- Open Access
The neoepitope landscape in pediatric cancers
© The Author(s). 2017
- Received: 1 June 2017
- Accepted: 10 August 2017
- Published: 31 August 2017
Neoepitopes derived from tumor-specific somatic mutations are promising targets for immunotherapy in childhood cancers. However, the potential for such therapies in targeting these epitopes remains uncertain due to a lack of knowledge of the neoepitope landscape in childhood cancer. Studies to date have focused primarily on missense mutations without exploring gene fusions, which are a major class of oncogenic drivers in pediatric cancer.
We developed an analytical workflow for identification of putative neoepitopes based on somatic missense mutations and gene fusions using whole-genome sequencing data. Transcriptome sequencing data were incorporated to interrogate the expression status of the neoepitopes.
We present the neoepitope landscape of somatic alterations including missense mutations and oncogenic gene fusions identified in 540 childhood cancer genomes and transcriptomes representing 23 cancer subtypes. We found that 88% of leukemias, 78% of central nervous system tumors, and 90% of solid tumors had at least one predicted neoepitope. Mutation hotspots in KRAS and histone H3 genes encode potential epitopes in multiple patients. Additionally, the ETV6-RUNX1 fusion was found to encode putative neoepitopes in a high proportion (69.6%) of the pediatric leukemia harboring this fusion.
Our study presents a comprehensive repertoire of potential neoepitopes in childhood cancers, and will facilitate the development of immunotherapeutic approaches designed to exploit them. The source code of the workflow is available at GitHub (https://github.com/zhanglabstjude/neoepitope).
- Pediatric cancer
- Gene fusions
Cancers are caused by somatically acquired alterations, including single nucleotide variations (SNVs), small insertion/deletions (indels), translocations, and other types of rearrangements. The genes affected by these mutations may produce altered proteins, some of which may lead to the emergence of tumor-specific immunogenic epitopes. While the neoepitopes generated from missense mutations have been investigated extensively [1–4], the immunogenicity of epitopes generated from other types of somatic alterations has remained largely unexplored until recently; now new methods, such as INTEGRATE-Neo , are being developed to support gene fusion-derived neoepitope discovery. Neoepitopes presented on the cell surface by major histocompatibility complex (MHC) molecules can be recognized by T cells and elicit immune responses. These may serve as important determinants in the natural immune response to cancer, and are potentially important targets for immunotherapy.
A key factor for antigen presentation and T-cell activation is the binding stability of the peptide–MHC complex at the cell surface. The affinity of an epitope for its cognate MHC molecule is typically measured by its IC50 value, where a lower value corresponds to a higher affinity. Previous analyses [1, 6–8] have suggested that an IC50 value ≤ 500 nM generally indicates moderate to high affinity of a peptide for MHC, while an IC50 value > 500 nM indicates low affinity. Based on machine learning approaches , computational algorithms have been developed for prediction of MHC class I peptide binding affinity, enabling a more comprehensive and systematic analysis of immunogenic mutations [10–14]. The accuracy of these approaches varied by the training data used to characterize the binding specificity of the MHC molecules. Consensus approaches combining two or more methods can increase the prediction accuracy when compared with empirical data [13, 15].
Preclinical studies in mice and humans have demonstrated that mutated tumor neoantigens can be recognized by cytotoxic T cells and anti-tumor responses can be induced by immunization with synthetic tumor-specific peptides [16–22]. Mounting clinical evidence has also shown that the neoepitope-specific T cells are important and effective in tumor rejection mediated by adoptive transfer of autologous tumor-infiltrating lymphocytes (TILs) or by immune checkpoint inhibitors [23–28].
As part of the St. Jude/Washington Pediatric Cancer Genome Project (PCGP) we have characterized > 1000 pediatric cancer genomes by whole-genome or whole-exome sequencing . The results have revealed a high variability of somatic mutation rate in different tumor types, ranging from 7.30 × 10−8 per base in infant acute lymphoblastic leukemia (ALL) to 1.32 × 10−5 per base in pediatric melanoma . Furthermore, we found that somatic alterations resulting in gene fusion represents a major class of oncogenic drivers in pediatric cancer. The genomic heterogeneity of pediatric cancer would require a comprehensive analysis of the neoepitope landscape of pediatric cancer to gain knowledge and insight into the feasibility of employing immunotherapy targeting cancer-specific neoepitopes in this patient population.
Patients, samples, and data
Tumor and matched normal samples were both sequenced in all cases. Matched normal samples were obtained either from peripheral blood, bone marrow, or adjacent normal tissue. Cancer samples were labeled using the following abbreviations: SJACT, adrenocortical tumor; SJAMLM7, acute myeloid leukemia M7; SJCBF, core binding factor acute myeloid leukemia; SJEPD, ependymoma; SJHGG, high grade glioma; SJHYPO, hypodiploid acute lymphoblastic leukemia (ALL); SJINF, infant ALL; SJLGG, low-grade glioma; SJMB, medulloblastoma; SJMEL, melanoma; SJOS, osteosarcoma; SJRB, retinoblastoma; SJRHB, rhabdomyosarcoma; and SJTALL T-lineage ALL. A paired-end WGS strategy was employed for all samples. The sequencing, alignment against human reference genome using BWA [31, 32], and the identification and validation of somatic alterations including missense mutations and gene fusions were described previously [33, 34]. Paired-end reads were aligned against the HG18 or HG19 genome builds depending on when the data were generated.
HLA typing and WGS validations
The default settings of Optitype were used for HLA analysis. HLA haplotypes derived from WGS were compared with those derived by clinical HLA typing using classic methods (e.g., sequence-specific oligonucleotides, sequence-specific primers, and Sanger sequencing) for 51 patients. All HLA assignments were high resolution per American Society of Histocompatibility and Immunogenetics and College of American Pathologists criteria at the time they were tested. Samples included in this study were tested between 2003 and 2017. For the earliest HLA typing in this set of samples, HLA assignments were made from high resolution sequence-specific primers (SSP; Life Technologies). Sequence-based typing used AlleleSEQR HLA typing kits (Abbott-Molecular) followed by capillary sequencing on an ABI 3130xL or 3500xL genetic analyzer (Life Technology) and analysis using Assign (Connexio Genomics) software. Sequences were compared to sequence-specific oligonucleotide (SSO) typing using LabType bead array test kits (One Lambda) analyzed using the LabScan200 bead array multiplex analyzer (Luminex) and HLA Fusion software (One Lambda). Ambiguities were resolved by sequence-specific primer PCR using SSP primer kits (Life Technologies).
Validation of WGS-based HLA typing was accomplished by comparisons with the clinical HLA Typing validation set. Accuracy was calculated based on the number of correct alleles at the HLA-A, HLA-B, and HLA-C loci. Homozygous loci were counted as two correct alleles if correctly called as homozygous, or one correct allele if it was called as heterozygous with one matching allele.
Haplotype correlation between HLA and population ethnicity
The HLA alleles called by Optitype were used to infer the HLA haplotypes in each patient using haplo.stats . The haplotype with the highest posterior probability was assigned to each patient. The HLA haplotype frequency in European, African, and east Asian populations was collected from Maiers et al.  to compare with the population structure inferred based on the SNP-based genotyping of the 540 patients and SNP data from the public 1000 Genomes (1KG) Project . For the 1KG cohort, we included the SNP data (phase 3) of 299 unrelated individuals with European ancestry (91), African ancestry (105), and East Asian ancestry (103). SNPs on the autosomes were included, and those with the following criteria were excluded: (1) missing genotype rate > 5%, (2) minor allele frequency < 0.01, and/or (3) Hardy–Weinberg p value < 0.005. A single SNP was selected per 700 kb on each chromosome. The final dataset contained 3418 SNPs for the 839 individuals. The Admixture model of STRUCTURE v2.3  was run 20 times (20,000 Monte Carlo Markov chain iterations after a burn-in of 10,000 iterations) using default settings and was supervised by the reference population information. The analyses with K =3 maximized the model probability and generated the highest consistency of clustering by assigning membership coefficients to all samples. CLUMPP  was used to collate replicate runs and calculate means of fractions of ancestry for each individual. The correlation between the HLA haplotype frequency and SNP-based population structure was evaluated by canonical correlation analysis.
Neoepitope prediction, RNA expression analysis, mutation signature analyses, and proteomics
Putative neoepitopes were identified by extracting a peptide covering nine tiling nonamers overlapping each missense mutation. Fusion proteins were identified in RNAseq using CICERO  (Li et al., unpublished data). Neoepitopes were predicted by obtaining the peptide sequence covering tiling nonamers overlapping each junction (Fig. 1b). NetMHCcons v1.1  was used to predict the affinity of each nonamer for each HLA receptor predicted in each sample. Nonamers were selected if the predicted IC50 ≤ 500 nM.
A subset of the patients (n = 270) had corresponding RNAseq data , which was used to identify the subset of predicted neoepitopes that are expressed. Expression was measured by counting the number of RNA-seq reads supporting the mutant variant, further requiring that at least one of the reads spans the full 27 bases encoding the nonameric peptide.
Mutation signature analyses were performed based on the mutation profiles for eight samples with mutations in the DNA mismatch repair genes or with a high mutation burden. WTSI Mutational Signature Framework was used for the mutation signature analyses .
Xenograft mouse models for three rhabdomyosarcoma (SJRHB011_E, SJRHB012_D, and SJRHB026_S) were used to assess whether the expression of neoantigenic transcripts would be a reliable metric for the presence of the mutant peptide. Briefly, proteomics data were generated by two-dimensional LC/LC-MS/MS (Stewart et al., unpublished data) and analyzed by the proteogenomics software JUMPg.  Specifically, a customized protein database was generated by translating flanking regions (±30 amino acids) of non-synonymous mutations, which was then concatenated with UniProt human and mouse proteins. MS/MS data were searched against the combined customized amino acid database using the hybrid search engine JUMP  and filtered to achieve 1% protein FDR. Spectra exclusively matching to mutation peptides were then manually examined and annotated.
Summary of neoepitope landscape in the PCGP cohort
Average number of mutationsb
Average number of neoepitope (≤500 nM)b
Average number of expressed neoepitopesb
HLA type prediction and validation
Accurate identification of HLA alleles in the patients is essential for patient-specific neoepitope prediction. To select an appropriate algorithm for HLA typing, we compared the performance of OptiType  with HLAminer  on 51 patients whose HLA alleles were typed in the current study using classic methods including sequence-specific oligonucleotides (SSO), sequence-specific primer (SSP), and Sanger sequence based testing (SBT) technologies (Additional file 2). Consistent with a prior report , OptiType achieved higher accuracy (94.1%) than HLAminer (75.5%); we therefore employed OptiType to characterize HLA class I alleles for the entire cohort.
We found that HLA-A*02:01 and HLA-B*07:02 were the most common alleles at HLA-A and HLA-B loci as they were present in 212 (39.3%) and 105 (19.6%) patients, respectively. For HLA-C, the most prevalent alleles were HLA-C*04:01 and HLA-C*07:01 present in 146 (27.0%) and 144 (26.7%) patients, respectively. Comparison of ethnicity projected from HLA-A-B-C alleles with those from genome-wide SNP analysis showed a significant association (p < 0.001), indicating the high accuracy of the HLA haplotype prediction.
Identification of potential neoepitopes based on missense mutations
Across the pediatric cancer cohort, the proportion of expressed missense mutations encoding neoepitopes is comparable across tumor class, including leukemias (0.38), CNS tumors (0.38), and solid tumors (0.36). Interestingly, melanoma had the highest number of expressed neoepitopes but the lowest proportion (0.29) among tumor types. For the adult TCGA data, we identified 36,230 expressed mutant alleles from 91,375 mutations in 295 tumors. The proportion of expressed mutant alleles encoding neoepitopes was 0.41 (14,753/36,284). Similar to the PCGP data, the number of expressed mutant alleles was strongly correlated with the number of expressed putative neoepitopes (R2 = 0.91, p value < 0.01) (Fig. 3).
Mismatch-repair deficient cancers have been predicted to have a high number of neoepitopes that might be recognized by the immune system . In the PCGP cohort, four high-grade gliomas (HGG)—SJHGG003_D, SJHGG030_D, SJHGG111_D, and SJHGG034_D—have a relatively high mutation burden (median = 6778, range 224–20,073) (Additional file 1). SJHGG003_D, SJHGG111_D, and SJHGG034_D harbored mutations in DNA mismatch repair genes (PMS2 or MSH6). All of the four hypermutators had ten or more neoepitopes with a mean of 6640 neoepitopes. The proportion of expressed mutant alleles encoding neoepitopes and the proportion of total missense mutations encoding neoepitopes is 38.4% (2797/7290) and 35.3% (11,959/33,853), respectively. We performed mutation signature analyses for the HGG hypermutators along with the four melanoma samples with high mutation burden (Additional file 3). Two major mutation signatures, which correspond to COSMIC signatures 1 and 14, are present in the hypermutators. The mutation signature 1 is correlated with the age of cancer diagnosis; the signature 14 has been observed in samples with high mutation burden . Two out of the three HGG tumors with signature 14 harbor bi-allelic loss-of-function mutations in PMS2, suggesting a potential link between signature 14 and PMS2 mutation. The major mutation signature in the melanoma samples is associated with ultraviolet light exposure .
Neoepitopes encoded by recurrent missense mutations
Neoepitopes derived from gene fusions
In the present study, we examined the neoepitope landscape of pediatric cancers based on the somatic missense mutations and gene fusions in tumors sequenced and analyzed through the PCGP. Neoepitopes identified from oncogenic mutations are ideal targets for immunotherapy, including tumor vaccines  and adoptively transferred tumor-reactive T cells . Alternatively, checkpoint blockade therapy might facilitate cytotoxic T lymphocyte recognition of these neoepitopes in a subset of patients. Similar approaches may be leveraged to target neoepitopes derived from fusion proteins that are known biomarkers for pediatric leukemias and some solid tumors. To facilitate neoepitope analysis by other research groups, we have deployed our workflow into the cloud under the DNANexus platform to support HLA typing and epitope prediction. These two analyses can be combined into a single workflow under DNAnexus.
The mutation rate in pediatric cancers is low compared to adult cancers . Consequently, the number of predicted neoepitopes per tumor in pediatric cancer (median 2, mean 26.2, range 0–7544) is much lower than those reported in adult cancers (median 112, range 8–610) . A separate analysis using functional and tetramer-binding assays to determine the proportion of these epitopes that elicit responses is in preparation.
Mutations in the DNA mismatch repair genes (MSH2, MSH6, MLH1, PMS2) can lead to high mutation rate and microsatellite instability. Importantly, mutations associated with neoepitopes in DNA mismatch repair-deficient cancers have been shown to be sensitive to immune checkpoint blockade, which is independent of the origin of tissue . The HGG hypermutators in the PCGP cohort with defects in the DNA mismatch repair machinery showed a mean of 8463 mutations per tumor as compared to ten mutations per tumor in the other samples. A mean of 2990 mutations in the hypermutators were found encoding neoepitopes as compared to four in mismatch repair-proficient cancers. The increase in the number of mutations and neoepitopes resulting from mismatch repair deficiency suggests an enhanced immune response in this subset of cancers  and is worth further investigation.
A recent study reported that tumor growth in a xenograft tumor model was significantly reduced by adoptive transfer of peripheral blood lymphocytes transduced with T-cell receptors (TCRs) derived from immunized HLA-A*11:01 transgenic mice. These TCRs were highly reactive to the KRAS G12V and G12D mutations . For the PCGP cohort, we found that four distinct KRAS mutations were able to generate putative neoepitopes predicted to be bound by either the HLA-A*11:01 allele (KRAS G13DV and G12D) or the HLA-A*03:01 allele (KRAS G12V and G12C). The HLA-A*11:01 allele was present in 64 patients (12%) in the PCGP cohort; the HLA-A*03:01 allele was present in 110 patients (20%). The high population frequency of the identified HLA alleles and the prevalence of epitopes with predicted high affinity to these HLA alleles suggest that they may be useful targets for future development of immunotherapy.
We additionally identified high affinity neoepitopes encoded by recurrent H3 K27M mutations and ETV6-RUNX1 gene fusions in a high proportion of tumors harboring these somatic alterations. The neoepitopes of histone H3 K27M mutations can be presented mainly by the HLA-A*30:01 allele that is present in 11.9% of African-Americans . The neoepitopes of ETV6-RUNX1 gene fusions can be bound by HLA-A*02:01, which is prevalent in Europeans and US Caucasians (47.8%) as well as other populations. These predicted neoepitopes are potentially important candidates for further immunogenicity testing.
The repertoire of putative neoepitopes identified in this study (Additional files 5 and 6) provides new fundamental knowledge on the formation of potentially targetable neoepitopes in childhood cancer and will serve as a valuable public resource for development of novel therapeutic strategies against these difficult to treat illnesses. To the best of our knowledge, this is the first comprehensive analysis of neoepitopes in pediatric cancers, which we hope will enable a broader range of research and open up new avenues for the treatment of pediatric cancer.
We thank Ms. Victoria Turner for assisting with validation analysis of HLA typing.
This study was supported by Cancer Center support grant P30 CA021765 from the National Cancer Institute and the American Lebanese Syrian Associated Charities of St Jude Children’s Research Hospital.
Availability of data and materials
All data generated or analyzed during this study are included in this published article and its supplementary information files. The PCGP sequencing data can be accessed via the St Jude Pediatric Cancer (PeCan) Data Portal (https://pecan.stjude.org/home).
JZ, PGT, DRG, and JRD contributed to the conception and design of the study. TC and RAC developed the bioinformatics pipeline and analyzed the data under the supervision of JZ, YL, ME, XC, and GW contributed to data analysis. YL, HW, MD, and JP carried out the proteomics experiments. PA and TG analyzed the clinical HLA haplotypes. JZ, TC, and RAC wrote the manuscript with critical review from PGT and DRG. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The use of human tissues for sequencing was approved by the institutional review board of St Jude Children’s Research Hospital in accordance with the principles of the Declaration of Helsinki. Written informed consent was provided by a parent or guardian of each child or by a patient who was 18 years of age or older.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Srivastava PK. Neoepitopes of cancers: looking back, looking ahead. Cancer Immunol Res. 2015;3:969–77.View ArticlePubMedGoogle Scholar
- Brown SD, Warren RL, Gibb EA, Martin SD, Spinelli JJ, Nelson BH, Holt RA. Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival. Genome Res. 2014;24:743–50.View ArticlePubMedPubMed CentralGoogle Scholar
- Rajasagi M, Shukla SA, Fritsch EF, Keskin DB, DeLuca D, Carmona E, Zhang W, Sougnez C, Cibulskis K, Sidney J, et al. Systematic identification of personal tumor-specific neoantigens in chronic lymphocytic leukemia. Blood. 2014;124:453–62.View ArticlePubMedPubMed CentralGoogle Scholar
- Gubin MM, Artyomov MN, Mardis ER, Schreiber RD. Tumor neoantigens: building a framework for personalized cancer immunotherapy. J Clin Invest. 2015;125:3413–21.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang J, Mardis ER, Maher CA. INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery. Bioinformatics. 2017;33:555–7.PubMedGoogle Scholar
- Sette A, Vitiello A, Reherman B, Fowler P, Nayersina R, Kast WM, Melief CJ, Oseroff C, Yuan L, Ruppert J, et al. The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. J Immunol. 1994;153:5586–92.PubMedGoogle Scholar
- Wentworth PA, Vitiello A, Sidney J, Keogh E, Chesnut RW, Grey H, Sette A. Differences and similarities in the A2.1-restricted cytotoxic T cell repertoire in humans and human leukocyte antigen-transgenic mice. Eur J Immunol. 1996;26:97–101.View ArticlePubMedGoogle Scholar
- Fritsch EF, Rajasagi M, Ott PA, Brusic V, Hacohen N, Wu CJ. HLA-binding properties of tumor neoepitopes in humans. Cancer Immunol Res. 2014;2:522–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Soria-Guerra RE, Nieto-Gomez R, Govea-Alonso DO, Rosales-Mendoza S. An overview of bioinformatics tools for epitope prediction: implications on vaccine development. J Biomed Inform. 2015;53:405–14.View ArticlePubMedGoogle Scholar
- Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, Brunak S, Lund O. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 2003;12:1007–17.View ArticlePubMedPubMed CentralGoogle Scholar
- Bui HH, Sidney J, Peters B, Sathiamurthy M, Sinichi A, Purton KA, Mothe BR, Chisari FV, Watkins DI, Sette A. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics. 2005;57:304–14.View ArticlePubMedGoogle Scholar
- Peters B, Sette A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinf. 2005;6:132.View ArticleGoogle Scholar
- Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, Salimi N, Damle R, Sette A, Peters B. The immune epitope database 2.0. Nucleic Acids Res. 2010;38:D854–62.View ArticlePubMedGoogle Scholar
- Lundegaard C, Lund O, Nielsen M. Prediction of epitopes using neural network based methods. J Immunol Methods. 2011;374:26–34.View ArticlePubMedGoogle Scholar
- Karosiene E, Lundegaard C, Lund O, Nielsen M. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics. 2012;64:177–86.View ArticlePubMedGoogle Scholar
- Lennerz V, Fatho M, Gentilini C, Frye RA, Lifke A, Ferel D, Wolfel C, Huber C, Wolfel T. The response of autologous T cells to a human melanoma is dominated by mutated neoantigens. Proc Natl Acad Sci U S A. 2005;102:16013–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Saeterdal I, Bjorheim J, Lislerud K, Gjertsen MK, Bukholm IK, Olsen OC, Nesland JM, Eriksen JA, Moller M, Lindblom A, Gaudernack G. Frameshift-mutation-derived peptides as tumor-specific antigens in inherited and spontaneous colorectal cancer. Proc Natl Acad Sci U S A. 2001;98:13255–60.View ArticlePubMedPubMed CentralGoogle Scholar
- Huang J, El-Gamil M, Dudley ME, Li YF, Rosenberg SA, Robbins PF. T cells associated with tumor regression recognize frameshifted products of the CDKN2A tumor suppressor gene locus and a mutated HLA class I gene product. J Immunol. 2004;172:6057–64.View ArticlePubMedPubMed CentralGoogle Scholar
- Castle JC, Kreiter S, Diekmann J, Lower M, van de Roemer N, de Graaf J, Selmi A, Diken M, Boegel S, Paret C, et al. Exploiting the mutanome for tumor vaccination. Cancer Res. 2012;72:1081–91.View ArticlePubMedGoogle Scholar
- Matsushita H, Vesely MD, Koboldt DC, Rickert CG, Uppaluri R, Magrini VJ, Arthur CD, White JM, Chen YS, Shea LK, et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature. 2012;482:400–4.View ArticlePubMedPubMed CentralGoogle Scholar
- DuPage M, Mazumdar C, Schmidt LM, Cheung AF, Jacks T. Expression of tumour-specific antigens underlies cancer immunoediting. Nature. 2012;482:405–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Gubin MM, Zhang X, Schuster H, Caron E, Ward JP, Noguchi T, Ivanova Y, Hundal J, Arthur CD, Krebber WJ, et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature. 2014;515:577–81.View ArticlePubMedPubMed CentralGoogle Scholar
- Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, Sucker A, Hillen U, Geukes Foppen MH, Goldinger SM, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015;350:207–11.View ArticlePubMedPubMed CentralGoogle Scholar
- Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, Lee W, Yuan J, Wong P, Ho TS, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348:124–8.View ArticlePubMedPubMed CentralGoogle Scholar
- van Rooij N, van Buuren MM, Philips D, Velds A, Toebes M, Heemskerk B, van Dijk LJ, Behjati S, Hilkmann H, El Atmioui D, et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J Clin Oncol. 2013;31:e439–42.View ArticlePubMedGoogle Scholar
- Lin EI, Tseng LH, Gocke CD, Reil S, Le DT, Azad NS, Eshleman JR. Mutational profiling of colorectal cancers with microsatellite instability. Oncotarget. 2015;6:42334–44.View ArticlePubMedPubMed CentralGoogle Scholar
- Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, Walsh LA, Postow MA, Wong P, Ho TS, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2014;371:2189–99.View ArticlePubMedPubMed CentralGoogle Scholar
- Gros A, Robbins PF, Yao X, Li YF, Turcotte S, Tran E, Wunderlich JR, Mixon A, Farid S, Dudley ME, et al. PD-1 identifies the patient-specific CD8(+) tumor-reactive repertoire infiltrating human tumors. J Clin Invest. 2014;124:2246–59.View ArticlePubMedPubMed CentralGoogle Scholar
- Downing JR, Wilson RK, Zhang J, Mardis ER, Pui CH, Ding L, Ley TJ, Evans WE. The Pediatric Cancer Genome Project. Nat Genet. 2012;44:619–22.View ArticlePubMedPubMed CentralGoogle Scholar
- Andersson AK, Ma J, Wang J, Chen X, Gedman AL, Dang J, Nakitandwe J, Holmfeldt L, Parker M, Easton J, et al. The landscape of somatic mutations in infant MLL-rearranged acute lymphoblastic leukemias. Nat Genet. 2015;47:330–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.View ArticlePubMedPubMed CentralGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. Genome Project Data Processing S: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang J, Ding L, Holmfeldt L, Wu G, Heatley SL, Payne-Turner D, Easton J, Chen X, Wang J, Rusch M, et al. The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature. 2012;481:157–63.View ArticlePubMedPubMed CentralGoogle Scholar
- Roberts KG, Li Y, Payne-Turner D, Harvey RC, Yang YL, Pei D, McCastlain K, Ding L, Lu C, Song G, et al. Targetable kinase-activating lesions in Ph-like acute lymphoblastic leukemia. N Engl J Med. 2014;371:1005–15.View ArticlePubMedPubMed CentralGoogle Scholar
- Lake SL, Lyon H, Tantisira K, Silverman EK, Weiss ST, Laird NM, Schaid DJ. Estimation and tests of haplotype-environment interaction when linkage phase is ambiguous. Hum Hered. 2003;55:56–65.View ArticlePubMedGoogle Scholar
- Maiers M, Gragert L, Klitz W. High-resolution HLA alleles and haplotypes in the United States population. Hum Immunol. 2007;68:779–88.View ArticlePubMedGoogle Scholar
- Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Hsi-Yang Fritz M, et al. An integrated map of structural variation in 2,504 human genomes. Nature. 2015;526:75–81.View ArticlePubMedPubMed CentralGoogle Scholar
- Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–59.PubMedPubMed CentralGoogle Scholar
- Jakobsson M, Rosenberg NA. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics. 2007;23:1801–6.View ArticlePubMedGoogle Scholar
- Secrier M, Li X, de Silva N, Eldridge MD, Contino G, Bornschein J, MacRae S, Grehan N, O'Donovan M, Miremadi A, et al. Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance. Nat Genet. 2016;48:1131–41.View ArticlePubMedGoogle Scholar
- Li Y, Wang X, Cho JH, Shaw TI, Wu Z, Bai B, Wang H, Zhou S, Beach TG, Wu G, et al. JUMPg: an integrative proteogenomics pipeline identifying unannotated proteins in human brain and cancer cells. J Proteome Res. 2016;15:2309–20.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang X, Li Y, Wu Z, Wang H, Tan H, Peng J. JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy. Mol Cell Proteomics. 2014;13:3663–73.View ArticlePubMedPubMed CentralGoogle Scholar
- Wu G, Diaz AK, Paugh BS, Rankin SL, Ju B, Li Y, Zhu X, Qu C, Chen X, Zhang J, et al. The genomic landscape of diffuse intrinsic pontine glioma and pediatric non-brainstem high-grade glioma. Nat Genet. 2014;46:444–50.View ArticlePubMedPubMed CentralGoogle Scholar
- Yarchoan M, Johnson 3rd BA, Lutz ER, Laheru DA, Jaffee EM. Targeting neoantigens to augment antitumour immunity. Nat Rev Cancer. 2017;17:209–22.View ArticlePubMedGoogle Scholar
- Szolek A, Schubert B, Mohr C, Sturm M, Feldhahn M, Kohlbacher O. OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics. 2014;30:3310–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Warren RL, Choe G, Freeman DJ, Castellarin M, Munro S, Moore R, Holt RA. Derivation of HLA types from shotgun sequence datasets. Genome Med. 2012;4:95.View ArticlePubMedPubMed CentralGoogle Scholar
- Bauer DC, Zadoorian A, Wilson LO, Melbourne Genomics Health A, Thorne NP. Evaluation of computational programs to predict HLA genotypes from genomic sequencing data. Brief Bioinform. 2016; doi:10.1093/bib/bbw097.
- Le DT, Durham JN, Smith KN, Wang H, Bartlett BR, Aulakh LK, Lu S, et al. Mismatch-repair deficiency predicts response of solid tumors to PD-1 blockade. Science. 2017;357:409–13.Google Scholar
- Bamford S, Dawson E, Forbes S, Clements J, Pettett R, Dogan A, Flanagan A, Teague J, Futreal PA, Stratton MR. The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br J Cancer. 2004;91:355.PubMedPubMed CentralGoogle Scholar
- Schumacher T, Bunse L, Pusch S, Sahm F, Wiestler B, Quandt J, Menn O, Osswald M, Oezen I, Ott M, et al. A vaccine targeting mutant IDH1 induces antitumour immunity. Nature. 2014;512:324–7.View ArticlePubMedGoogle Scholar
- Robbins PF, Lu YC, El-Gamil M, Li YF, Gross C, Gartner J, Lin JC, Teer JK, Cliften P, Tycksen E, et al. Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat Med. 2013;19:747–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, Skora AD, Luber BS, Azad NS, Laheru D. PD-1 blockade in tumors with mismatch-repair deficiency. New Engl J Med. 2015;372:2509–20.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang QJ, Yu Z, Griffith K, Hanada K, Restifo NP, Yang JC. Identification of T-cell receptors targeting KRAS-mutated human tumors. Cancer Immunol Res. 2016;4:204–14.View ArticlePubMedGoogle Scholar