Conformational plasticity of RAS Q61 family of neoepitopes results in distinct features for targeted recognition

The conformational landscapes of peptide/human leucocyte antigen (pHLA) protein complexes encompassing tumor neoantigens provide a rationale for target selection towards autologous T cell, vaccine, and antibody-based therapeutic modalities. Here, using complementary biophysical and computational methods, we characterize recurrent RAS55-64 Q61 neoepitopes presented by the common HLA-A*01:01 allotype. We integrate sparse NMR restraints with Rosetta docking to determine the solution structure of NRASQ61K/HLA-A*01:01, which enables modeling of other common RAS55-64 neoepitopes. Hydrogen/deuterium exchange mass spectrometry experiments alongside molecular dynamics simulations reveal differences in solvent accessibility and conformational plasticity across a panel of common Q61 neoepitopes that are relevant for recognition by immunoreceptors. Finally, we predict binding and provide structural models of NRASQ61K antigens spanning the entire HLA allelic landscape, together with in vitro validation for HLA-A*01:191, HLA-B*15:01, and HLA-C*08:02. Our work provides a basis to delineate the solution surface features and immunogenicity of clinically relevant neoepitope/HLA targets for cancer therapy.

Antigen-specificity measurements are the key to understanding T cell responses

Antigen-specific T cells play a central role in the adaptive immune response and come in a wide range of phenotypes. T cell receptors (TCRs) mediate the antigen-specificities found in T cells. Importantly, high-throughput TCR sequencing provides a fingerprint which allows tracking of specific T cells and their clonal expansion in response to particular antigens. As a result, many studies have leveraged TCR sequencing in an attempt to elucidate the role of antigen-specific T cells in various contexts. Here, we discuss the published approaches to studying antigen-specific T cells and their specific TCR repertoire. Further, we discuss how these methods have been applied to study the TCR repertoire in various diseases in order to characterize the antigen-specific T cells involved in the immune control of disease.

iTTCA-MFF: identifying tumor T cell antigens based on multiple feature fusion

Cancer is a terrible disease, recent studies reported that tumor T cell antigens (TTCAs) may play a promising role in cancer treatment. Since experimental methods are still expensive and time-consuming, it is highly desirable to develop automatic computational methods to identify tumor T cell antigens from the huge amount of natural and synthetic peptides. Hence, in this study, a novel computational model called iTTCA-MFF was proposed to identify TTCAs. In order to describe the sequence effectively, the physicochemical (PC) properties of amino acid and residue pairwise energy content matrix (RECM) were firstly employed to encode peptide sequences. Then, two different approaches including covariance and Pearson's correlation coefficient (PCC) were used to collect discriminative information from PC and RECM matrixes. Next, an effective feature selection approach called the least absolute shrinkage and selection operator (LAASO) was adopted to select the optimal features. These selected optimal features were fed into support vector machine (SVM) for identifying TTCAs. We performed experiments on two different datasets, experimental results indicated that the proposed method is promising and may play a complementary role to the existing methods for identifying TTCAs. The datasets and codes can be available at https://figshare.com/articles/online_resource/iTTCA-MFF/17636120 .

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Targeting the DNA damage response in immuno-oncology: developments and opportunities

Immunotherapy has revolutionized cancer treatment and substantially improved patient outcome with regard to multiple tumour types. However, most patients still do not benefit from such therapies, notably because of the absence of pre-existing T cell infiltration. DNA damage response (DDR) deficiency has recently emerged as an important determinant of tumour immunogenicity. A growing body of evidence now supports the concept that DDR-targeted therapies can increase the antitumour immune response by (1) promoting antigenicity through increased mutability and genomic instability, (2) enhancing adjuvanticity through the activation of cytosolic immunity and immunogenic cell death and (3) favouring reactogenicity through the modulation of factors that control the tumour-immune cell synapse. In this Review, we discuss the interplay between the DDR and anticancer immunity and highlight how this dynamic interaction contributes to shaping tumour immunogenicity. We also review the most innovative preclinical approaches that could be used to investigate such effects, including recently developed ex vivo systems. Finally, we highlight the therapeutic opportunities presented by the exploitation of the DDR-anticancer immunity interplay, with a focus on those in early-phase clinical development.

Development of neoantigens: from identification in cancer cells to application in cancer vaccines

Introduction: The discovery of neoantigens as mutated proteins specifically expressed in tumor cells but not in normal cells has led to improved cancer vaccines. Targeting neoantigens can induce anti-tumor T-cell responses to destroy tumors without damaging healthy cells. Extensive advances in genome sequencing technology and bioinformatics analysis have made it possible to discover and design effective neoantigens for use in therapeutic cancer vaccines. Neoantigens-based therapeutic personalized vaccines have shown promising results in cancer immunotherapy.Areas covered: We discuss the types of cancer neoantigens that can be recognized by the immune system in this review. We also summarize the detection, identification, and design of neoantigens and their appliction in developing cancer vaccines. Finally, clinical trials of neoantigen-based vaccines, their advantages, and their limitations are reviewed. From 2015 to 2020, the authors conducted a literature search of controlled randomized trials and laboratory investigations that that focused on neoantigens, their use in the design of various types of cancer vaccines.Expert opinion: Neoantigens are cancer cell-specific antigens, which their expression leads to the immune stimulation against tumor cells. The identification and delivery of specific neoantigens to antigen-presenting cells (APCs) with the help of anti-cancer vaccines promise novel and more effective cancer treatments.

The temporal mutational and immune tumour microenvironment remodelling of HER2-negative primary breast cancers

The biology of breast cancer response to neoadjuvant therapy is underrepresented in the literature and provides a window-of-opportunity to explore the genomic and microenvironment modulation of tumours exposed to therapy. Here, we characterised the mutational, gene expression, pathway enrichment and tumour-infiltrating lymphocytes (TILs) dynamics across different timepoints of 35 HER2-negative primary breast cancer patients receiving neoadjuvant eribulin therapy (SOLTI-1007 NEOERIBULIN-NCT01669252). Whole-exome data (N = 88 samples) generated mutational profiles and candidate neoantigens and were analysed along with RNA-Nanostring 545-gene expression (N = 96 samples) and stromal TILs (N = 105 samples). Tumour mutation burden varied across patients at baseline but not across the sampling timepoints for each patient. Mutational signatures were not always conserved across tumours. There was a trend towards higher odds of response and less hazard to relapse when the percentage of subclonal mutations was low, suggesting that more homogenous tumours might have better responses to neoadjuvant therapy. Few driver mutations (5.1%) generated putative neoantigens. Mutation and neoantigen load were positively correlated (R2 = 0.94, p = <0.001); neoantigen load was weakly correlated with stromal TILs (R2 = 0.16, p = 0.02). An enrichment in pathways linked to immune infiltration and reduced programmed cell death expression were seen after 12 weeks of eribulin in good responders. VEGF was downregulated over time in the good responder group and FABP5, an inductor of epithelial mesenchymal transition (EMT), was upregulated in cases that recurred (p < 0.05). Mutational heterogeneity, subclonal architecture and the improvement of immune microenvironment along with remodelling of hypoxia and EMT may influence the response to neoadjuvant treatment.

Changing the landscape of tumor immunology: novel tools to examine T cell specificity

Immunotherapy has established itself as a stalwart arm in patient care and with precision medicine forms the new paradigm in cancer treatment. T cells are an important group of immune cells capable of potent cancer immune surveillance and immunity. The advent of bioinformatics, particularly more recent advances incorporating algorithms employing machine learning, provide a seemingly limitless ability for T cell analysis and hypothesis generation. Such endeavors have become indispensable to research efforts accelerating and evolving to such an extent that there exists an appreciable gap between knowledge and proof of function and application. Exciting new technologies such as DNA barcoding, cytometry by time-of-flight (CyTOF), and peptide-exchangeable pHLA multimers inclusive of rare and difficult HLA alleles offer high-throughput cell-by-cell analytical capabilities. These outstanding recent contributions to T cell research will help close this gap and potentially bring practical benefit to patients.

Neoantigen prediction and computational perspectives towards clinical benefit: recommendations from the ESMO Precision Medicine Working Group

BACKGROUND

The use of next-generation sequencing technologies has enabled the rapid identification of non-synonymous somatic mutations in cancer cells. Neoantigens are mutated peptides derived from somatic mutations not present in normal tissues that may result in the presentation of tumour-specific peptides capable of eliciting antitumour T-cell responses. Personalised neoantigen-based cancer vaccines and adoptive T-cell therapies have been shown to prime host immunity against tumour cells and are under clinical trial development. However, the optimisation and standardisation of neoantigen identification, as well as its delivery as immunotherapy are needed to increase tumour-specific T-cell responses and, thus, the clinical efficacy of current cancer immunotherapies.

METHODS

In this recommendation article, launched by the European Society for Medical Oncology (ESMO), we outline and discuss the available framework for neoantigen prediction and present a systematic review of the current scientific evidence.

RESULTS

A number of computational pipelines for neoantigen prediction are available. Most of them provide peptide major histocompatibility complex (MHC) binding affinity predictions, but more recent approaches incorporate additional features like variant allele fraction, gene expression, and clonality of mutations. Neoantigens can be predicted in all cancer types with high and low tumour mutation burden, in part by exploiting tumour-specific aberrations derived from mutational frameshifts, splice variants, gene fusions, endogenous retroelements and other tumour-specific processes that could yield more potently immunogenic tumour neoantigens. Ongoing clinical trials will highlight those cancer types and combinations of immune therapies that would derive the most benefit from neoantigen-based immunotherapies.

CONCLUSIONS

Improved identification, selection and prioritisation of tumour-specific neoantigens are needed to increase the scope of benefit from cancer vaccines and adoptive T-cell therapies. Novel pipelines are being developed to resolve the challenges posed by high-throughput sequencing and to predict immunogenic neoantigens.

News on immune checkpoint inhibitors as immunotherapy strategies in adult and pediatric solid tumors

Immune checkpoint inhibitors (ICIs) have shown unprecedented benefits in various adult cancers, and this success has prompted the exploration of ICI therapy even in childhood malignances. Although the use of ICIs as individual agents has achieved disappointing response rates, combinational therapies are likely to promise better results. However, only a subset of patients experienced prolonged clinical effects, thus suggesting the need to identify robust bio-markers that predict individual clinical response or resistance to ICI therapy as the main challenge. In this review, we focus on how the use of ICIs in adult cancers can be translated into pediatric malignances. We discuss the physiological mechanism of action of each IC, including PD-1, PD-L1 and CTLA-4 and the new emerging ones, LAG-3, TIM-3, TIGIT, B7-H3, BTLA and IDO-1, and evaluate their prognostic value in both adult and childhood tumors. Furthermore, we offer an overview of preclinical models and clinical trials currently under investigation to improve the effectiveness of cancer immunotherapies in these patients. Finally, we outline the main predictive factors that influence the efficacy of ICIs, in order to lay the basis for the development of a pan-cancer immunogenomic model, able to direct young patients towards more specific immunotherapy.

Tumor-Infiltrating T Cells From Clear Cell Renal Cell Carcinoma Patients Recognize Neoepitopes Derived From Point and Frameshift Mutations

Mutation-derived neoantigens are important targets for T cell-mediated reactivity toward tumors and, due to their unique tumor expression, an attractive target for immunotherapy. Neoepitope-specific T cells have been detected across a number of solid cancers with high mutational burden tumors, but neoepitopes have been mostly selected from single nucleotide variations (SNVs), and little focus has been given to neoepitopes derived from in-frame and frameshift indels, which might be equally important and potentially highly immunogenic. Clear cell renal cell carcinomas (ccRCCs) are medium-range mutational burden tumors with a high pan-cancer proportion of frameshift mutations. In this study, the mutational landscape of tumors from six RCC patients was analyzed by whole-exome sequencing (WES) of DNA from tumor fragments (TFs), autologous tumor cell lines (TCLs), and tumor-infiltrating lymphocytes (TILs, germline reference). Neopeptides were predicted using MuPeXI, and patient-specific peptide–MHC (pMHC) libraries were created for all neopeptides with a rank score < 2 for binding to the patient's HLAs. T cell recognition toward neoepitopes in TILs was evaluated using the high-throughput technology of DNA barcode-labeled pMHC multimers. The patient-specific libraries consisted of, on average, 258 putative neopeptides (range, 103–397, n = 6). In four patients, WES was performed on two different sources (TF and TCL), whereas in two patients, WES was performed only on TF. Most of the peptides were predicted from both sources. However, a fraction was predicted from one source only. Among the total predicted neopeptides, 16% were derived from frameshift indels. T cell recognition of 52 neoepitopes was detected across all patients (range, 4–18, n = 6) and spanning two to five HLA restrictions per patient. On average, 21% of the recognized neoepitopes were derived from frameshift indels (range, 0–43%, n = 6). Thus, frameshift indels are equally represented in the pool of immunogenic neoepitopes as SNV-derived neoepitopes. This suggests the importance of a broad neopeptide prediction strategy covering multiple sources of tumor material, and including different genetic alterations. This study, for the first time, describes the T cell recognition of frameshift-derived neoepitopes in RCC and determines their immunogenic profile.

APOBEC3B-mediated corruption of the tumor cell immunopeptidome induces heteroclitic neoepitopes for cancer immunotherapy

APOBEC3B, an anti-viral cytidine deaminase which induces DNA mutations, has been implicated as a mediator of cancer evolution and therapeutic resistance. Mutational plasticity also drives generation of neoepitopes, which prime anti-tumor T cells. Here, we show that overexpression of APOBEC3B in tumors increases resistance to chemotherapy, but simultaneously heightens sensitivity to immune checkpoint blockade in a murine model of melanoma. However, in the vaccine setting, APOBEC3B-mediated mutations reproducibly generate heteroclitic neoepitopes in vaccine cells which activate de novo T cell responses. These cross react against parental, unmodified tumors and lead to a high rate of cures in both subcutaneous and intra-cranial tumor models. Heteroclitic Epitope Activated Therapy (HEAT) dispenses with the need to identify patient specific neoepitopes and tumor reactive T cells ex vivo. Thus, actively driving a high mutational load in tumor cell vaccines increases their immunogenicity to drive anti-tumor therapy in combination with immune checkpoint blockade.

Next-generation computational tools for interrogating cancer immunity

The remarkable success of cancer therapies with immune checkpoint blockers is revolutionizing oncology and has sparked intensive basic and translational research into the mechanisms of cancer-immune cell interactions. In parallel, numerous novel cutting-edge technologies for comprehensive molecular and cellular characterization of cancer immunity have been developed, including single-cell sequencing, mass cytometry and multiplexed spatial cellular phenotyping. In order to process, analyse and visualize multidimensional data sets generated by these technologies, computational methods and software tools are required. Here, we review computational tools for interrogating cancer immunity, discuss advantages and limitations of the various methods and provide guidelines to assist in method selection.

Immunotherapy for Cutaneous T-Cell Lymphoma: Current Landscape and Future Developments

Mycosis fungoides (MF) and Sézary syndrome (SS) are chronic, progressive primary cutaneous T-cell lymphomas (CTCLs) for which there are no curative treatments. Skin-directed therapies, such as phototherapy, radiation therapy, or topical nitrogen mustard, provide only short-term remissions. Numerous attempts with different chemotherapeutic regimes failed to achieve meaningful clinical responses. Immunotherapy seems to be a promising avenue to achieve long-term disease control in CTCL. There is compelling evidence indicating that MF and SS are immunogenic lymphomas, which can be recognized by the patient's immune system. However, CTCL uses different strategies to impair host's immunity, eg, via repolarizing the T-cell differentiation from type I to type II, recruiting immunosuppressive regulatory T-cells, and limiting the repertoire of lymphocytes in the circulation. Many currently used therapies, such as interferon-α, imiquimod, extracorporeal phototherapy, and allogeneic bone marrow transplant, seem to exert their therapeutic effect via activation of the antitumor cytotoxic response and reconstitution of the host's immune system. It is likely that novel immunotherapies such as immune checkpoint inhibitors, cancer vaccines, and chimeric antigen receptor-T cells will help to manage CTCL more efficiently. We also discuss how current genomic techniques, such as estimating the mutational load by whole genome sequencing and neoantigen calling, are likely to provide clinically useful information facilitating personalized immunotherapy of CTCL.

Empty peptide-receptive MHC class I molecules for efficient detection of antigen-specific T cells

The peptide-dependent stability of MHC class I molecules poses a substantial challenge for their use in peptide-MHC multimer–based approaches to comprehensively analyze T cell immunity. To overcome this challenge, we demonstrate the use of functionally empty MHC class I molecules stabilized by a disulfide bond to link the α1and α2helices close to the F pocket. Peptide-loaded disulfide-stabilized HLA-A*02:01 shows complete structural overlap with wild-type HLA-A*02:01. Peptide-MHC multimers prepared using disulfide-stabilized HLA-A*02:01, HLA-A*24:02, and H-2Kbcan be used to identify antigen-specific T cells, and they provide a better staining index for antigen-specific T cell detection compared with multimers prepared with wild-type MHC class I molecules. Disulfide-stabilized MHC class I molecules can be loaded with peptide in the multimerized form without affecting their capacity to stain T cells. We demonstrate the value of empty-loadable tetramers that are converted to antigen-specific tetramers by a single-step peptide addition through their use to identify T cells specific for mutation-derived neoantigens and other cancer-associated antigens in human melanoma.

Oncolytic adenovirus drives specific immune response generated by a poly-epitope pDNA vaccine encoding melanoma neoantigens into the tumor site

Background

DNA vaccines against cancer held great promises due to the generation of a specific and long-lasting immune response. However, when used as a single therapy, they are not able to drive the generated immune response into the tumor, because of the immunosuppressive microenvironment, thus limiting their use in humans. To enhance DNA vaccine efficacy, we combined a new poly-epitope DNA vaccine encoding melanoma tumor associated antigens and B16F1-specific neoantigens with an oncolytic virus administered intratumorally.

Methods

Genomic analysis were performed to find specific mutations in B16F1 melanoma cells. The antigen gene sequences were designed according to these mutations prior to the insertion in the plasmid vector. Mice were injected with B16F1 tumor cells (n = 7–9) and therapeutically vaccinated 2, 9 and 16 days after the tumor injection. The virus was administered intratumorally at day 10, 12 and 14. Immune cell infiltration analysis and cytokine production were performed by flow cytometry, PCR and ELISPOT in the tumor site and in the spleen of animals, 17 days after the tumor injection.

Results

The combination of DNA vaccine and oncolytic virus significantly increased the immune activity into the tumor. In particular, the local intratumoral viral therapy increased the NK infiltration, thus increasing the production of different cytokines, chemokines and enzymes involved in the adaptive immune system recruitment and cytotoxic activity. On the other side, the DNA vaccine generated antigen-specific T cells in the spleen, which migrated into the tumor when recalled by the local viral therapy. The complementarity between these strategies explains the dramatic tumor regression observed only in the combination group compared to all the other control groups.

Conclusions

This study explores the immunological mechanism of the combination between an oncolytic adenovirus and a DNA vaccine against melanoma. It demonstrates that the use of a rational combination therapy involving DNA vaccination could overcome its poor immunogenicity. In this way, it will be possible to exploit the great potential of DNA vaccination, thus allowing a larger use in the clinic.

Electronic supplementary material

The online version of this article (10.1186/s40425-019-0644-7) contains supplementary material, which is available to authorized users.

Fibroblasts in cancer: Defining target structures for therapeutic intervention

The functional importance of the tumor stroma for cancer growth and progression is increasingly recognized, but has not resulted in notable therapeutic developments yet. Within the mesenchymal tumor microenvironment, cancer-associated fibroblasts take the center stage and fuel tumor progression in various ways including malignant cell potentiation, immune regulation and fibrosis. However, recent studies have demonstrated pronounced heterogeneity of the fibroblastic tumor stroma, which comprises a plethora of individual cell subsets with varying phenotypes and functions, some of which suppress malignant growth through immune engagement or crosstalk with the tumor vasculature. This article summarizes the various levels at which the fibroblastic tumor stroma may impact cancer progression and highlights potential target structures for future therapeutic intervention(s).

Improved peptide-MHC class II interaction prediction through integration of eluted ligand and peptide affinity data

Major histocompatibility complex (MHC) class II antigen presentation is a key component in eliciting a CD4+ T cell response. Precise prediction of peptide-MHC (pMHC) interactions has thus become a cornerstone in defining epitope candidates for rational vaccine design. Current pMHC prediction tools have, so far, primarily focused on inference from in vitro binding affinity. In the current study, we collate a large set of MHC class II eluted ligands generated by mass spectrometry to guide the prediction of MHC class II antigen presentation. We demonstrate that models developed on eluted ligands outperform those developed on pMHC binding affinity data. The predictive performance can be further enhanced by combining the eluted ligand and pMHC affinity data in a single prediction model. Furthermore, by including ligand data, the peptide length preference of MHC class II can be accurately learned by the prediction model. Finally, we demonstrate that our model significantly outperforms the current state-of-the-art prediction method, NetMHCIIpan, on an external dataset of eluted ligands and appears superior in identifying CD4+ T cell epitopes.

Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides

BACKGROUND

Targeting epitopes derived from neo-antigens (or "neo-epitopes") represents a promising immunotherapy approach with limited off-target effects. However, most peptides predicted using MHC binding prediction algorithms do not induce a CD8 + T cell response, and there is a crucial need to refine the predictions to readily identify the best antigens that could mediate T-cell responses. Such a response requires a high enough number of epitopes bound to the target MHC. This number is correlated with both the peptide-MHC binding affinity and the number of peptides reaching the ER. Beyond this, the response may be affected by the properties of the neo-epitope mutated residues.

METHODS

Herein, we analyzed several experimental datasets from cancer patients to elaborate better predictive algorithms for T-cell reactivity to neo-epitopes.

RESULTS

Indeed, potent classifiers for epitopes derived from neo-antigens in melanoma and other tumors can be developed based on biochemical properties of the mutated residue, the antigen expression level and the peptide processing stage. Among MHC binding peptides, the present classifiers can remove half of the peptides falsely predicted to activate T cells while maintaining the absolute majority of reactive peptides.

CONCLUSIONS

The classifier properties further highlight the contribution of the quantity of peptides reaching the ER and the mutation type to CD8 + T cell responses. These classifiers were then validated on neo-antigens obtained from other datasets, confirming the validity of our prediction. Algorithm Availability: http://peptibase.cs.biu.ac.il/Tcell_predictor/ or by request from the authors as a standalone code.

The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy

Checkpoint inhibitor-based immunotherapies that target cytotoxic T lymphocyte antigen 4 (CTLA4) or the programmed cell death 1 (PD1) pathway have achieved impressive success in the treatment of different cancer types. Yet, only a subset of patients derive clinical benefit. It is thus critical to understand the determinants driving response, resistance and adverse effects. In this Review, we discuss recent work demonstrating that immune checkpoint inhibitor efficacy is affected by a combination of factors involving tumour genomics, host germline genetics, PD1 ligand 1 (PDL1) levels and other features of the tumour microenvironment, as well as the gut microbiome. We focus on recently identified molecular and cellular determinants of response. A better understanding of how these variables cooperate to affect tumour-host interactions is needed to optimize the implementation of precision immunotherapy.

Significant differences in T cell receptor repertoires in lung adenocarcinomas with and without epidermal growth factor receptor mutations

Recent clinical trials of non-small cell lung cancer with immune checkpoint inhibitors revealed that patients with epidermal growth factor receptor (EGFR) mutations had more unfavorable outcomes compared with those with wild-type EGFR. However, the underlying mechanism for the link between EGFR mutations and immune resistance remains unclear. We performed T cell receptor (TCR) repertoire analysis of resected lung adenocarcinoma tissues with and without EGFR mutations to investigate the characteristics of TCR repertoires. We collected a total of 39 paired (normal and tumor) lung tissue samples (20 had EGFR mutations) and conducted TCR repertoire analysis as well as whole-exome sequencing (WES) and transcriptome analysis. The TCR diversity index in EGFR-mutant tumors was significantly higher than that in EGFR-wild-type tumors (median [range] 552 [162-1,135] vs 230 [30-764]; P < .01), suggesting higher T cell clonal expansion in EGFR-wild-type tumors than in EGFR-mutant tumors. In WES, EGFR-mutant tumors showed lower numbers of non-synonymous mutations and predicted neoantigens than EGFR-wild-type tumors (P < .01, P = .03, respectively). The number of non-synonymous mutations revealed a positive correlation with the sum of frequencies of the TCRβ clonotypes of 1% or higher in tumors (r = .52, P = .04). The present study demonstrates significant differences in TCR repertoires and the number of predicted neoantigens between EGFR-mutant and wild-type lung tumors. Our findings provide important information for understanding the molecular mechanism behind EGFR-mutant patients showing unfavorable responses to immune checkpoint inhibitors.

Combination of checkpoint inhibitors with other agents as a strategy to improve anti-cancer effect - a glimpse to the future

In the last years, a remarkable progress has been made in the clinical application of novel immunotherapy agents, the so called 'checkpoint inhibitors,' that has revolutionized the treatment of many malignant tumors. Their design has been based on the immune-mediated mechanisms of antitumor activity circle, such as antigen release and presentation, activation and trafficking of T-cells into tumors, depletion of immunosuppression, and immunogenic cell death. Various combinations of checkpoint inhibitors are being designed and/or tested, such as double checkpoint blockade, combination with chemotherapy, radiotherapy, molecularly targeted agents, and other immune-directed strategies.

The function and dysfunction of memory CD8+ T cells in tumor immunity

The generation and maintenance of CD8+ T cell memory is crucial to long-term host survival, yet the basic tenets of CD8+ T cell immunity are still being established. Recent work has led to the discovery of tissue-resident memory cells and refined our understanding of the transcriptional and epigenetic basis of CD8+ T cell differentiation and dysregulation. In parallel, the unprecedented clinical success of immunotherapy has galvanized an intense, global research effort to decipher and de-repress the anti-tumor response. However, the progress of immunotherapy is at a critical juncture, since the efficacy of immuno-oncology agents remains confined to a fraction of patients and often fails to provide durable benefit. Unlocking the potential of immunotherapy requires the design of strategies that both induce a potent effector response and reliably forge stable, functional memory T cell pools capable of protecting from recurrence or relapse. It is therefore essential that basic and emerging concepts of memory T cell biology are rapidly and faithfully transposed to advance therapeutic development in cancer immunotherapy. This review highlights seminal and recent reports in CD8+ T cell memory and tumor immunology, and evaluates recent data from solid cancer specimens in the context of the key paradigms from preclinical models. We elucidate the potential significance of circulating effector cells poised downstream of neoantigen recognition and upstream of T cell dysfunction and propose that cells in this immunological 'sweet spot' may be key anti-tumor effectors.

Present status and future perspective of peptide-based vaccine therapy for urological cancer

Use of peptide‐based vaccines as therapeutics aims to elicit immune responses through antigenic epitopes derived from tumor antigens. Peptide‐based vaccines are easily synthesized and lack significant side‐effects when given in vivo. Peptide‐based vaccine therapy against several cancers including urological cancers has made progress for several decades, but there is no worldwide approved peptide vaccine. Peptide vaccines were also shown to induce a high frequency of immune response in patients accompanied by clinical efficacy. These data are discussed in light of the recent progression of immunotherapy caused by the addition of immune checkpoint inhibitors thus providing a general picture of the potential therapeutic efficacy of peptide‐based vaccines and their combination with other biological agents. In this review, we discuss the mechanism of the antitumor effect of peptide‐based vaccine therapy, development of our peptide vaccine, recent clinical trials using peptide vaccines for urological cancers, and perspectives of peptide‐based vaccine therapy.