REVIEW Preclinical and clinical development of neoantigen vaccines L. Li1,2, S. P. Goedegebuure1,2& W. E. Gillanders1,2* 1Department of Surgery, Washington University School of Medicine, St Louis;2The Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St Louis, USA *Correspondence to : Dr William E. Gillanders, Department of Surgery, Washington University School of Medicine, Campus Box 8109, 660 South Euclid Avenue, St Louis, MO 63110, USA. Tel: þ1-314-747-0072; E-mail: gillandersw@wustl.edu Cancer neoantigens are antigens that result from somatic mutations present in individual cancers. Neoantigens are considered important targets for cancer immunotherapy because of their immunogenicity and lack of expression in normal tissues. Next-generation sequencing technologies and computational analysis have recently made neoantigen discovery possible. Althoughneoantigens are important targets of checkpoint blockade therapy, neoantigen vaccines are currently being investigated inpreclinical models and early-phase human clinical trials. Preliminary results from these clinical trials demonstrate that dendriticcell, synthetic long peptide, and RNA-based neoantigen vaccines are safe, and capable of inducing both CD8 þand CD4 þ neoantigen-specific T-cell responses. We and others are testing neoantigen vaccines in melanoma, breast cancer, non-small-celllung cancer and other cancer types. Since cancers have evolved mechanisms to escape immune control, it is particularlyimportant to study the efficacy of neoantigen vaccines in combination with other immunotherapies including checkpointblockade therapy, and immune therapies targeting the immunosuppressive tumor microenvironment. Key words :cancer vaccine, neoantigen, immunotherapy, clinical trial Introduction Cancer immunotherapy has evolved into one of the most promis- ing cancer treatment modalities. The goal of cancer immunother- apy is to harness the immune system for the selective destruction of cancers while leaving normal tissues unharmed. Two immunecheckpoints, cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death protein 1 (PD-1), have been the focus of most clinical trials. Antibodies targeting CTLA- 4 and PD-1/PD-L1 are relatively well-tolerated, and can elicit durable antitumor responses in a subset of patients with mela-noma [ 1–3], non-small-cell lung cancer (NSCLC), bladder carcinoma and other cancers [ 4]. Although antibodies targeting CTLA-4 and PD-1/PD-L1 represent the greatest success of cancer immunotherapy, substantial efforts are underway to develop additional immunotherapies that can be used alone or in combi-nation with these checkpoint blockade therapies. A dynamic interplay exists between cancer cells and the immune system, as described in the cancer immunoediting hypothesis [ 5– 7]. It is now understood that the immune system is capable of both suppressing tumor growth, and/or promoting tumor progression.In terms of suppressing tumor growth, the innate and adaptive immune systems can speciﬁcally recognize cancers as nonself and mount antitumor immune responses. Such beneﬁcial immuneresponses can be induced and/or enhanced by checkpoint blockade therapies, adoptive cell therapies or cancer vaccine approaches. In terms of promoting tumor progression, tumors leverage immune regulatory networks to circumvent immune control and promote growth [ 6,8]. To maximize efﬁcacy, it is likely that next- generation immunotherapies will successfully integrate multiple strategies targeting elements of this dynamic interplay. The most promising strategies include, but are not limited to (i) agents thattarget immune checkpoints, (ii) approaches that inhibit immuno- suppression in the tumor microenvironment (e.g. targeting regula- tory T cells, myeloid-derived suppressor cells, tumor-associated macrophages etc.), (iii) adoptive transfer of ex vivo expanded and/ or genetically engineered T-cell receptor (TCR) or chimeric anti-gen receptor T cells, and (iv) cancer vaccines designed to elicit robust antitumor immune responses. In this review we focus on the development of neoantigen vaccines with an emphasis on bothpreclinical development and initial clinical experience. VCThe Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.Annals of Oncology 28 (Supplement 12): xii11–xii17, 2017 doi:10.1093/annonc/mdx681 Cancer neoantigens Traditionally, cancer vaccines have targeted the so-called tumor- associated antigens (TAA). TAA are typically proteins present in normal tissues but overexpressed in cancers. Examples of TAA include HER2, MART-1, MUC1, tyrosinase, MAGE, mammaglo-bin-A and NY-ESO-1. Cancer vaccines targeting TAA are pre- sumed capable of inducing T-cell responses to these ‘self’ proteins due to one or more of the following reasons (i) incomplete thymic depletion and/or peripheral tolerance of TAA-reactive T cells; (ii) extremely low expression of TAA in the periphery; (iii) low TCR binding afﬁnity of TAA-reactive T cells; or (iv) restricted TAA expression pattern during development. Unfortunately, mostclinical trials targeting TAA have failed to demonstrate durable beneﬁcial effects compared with standard of care treatment [ 9]. In contrast, neoantigens are tumor-speciﬁc antigens resulting from somatic DNA alterations [e.g. nonsynonymous point muta-tions, insertion-deletions (so-called indels), gene fusions and/or frameshift mutations]. Neoantigens typically have a high predicted binding afﬁnity to major histocompatibility complex (MHC) molecules. Cancer vaccines targeting neoantigens have generated great enthusiasm given the potential advantages of targeting protein sequences that are not present in normal tissues including decreased central immune tolerance, and improved safety proﬁle.This enthusiasm for targeting neoantigens has been enhanced recently, as a strong correlation between somatic tumor mutation burden and favorable clinical beneﬁt of checkpoint blockade therapy has been established in melanoma [ 10,11], NSCLC [ 12] and colorectal cancer [ 13]. In preclinical models, low mutation burden has been shown to result in a lack of immunoediting in the murine KPC pancreatic cancer model, while introduction of a neoantigen (OVA) results in tumor elimination [ 14]. These observations suggest an important role for neoantigens in the clini- cal response to checkpoint blockade therapy and the potential value of mutation burden as a predictive biomarker [ 15]. A clinical trial is ongoing to study the relationship between tumor mutationburden, predicted neoantigen burden, and clinical response in patients with advanced melanoma or bladder cancer treated with nivolumab ( a-PD-1) or nivolumab plus ipilimumab ( a-CTLA-4) (NCT02553642). Cancer neoantigen identification The mutational landscape of cancer is complex as demonstrated by genomic analyses of breast cancer [ 16], melanoma [ 17], liver cancer [18] and many other major cancer types [ 19,20]. Advances in next- generation sequencing (NGS) technologies have enabled rapid and cost-effective comparisons between tumor and normal sequences, which is the starting point of cancer neoantigen identiﬁcation [ 21] (Figure 1). Many somatic mutations detected by DNA sequencing are not expressed (i.e. noncoding mutations, nonsense mutations or monoallelic expression). For thos e expressed mutations resulting in an altered amino acid sequence, the abnormal amino acid sequences have to be successfully translated and then processed into short peptide fragments and displayed on the cell surface in the context of MHC molecules in order to be recognized by the immune system.Antigen processing and presentation is a complex process involving multiple steps that can impact neoantigen presentation [ 22].Antigen processing is different for MHC class I and class II mole- cules. MHC class I molecules present 8–10 amino acid peptides pro-duced endogenously or acquired by p rofessional antigen presenting cells. MHC class II molecules present longer peptides (11–20 amino acids) derived from exogenous proteins. Therefore, predictingwhether a somatic mutation can create a neoantigen depends onseveral key factors: (i) whether the somatic mutation is expressed atthe protein level, (ii) whether t he mutant protein can be naturally processed into an appropriate peptide for presentation, (iii) thebinding afﬁnity of the mutant peptide to the patient‘s autologousMHC molecules and (iv) the afﬁnity of the mutant peptide/MHCcomplex to the TCR of responding T cells. It is currently believedthat most somatic mutations det ected by sequencing do not result in effective neoantigens [ 23]. With the large number of somatic mutations, and the intrinsic polymorphism of human MHC molecules [i.e. human leukocyteantigen (HLA) molecules], it remains a challenging task to accu-rately predict and prioritize neoantigen candidates. A number ofcomputational tools have been developed, which includesequence-based and structure-based algorithms to predict pep-tide–MHC binding (reviewed in Refs. [ 24–29]). Structure-based approaches require experimentally acquired crystallographicdata for model building as well as signiﬁcant computing powerfor simulations. This limits their current application despite anincreased accuracy in epitope prediction [ 28]. On the other hand, in sequence-based predictions, supervised machine learningmodules are applied with methods ranging from simple position- speciﬁc scoring matrices (PSSM, used by SYFPEITHI [ 30] and BIMAS [ 31]), to more sophisticated artiﬁcial neural networks (ANN, used by NetMHC [ 32]), support vector machines or hid- den Markov models. These algorithms depend heavily on the sizeand quality of the training datasets of known MHC binding pep-tides available, and because of this, the predictive performance ofsome MHC alleles is still in need of further improvement.Nevertheless the computational methods that predict bindingafﬁnity of peptides to MHC class I molecules are the most accu-rate so far and have been used successfully in studies to identifyneoantigens [ 33–36]. The ideal neoantigen prediction approach would integrate ﬁlters based on all of the biologic processes discussed above (i.e. proteasomal cleavage, MHC binding, TCR recognition) in assessing the potential immunogenicity of neoan-tigen candidates. To this end, the Immune Epitope Database andAnalysis Resource (IEDB, www.iedb.org) hosts an array of toolsto facilitate peptide–MHC binding prediction with the options ofintegrating proteasomal processing (NetCHOP and NetCTL)and TAP transport prediction [ 37]. We have found in our pre- clinical studies that median afﬁnity is an effective way to predictneoantigens [ 33] although we are not aware that the median approach and NetMHCpan have been directly compared. Inthese studies, we calculated a median afﬁnity for each neoantigenusing multiple epitope prediction algorithms (NetMHCpan,ANN, SMM and SMMPMBEC). Additional ﬁlters were applied to prioritize neoantigen candidates: (i) elimination of hypotheti- cal (Riken) proteins; (ii) use of NetCHOP, an antigen processingalgorithm to eliminate epitopes that are not likely to be proteo-lytically produced by the constitutive- or immune-proteasomeand (iii) prioritization of neoantigens where the mutant epitopehas a higher predicted binding afﬁnity than the correspondingwildtype sequence.ReviewAnnals of Oncology xii12 | Li et al. Volume 28 | Supplement 12 | December 2017 CD4þT cells, which recognize antigens presented by MHC class II molecules, contribute to antitumor immunity. However,the computational methods available to predict MHC class II epitopes are less informative than the MHC class I algorithms because of the more promiscuous nature of peptide binding toMHC class II molecules [ 38], and lack of robust training datasets. Unlike MHC class I molecules, whose peptide binding groove tends to be closed at both ends, the ends of the MHC class II pep-tide binding grove are open, allowing the accommodation of lon- ger peptides [ 39]. The core binding motif of a set of peptides (with variable lengths) that bind to a particular MHC class IImolecule is more difﬁcult to identify. In addition, the proteolytic degradation process of MHC class II-bound peptides is less well characterized. Despite these difﬁculties, Kreiter et al. [ 40] suc- cessfully prioritized MHC class II neoantigens based solely on expression levels and predicted MHC class II binding afﬁnity using tools available at IEDB. The relevance of these MHC class IIneoantigen predictions was conﬁrmed as vaccination with syn- thetic polyepitope mRNA led to complete rejection of estab- lished, aggressively growing syngeneic tumors (B16F10 andCT26) in mice [ 40]. In addition, recent results from two phase I clinical trials [ 34,35] highlight the importance of neoantigen- speciﬁc CD4 þT-cell responses following neoantigen vaccina- tion. These trials, which will be discussed in more detail below, investigated personalized synthetic long peptide (SLP) [ 34] and polyepitope mRNA neoantigen vaccines [ 35] in patients with advanced melanoma. Both SLP and polyepitope mRNA approaches were able to generate CD8 þand CD4 þneoantigen- speciﬁc T-cell responses. This result is notable because the study by Ott et al. [ 34] did not attempt to identify MHC class II- restricted neoantigens for inclusion in the neoantigen vaccine,and Sahin et al. [ 35] found that /C2420% of the T-cell responses were induced to neoantigens predicted to bind poorly to HLA class I and II. These results underscore the need to furtherimprove the in silico prediction algorithms for both MHC class I and II epitopes. Other methods have been used to identify cancer neoantigens. Mass spectrometry analyses of peptides eluted from peptide–MHC complexes have enabled the characterization of the HLA ligandome or immunopeptidome [ 41,42]. A series of clinical tri- als targeting the HLA ligandome have been completed in HLA-A2 þrenal-cell carcinoma patients [ 43]. Another strategy is based on the functional analysis of an individual patient’s peripheral blood mononuclear cells (PBMC) or tumor-inﬁltratinglymphocytes (TIL). Pre-existing neoantigen-reactive T cells can be stimulated and detected by tetramer/multimer staining (ﬂowcytometry analysis) or cytokine secreting assay (e.g. IFN- c ELISPOT). Recently, novel platforms that screen a patient’sPBMC against a neoantigen library has been proposed. Theseplatforms have been successfully applied to identify CD4 þand CD8þT-cell antigens in infectious diseases and cancer [ 44,45]. However, these types of biologic assays are currently costly, tech-nically challenging, and may also fail to identify subdominant and/or cryptic neoantigens that do not naturally induce immune responses yet can be activated through vaccination [ 46]. Preclinical studies Recent studies have shown that neoantigen vaccine approaches are able to induce robust antitumor responses in mice [ 33,40, 47]. In the B16F10 murine melanoma model, Castle et al. [ 47] vaccinated C57BL/6 mice with SLPs derived from 50 validated mutations. Sixteen of these peptides were immunogenic as deter-mined by IFN- cELISPOT assay. Peptide vaccination against two mutant antigens MUT30 (Kif18b K739N) and MUT44 (Cpsf3lD314N) was able to confer a marked in vivo antitumor effect in both preventive and therapeutic settings [ 47]. In follow-up stud- ies, the same group found that the majority of the immunogenic‘mutanome’ of B16F10 and CT26 tumors was CD4 þT-cell spe- ciﬁc, and mRNA vaccines encoding CD4 þT-cell neoantigens were able to induce potent antitumor immunity [ 40]. In collabo- ration with Schreiber et al., we identiﬁed two neoantigens respon- sible for tumor rejection following immune checkpoint blockade witha-CTLA-4 or a-PD-1 antibodies [ 33] in the murine sarcoma model T3. Vaccination with SLP incorporating these two mutantepitopes, namely Lama4 G1254V and Alg8 A506T, induced anti-tumor immunity comparable to checkpoint blockade immuno-therapy [ 33]. We have since conﬁrmed and extended these results in the murine 4T1 and E0771 breast tumor models, identifyingneoantigens that can be successfully targeted with both SLP andpolyepitope DNA neoantigen vaccines (unpublished data).Employing similar strategies, Yadav et al. [ 36] successfully identi- ﬁed and validated several neoantigens in the MC-38 and TRAMP-C1 tumors. The immunodeﬁcient NOD.scid.gamma (NSG) [ 48,49] and related mouse models have made it possible to study human can-cer cell lines and patient-derived xenografts (PDX) in vivo .W e have successfully established PDX by injecting human breast Tumor/normal exome sequencing and cDNA- capture sequencing to identify somatic mutationsComputational analysis, variant calling, neoantigen identification and prioritizationNeoantigen vaccine manufacture and product release testsNeoantigen vaccine administration Figure 1. Neoantigen vaccine design and manufacture.Annals of Oncology Review Volume 28 | Supplement 12 | December 2017 doi:10.1093/annonc/mdx681 | xii13 cancer cells into NSG mice [ 50]. Two neoantigens, ROBO3 A1265V and PALB2 H198D were identiﬁed in the WHIM30 PDX and parental tumor by computational analysis and in vitro stud- ies. Adoptive transfer of autologous PBMCs stimulated in vitro with mutant ROBO3 and PALB2 peptides resulted in decreased tumor growth [ 50]. Integrating PDX models into the neoantigen discovery pipeline offers great opportunities. Recently the TRON Cell Line Portal (TCLP) [ 51,52] has been assembled which, among others, catalog the HLA type, expression and neoepitope candidates of 1082 human cancer cell lines. TCLP (available at celllines.tron-mainz.de) is the product of data-mining and re- analyzing the public databases generated by the Catalogue of Somatic Mutations In Cancer (COSMIC) [ 53,54], the Cancer Cell Line Encyclopedia (CCLE) [ 55] and Klijn et al. [ 56]. This val- uable resource will help researchers select cancer cell lines based on the HLA type and expression, as well as provide therapeutic target for the development of cancer immunotherapy. Current clinical trials The presence of neoantigen-speciﬁc CD8 þand CD4 þT cells in TILs from melanoma patients responding to checkpoint block- ade therapy [ 57–60], and promising results from preclinical stud- ies have generated signiﬁcant interest in the clinical development of neoantigen vaccines (Table 1). Results from several phase I clinical trials [ 34,35,46] in patients with advanced melanoma are quite encouraging, even though the number of patients treated in these studies is small. Main characteristics of current neoantigen vaccine platforms are summarized in Table 2. Carreno et al. [ 46] were the ﬁrst to report that neoantigen-pulsed DC can induceneoantigen-speciﬁc T-cell responses in melanoma patients (NCT00683670). The initial report details the results of vaccinat- ing three patients. Seven neoantigens with the highest binding scores to HLA-A*02:01 were prioritized in each patient. DC vac- cination augmented pre-existing immunity to neoantigens, and induced neoantigen-speciﬁc T-cell responses. In addition, the frequency of most existing pre-vaccine TCR- bclonotypes was increased and previously undetected clonotypes were revealed, indicating that vaccination promotes a more diverse T-cell reper- toire [ 46]. However, clinical presentation and responses to neoantigen-pulsed DC vaccine were not reported for these mela- noma patients. Two additional papers co-published in Nature by Ott et al. [34] and Sahin et al. [ 35] conﬁrm the potential of neoantigen vac- cines in treating melanoma patients. These two studies employed similar strategies to identify neoantigens based on NGS data from cancers and normal cells. Computational algorithms were used to predict the ability of neoantigens to bind MHC class I molecules and to prioritize candidate neoantigens. Ott et al. vaccinated six patients with SLP (up to 20 total peptides in 4 pools for each patient) after surgical resection of the tumors (NCT01970358). Four of the six patients vaccinated showed no disease recurrence during follow-up of 20–32 months after vaccination. The remain- ing two participants had disease recurrence but both achieved a complete response after treatment with anti-PD-1 antibody. Sahin et al. [ 61] created synthetic RNA vaccines, each encoding ﬁve 27-mer neoantigens (NCT02035956). Such RNA molecules were previously shown to be readily taken up by lymph noderesident DCs. Up to 10 mutations were targeted in each patient’s tumor (two RNA vaccines). Of the 13 patients vaccinated via intranodal injection of the RNA vaccine, 8 remained tumor free throughout the follow-up period. The other ﬁve participants had tumor relapse. However, after PD-1 blockade therapy, tumor regression occurred in one of these patients. Another patient was noted to have outgrowth of b2-microglobulin deﬁcient cancer cells, indicating MHC loss as an acquired immune escape mecha- nism. ‘Off-the-shelf’ RNA vaccines targeting shared TAAs were also administered in patients with NY-ESO-1 þand/or tyrosinaseþmelanoma, but their contribution to the antitumor immunity was not studied. Immune monitoring analyses of patients’ PBMCs (IFN- cELISPOT, intracellular cytokine stain- ing, multimer staining) in both studies revealed that SLP and RNA vaccines can (i) enhance pre-existing but weak neoantigen- speciﬁc T-cell responses and (ii) generate de novo neoantigen- speciﬁc T-cell responses [ 34,35]. As noted above, the majority of the ex vivo IFN-cresponses were generated by CD4 þT cells. Both studies also found that vaccination resulted in an expansion of the neoantigen-speciﬁc T-cell repertoire. Taken together, these studies provide strong rationale for further clinical development and testing of neoantigen vaccines. We and others have initiated clinical trials of neoantigen vac- cines in breast and other cancer types (Table 1). For instance, we have initiated trials and are currently recruiting patients with triple-negative breast cancer (TNBC) who do not have a patho-logic complete response after neoadjuvant chemotherapy. These patients typically have no gross evidence of disease following standard of care therapy (neoadjuvant chemotherapy, surgery and radiation therapy) but are at high-risk for disease recurrence. Targeting this patient population provides a window-of- opportunity to identify neoantigens and manufacture personal- ized cancer vaccines, maximizing the potential beneﬁt from the vaccine as the regulatory networks associated with metastatic dis- ease are not present. One trial (NCT02427581) is designed to vac- cinate TNBC patients with SLP admixed with poly-ICLC as adjuvant. In a companion trial (NCT02348320), we synthesize polyepitope neoantigen DNA vaccines and vaccinate patients intramuscularly using an electroporation device. Exome and RNA sequencing, neoantigen prediction and vaccine production has been completed for 12 patients, and 11 subjects have com- pleted all scheduled vaccinations. The primary objective of these trials is to assess the safety of personalized neoantigen vaccines. Meanwhile, pre- and post-vaccine PBMC have been collected at various time points from vaccinated patients, and cryopreserved. Neoantigen-speciﬁc T-cell responses will be assessed using IFN- c ELISPOT assay, multiparametric ﬂow cytometry analysis, and related techniques. Challenges Despite recent advances, many challenges remain in the develop-ment of neoantigen vaccines. First, both the cost and time to manufacture neoantigen vaccines have to be reduced. Although the cost of DNA/RNA sequencing has decreased signiﬁcantly [21], it remains costly and time-consuming to identify and vali- date candidate neoantigens. Manufacture of neoantigen vaccines under good manufacturing practice conditions is also veryReviewAnnals of Oncology xii14 | Li et al. Volume 28 | Supplement 12 | December 2017 expensive. Currently the time from tissue acquisition to vaccine delivery ranges from 3 to 5 months [ 34,35]. This will need to be improved in order to beneﬁt patients with metastatic disease.Second, neoantigen prediction algorithms require furtheroptimization. These include strategies to better predict MHC class I and II neoantigens, as well as potential neoantigens result-ing from genetical alterations other than missense mutations,such as gene fusions and indels. Given the recent ﬁndings thatTable 1. Selected clinical trials targeting cancer neoantigens ClinicalTrial.gov identifier Phase Enrollment statusaCancer type Formulation Additional intervention Polyepitope plasmid DNA NCT02348320 I Recruiting TNBC Electroporation Neoadjuvant chemotherapyNCT03122106 I Not yet recruiting Pancreatic cancer Electroporation Adjuvant chemotherapy Polyepitope coding RNA NCT02035956 I Not recruiting Melanoma Intranodal injection Pembrolizumab b[33] NCT02316457 I Recruiting TNBC Intranodal injection Synthetic peptide NCT01970358 I Recruiting Melanoma Poly-ICLC (NeoVax) Pembrolizumabb[32] NCT02427581 I Recruiting TNBC Poly-ICLC Neoadjuvant chemotherapyNCT02600949 II By invitation only PDA and CRC IFA, topical imiquimod ChemotherapyNCT02721043 II Recruiting Solid tumors Poly-ICLC LenalidomideNCT02510950 I Recruiting Glioblastoma Poly-ICLC TemozolomideNCT02992977 I Recruiting Advanced cancer AutoSynVax TMc NCT02933073 I Recruiting Ovarian cancer OncoImmunome ChemotherapyNCT02897765 I Recruiting Multiple Poly-ICLC (Neo-PV-01) Nivolumab aAs of 15 August 2017. bOnly to patients with disease recurrence. cHSP70 conjugated short peptides. TNBC, triple-negative breast cancer; PDA, pancreatic ductal adenocarcinoma; CRC, colorectal cancer; IFA, incomplete Freund‘s adjuvant. Table 2. Main characteristics of current neoantigen vaccine platforms Vaccine platform Advantages Disadvantages Clinical trials Synthetic long peptide vaccine•Stable in storage •Low toxicity •Elicit both CD8 and CD4 T-cell responses •Safe •Repeated vaccination possible•Requires co-administration of an appropriateadjuvant •Multiple peptides need to be manufactured •Immune responses may be weak and/or tran-sient [although this appears to be more of aconcern with short peptides (8–10 AA) com-pared with long peptides (25–30 AA)]•NCT01970358 •NCT02427581 •NCT02600949 •NCT02721043 •NCT02510950 RNA vaccine •Activation of TLR3, TLR7, TLR8 •No potential for integration into the genome (cf. DNA vaccine)•Manufacture more complex •Subject to RNase degradation, although modi-ﬁcation could potentially extend the half-life•NCT02035956 •NCT02316457 Dendritic cell vaccine •Dendritic cells represent the most impor- tant cell for CD8 T-cell priming •Dendritic cells can be modiﬁed to expressboth neoantigens and costimulatorymolecules•Labor intensive and high cost •Requires ex vivo expansion, maturation, and activation •Short half-life in vivo•NCT00683670 •NCT01885702 DNA vaccine •Capable of delivery of multiple antigens in a single vaccine •Flexible platform allowing molecularengineering •Relatively straightforward manufacturingprocess that is readily scaled for personal-ized intervention•Limited success in humans with ﬁrst genera-tion delivery platforms, success may bedependent on electroporation •Limited potential for integration into thegenome•NCT02348320 •NCT03122106Annals of Oncology Review Volume 28 | Supplement 12 | December 2017 doi:10.1093/annonc/mdx681 | xii15 CD4þT-cell responses to neoantigen vaccines were more com- mon than CD8 þT-cell responses [ 34,35] even when the neoanti- gens included in the vaccines were prioritized based on predictedMHC class I binding, it is clear that neoantigen prediction algo- rithms can be improved. The clinical response to cancer immunotherapies has distinct kinetics compared with the response to cytotoxic or small mole-cule therapies. Cancer immunotherapies are frequently evaluated using immune-related response criteria (irRC) [ 62,63]. Neoantigen vaccines will also leverage these immune-relatedresponse criteria, but will also rely on effective immune monitor- ing to assess vaccine-induced immune responses before clinical end points are reached. Unfortunately, there is still a lack of reli-able immune response biomarkers that are predictive of antitu- mor immunity and, ultimately a survival beneﬁt. Further investigation is needed to identify relevant immune response bio- markers using a systematic approach. From a technical point-of- view, immunological assays currently available (e.g. ELISPOT,ﬂow cytometry-based multimer staining and intracellular cyto- kine staining) have a reputation of inconsistency among different laboratories. For instance, one study found the inter-laboratoryvariation of ELISPOT can be as high as 50% [ 64]. Clearly, stand- ardized and harmonized procedures from specimen banking, assay validation, to result reporting are warranted for successfulclinical development. Concluding remarks Neoantigen vaccines have shown encouraging results in terms ofinducing neoantigen-speciﬁc T-cell responses [ 34,35]. RNA, SLP, dendritic cell and DNA neoantigen vaccines are being rigor- ously tested in phase I clinical trials. NGS technologies and com- putational algorithms demonstrate great promise but still need tobe optimized to most effectively prioritize candidate neoantigens. Given that cancers can escape immune control through various mechanisms, including some that are not fully understood, it willbe important to explore the efﬁcacy of neoantigen vaccines in combination with other immunotherapies including checkpoint blockade therapy, and emerging therapies targeting the immuno-suppressive tumor microenvironment. Acknowledgement We would like to thank Enid McIntosh for administrativeassistance.