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Cytotoxic T lymphocyte (CTL)-based cancer immunotherapies have shown great promise for inducing clinical regressions by targeting tumor-associated antigens (TAA). To expand the TAA landscape of pancreatic ductal adenocarcinoma (PDAC), we performed tandem mass spectrometry analysis of HLA class I-bound peptides from 35 PDAC patient tumors. This identified a shared HLA-A*0101 restricted peptide derived from co-transcriptional activator Vestigial-like 1 (VGLL1) as a putative TAA demonstrating overexpression in multiple tumor types and low or absent expression in essential normal tissues. Here we show that VGLL1-specific CTLs expanded from the blood of a PDAC patient could recognize and kill in an antigen-specific manner a majority of HLA-A*0101 allogeneic tumor cell lines derived not only from PDAC, but also bladder, ovarian, gastric, lung, and basal-like breast cancers. Gene expression profiling reveals VGLL1 as a member of a unique group of cancer-placenta antigens (CPA) that may constitute immunotherapeutic targets for patients with multiple cancer types. Cytotoxic T lymphocyte (CTL)-based immunotherapies can induce tumor regressions by targeting HLA class I-bound tumor-associated peptides. Here, the authors identified a peptide derived from Vestigial-like 1 (VGLL1) as a shared, potentially therapeutic CTL target expressed by multiple cancer types.

Cancer cells develop under immune surveillance, thus necessitating immune escape for successful growth. Loss of MHC class I expression provides a key immune evasion strategy in many cancers, although the molecular mechanisms remain elusive. MHC class I transactivator (CITA), known as “NLRC5” [NOD-like receptor (NLR) family, caspase recruitment (CARD) domain containing 5], has recently been identified as a critical transcriptional coactivator of MHC class I gene expression. Here we show that the MHC class I transactivation pathway mediated by CITA/NLRC5 constitutes a target for cancer immune evasion. In all the 21 tumor types we examined, NLRC5 expression was highly correlated with the expression of MHC class I, with cytotoxic T-cell markers, and with genes in the MHC class I antigen-presentation pathway, including LMP2/LMP7, TAP1, and β2-microglobulin. Epigenetic and genetic alterations in cancers, including promoter methylation, copy number loss, and somatic mutations, were most prevalent in NLRC5 among all MHC class I-related genes and were associated with the impaired expression of components of the MHC class I pathway. Strikingly, NLRC5 expression was significantly associated with the activation of CD8⁺ cytotoxic T cells and patient survival in multiple cancer types. Thus, NLRC5 constitutes a novel prognostic biomarker and potential therapeutic target of cancers.

The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC complexes. Several attempts have been made to use molecular docking for such analyses, but pMHC structure remains too challenging for even state-of-the-art docking tools. To overcome these limitations, we describe the use of an incremental meta-docking approach for structural prediction of pMHC complexes. Previous methods applied in this context used specific constraints to reduce the complexity of this prediction problem, at the expense of generality. Our strategy makes no assumption and can potentially be used to predict binding modes for any pMHC complex. Our method has been tested in a re-docking experiment, reproducing the binding modes of 25 pMHC complexes whose crystal structures are available. This study is a proof of concept that incremental docking strategies can lead to general geometry prediction of pMHC complexes, with potential applications for immunotherapy against cancer or infectious diseases.

Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide–MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide–MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide–MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.

The cytoplasmic domain of MHC class I (MHC-I) molecules contains a single, highly conserved tyrosine residue (Y320). In previous work, we found that mice expressing a Y320F-mutated form of H-2K b had reduced capacity to generate K b -restricted cytotoxic T lymphocyte (CTL) responses following viral infection, due at least in part to defects in endolysosomal trafficking of H-2K b and antigen cross-presentation by dendritic cells (DCs). In this study, we investigated whether there are additional, post-presentation dependencies on Y320 for T cell priming. We engineered both human- and mouse-derived antigen-presenting cells (APCs) to express either wild-type MHC-I or variants of MHC-I containing Y320F or Y320E mutations. We found that Y320E-mutated HLA-A*0201 elicited enhanced in vitro priming and expansion of human antigen-specific CD8+ T cells, which showed a unique transcriptional profile compared to T cells primed with APCs expressing either WT or Y320F-mutated A*0201. Furthermore, the Y320E variant of H-2K b expressed in the context of a murine DC vaccine model induced altered T cell differentiation kinetics while improving both anti-tumor immunity and augmenting the magnitude of memory CD8+ T cell responses in vivo . These results suggest that Y320 phosphorylation of MHC-I may play a role in determining the fate and function of CD8+ T cells and suggest a novel strategy for improving DC-based cancer immunotherapies.

Checkpoint blockade-mediated immunotherapy is emerging as an effective treatment modality for multiple cancer types. However, cancer cells frequently evade the immune system, compromising the effectiveness of immunotherapy. It is crucial to develop screening methods to identify the patients who would most benefit from these therapies because of the risk of the side effects and the high cost of treatment. Here we show that expression of the MHC class I transactivator ( CITA ), NLRC5 , is important for efficient responses to anti-CTLA-4 and anti-PD1 checkpoint blockade therapies. Melanoma tumors derived from patients responding to immunotherapy exhibited significantly higher expression of NLRC5 and MHC class I-related genes compared to non-responding patients. In addition, multivariate analysis that included the number of tumor-associated non-synonymous mutations, predicted neo-antigen load and PD-L2 expression was capable of further stratifying responders and non-responders to anti-CTLA4 therapy. Moreover, expression or methylation of NLRC5 together with total somatic mutation number were significantly correlated with increased patient survival. These results suggest that NLRC5 tumor expression, alone or together with tumor mutation load constitutes a valuable predictive biomarker for both prognosis and response to anti-CTLA-4 and potentially anti-PD1 blockade immunotherapy in melanoma patients.

Immunotherapy has become one of the most promising avenues for cancer treatment, making use of the patient's own immune system to eliminate cancer cells. Clinical trials with T-cell-based immunotherapies have shown dramatic tumor regressions, being effective in multiple cancer types and for many different patients. Unfortunately, this progress was tempered by reports of serious (even fatal) side effects. Such therapies rely on the use of cytotoxic T-cell lymphocytes, an essential part of the adaptive immune system. Cytotoxic T-cells are regularly involved in surveillance and are capable of both eliminating diseased cells and generating protective immunological memory. The specificity of a given T-cell is determined through the structural interaction between the T-cell receptor (TCR) and a peptide-loaded major histocompatibility complex (MHC); i.e., an intracellular peptide-ligand displayed at the cell surface by an MHC molecule. However, a given TCR can recognize different peptide-MHC (pMHC) complexes, which can sometimes trigger an unwanted response that is referred to as T-cell cross-reactivity. This has become a major safety issue in TCR-based immunotherapies, following reports of melanoma-specific T-cells causing cytotoxic damage to healthy tissues (e.g., heart and nervous system). T-cell cross-reactivity has been extensively studied in the context of viral immunology and tissue transplantation. Growing evidence suggests that it is largely driven by structural similarities of seemingly unrelated pMHC complexes. Here, we review recent reports about the existence of pMHC \"hot-spots\" for cross-reactivity and propose the existence of a TCR interaction profile (i.e., a refinement of a more general TCR footprint in which some amino acid residues are more important than others in triggering T-cell cross-reactivity). We also make use of available structural data and pMHC models to interpret previously reported cross-reactivity patterns among virus-derived peptides. Our study provides further evidence that structural analyses of pMHC complexes can be used to assess the intrinsic likelihood of cross-reactivity among peptide-targets. Furthermore, we hypothesize that some apparent inconsistencies in reported cross-reactivities, such as a preferential directionality, might also be driven by particular structural features of the targeted pMHC complex. Finally, we explain why TCR-based immunotherapy provides a special context in which meaningful T-cell cross-reactivity predictions can be made.

Peptide-HLA class I (pHLA) complexes on the surface of tumor cells can be targeted by cytotoxic T-cells to eliminate tumors, and this is one of the bases for T-cell-based immunotherapies. However, there exist cases where therapeutic T-cells directed towards tumor pHLA complexes may also recognize pHLAs from healthy normal cells. The process where the same T-cell clone recognizes more than one pHLA is referred to as T-cell cross-reactivity and this process is driven mainly by features that make pHLAs similar to each other. T-cell cross-reactivity prediction is critical for designing T-cell-based cancer immunotherapies that are both effective and safe. Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs. We show our method can accurately separate cross-reactive from non-crossreactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. PepSim can be generalized to work on any dataset of class I peptide-HLAs and is freely available as a web server at pepsim.kavrakilab.org.

The epidermal growth factor receptor (EGFR) plays crucial roles in several important biological functions such as embryogenesis, epithelial tissue development, and cellular regeneration. However, in multiple solid tumor types overexpression and/or activating mutations of the EGFR gene frequently occur, thus hijacking the EGFR signaling pathway to promote tumorigenesis. Non-small cell lung cancer (NSCLC) tumors in particular often contain prevalent and shared EGFR mutations that provide an ideal source for public neoantigens (NeoAg). Studies in both humans and animal models have confirmed the immunogenicity of some of these NeoAg peptides, suggesting that they may constitute viable targets for cancer immunotherapies. Peptide vaccines targeting mutated EGFR have been tested in multiple clinical trials, demonstrating an excellent safety profile and encouraging clinical efficacy. For example, the CDX-110 (rindopepimut) NeoAg peptide vaccine derived from the EGFRvIII deletion mutant in combination with temozolomide and radiotherapy has shown efficacy in treating EGFRvIII-harboring glioblastoma multiforme (GBM) patients undergone surgery in multiple Phase I and II clinical trials. Furthermore, pilot clinical trials that have administered personalized NeoAg peptides for treating advanced-stage NSCLC patients have shown this approach to be a feasible and safe method to increase antitumor immune responses. Amongst the vaccine peptides administered, EGFR mutation-targeting NeoAgs induced the strongest T cell-mediated immune responses in patients and were also associated with objective clinical responses, implying a promising future for NeoAg peptide vaccines for treating NSCLC patients with selected EGFR mutations. The efficacy of NeoAg-targeting peptide vaccines may be further improved by combining with other modalities such as tyrosine kinase or immune checkpoint inhibitor (ICI) therapy, which are currently being tested in animal models and clinical trials. Herein, we review the most current basic and clinical research progress on EGFR-targeted peptide vaccination for the treatment of NSCLC and other solid tumor types.

Background Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use five datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies. Results Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneficial. Instead, it is more efficient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and efficiency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional benefits of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered. Conclusions Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking.