoantigen signature in
`the validation set suggests that this may pro-
`vide a generally applicable tool for prediction
`of a benefit from immunotherapy, further stud-
`ies will be needed to investigate the role of
`MHC class II molecules and the relative effects
`and characteristics of neoantigens in different
`cancers.
`Our use of whole-exome sequencing to iden-
`tify a genetic basis associated with a benefit from
`
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`CTLA-4 blockade provides proof of principle that
`tumor genomics can inform responses to im-
`munotherapy. For the field of cancer genetics,
`these data suggest a need for an expanded defi-
`nition of the previous categories of driver and
`passenger mutations. Our data show that exonic
`missense mutations in general confer increased
`MHC class I binding (Fig. S5A and S5B in the
`Supplementary Appendix) and confirm the hy-
`pothesis36 that some mutations formerly catego-
`rized as passengers may in