neoantigen load, antigen presentation machinery, and ... · 0.0423; fig. 2h). these data suggest...

Research Article

Neoantigen Load, Antigen PresentationMachinery, and Immune Signatures DeterminePrognosis in Clear Cell Renal Cell CarcinomaHirokazu Matsushita1,Yusuke Sato2,3,Takahiro Karasaki1,Tohru Nakagawa2, Haruki Kume2,Seishi Ogawa3, Yukio Homma2, and Kazuhiro Kakimi1

Abstract

Tumors commonly harbor multiple genetic alterations, someof which initiate tumorigenesis. Among these, some tumor-specific somatic mutations resulting in mutated protein havethe potential to induce antitumor immune responses. Toexamine the relevance of the latter to immune responses inthe tumor and to patient outcomes, we used datasets of whole-exome and RNA sequencing from 97 clear cell renal cellcarcinoma (ccRCC) patients to identify neoepitopes predictedto be presented by each patient's autologous HLA molecules.We found that the number of nonsilent or missense mutationsdid not correlate with patient prognosis. However, combiningthe number of HLA-restricted neoepitopes with the cell surfaceexpression of HLA or b2-microglobulin (b2M) revealed that anA-neohi/HLA-Ahi or ABC-neohi/b2Mhi phenotype correlatedwith better clinical outcomes. Higher expression of immune-

related genes from CD8 T cells and their effector molecules[CD8A, perforin (PRF1) and granzyme A (GZMA)], however,did not correlate with prognosis. This may have been due to theobserved correlation of these genes with the expression of othergenes that were associated with immunosuppression in thetumor microenvironment (CTLA-4, PD-1, LAG-3, PD-L1, PD-L2, IDO1, and IL10). This suggested that abundant neoepitopesassociated with greater antitumor effector immune responseswere counterbalanced by a strongly immunosuppressivemicroenvironment. Therefore, immunosuppressive moleculesshould be considered high-priority targets for modulatingimmune responses in patients with ccRCC. Blockade of thesemolecular pathways could be combined with immunothera-pies targeting neoantigens to achieve synergistic antitumoractivity. Cancer Immunol Res; 4(5); 463–71. �2016 AACR.

IntroductionImmune checkpoint blockade therapy has now unequivo-

cally demonstrated robust and durable clinical responses inpatients with previously refractory tumors. The existence ofantitumor immunity and its contribution to the treatment ofcancer is now widely appreciated (1, 2). It is thought that thegeneration of CTLs plays a pivotal role in mediating antitumorimmunity. Several reports support the notion that amino acidsubstitutions originating from tumor-specific somatic muta-tions can be recognized by tumor-specific CTLs (3–10). T cellsrecognizing such individual tumor-specific mutations will nothave been subject to central tolerance induction in the thymus.These mutations are therefore likely to represent highly immu-nogenic neoantigens which can be targeted for tumor immunerejection (11–13). Recent reports suggest that the number of

candidate neoantigens predicted by next-generation sequenc-ing (NGS) and MHC class I binding algorithms is correlatedwith clinical outcomes in melanoma and lung cancer patientsreceiving anti-CTLA-4 or anti-PD-1 immune checkpoint block-ade antibody therapy (14, 15). These findings were interpretedas indicating that endogenous immune responses against muta-tion-derived neoantigens were augmented by blocking regula-tory mechanisms inhibiting antitumor T-cell function.

Clear cell renal cell carcinoma (ccRCC) appears to be animmune-sensitive tumor as suggested by early attempts at immu-nomodulating therapies, such as IL2 and/or IFNa (16, 17). Theresults of recent clinical trials of immune checkpoint blockadetherapy in ccRCC suggest that this cancer may also be sensitive tothese agents, similar tomelanomaor lung cancer, despite its lowerlevels of somatic mutations (18–20). Therefore, we examined theimpact of neoantigens in ccRCCpatients and investigatedwhetherthe number of candidate neoantigenic T-cell epitopes present inccRCC correlates with clinical outcomes and immune signaturesin the local tumor microenvironment.

Materials and MethodsPatients

ccRCC patients (N ¼ 97) from our previous study (21) wereinvestigated in this cohort. The clinicopathologic characteristics ofthese patients are shown in Table 1. Most of the patients hadreceived standard therapy with surgery alone or surgery pluscytokines, tyrosine kinase inhibitors, and mTOR inhibitorsaccording to the guidelines in the JapaneseUrological Association(22) and the National Comprehensive Cancer Network (NCCN).

1Department of Immunotherapeutics, The University of Tokyo Hospi-tal, Tokyo, Japan. 2Department of Urology, The University of TokyoHospital,Tokyo, Japan. 3Department of PathologyandTumor Biology,Graduate School of Medicine, Kyoto University, Kyoto, Japan.

Note: Supplementary data for this article are available at Cancer ImmunologyResearch Online (http://cancerimmunolres.aacrjournals.org/).

Corresponding Author: Kazuhiro Kakimi, Department of Immunotherapeutics,The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo 113-8655,Japan. Phone: 813-5805-3161; Fax: 813-5805-3164; E-mail:[email protected]

doi: 10.1158/2326-6066.CIR-15-0225

�2016 American Association for Cancer Research.

CancerImmunologyResearch

www.aacrjournals.org 463

on October 7, 2020. © 2016 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from

Published OnlineFirst March 15, 2016; DOI: 10.1158/2326-6066.CIR-15-0225

http://cancerimmunolres.aacrjournals.org/

Nopatients had received immune checkpoint blockade therapy inthis cohort.

Whole-exome and RNA sequencing dataThe cohort used in the current study consisted of the 97 cases

from our previous study in which HLA typing (97 cases), whole-exome sequencing (106 cases), and RNA sequencing data (RNA-seq; 100 cases) were all available (21). The mean coverage bywhole-exome sequencing was 128�, and 89% of targeted regionswere covered by�20 independent reads. Somaticmutations weredetected using the EBCall (Empirical Bayesian mutation Calling)algorithm (23). To validate somatic mutations, PCR-based deepsequencing was performed for randomly selected single-nucleo-tide variants (SNV) and indels. True-positive rateswere 96%(504/525) for SNVs and 96% (55/57) for indels. In RNA sequencing,130million readswith 100base lengthper sampleswere obtainedof which 105 million reads were mapped on RefSeq gene.

HLA typingHLA types were assigned from exome sequencing data from

normal tissues or peripheral blood mononuclear cells using HLAtyping software (Omixon target HLA). Three patients with ambig-uous predictions were excluded from the analysis, resulting in the97 ccRCC patients included here.

MHC class I binding predictionMutated peptides derived from missense mutations from

exome sequencing data were used for MHC class I bindingprediction. The missense mutations in genes with low expressionthat have FPKM value (fragments per kilobase of transcripts per

million fragments sequenced) of less than 1 were eliminated.Long peptides containing the predicted mutation or of wild typewere assessed using the Immune Epitope Database and AnalysisResource (http://www.iedb.org/) offline; all possible 9- and 10-mer peptides were selected, each predicted to bind to a specificHLA allele for each patient (24). Ranking of predicted bindingscores was byMHC class I epitope binding algorithms (NetMHC-pan v2.8). Mutated peptides that had an IC50 value below 500nmol/L were regarded as candidate neoepitopes.

Analyses of immunologic signatures in the tumormicroenvironment

The immunologic signature in the tumorwas evaluated by geneexpression analysis of infiltrating immune cells (CD8A, CD4,CD19, KLRF1, and CD68), phenotypic markers of T cells (CD27,CD28, 4-1BB, OX40, and GITR), and effector molecules of CD8 Tcells (PRF1, GZMA, IFNg , and TNFa) using FPKM values from theRNA-seq data.

Statistical analysisSurvival times were calculated as the number of days from

surgery to death, or the last time the patientwas known to be alive,andplotted as Kaplan–Meier survival curves. The log-rank testwasused to examine the significance of differences in the survivalbetween groups. Comparison of results was performed by anunpaired, two-tailed Student t test with GraphPad Prism 5(GraphPad Software, Inc.). A value of P < 0.05 was consideredstatistically significant.

ResultsSomatic mutation number does not correlate with ccRCCprognosis

Neoantigens derived from tumor-specific somatic mutationsare now acknowledged as major targets for antitumor immuneresponses. It has been reported that the number of mutationscorrelates with prognosis in melanoma and lung cancer patientsunder checkpoint blockade immunotherapy. To determinewhether the same may apply to ccRCC (not on immunomod-ulatory therapy), we investigated the number of tumor-specificsomatic mutations in this tumor type. The clinicopathologiccharacteristics of these 97 ccRCC patients are summarizedin Table 1. These patients received standard treatments; TNMstaging and Fuhrman grade were strong prognostic values forthese patients (Supplementary Fig. S1). The number of nonsilentmutations in these patients ranged from 7 to 155 (median, 50).Most of them were missense mutations (5–130, median,41; Table 2 and Supplementary Table S1). We divided thepatients into two groups with values above or below the medianand plotted Kaplan–Meier survival curves. Survival betweengroups with a higher or lower number of nonsilent mutationswas not significantly different (Fig. 1A). The same analysis wastrue for the missense mutations as well (Fig. 1B). These resultsdocument that the number of mutations did not correlate withccRCC prognosis.

Neoepitope number does not correlate with ccRCC prognosisWe determined the most likely neoepitopes among the tumor-

specific mutations in each patient based on MHC class I bindingprediction scores according to NetMHCpan v2.8. Peptides pre-dicted to bind to each patient's HLA molecules with high-affinity

Table 1. Clinicopathologic characteristics of 97 ccRCC patients

N

All cases 97SexMale 75Female 22

Age, years<60 39�60 58

pT1a 371b 272 113 214 1

N0 901 32 4

M0 851 12

Fuhrman grade1 132 563 214 5Undetermined 2

Clinical outcomeAlive 74Dead 23

Abbreviations: M, metastasis; N, nodes; pT, primary tumor.

Matsushita et al.

Cancer Immunol Res; 4(5) May 2016 Cancer Immunology Research464




(IC50 < 500 nmol/L) were considered to be likely neoepitopes.Because missense mutations represent the majority of the non-silent mutations, we focused on neoepitopes derived from thesemutations. AlthoughHLA class I–restricted epitopes range from 8up to 11 or more amino acids, 9- and 10-mer peptides wereexamined in this study because they account for 90% of theneoepitopes which induce T-cell responses in humans (25).The number of HLA-A–restricted neoepitopes (A-neo) deter-mined by the estimated binding affinity ranged from 0 to 88(median, 20). Similarly, 0 to 47 (median, 11) HLA-B–restrictedneoepitopes (B-neo), and 0 to 69 (median, 14) HLA-C–restricted(C-neo) neoepitopes were identified (Table 2 and SupplementaryTables S2 and S3). Then, the total number of neoepitopes pre-sented by HLA-A, -B, and -C molecules (ABC-neo) ranged from 2to 139 (median, 54). We divided these patients into two groupsaccording to higher or lower predicted numbers of neoepitopes,designated ABC-neohi (�54 neoepitopes) and ABC-neolo (with<54). The ABC-neohi group did experience better clinical outcomecompared with the ABC-neolo group, but this difference did notachieve statistical significance (log-rank test, P¼ 0.2413; Fig. 1C).Similar results for correlations with survival were obtained forHLA-A–restricted as well as for HLA-A, -B, and -C–restrictedneoepitopes (ABC-neo) in that patients in the A-neohi group(defined as �20 neoepitopes) tended to have a better prognosisthan those in the low group (<20 neoepitopes), again without

achieving statistical significance (log-rank test, P ¼ 0.096; Fig.1D). In contrast, there was no difference at all between B-neohi

(�11) and B-neolo (<11) groups or between C-neohi (�14) andC-neolo (<14) groups (Fig. 1E and F).

Neoepitope antigen presentation correlates with ccRCCprognosis

It is widely accepted that tumors can evade antitumor immuneresponses by thedownregulation or loss of expression of theMHCclass I molecules responsible for antigen presentation (26, 27).We therefore evaluated the expression of HLA-A, HLA-B, HLA-C,and b2-microglobulin (b2M) using RNA-seq data (21). The FPKMvalue of HLA-A, HLA-B, HLA-C, and b2M expression was 22,427–216,795 (median, 79,336), 18,298–309,293 (median, 93,251),18,808–180,437 (median, 53,744), and 52,111–709,571 (medi-an, 253,455), respectively (Supplementary Table S2). Patientswhose tumors had high expression of HLA-A, HLA-B, HLA-C, orb2M tended to have a better prognosis than those with lowexpression, but again this difference did not achieve statisticalsignificance (P¼ 0.0966, P¼ 0.2177, P¼ 0.2308, and P¼ 0.0797,respectively; Fig. 2A–D). Next, we examined these patients interms of the number of neoepitopes and the levels of HLA or b2Mexpression. For this, patients were divided into four groups: (i)neohi/HLAhi; (ii) neohi/HLAlo; (iii) neolo/HLAhi; and (iv)neolo/HLAlo. As shown in Fig. 2E, patients with A-neohi/HLA-Ahi

had longer overall survival (OS) than those with A-neolo/HLA-Alo

and this time statistical significance was achieved (P ¼ 0.027).However, there was no statistical significance between B-neohi/HLA-Bhi andB-neolo/HLA-Blo or betweenC-neohi/HLA-Chi andC-neolo/HLA-Clo (Fig. 2F and G). As for b2M expression, ABC-neowas combined with b2M. Patients with ABC-neohi/b2Mhi alsohad significantly better OS than those with A-neolo/b2Mlo (P ¼0.0423; Fig. 2H). These data suggest that antigen presentationof neoepitopes correlates with a better prognosis for ccRCCpatients.

Neoepitope presentation determines immune signaturesAntigen presentation of neoepitopes should result in recogni-

tion by tumor-specific T cells and the induction of antitumorimmune responses in the host and their presence within the

Ove

rall

surv

ival

Months after surgery

Ove

rall

surv

ival


NS

A BLow

High

Low

High

NS

C

Ove

rall

surv

ival


A-neohi

A-neolo

NS

Ove

rall

surv

ival


B-neohi

B-neolo

NS

Ove

rall

surv

ival

Ove

rall

surv

ival

C-neohi

C-neolo


NS

ABC-neohi

ABC-neolo

NS


D E F

00

20406080

100

50 100 150 00

20406080

100

50 100 150 00

20406080

100

50 100 150

00

20406080

100

50 100 15000

20406080

100

50 100 15000

20406080

100

50 100 150

Figure 1.The number of somatic mutations andneoepitopes was not correlated withprognosis in 97 ccRCC patients. A,Kaplan–Meier survival curves forccRCC patients with 50 or morenonsilent mutations (high), patientswith fewer than 50 nonsilentmutations(low), patients with 41 or moremissense mutations (high), andpatients with fewer than 41 missensemutations (low; B). Kaplan–Meiersurvival curves in 97 ccRCC patientsstratified according to the number oftotal HLA-restricted neoepitopes(ABC-neo; C) and the number of eachHLA-restricted neoepitope, A-neo (D),B-neo (E), or C-neo (F). Statisticalanalysis was done by log-rank test. NS,not statistically significant.

Table 2. The number of nonsilent mutations and the number of HLA-restrictedneoepitopes derived from missense mutations in 97 ccRCC patients

Median value Range NNonsilent mutations (total) 50 7–155 4,859

Missense 41 5–130 3,983Indel 5 0–13 521Splice 1 0–5 126Nonsense 2 0–8 221Read-through changes 0 0–1 8

Median value Range NHLA-Restricted neoepitopes (ABC-neo) 54 2–139 5,186

HLA-A (A-neo) 20 0–88 2,198HLA-B (B-neo) 11 0–47 1,226HLA-C (C-neo) 14 0–69 1,762

Neoantigens and Immune Signature of ccRCC Patients

www.aacrjournals.org Cancer Immunol Res; 4(5) May 2016 465




tumor. Therefore, we examined the expression of markers ofinfiltrating immune cells such as CD8A, CD4, CD19, KLRF1, andCD68, as well as T-cell costimulatory molecules such as CD27,CD28, 4-1BB, OX40, and GITR, and finally, effector moleculessuch as perforin (PRF1), granzyme A (GZMA), IFNg , and TNFa inthe tumor tissueusingRNA-seqdata (Fig. 3). Asmentioned above,patients were divided into 4 groups in terms of the number ofpotential A-neoepitopes present and the level of HLA-A expres-sion. As expected, CD8A expression in patientswith A-neohi/HLA-Ahi was significantly higher than in A-neolo/HLA-Alo (P ¼0.0309; Fig. 3A). The expression of other immune cell markersin the four groups was similar. However, patients in the A-neohi/HLA-Ahi group had significantly higher expression of the cytotoxiceffector molecules PRF1 and GZMA than in the A-neolo/HLA-Alo

group (P¼ 0.0058 and P¼ 0.0094, respectively). Similarly, CD8Aexpression in patients in the ABC-neohi/b2Mhi group was signif-icantlyhigher than in theABC-neolo/b2Mlogroup(P¼0.0238; Fig.3B). GZMA expression in ABC-neohi/b2Mhi patients was alsosignificantly higher in the ABC-neolo/b2Mlo group (P ¼0.0317). Taken together, these results indicate that presentationof A-neo with high HLA-A or ABC-neo with high b2M expressioncorrelates with an intratumoral immune signature of CD8 T cellsand their effector molecules.

Expression of effector immune signature genes does notcorrelate with prognosis

Next, we examined whether the expression of these immune-related genes had any impact on prognosis. Again, we divided thepatients into low and high groups according to the expression ofeach of these genes but failed to find any correlation with survival(Fig. 4). None of these genes by itself was correlated with prog-nosis in ccRCCpatients. Notably, even expression of genes relatedto CD8 T cells and their effector molecules, such as CD8A, PRF1,or GZMA was not associated with better prognosis, despite theirexpression being associated with neoepitope load and antigenpresentation.

Positive and negative immune response signatures coexistAs noted above, the number of A-neo or ABC-neo present and

higher expression of HLA-A or b2M tended to correlate with betterprognosis andwith immune signatures of effector CD8T cells andmolecules. However, the expression of CD8 T-cell genes andeffector molecules such as CD8A, PRF1, and GZMA did notcorrelatewith better prognosis. Therefore, we divided these ccRCCpatients into two groups according to their higher- or lower-thanmedian expression of CD8 genes, PRF1, or GZMA and examinedthe expression of genes associated with negative regulation ofimmune responses. As shown in Fig. 5, the immune checkpointmolecules CTLA-4, PD-1, and LAG-3, but not Tim-3, were highlyexpressed in patients with a CD8Ahi pattern relative to CD8Alo

patients (Fig. 5A; P<0.0001). PD-L1, PD-L2, and IDO1,which areinduced by IFNg , were alsomore highly expressed in CD8Ahi thanCD8Alo patients (Fig. 5A). The same was true for IL10 (Fig. 5A),whereas NOS1 and VEGFA were expressed at equal levels in allpatients. Similarly, the expression of these negative regulatorygenes (CTLA-4, PD-1, LAG-3, PD-L1, PD-L2, IDO1, and IL10),wasassociated with PRF1 (Fig. 5B) and GZMA (Fig. 5C). These resultsindicate that the immunosuppressive microenvironment appearsto be dominant in ccRCC tumors.

DiscussionIn this study, we analyzed data from whole-exome and RNA

sequencing in tumors from 97 ccRCC patients and found that thenumber of mutations and potential neoantigens derived fromthem was not associated with prognosis. However, when thenumber of neoantigens and the expression of HLA-A or b2Mwereconsidered together, patients grouped asA-neohi/HLA-Ahi or ABC-neohi/b2Mhi hadbetter clinical outcomes andhigher expression ofimmune-related CD8 T-cell genes and their effector molecules.Nonetheless, the latter were not themselves correlated with prog-nosis, possibly due to coexpression of genes that were associatedwith immunosuppression in the tumor microenvironment.

Ove

rall

surv

ival


HLA-Ahi

HLA-Alo

NS

Ove

rall

surv

ival


HLA-Bhi

HLA-Blo

NS Ove

rall

surv

ival HLA-Chi

HLA-Clo


NS Ove

rall

surv

ival


β2M hi

β2Mlo

NS

Ove

rall

surv

ival


A-neo/HLA-A

hi/lo

hi/hi vs. lo/lo P = 0.027 Ove

rall

surv

ival


B-neo/HLA-B

hi/hi vs. lo/lo NS Ove

rall

surv

ival

C-neo/HLA-C


Ove

rall

surv

ival


ABC-neo/β2M

hi/hi

lo/lolo/hi hi/lo

hi/hi

lo/lo

lo/hi

hi/lohi/hi

lo/lo

lo/hihi/lo

hi/hi

lo/lo

lo/hi

hi/hi vs. lo/lo NS hi/hi vs. lo/lo P = 0.0423

A B C D

E F G H

00

20

40

60

80

100

50 100 150 00

20

40

60

80

100

50 100 150 00

20

40

60

80

100

50 100 150

00

20

40

60

80

100

50 100 15000

20

40

60

80

100

50 100 15000

20

40

60

80

100

50 100 15000

20

40

60

80

100

50 100 150

00

20

40

60

80

100

50 100 150

Figure 2.Antigen presentation of neoepitopes correlates with prognosis of ccRCC patients. Kaplan–Meier survival curves in 97 ccRCC patients stratified according toHLA-A (A), HLA-B (B), HLA-C (C), or b2M (D) expression, and the combination of the number of each neoepitope and their corresponding HLA expression,A-neo/HLA-A (E), B-neo/HLA-B (F), or C-neo/HLA-C (G), and the combination of ABC-neo and b2M expression (ABC-neo/b2M; H). Statistical analysis wasdone by log-rank test. A-neo, HLA-A-restricted neoepitopes.

Matsushita et al.





P = 0.0309NS

NS

NSNS

NSCD8A CD4

FPK

M

NSNS

NSKLRF1

NSNS

NSCD68

NSNS

NSCD19

NSNS

GZMAPRF1

FPK

M

P = 0.0094NS

NS

P = 0.0058

A

B

NSNS

NSIFNg

NSNS

NSTNFa

CD27 CD28 4-1BB GITR OX40 P = 0.0361NS

NS

NSNS

NS

NSNS

NS

NSNS

NS

NSNS

NS

P = 0.0238NS

NS

NSNS

NSCD8A CD4

P = 0.0347NS

P = 0.0140KLRF1

NSNS

NSCD68

NSNS

NSCD19

CD27 CD28NS

NSNS

NS

NSNS

NSNS

NS

NSNSP = 0.0457

GZMAPRF1P = 0.0317NS

IFNg TNFa

NS

NS

FPK

MFP

KM

FPK

MFP

KM

4-1BB GITR OX40

NS

NS NSNS

NS

NSNS

NS

NSNS

NS

0 0 0

100

200

300

0 0

10,000

20,000

30,000

40,000

100

200

300400

2,000

4,000

6,000

0

2,000

4,000

6,000

0

500

1,000

1,500

0

500

1,000

1,500

2,000

8,000

10,000

0

2,000

4,000

6,000

8,000

10,000

0

200

400

600

800

1,000

0

200

400

600

800

0

200

400

600

800

1,000

0

1,000

2,000

3,000

4,000

5,000

0

1,000

2,000

3,000

4,000

5,000

5,000

10,000

0 0 0

100

200

300

0 10,000

20,000

30,000

40,000

100

200

300400

2,000

4,000

6,000

0

2,000

4,000

6,000

0

500

1,000

1,500

0

500

1,000

1,500

2,000

8,000

10,000

0

2,000

4,000

6,000

8,000

10,000

0

200

400

600

800

1,000

0

200

400

600

800

0

200

400

600

800

1,000

0

1,000

2,000

3,000

4,000

5,000

0

1,000

2,000

3,000

4,000

5,000

5,000

10,000

Figure 3.Antigen presentation of neoepitopes correlates with CD8 T-cell gene expression levels and effector molecules in the tumor. The levels of immune-relatedgene expression were determined in patients grouped as A-neohi/HLA-Ahi, A-neohi/HLA-Alo, A-neolo/HLA-Ahi, and A-neolo/HLA-Alo (A); and ABC-neohi/b2Mhi,ABC-neohi/b2Mlo, ABC-neolo/b2Mhi, and ABC-neolo/b2Mlo (B). NS, not statistically significant.






It has been reported that mutational rates differ betweendifferent cancers (28). Melanomas have the highest mutationalrate (median, 13.2 mutations per Mb) with non–small cell lungcancers (NSCLC) having the second highest rate (median, 8.17and 6.43 mutations per Mb for the squamous and nonsqua-mous subtype, respectively; ref. 28). Rizvi and colleagues report-ed that in 34 lung cancer patients, each had between 11 and asmany as 1,192 missense mutations (median, 201) resulting in apredicted range of 8–610 neoepitopes (median, 112; ref. 15). Itwas also reported that the mutation rate and neoepitope loadwere indications for sensitivity to immune checkpoint blockadetherapy and correlated with longer OS in melanoma and lungcancer patients (14, 15).

In ccRCCpatients, themutation ratewas reported asmedium tolow (median, 1.53 mutations per Mb; ref. 28). Consistent withthis, in our 97 ccRCC patients, each had 5–130 missense muta-tions (median, 41) and 2–139 predicted neoepitopes (ABC-neo;median, 54; Table 2). Unlike results inmelanoma and lung cancerpatients, the mutational and neoepitope load was not correlatedwith prognosis in the ccRCC patients in the current study. Thismight be due to the small number of mutations and low neoe-pitope load in ccRCC patients. Alternatively, it might be becauseour ccRCC patients had never received immune checkpoint inhi-bitors such as anti-PD-1 antibody. In fact, most of the patientsreported here had received standard therapy with surgery(nephrectomy) alone or surgery plus cytokines, tyrosine kinaseinhibitors, and mTOR inhibitors according to evidence-basedclinical practice guidelines for renal cell carcinoma provided bythe Japanese Urological Association (22) and the NCCN Guide-lines. Whether high mutation/neoepitope load shows any posi-tive association with prognosis in ccRCC patients who do receiveimmune checkpoint blockade therapy remains to be investigated,as in the melanoma and lung cancer patients mentioned above.

Nevertheless, we did find that ccRCC patients with a highpotential neoepitope load and higher-than-median HLA orb2M expression tended to have a better prognosis, coupled withan immunologic signature of cytotoxic T-cell response in thetumor, as assessed by their expression of CD8, PRF1, and GZMA(Figs. 2 and 3). Therefore, these results indicate that intactneoantigen presentation machinery, but not mutation/neoanti-gen load alone, may contribute to a more favorable prognosisassociatedwith the presence of an effector immunologic signatureof CD8 T cells and molecules.

However, we found that the expression of CD8A, PRF1, orGZMAdid not correlate with prognosis in ccRCC patients (Fig. 4).Consistent with our results, Nakano and colleagues performedIHC and demonstrated that the infiltration of CD4 and CD8lymphocytes was correlated with poor survival in ccRCC patients,rather than better survival, as was expected (29). Increased CD8 T-cell infiltration was also associated with poor prognosis anddisease progression after IFN therapy (30). Although at first sightparadoxical, these findings may be explained by the fact thatimmune signatures of CD8 T cells and their effector moleculescoexisted with the expression of immunosuppressive moleculesin the tumor (Fig. 5). These results are also consistent withprevious reports that the immunosuppressive ligand PD-L1 andthe enzyme IDO are induced by the IFNg produced by infiltratingT cells (31, 32). In fact, a statistically significant correlationbetween IFNg and PD-L1 expression or the presence of IDO inthe tumor was observed (Supplementary Fig. S2).

Neoantigens are potential targets of antitumor immuneresponses constitutively as well as under immunotherapeuticconditions (33, 34). However, antitumor immune responsesmay themselves induce counter-regulatorymechanisms to inhibitthem as part of physiologic feedback control of immune reactiv-ity. Indeed, immune checkpoint molecules (CTLA-4, PD-1,

10080604020

0

100

NS

Low

High

NS

Low

High

NS

Low

High

NS

Low

High

NS

Low

High

NS

Low

High

NS

Low

High

NS

Low

High

NS

Low

High

NS

LowHigh

NS

Low

High

NS

Low

High

NS

Low

High

NS

Low

High

CD8A CD4 CD19 KLRF1 CD68

GITROX404-1BBCD28CD27

Ove

rall

surv

ival


PRF1 GZMA IFNg TNFa

80604020

00 50 100 150 0 50 100 150

10080604020

00 50 100 150

10080604020

00 50 100 150

10080604020

00 50 100 150

10080604020

00 50 100 150

10080604020

00 50 100 150

10080604020

00 50 100 150

10080604020

00 50 100 150

10080604020

00 50 100 150

10080604020

00 50 100 150

100

80604020

00 50 100 150

10080604020

00 50 100 150

10080

604020

00 50 100 150

Figure 4.Immune cell markers, T-cell costimulatory molecules, and effector molecules of CD8 T cells in the tumor are not correlated with prognosis. Kaplan–Meier survivalcurves in 97 ccRCCpatients stratified according to the expression of the indicated genes. Statistical analysiswasdoneby log-rank test. NS, not statistically significant.

Matsushita et al.





500

400

300

200

100

0

100

50

0

200

150

250

100

50

0

200

150

250

100

50

0

200

150

250

0

1,000

2,000

3,000

1,000

0

2,000

3,000

4,000

5,000

1,000

0

2,000

3,000

4,000

5,000

0

10,000

5,000

15,000

20,000

0

10,000

5,000

15,000

20,000

0

10,000

5,000

15,000

20,000

0

100,000

50,000

150,000

0

100,000

50,000

150,000

0

100,000

50,000

150,000

2,500

2,000

1,500

1,000

500

0

2,000

1,500

1,000

500

0

2,000

1,500

1,000

500

0

2,000

4,000

6,0003,000500

400

300

200

100

0

500

400

300

200

100

0

2,000

1,000

0

3,000

2,000

1,000

0

1,500

500

0

2,000

1,000

1,500

500

0

2,0005,000

4,000

3,000

2,000

1,000

0

5,000

4,000

3,000

2,000

1,000

0

6,000

4,000

2,000

0

1,000

1,500

500

0

2,000

1,000

1,500

500

0

2,000

1,000

0

2,000

2,500

1,500

1,000

500

0

2,000

2,500

1,500

1,000

500

0

CD8Ahi CD8Alo CD8Ahi CD8Alo CD8Ahi CD8Alo CD8Ahi CD8Alo CD8Ahi CD8Alo

CD8Ahi CD8AloCD8Ahi CD8AloCD8Ahi CD8AloCD8Ahi CD8AloCD8Ahi CD8Alo

PRF1hi PRF1lo PRF1hi PRF1lo PRF1hi PRF1lo PRF1hi PRF1lo PRF1hi PRF1lo

PRF1hi PRF1loPRF1hi PRF1loPRF1hi PRF1loPRF1hi PRF1loPRF1hi PRF1lo

GZMAhi GZMAlo GZMAhi GZMAlo GZMAhi GZMAlo GZMAhi GZMAlo GZMAhi GZMAlo

GZMAhi GZMAloGZMAhi GZMAloGZMAhi GZMAloGZMAhi GZMAloGZMAhi

P < 0.0001 P = 0.0065 P = 0.0002

P = 0.0165P < 0.0001P < 0.0001P < 0.0001

P < 0.0001

P < 0.0001 P < 0.0001 P < 0.0001 P = 0.012

P = 0.0048 P = 0.0050

NSVEGFAIL10NOS1IDO1PD-L2

CTLA-4 PD-1 LAG-3 Tim-3NS

PD-L1

NS

NS

NSNOS1IDO1PD-L2

CTLA-4

IDO1 NOS1NS

NS

IL10 VEGFANSP < 0.0001

P < 0.0001 P < 0.0001 P < 0.0001 P = 0.0044

P = 0.0036 P = 0.0004PD-L2

CTLA-4

FPK

MFP

KM

FPK

MFP

KM

FPK

MFP

KM

A

B

C

PD-1 LAG-3 Tim-3 PD-L1

PD-1 LAG-3 Tim-3 PD-L1

IL10 VEGFA

NS

GZMAlo

Figure 5.Immune signature of CD8 T cells and effector molecules correlates with the expression of immunoregulatory genes in the tumor microenvironment.Immunoregulatory gene expression in patients grouped as CD8Ahi versus CD8Alo (A), PRF1hi versus PRF1lo (B), and GZMAhi versus GZMAlo (C). CD8A, PRF1, andGZMA gene expression was determined as higher or lower than the median. NS, not statistically significant.






LAG-3) and their ligands (PD-L1 and PD-L2) as well as IDO andIL10 are upregulated concordantly with higher CD8, PFR1, andGZMA gene expression. Therefore, strategies to terminate theinduction of such adaptive resistance (35) and overcome theseimmunosuppressive microenvironmental factors are required toimprove the treatment of ccRCC. These considerations mayexplain why immune checkpoint blockade is effective in only afraction of ccRCC patients (20%–30%; refs. 18–20).

Recently, novel strategies to actively induce immune responsestargeting neoantigens have been developed (36–38). However, aswe demonstrated here, the adaptive resistance operating in ccRCCmight also hamper the response to a vaccination targeting neoan-tigens. Thus, for the induction of synergistic antitumor activity,blockade of these immunosuppressive molecules combined withcancer vaccines against neoantigens could be an ideal strategy inccRCC patients.

The current study has some limitations. It was based on theprediction of candidate neoepitopes by computer algorithms, asin other earlier reports (39–42). Antigen presentation as reflectedin the values of A-neo/HLA-A or ABC-neo/b2M correlated withbetter prognosis of ccRCCpatients, although B-neo/HLA-B andC-neo/HLA-C did not. Whether these results are attributable to thedominant role of HLA-A and neoepitopes restricted by it inimmune responses in ccRCC patients or whether there is aproblem with the accuracy of prediction of HLA-B- or HLA-C–restricted neoepitopes remains unclear (24). Future studies needto determine whether in silico–predicted neoantigens do indeedinduce T-cell responses in ccRCC patients and whether these aredifferent among HLA types. At any rate, the integrated analysis ofgenetic and immunologic landscape using NGS technologiesbecomes the main stream for the development of effective cancerimmunotherapies (41, 42).

Although ccRCC is accepted as an immune-sensitive cancer,only a very limited number of candidate tumor-associated anti-gens recognized by CTLs have been identified so far (43–47).Shared tumor antigens, such as cancer-testis antigens, are alsoexpressed in a limitedmanner (48, 49). In a recent report (41), theexpression of several endogenous retrovirus (ERV) genes waspositively correlated with immune cytolytic activity in ccRCCpatients, suggesting that ERV is likely to be involved in antitumorimmunity in these patients. However, the associations betweenERV-derived epitopes and prognosis, or between cytolytic activity

and prognosis in ccRCCpatients, have not been determined. Herewe demonstrated that neoantigen load, antigen presentationmachinery, and immune signatures in the tumor do influencethe prognosis of patientswith ccRCC.We suggest that neoantigensmight be major targets of choice for manipulating immuneresponses to control ccRCC, as in melanoma and lung cancer.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

Authors' ContributionsConception and design: H. Matsushita, K. KakimiDevelopment of methodology: K. KakimiAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): H. Matsushita, Y. Sato, H. Kume, S. Ogawa,Y. Homma, K. KakimiAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): H. Matsushita, Y. Sato, T. Karasaki, T. Nakagawa,K. KakimiWriting, review, and/or revision of the manuscript: H. Matsushita, Y. Sato,T. Nakagawa, H. Kume, Y. Homma, K. KakimiAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): Y. Sato, H. Kume, S. Ogawa, Y. Homma,K. KakimiStudy supervision: Y. Homma, K. Kakimi

AcknowledgmentsThe authors thankMs.ManamiMiyai (Department of Immunotherapeutics,

The University of Tokyo Hospital and Medinet Co. Ltd.) for excellent technicalassistance.

Grant SupportPart of this study was performed as a research program of the Project for

Development of Innovative research on Cancer Therapeutics (P-Direct), Min-istry of Education, Culture, Sports, Science and Technology of Japan (to K.Kakimi); supported in part also by a Grant-in-Aid for Scientific Research of theMinistry of Education, Culture, Sports, Science and Technology of Japan (to H.Matsushita and K. Kakimi) and the Princess Takamatsu Cancer Research Fund(13-24522; to H. Matsushita).

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received September 9, 2015; revised December 28, 2015; accepted February5, 2016; published OnlineFirst March 15, 2016.

References1. Postow MA, Callahan MK, Wolchok JD. Immune checkpoint blockade in

cancer therapy. J Clin Oncol 2015;33:1974–82.2. Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: a

common denominator approach to cancer therapy. Cancer Cell 2015;27:450–61.

3. Lennerz V, FathoM,Gentilini C, Frye RA, LifkeA, FerelD, et al. The responseof autologous T cells to a human melanoma is dominated by mutatedneoantigens. Proc Natl Acad Sci U S A 2005;102:16013–8.

4. Castle JC, Kreiter S, Diekmann J, Lower M, van de Roemer N, de Graaf J,et al. Exploiting the mutanome for tumor vaccination. Cancer Res2012;72:1081–91.

5. Matsushita H, VeselyMD, Koboldt DC, Rickert CG, Uppaluri R,Magrini VJ,et al. Cancer exome analysis reveals a T-cell-dependent mechanism ofcancer immunoediting. Nature 2012;482:400–4.

6. Robbins PF, Lu YC, El-Gamil M, Li YF, Gross C, Gartner J, et al. Miningexomic sequencing data to identify mutated antigens recognized byadoptively transferred tumor-reactive T cells. Nat Med 2013;19:747–52.

7. van Rooij N, van Buuren MM, Philips D, Velds A, Toebes M, Heemskerk B,et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity inan ipilimumab-responsive melanoma. J Clin Oncol 2013;31:e439–42.

8. Wick DA, Webb JR, Nielsen JS, Martin SD, Kroeger DR, Milne K, et al.Surveillance of the tumor mutanome by T cells during progression fromprimary to recurrent ovarian cancer. Clin Cancer Res 2014;20:1125–34.

9. Rajasagi M, Shukla SA, Fritsch EF, Keskin DB, DeLuca D, Carmona E, et al.Systematic identification of personal tumor-specific neoantigens in chroniclymphocytic leukemia. Blood 2014;124:453–62.

10. Duan F, Duitama J, Al Seesi S, Ayres CM, Corcelli SA, Pawashe AP, et al.Genomic and bioinformatic profiling of mutational neoepitopes revealsnew rules to predict anticancer immunogenicity. J Exp Med 2014;211:2231–48.

11. Srivastava PK. Peptide-binding heat shock proteins in the endoplasmicreticulum: role in immune response to cancer and in antigen presentation.Adv Cancer Res 1993;62:153–77.

12. Heemskerk B, Kvistborg P, Schumacher TN. The cancer antigenome. EMBOJ 2013;32:194–203.

Matsushita et al.





13. SrivastavaPK.Neoepitopesof cancers: looking back, looking ahead. CancerImmunol Res 2015;3:969–77.

14. Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, et al.Genetic basis for clinical response to CTLA-4 blockade in melanoma.N Engl J Med 2014;371:2189–99.

15. Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, et al.Cancer immunology. Mutational landscape determines sensitivity to PD-1blockade in non-small cell lung cancer. Science 2015;348:124–8.

16. CoppinC, Porzsolt F, AwaA, Kumpf J, ColdmanA,Wilt T. Immunotherapyfor advanced renal cell cancer. Cochrane Database Syst Rev 2005:CD001425.

17. Yang JC, Childs R. Immunotherapy for renal cell cancer. J Clin Oncol2006;24:5576–83.

18. TopalianSL,Hodi FS, Brahmer JR,Gettinger SN, SmithDC,McDermottDF,et al. Safety, activity, and immune correlates of anti-PD-1 antibody incancer. N Engl J Med 2012;366:2443–54.

19. Brahmer JR, Tykodi SS, ChowLQ,HwuWJ, Topalian SL,HwuP, et al. Safetyand activity of anti-PD-L1 antibody in patients with advanced cancer. NEngl J Med 2012;366:2455–65.

20. Champiat S, Ferte C, Lebel-Binay S, Eggermont A, Soria JC. Exomics andimmunogenics: Bridging mutational load and immune checkpoints effi-cacy. Oncoimmunology 2014;3:e27817.

21. Sato Y, Yoshizato T, Shiraishi Y, Maekawa S, Okuno Y, Kamura T, et al.Integrated molecular analysis of clear-cell renal cell carcinoma. Nat Genet2013;45:860–7.

22. Fujioka T,ObaraW. Evidence-based clinical practice guideline for renal cellcarcinoma: the Japanese Urological Association 2011 update. Int J Urol2012;19:496–503.

23. Shiraishi Y, Sato Y, Chiba K, Okuno Y, Nagata Y, Yoshida K, et al. Anempirical Bayesian framework for somatic mutation detection from cancergenome sequencing data. Nucleic Acids Res 2013;41:e89.

24. Hoof I, Peters B, Sidney J, Pedersen LE, Sette A, LundO, et al. NetMHCpan,a method for MHC class I binding prediction beyond humans. Immuno-genetics 2009;61:1–13.

25. Fritsch EF, Rajasagi M, Ott PA, Brusic V, Hacohen N, Wu CJ. HLA-bindingproperties of tumor neoepitopes in humans. Cancer Immunol Res2014;2:522–9.

26. Khong HT, Wang QJ, Rosenberg SA. Identification of multiple antigensrecognized by tumor-infiltrating lymphocytes from a single patient: tumorescape by antigen loss and loss of MHC expression. J Immunother2004;27:184–90.

27. Restifo NP, Marincola FM, Kawakami Y, Taubenberger J, Yannelli JR,Rosenberg SA. Loss of functional beta 2-microglobulin in metastaticmelanomas from five patients receiving immunotherapy. J Natl CancerInst 1996;88:100–8.

28. AlexandrovLB,Nik-Zainal S,WedgeDC,Aparicio SA, Behjati S, BiankinAV,et al. Signatures of mutational processes in human cancer. Nature2013;500:415–21.

29. Nakano O, Sato M, Naito Y, Suzuki K, Orikasa S, Aizawa M, et al.Proliferative activity of intratumoral CD8(þ) T-lymphocytes as a prog-nostic factor in human renal cell carcinoma: clinicopathologic demon-stration of antitumor immunity. Cancer Res 2001;61:5132–6.

30. Igarashi T, Takahashi H, Tobe T, Suzuki H,Mizoguchi K, Nakatsu HO, et al.Effect of tumor-infiltrating lymphocyte subsets on prognosis and suscep-tibility to interferon therapy in patients with renal cell carcinoma. Urol Int2002;69:51–6.

31. Dong H, Strome SE, Salomao DR, Tamura H, Hirano F, Flies DB, et al.Tumor-associated B7-H1 promotes T-cell apoptosis: a potential mecha-nism of immune evasion. Nat Med 2002;8:793–800.

32. Heitger A. Regulation of expression and function of IDO in humandendritic cells. Curr Med Chem 2011;18:2222–33.

33. Gubin MM, Zhang X, Schuster H, Caron E, Ward JP, Noguchi T, et al.Checkpoint blockade cancer immunotherapy targets tumour-specificmutant antigens. Nature 2014;515:577–81.

34. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy.Science 2015;348:69–74.

35. Pardoll DM. The blockade of immune checkpoints in cancer immuno-therapy. Nat Rev Cancer 2012;12:252–64.

36. Tran E, Turcotte S, Gros A, Robbins PF, Lu YC, Dudley ME, et al. Cancerimmunotherapy based onmutation-specific CD4þ T cells in a patient withepithelial cancer. Science 2014;344:641–5.

37. Carreno BM, Magrini V, Becker-Hapak M, Kaabinejadian S, Hundal J, PettiAA, et al. Cancer immunotherapy. A dendritic cell vaccine increases thebreadth and diversity of melanoma neoantigen-specific T cells. Science2015;348:803–8.

38. Gubin MM, Artyomov MN, Mardis ER, Schreiber RD. Tumor neoantigens:building a framework for personalized cancer immunotherapy. J ClinInvest 2015:125:3413–21.

39. Segal NH, Parsons DW, Peggs KS, Velculescu V, Kinzler KW, Vogelstein B,et al. Epitope landscape in breast and colorectal cancer. Cancer Res2008;68:889–92.

40. Brown SD, Warren RL, Gibb EA, Martin SD, Spinelli JJ, Nelson BH,et al. Neo-antigens predicted by tumor genome meta-analysiscorrelate with increased patient survival. Genome Res 2014;24:743–50.

41. Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and geneticproperties of tumors associated with local immune cytolytic activity. Cell2015;160:48–61.

42. Angelova M, Charoentong P, Hackl H, Fischer ML, Snajder R, KrogsdamAM, et al. Characterization of the immunophenotypes and antigenomes ofcolorectal cancers reveals distinct tumor escape mechanisms and noveltargets for immunotherapy. Genome Biol 2015;16:64.

43. Gaugler B, Brouwenstijn N, Vantomme V, Szikora JP, Van der Spek CW,Patard JJ, et al. A new gene coding for an antigen recognized by autologouscytolytic T lymphocytes on a human renal carcinoma. Immunogenetics1996;44:323–30.

44. Brandle D, Brasseur F, Weynants P, Boon T, Van den Eynde B. A mutatedHLA-A2 molecule recognized by autologous cytotoxic T lymphocytes on ahuman renal cell carcinoma. J Exp Med 1996;183:2501–8.

45. Vissers JL, De Vries IJ, Schreurs MW, Engelen LP, Oosterwijk E, Figdor CG,et al. The renal cell carcinoma-associated antigen G250 encodes a humanleukocyte antigen (HLA)-A2.1-restricted epitope recognized by cytotoxicT lymphocytes. Cancer Res 1999;59:5554–9.

46. Dannenmann SR, Hermanns T, Bransi A, Matter C, von Boehmer L,Stevanovic S, et al. Spontaneous peripheral T-cell responses toward thetumor-associated antigen cyclin D1 in patients with clear cell renal cellcarcinoma. Cancer Immunol Res 2013;1:288–95.

47. Takahashi Y,HarashimaN, Kajigaya S, YokoyamaH,Cherkasova E,McCoyJP, et al. Regression of human kidney cancer following allogeneic stem celltransplantation is associated with recognition of an HERV-E antigen byT cells. J Clin Invest 2008;118:1099–109.

48. Coral S, Sigalotti L, AltomonteM, Engelsberg A, Colizzi F, Cattarossi I, et al.5-aza-20-deoxycytidine-induced expression of functional cancer testis anti-gens in human renal cell carcinoma: immunotherapeutic implications.Clin Cancer Res 2002;8:2690–5.

49. Soga N, Hori Y, Yamakado K, Ikeda H, Imai N, Kageyama S, et al. Limitedexpression of cancer-testis antigens in renal cell carcinoma patients. MolClin Oncol 2013;1:326–30.






2016;4:463-471. Published OnlineFirst March 15, 2016.Cancer Immunol Res Hirokazu Matsushita, Yusuke Sato, Takahiro Karasaki, et al. CarcinomaSignatures Determine Prognosis in Clear Cell Renal Cell Neoantigen Load, Antigen Presentation Machinery, and Immune

Updated version

10.1158/2326-6066.CIR-15-0225doi:

Access the most recent version of this article at:

Material

Supplementary

http://cancerimmunolres.aacrjournals.org/content/suppl/2016/03/15/2326-6066.CIR-15-0225.DC1

Access the most recent supplemental material at:

Cited articles

http://cancerimmunolres.aacrjournals.org/content/4/5/463.full#ref-list-1

This article cites 48 articles, 22 of which you can access for free at:

Citing articles

http://cancerimmunolres.aacrjournals.org/content/4/5/463.full#related-urls

This article has been cited by 6 HighWire-hosted articles. Access the articles at:

E-mail alerts related to this article or journal.Sign up to receive free email-alerts

Subscriptions

Reprints and

[email protected]

To order reprints of this article or to subscribe to the journal, contact the AACR Publications Department

Permissions

Rightslink site. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC)

.http://cancerimmunolres.aacrjournals.org/content/4/5/463To request permission to re-use all or part of this article, use this link



http://cancerimmunolres.aacrjournals.org/lookup/doi/10.1158/2326-6066.CIR-15-0225

http://cancerimmunolres.aacrjournals.org/content/suppl/2016/03/15/2326-6066.CIR-15-0225.DC1

http://cancerimmunolres.aacrjournals.org/content/4/5/463.full#ref-list-1

http://cancerimmunolres.aacrjournals.org/content/4/5/463.full#related-urls

http://cancerimmunolres.aacrjournals.org/cgi/alerts

mailto:[email protected]

http://cancerimmunolres.aacrjournals.org/content/4/5/463


neoantigen load, antigen presentation machinery, and ... · 0.0423; fig. 2h). these data suggest...

Documents