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Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial


Neoantigens, which are derived from tumour-specific protein-coding mutations, are exempt from central tolerance, can generate robust immune responses1,2 and can function as bona fide antigens that facilitate tumour rejection3. Here we demonstrate that a strategy that uses multi-epitope, personalized neoantigen vaccination, which has previously been tested in patients with high-risk melanoma4,5,6, is feasible for tumours such as glioblastoma, which typically have a relatively low mutation load1,7 and an immunologically ‘cold’ tumour microenvironment8. We used personalized neoantigen-targeting vaccines to immunize patients newly diagnosed with glioblastoma following surgical resection and conventional radiotherapy in a phase I/Ib study. Patients who did not receive dexamethasone—a highly potent corticosteroid that is frequently prescribed to treat cerebral oedema in patients with glioblastoma—generated circulating polyfunctional neoantigen-specific CD4+ and CD8+ T cell responses that were enriched in a memory phenotype and showed an increase in the number of tumour-infiltrating T cells. Using single-cell T cell receptor analysis, we provide evidence that neoantigen-specific T cells from the peripheral blood can migrate into an intracranial glioblastoma tumour. Neoantigen-targeting vaccines thus have the potential to favourably alter the immune milieu of glioblastoma.

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Fig. 1: Generation of a personal neoantigen-targeting vaccine for newly diagnosed patients with glioblastoma that had unmethylated MGMT promoters.
Fig. 2: Vaccination induces circulating neoantigen-specific T cell responses in patients who did not receive dexamethasone during vaccine priming.
Fig. 3: Increase in T cell infiltration evident in patients with circulating neoantigen-specific T cell responses.
Fig. 4: TCRαβ clonotypes are shared between tumour-associated T cells and neoantigen-reactive T cells in peripheral blood, with select clones demonstrated to be specific for neoantigens targeted by the vaccine.

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Data availability

WES and bulk RNA-seq data generated and analysed during the current study are available through dbGaP ( with accession number phs001519.v1.p1. All other data are available from the corresponding author upon reasonable request.


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We thank the Ben and Catherine Ivy Foundation, the Blavatnik Family Foundation and the Mathers Foundation for supporting this research. We acknowledge support from the Broad Institute SPARC program and National Institutes of Health (NCI-1RO1CA155010-02 (to C.J.W.)), NHLBI-5R01HL103532-03 (to C.J.W.), Francis and Adele Kittredge Family Immuno-Oncology and Melanoma Research Fund (to P.A.O.), Faircloth Family Research Fund (to P.A.O.), NIH/NCI R21 CA216772-01A1 (to D.B.K.), NCI-SPORE-2P50CA101942-11A1 (to D.B.K.); NHLBI-T32HL007627 (to J.B.I.); NCI (R50CA211482) (to S.A.S.), Zuckerman STEM Leadership Program (to I.T.); Benoziyo Endowment Fund for the Advancement of Science (to I.T.); P50 CA165962 (SPORE) and P01 CA163205 (to K.L.L.); DFCI Center for Cancer Immunotherapy Research fellowship (to Z.H.); Howard Hughes Medical Institute Medical Research Fellows Program (to A.J.A.); and American Cancer Society PF-17-042-01–LIB (to N.D.M.). C.J.W. is a scholar of the Leukemia and Lymphoma Society. We thank the Center for Neuro-Oncology, J. Russell and Dana-Farber Cancer Institute (DFCI) Center for Immuno-Oncology (CIO) staff; B. Meyers, C. Harvey and S. Bartel (Clinical Pharmacy); M. Severgnini, K. Kleinsteuber and E. McWilliams, (CIO laboratory); M. Copersino (Regulatory Affairs); T. Bowman (DFHCC Specialized Histopathology Core Laboratory); A. Lako (CIO); M. Seaman and D. H. Barouch (BIDMC); the Broad Institute’s Biological Samples, Genetic Analysis and Genome Sequencing Platforms; J. Petricciani and M. Krane for regulatory advice; B. McDonough (CSBio), I. Javeri and K. Nellaiappan (CuriRx) for peptide development.

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Nature thanks M. Lim and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Authors and Affiliations



D.A.R. was the principal investigator. P.A.O. is the investigational new drug holder. D.A.R., C.J.W., N.H., P.A.O., D.B.K. and E.F.F. directed the overall study design. Immune monitoring by ELISPOT and intracellular cytokine staining was performed by D.B.K. with help of L.L., P.M.L., W.Z. and Z.H. Multiplex immunofluorescence staining was performed and analysed by K.F., E.G., S.J.R. and A.J.A. scRNA-seq and TCR sequencing was performed and analysed by D.B.K., N.D.M., I.T., S.L., J. Sun, A.J.A., G.O., P.M.L., A.R.R., M.S.K., M.L.S., K.J.L., S.A.S., A.R., R.L.A., L.R.O. and K.W.W. TCR cloning and reconstruction experiments were performed by A.J.A., D.B.K., G.O., Z.H. and P.M.L. The patient-derived cell line was generated by K.P. and K.L.L. J. Sun, S.A.S., J. Stevens, W.J.L. and E.F.F. analysed sequencing data and selected neoantigen targets. H.D. and J.R. directed the preparation of vaccines. A.G.-H., L.E. and D.N. designed and performed statistical analyses. J. Stevens and W.J.L. performed HLA typing. A.M.S. helped to design the vaccine formulation. C.M., O.O., J.E.G., S.C. and J.B.I. supported sample collection and coordinated clinical research. D.A.R., P.Y.W. and E.A.C. oversaw clinical care and provided patient samples. K.L.L., E.G., S.J.R., K.F. and S.A. performed pathology reviews. M.H., N.J.L., S.G., J. Sun, S.A.S. and G.G. helped to devise the computational pipeline. N.H., C.J.W., E.F.F. and E.S.L. developed the overall neoantigen vaccine strategy. D.B.K., A.J.A., D.A.R., N.H. and C.J.W. wrote the manuscript, on which all co-authors commented.

Corresponding author

Correspondence to David A. Reardon.

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Competing interests

D.A.R. is an advisor to Abbvie, Agenus, Bristol-Myers Squibb, Celldex, EMD Serono, Genentech/Roche, Inovio, Merck, Merck KGaA, Monteris, Novocure, Oncorus, Oxigene, Regeneron, Stemline and Taiho Oncology; he has received research funding support from Acerta Phamaceuticals, Agenus, Celldex, EMD Serono, Incyte, Midatech, Omniox and Tragara. D.B.K. has previously advised Neon Therapeutics and owns equity in Aduro Biotech, Agenus, Ampliphi BioSciences, Biomarin Pharmaceutical, Bristol-Myers Squibb, Celldex Therapeutics, Editas Medicine, Exelixis, Gilead Sciences, IMV, Lexicon Pharmaceuticals, Sangamo Therapeutics and Stemline Therapeutics. J. Sun is a current employee of Moderna Therapeutics. C.J.W. is a founder of Neon Therapeutics and member of its scientific advisory board. P.A.O. has received research funding from and has advised Neon Therapeutics, Bristol-Meyers Squibb, Merck, CytomX, Pfizer, Novartis, Celldex, Amgen, AstraZeneca/MedImmune, Armo BioSciences and Roche/Genentech. K.J.L. is a paid consultant for Integrated DNA Technologies. E.F.F. is a founder and employee of Neon Therapeutics. N.H. is a founder of Neon Therapeutics and member of its scientific advisory board and an advisor for IFM therapeutics. K.W.W. is a paid consultant for Novartis, serves on the scientific advisory board for TCR2, Nextech and T-Scan, and receives sponsored research funding from Novartis, BMS and Astellas that is not related to the topic of this manuscript. S.J.R. receives research funding from Bristol-Myers Squibb, Merck, KITE Pharmaceuticals and Affimed Pharmaceuticals, and is on a scientific medical board for Perkin-Elmer. J.R. has consulted for Celgene, Draper Labs and Clarus Ventures. A.R. is a founder of Celsius Therapeutics and a member of the scientific advisory board for ThermoFisher Scientific, Driver Group and Syros Pharmaceuticals. E.S.L. is a founder of Neon Therapeutics and a member of its board of directors. G.G. is receiving research funds from IBM and Pharmacyclics, and is an inventor on patent applications related to MuTect and ABSOLUTE. N.J.L. has advised Neon Therapeutics, is a current advisor of New England Biolabs and is a member of the scientific advisory board for Genturi. E.A.C. is currently an advisor to Advantagene and DNAtrix, and has equity interest in DNATrix; he has previously advised Oncorus, Merck, Tocagen, Ziopharm, StemCell and NanoTx. P.Y.W. has no directly relevant conflicts; he currently sits on advisory boards for Abbvie, AstraZeneca, Eli Lilly, Genentech/Roche, Immunomic Therapeutics, Puma, Vascular Biogenics, Taiho and Deciphera, received speaker fees for Merck and received research support from Agios, AstraZeneca, Beigene, Eli Lilly, Genentech/Roche, Kadmon, Karyopharm, Kazia, Merck, Novartis, Oncoceutics, Sanofi-Aventis and VBI Vaccines. A.M.S. is CEO and CSO of Oncovir. M.S.K. is a current employee of Celsius Therapeutics. S.A.S has previously advised Neon Therapeutics and has equity in 152 Therapeutics. C.J.W. is subject to a conflict of interest management plan for the reported studies because of her competing financial interests in Neon Therapeutics. Under this plan, C.J.W. may not access identifiable data for human subjects or otherwise participate directly in the Institutional Review Board-approved protocol reported herein. C.J.W.’s contributions to the overall strategy and data analyses occurred on a de-identified basis. Patent applications have been filed on aspects of the described work entitled as follows: ‘Compositions and methods for personalized neoplasia vaccines’ (N.H., E.F.F. and C.J.W.), ‘Methods for identifying tumour specific neo-antigens’ (N.H. and C.J.W.), ‘Formulations for neoplasia vaccines’ (E.F.F.) and ‘Combination therapy for neoantigen vaccine’ (N.H., C.J.W. and E.F.F.). The Dana-Farber Cancer Institute, the lead site of this trial, has a proprietary and financial interest in the personalized neoantigen vaccine. As a result of its licensing activities, Dana-Farber Cancer Institute also holds equity in Neon Therapeutics. The remaining authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Mutational landscape of patient GBM tumours.

a, The overall mutational landscape of the GBM tumours from 10 enrolled patients (top, number of mutations per Mb; bottom, distribution of nucleotide changes) and the presence of mutations in genes that have previously been identified as recurrent in GBM samples from The Cancer Genome Atlas (TCGA) (n = 290) by the MutSig2CV algorithm51 (middle; genes are ordered on the basis of the significance of recurrence reported in the TCGA). b, Expression profiles of the GBM specimens from patients at study entry, according to RNA-seq analysis (Methods). Tumour RNA-seq data were available for 9 out of 10 study subjects (red dots, vaccinated, n = 7; blue dots, not included in study because of progressive disease, n = 1; black dots, not included in study because of insufficient numbers of binders, n = 1) and were normalized as transcripts per million base pairs (TPM). These data were compared to expression data from normal brain tissue (GTEx data, blue box) and a published cohort of GBMs (TCGA, red box). This analysis revealed that tumour samples from the patients showed canonical GBM expression profiles such as EGFR upregulation (consistent with EGFR amplification and polysomy 7), MDM4 upregulation (consistent with p53 pathway dysregulation), TERT upregulation, upregulation of genes associated with glial intermediate filaments (for example, GFAP, VIM and NES), upregulation of markers of GBM stemness (for example, SOX2 and NOTCH2), Rb pathway dysregulation (for example, CDKN2A and CDKN2B downregulation, and CDK4 and CDK6 upregulation), PTEN downregulation (consistent with monosomy 10) and upregulation of IL13RA2 and BIRC5 (which encodes survivin). The upper, middle and lower hinges of the box plot are 75th, 50th and 25th quartiles, the whiskers extend to 1.5× the interquartile range below and above the lower and upper hinge, respectively, and points above or below the whiskers represent outliers. c, Numbers of mutations and predicted epitopes for all study subjects (Supplementary Table 4). Red lines, patients for whom vaccines were generated (n = 8); grey solid line, patient who was removed from the study before vaccine administration owing to progressive disease (n = 1); grey dotted line, patient had an insufficient number of mutations to proceed to vaccine generation (n = 1).

Extended Data Fig. 2 Mapping of CD4+ and CD8+ T cell responses to individual ASP and EPT to the immunizing peptides for patients 7 and 8.

ASP and EPT that cover the immunizing peptides (IMPs) are shown for the immunizing peptide(s) that induced T cell responses. Blue bold, germline amino acid; red bold, mutated amino acid. Blue underline, predicted class I epitopes (IC50 < 500 nM) based on NetMHCpan16,32.

Extended Data Fig. 3 Neoantigen-specific T cell responses generated by vaccine in patients.

a, Ex vivo IFNγ ELISPOT observed in a patient treated with dexamethasone during vaccine priming (patient 4, left) showed no response at 12 weeks after vaccination, whereas a patient who did not receive dexamethasone (patient 8, right) showed strong response already at 8 weeks after vaccination. DMSO, dimethyl sulfoxide; CEF, peptides from cytomegalovirus, Epstein–Barr virus and influenza virus. b, Deconvolution of post-vaccination CD4+ and CD8+ immune responses following stimulation to the neoantigen assay peptide pools using IFNγ ELISPOT assays. n = 3 biologically independent samples, data are mean ± s.d. c, SLX4MUT and ARHGAP35MUT CD8+ T cell responses are only detected by ELISPOT assay in samples 16 weeks after vaccination, not pre-vaccination samples after stimulation in vitro for three weeks. Control, DMSO. d, Representative IFNγ ELISPOT responses from dominant neoantigen-specific T cell lines established from week 8 or week 16 PBMCs of vaccinated patients against minigene (MG)-transfected patient B cells, demonstrating epitope processing and presentation; ELISPOT experiments were performed in triplicate wells per time point. e, Analysis of ex vivo T cell responses to neoantigen assay peptide pools using intracellular cytokine staining followed by flow cytometry. Data are representative of results from three independent experiments. PHA, phytohaemagglutinin. f, IL-2 producing pool A-reactive T cells isolated ex vivo from patient 8 express CD45RO (memory) and PD-1 (activation) markers. Data are representative of results from two independent experiments.

Extended Data Fig. 4 Characterization of tumour expression markers and HLA class I and class II in GBM.

a, The GBM tumour cell line from patient 7 that was established from the initial resection is positive for GBM tumour markers by immunohistochemistry. b, The GBM tumour cell line generated from initial resection tissue of patient 7 has low HLA-class I expression that is upregulated by IFNγ treatment and is negative for HLA-class II DR expression. This experiment was repeated four times. c, Immunohistochemistry analysis of HLA-class I and II expression on diagnostic sections of GBM tumours from the eight patients enrolled and vaccinated in the study. Semi-quantitative scoring was performed for the intensity of positive staining of tumour cell membranes or cytoplasm for class I or II (0, negative; 1, weak; 2, moderate; 3, strong) and for the percentage of positive staining malignant cells (0–100%). A cumulative H score was obtained by multiplying intensity score by the percentage of malignant cells with positive staining. This experiment was performed once, with the available resection tissue. d, Neoantigen-specific CD8+ T cells generated by vaccination of patient 7 do not recognize the GBM tumour cell line generated from initial resection tissue of patient 7 by TNF ELISPOT assay with or without IFNγ treatment to upregulate HLA-class I expression on tumour cells. Data were generated from one experiment with three replicate wells. Additional direct tumour recognition assays performed with IFNγ ELISPOT demonstrated similar results (data not shown). e, IFNγ secretion by the neoantigen-specific CD4+ T cell line generated from PBMCs of patient 7 against autologous dendritic cells (DCs) co-cultured with irradiated autologous GBM. Class II blocking antibody (ab) attenuated the IFNγ response. All T cell lines originated from week 16 PBMCs; ELISPOT experiments were performed in triplicate wells per time point.

Extended Data Fig. 5 Analysis of tumour morphology, immune infiltration at initial resection and relapse, and TCR clonotypes of patient 8.

a, Haematoxylin and eosin staining of initial and relapse resection samples show the most-prominent changes for patients 4, 7 and 8, including high levels of perivascular lymphocytes, extensive cystic changes and necrosis post-treatment in patient 7, low tumour content and patchy lymphocytic infiltration in patient 8 and sarcomatous morphology with myomatous changes in patient 4. b, No FOXP3+ and CD25+ CD4+ cells were detected in matched initial and relapse tumour sections, evaluated by multiplex immunofluorescence. c, No correlation was observed between the number of predicted neoantigens and the extent of T cell infiltration in the initial resection sample. Patient ID numbers shown for each data point. d, Schema of single-cell TCR analysis of neoantigen-reactive T cells isolated from post-vaccination PBMCs of patient 8 and comparison to bulk TCR sequencing of cDNA from pre- and post-vaccination PBMCs and from initial and relapse fresh-frozen tumour biopsies. e, TCR clonotypes observed in neoantigen-reactive T cell lines generated from PBMCs of patient 8, based on single-cell-targeted TCRαβ sequencing (see Methods). This experiment was performed once, with the available resection tissue.

Extended Data Fig. 6 Analysis of tumour-associated T cells from patient 7.

a, Expression levels (log2(TPM/10 + 1)) of selected marker genes for regulatory (Treg), cytotoxic and naive/memory T cell phenotypes; and expression of co-inhibitory receptors and effector cytokines based on scRNA-seq of tumour-associated CD4+ and CD8+ T cells from the relapse specimen of patient 7 (top). The average levels of these gene sets were used to define the expression scores of these signatures (bottom) (Supplementary Table 10). b, Expression levels of selected marker genes for all tumour-associated CD3+ T cells from patient 7, including those unable to be resolved as CD4 or CD8 (ND, not determined) based on scRNA-seq (Supplementary Table 10). c, Expression program associated with cytotoxicity in CD8+ (x axis) and CD4+ (y axis) T cells. For CD8, we compared all CD8+ T cells to non-Treg CD4+ T cells. For CD4, we divided the non-Treg CD4+ T cells by their average expression of a predefined cytotoxic signature (PRF1, NKG7, GZMK, GZMA and CST7) into three groups (low, medium and high) and compared the high to the low groups. The expression log2 ratios of all expressed genes for these two comparisons (CD8 and CD4) are shown. To identify significant differences, we performed a one-sided permutation test, which shuffled the assignments of cells to groups 100,000 times and defined P values based on the number of times the shuffled log2 ratios are higher (or lower for negative effects) than the observed log2 ratios. P values were adjusted by Bonferroni correction and significant genes were defined as those with an adjusted P value below 0.05 and a fold change above 2 (Supplementary Table 10). Significant genes are shown as red dots; significant genes that have also previously been associated with cytotoxic CD4+ T cells9 are shown in blue font. d, Multiplex immunofluorescence demonstrates increase in CD8+PD-1+ cell infiltration at relapse in patients who did not receive dexamethasone during vaccine priming (red). Infiltrates were determined by enumerating the mean number of CD8+PD-1+ cells in 20× fields. The number of fields evaluated per sample was: 4 fields for relapse samples from patients 7 and 8; 5 fields for initial and relapse samples from patient 3, relapse samples from patient 5 and initial samples from patient 8; and 6 fields for initial and relapse samples from patient 4, and initial samples from patients 5 and 7. Data are mean ± s.e.m. P values are two-sided and based on model F-tests. e, Enumeration of the SOX2+PD-L1+ cells in matched initial and relapse tumour sections, evaluated by multiplex immunofluorescence. Infiltrates were determined by enumerating the mean number SOX2+PD-L1+ cells in 20× fields as in d. Data are mean ± s.e.m.

Extended Data Fig. 7 Single-cell TCR sequencing analysis of tumour-associated T cells from patient 7.

a, Single-cell TCR sequencing analyses of tumour-associated T cells, CD4+ neoantigen assay peptide pool-reactive T cells from peripheral blood ex vivo and CD8+ neoantigen-reactive T cell lines (stimulated with ARHGAP35MUT and SLX4MUT) show enrichment of particular clonotypes. b, Sorting strategy for isolating neoantigen pool-reactive CD4+ T cells from peripheral blood ex vivo. Negative control, DMSO. c, Six TCRs identified in both tumour-associated and neoantigen-reactive T cells from peripheral blood were successfully cloned and expressed in the reporter cell line, as verified by stabilized CD3 surface expression; this experiment was repeated twice in independent experiments. The CDR3 sequences of these TCRs can be found in Supplementary Table 9. d, The largest CD8+ clone detected among the neoantigen-reactive T cell lines established from peripheral blood of patient 7 was experimentally confirmed to be specific for EPT12A, the MHC class I predicted epitope of ARHGAP35MUT. Two-sample two-sided t-tests with Welch correction were used for the comparisons; n = 4 biologically independent replicates; data are mean ± s.d. e, CD4+ ARHGAP35-specific H02 and F10 TCRs (as described in Fig. 4) discriminate between the mutant and wild-type form of the peptide. n = 2 biologically independent samples, each with two technical replicates; data are mean ± s.e.m. f, The ARHGAP35 mutation in the tumour of patient 7 is present in both the initial and relapse tumour specimens, as visualized by the IGV30.

Extended Data Fig. 8 Comparison of single-cell expression profiles of circulating neoantigen-stimulated CD4+ T cells and tumour-associated CD4+ T cells isolated from patient 7.

a, Single-cell transcriptome analysis of CD4+ tumour-associated T cells (n = 185), freshly isolated at the time of relapse and neoantigen-reactive CD4+ cells isolated from post-vaccination (week 16) peripheral blood ex vivo (n = 104) of patient 7 (excluding CD4 dropouts). Tumour-associated T cells expressed more granzyme A than circulating neoantigen-reactive CD4+ cells and showed higher expression of the co-inhibitory molecule TIGIT (Supplementary Table 10). b, Significantly altered expression of CCR7, GZMA, LAG3, PD1 and TIGIT was detected between CD4+ cells from the blood versus brain by two-sided Wilcoxon rank-sum tests. The expression levels of ARHGAP35MUT-specific single T cells (clones H02 (red cross) and F10 (green box)), as described in Fig. 4, are marked. c, Vaccination peptide pool-reactive T cells from patients 7 and 8 PBMCs were stained for TIGIT and TIM3, confirming the minimal expression of these markers on neoantigen-reactive IFNγ-producing T cells in the periphery, compared to IFNγ controls, as suggested by the single-cell transcriptome data in a. Data are representative of results from two independent experiments.

Extended Data Fig. 9 Detection of ARHGAP35MUT-specific T cells in patient 7, week 16.

a, Thawed PBMCs from week 16 collected from patient 7 revealed 60% of the live PBMCs to be CD3+ T cells. Data are representative of results from two independent experiments. b, Thawed PBMCs from patient 7 at week 16 were tested ex vivo by ELISPOT, in which 2 × 105 PBMCs were added per well and exposed overnight to 10 μg ml−1 of peptides covering ARHGAP35MUT (ASP34 or ASP35 peptides) compared to negative control (OVA peptide). Positive control, CEF peptides. Experiment was performed once in triplicate wells. c, Results of ex vivo ELISPOT (n = 3 biologically independent samples). Together, these results indicate that the frequencies of ASP35- and ASP34-reactive T cells were 39 and 29 (after subtracting background) per 360,000 T cells, respectively. Detection of ASP35-reactive T cells (F10) and ASP34-reactive T cells (H02) in brain at relapse was 1 each among 277 single intracranial T cells (Fig. 4c). The rate of T cells that recognize immunizing neoantigens is highly enriched in the brain compared to the periphery, P = 0.030 for ASP35 and P = 0.023 for ASP34, two-sided Poisson test.

Supplementary information

Supplementary Information

This file contains Supplementary Information for Methods, and Supplementary Tables 3, 4, 6 and 12.

Reporting Summary

Supplementary Table 1

QC metrics of (a) whole-exome sequencing and (b) RNA sequencing for Patients 1-10.

Supplementary Table 2

Somatic mutations identified from enrolled patients.

Supplementary Table 5

Expression and class I prediction related to immunizing peptides.

Supplementary Table 7

Primer sequences for targeted TCR amplification.

Supplementary Table 8

TCR Sequences from (a) patient 8 SHANK2MUT-reactive T cells; (b) patient 8 SVEP1MUT-reactive T cells; and (c) patient 8 SHANK2MUT-reactive T cells after short (24h) stimulation.

Supplementary Table 9

Patient 8 CDR3 sequences from analysis of bulk RNA samples: Alpha (a) and beta (c) clonotypes from pre-vaccination blood sample; Alpha (b) and beta (d) clonotypes from 16-week post-vaccination blood sample; Alpha (e) and beta (g) clonotypes from initial tumour sample; Alpha (f) and beta (h) clonotypes from relapse tumour sample.

Supplementary Table 10

Cytotoxic gene expression in CD4+ and CD8+ tumour-associated T cells.

Supplementary Table 11

TCR sequences from (a) patient 7 tumour associated T cells; patient 7 neoantigen-reactive (b) CD4+ and (c) CD8+ T cells from peripheral blood.

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Keskin, D.B., Anandappa, A.J., Sun, J. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).

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