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In this work, we proposed nµPEF, a novel pulse configuration combining nanosecond and microsecond pulses (nµPEF), to enhance tumor ablation in irreversible electroporation (IRE) for oncological therapy. nµPEF demonstrated improved efficacy in inducing immunogenic cell death, positioning it as a potential candidate for next-generation ablative therapy. However, the immune response elicited by nµPEF alone was insufficient to effectively suppress distant tumors. To address this limitation, we developed PPR@CM-PD1, a genetically engineered nanovesicle. PPR@CM-PD1 employed a polyethylene glycol-polylactic acid-glycolic acid (PEG-PLGA) nanoparticle encapsulating the immune adjuvant imiquimod and coated with a genetically engineered cell membrane expressing programmed cell death protein 1 (PD1). This design allowed PPR@CM-PD1 to target both the innate immune system through toll-like receptor 7 (TLR7) agonism and the adaptive immune system through programmed cell death protein 1/programmed cell death-ligand 1 (PD1/PDL1) checkpoint blockade. In turn, nµPEF facilitated intratumoral infiltration of PPR@CM-PD1 by modulating the tumor stroma. The combination of nµPEF and PPR@CM-PD1 generated a potent and systemic antitumor immune response, resulting in remarkable suppression of both nµPEF-treated and untreated distant tumors (abscopal effects). This interdisciplinary approach presents a promising perspective for oncotherapy and holds great potential for future clinical applications., (© 2023 Wiley-VCH GmbH.)

The transport of peptides from the cytoplasm to the endoplasmic reticulum (ER) by transporters associated with antigen processing (TAP) is a critical step in the intracellular presentation of cytotoxic T lymphocyte (CTL) epitopes. The development and application of computational methods, especially deep learning methods and new neural network strategies that can automatically learn feature representations with limited knowledge, provide an opportunity to develop fast and efficient methods to identify TAP-binding peptides. Herein, this study presents a comprehensive analysis of TAP-binding peptide sequences to derive TAP-binding motifs and preferences for N-terminal and C-terminal amino acids. A novel recurrent neural network (RNN)-based method called DeepTAP, using bidirectional gated recurrent unit (BiGRU), was developed for the accurate prediction of TAP-binding peptides. Our results demonstrated that DeepTAP achieves an optimal balance between prediction precision and false positives, outperforming other baseline models. Furthermore, DeepTAP significantly improves the prediction accuracy of high-confidence neoantigens, especially the top-ranked ones, making it a valuable tool for researchers studying antigen presentation processes and T-cell epitope screening. DeepTAP is freely available at https://github.com/zjupgx/deeptap and https://pgx.zju.edu.cn/deeptap., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Fig. 2 was created with BioRender.com., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

In recent years, neoantigens have been recognized as ideal targets for tumor immunotherapy. With the development of neoantigen-based tumor immunotherapy, comprehensive neoantigen databases are urgently needed to meet the growing demand for clinical studies. We have built the tumor-specific neoantigen database (TSNAdb) previously, which has attracted much attention. In this study, we provide TSNAdb v2.0, an updated version of the TSNAdb. TSNAdb v2.0 offers several new features, including (1) adopting more stringent criteria for neoantigen identification, (2) providing predicted neoantigens derived from three types of somatic mutations, and (3) collecting experimentally validated neoantigens and dividing them according to the experimental level. TSNAdb v2.0 is freely available at https://pgx.zju.edu.cn/tsnadb/., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Tumor-specific neoantigens play important roles in tumor immunotherapy. How to predict neoantigens accurately and efficiently has attracted much attention. TSNAD is the first one-stop neoantigen prediction tool from next-generation sequencing data, and TSNAdb provides both predicted and validated neoantigens based on pan-cancer immunogenomics analyses. In this chapter, we describe the usage of TSNAD and TSNAdb for the clinical application of neoantigens. The latest version of TSNAD is available at https://pgx.zju.edu.cn/tsnad , and the latest version of TSNAdb is available at https://pgx.zju.edu.cn/tsnadb ., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Current cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored. Due to the over-excess mutations unrelated to cancer, the great challenge is to identify somatic mutations that are cancer-driven. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model: while the ground component corresponds to passenger mutations, the rapidly evolving component corresponds to driver mutations. Then, we implemented an empirical Bayesian procedure to calculate the posterior probability of a site being cancer-driven. Based on these, we developed a software CanDriS (Cancer Driver Sites) to profile the potential cancer-driving sites for thousands of tumor samples from the Cancer Genome Atlas and International Cancer Genome Consortium across tumor types and pan-cancer level. As a result, we identified that approximately 1% of the sites have posterior probabilities larger than 0.90 and listed potential cancer-wide and cancer-specific driver mutations. By comprehensively profiling all potential cancer-driving sites, CanDriS greatly enhances our ability to refine our knowledge of the genetic basis of cancer and might guide clinical medication in the upcoming era of precision medicine. The results were displayed in a database CandrisDB (http://biopharm.zju.edu.cn/candrisdb/)., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Neoantigen, as an important member of tumor-specific antigens, has attracted a great deal of attention as a target for immunotherapy. Neoantigens are potential targets for personalized vaccines and adoptive cell transfer therapies. However, most of the neoantigen-targeted immunotherapies in the process are customized and costly. So, we are inclined to find shared neoantigens suitable for more patients. With the help of existing neoantigen prediction algorithms, we found that the most frequent shared neoantigens occurred in more than 1% of patients for 17 tumor types and the ten most frequent shared neoantigens covered approximately 50% of pancreatic cancer patients, providing a potential list of targets for off-the-shelf immunotherapy.

Tumor-specific neoantigens have attracted much attention since they can be used as biomarkers to predict therapeutic effects of immune checkpoint blockade therapy and as potential targets for cancer immunotherapy. In this study, we developed a comprehensive tumor-specific neoantigen database (TSNAdb v1.0), based on pan-cancer immunogenomic analyses of somatic mutation data and human leukocyte antigen (HLA) allele information for 16 tumor types with 7748 tumor samples from The Cancer Genome Atlas (TCGA) and The Cancer Immunome Atlas (TCIA). We predicted binding affinities between mutant/wild-type peptides and HLA class I molecules by NetMHCpan v2.8/v4.0, and presented detailed information of 3,707,562/1,146,961 potential neoantigens generated by somatic mutations of all tumor samples. Moreover, we employed recurrent mutations in combination with highly frequent HLA alleles to predict potential shared neoantigens across tumor patients, which would facilitate the discovery of putative targets for neoantigen-based cancer immunotherapy. TSNAdb is freely available at http://biopharm.zju.edu.cn/tsnadb., (Copyright © 2018 The Authors. Production and hosting by Elsevier B.V. All rights reserved.)