Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors

Biomarkers to predict the benefits of immune‑checkpoint blockade‑based therapy in patients with malignant peritoneal mesothelioma (Review)

Malignant peritoneal mesothelioma (MPeM) is a type of rare and highly lethal tumor. Immune checkpoint blockade (ICB)-based therapy has shown encouraging clinical activity for MPeM. However, no definitive biomarkers have been identified for predicting which patients with MPeM will benefit from ICB-based therapy. At present, there are several novel potential biomarkers proposed for predicting the response to ICB-based therapy, and biomarkers available in MPeM cells and in the tumor microenvironment have been identified with the potential to predict the efficacy of ICB-based therapy in MPeM. According to the molecular characteristics of MPeM itself, the feasibility of biomarkers in practice, and the body of available evidence, we hypothesize that the following five types of biomarkers can be used to predict the response of ICB-based therapy in patients with MPeM: Tertiary lymphoid structures, immune checkpoints and their ligands, fusion gene neoantigen burden, BRCA1-associated protein-1 haploinsufficiency and transcriptome-based biomarkers. The present review discusses the value and limitations of each type of biomarker, and potential solutions to address the limitations are proposed. The aim of the present review is to provide a background for future studies on ICB-based therapy for MPeM.

Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens

Recent breakthroughs in cancer immunotherapies have emphasized the importance of harnessing the immune system for treating cancer. Vaccines, which have traditionally been used to promote protective immunity against pathogens, are now being explored as a method to target cancer neoantigens. Over the past few years, extensive preclinical research and more than a hundred clinical trials have been dedicated to investigating various approaches to neoantigen discovery and vaccine formulations, encouraging development of personalized medicine. Nucleic acids (DNA and mRNA) have become particularly promising platform for the development of these cancer immunotherapies. This shift towards nucleic acid-based personalized vaccines has been facilitated by advancements in molecular techniques for identifying neoantigens, antigen prediction methodologies, and the development of new vaccine platforms. Generating these personalized vaccines involves a comprehensive pipeline that includes sequencing of patient tumor samples, data analysis for antigen prediction, and tailored vaccine manufacturing. In this review, we will discuss the various shared and personalized antigens used for cancer vaccine development and introduce strategies for identifying neoantigens through the characterization of gene mutation, transcription, translation and post translational modifications associated with oncogenesis. In addition, we will focus on the most up-to-date nucleic acid vaccine platforms, discuss the limitations of cancer vaccines as well as provide potential solutions, and raise key clinical and technical considerations in vaccine development.

BCL2A1 neoepitope-elicited cytotoxic T lymphocytes are a promising individualized immunotherapy of pancreatic cancer

Conventional treatments have shown a limited efficacy for pancreatic cancer, and immunotherapy is an emerging option for treatment of this highly fatal malignancy. Neoantigen is critical to improving the efficacy of tumor-specific immunotherapy. The cancer and peripheral blood specimens from an HLA-A0201-positive pancreatic cancer patient were subjected to next-generation sequencing, and bioinformatics analyses were performed to screen high-affinity and highly stable neoepitopes. The activation of cytotoxic T lymphocytes (CTLs) by dendritic cells (DCs) loaded with mutBCL2A111-20 neoepitope targeting a BCL2A1 mutant epitope was investigated, and the cytotoxicity of mutBCL2A111-20 neoepitope-specific CTLs to pancreatic cancer cells was evaluated. The mutBCL2A111-20 neoepitope was found to present a high immunogenicity and induce CTLs activation and proliferation, and these CTLs were cytotoxic to mutBCL2A111-20 neoepitope-loaded T2 cells and pancreatic cancer PANC-1-Neo and A2-BxPC-3-Neo cells that overexpressed mutBCL2A111-20 neoepitopes, appearing to be a targeting neoepitope specificity. In addition, high BCL2A1 expression correlated with a low 5-yr progression-free interval among pancreatic cancer patients. Our findings provide experimental supports to individualized T cell therapy targeting mutBCL2A111-20 neoepitopes, and provide an option of immunotherapy for pancreatic cancer.

A Subset of Microsatellite Unstable Cancer Genomes Prone to Short Insertions over Deletions Is Associated with Elevated Anticancer Immunity

Deficiencies in DNA mismatch repair (MMRd) leave characteristic footprints of microsatellite instability (MSI) in cancer genomes. We used data from the Cancer Genome Atlas and International Cancer Genome Consortium to conduct a comprehensive analysis of MSI-associated cancers, focusing on indel mutational signatures. We classified MSI-high genomes into two subtypes based on their indel profiles: deletion-dominant (MMRd-del) and insertion-dominant (MMRd-ins). Compared with MMRd-del genomes, MMRd-ins genomes exhibit distinct mutational and transcriptomic features, including a higher prevalence of T>C substitutions and related mutation signatures. Short insertions and deletions in MMRd-ins and MMRd-del genomes target different sets of genes, resulting in distinct indel profiles between the two subtypes. In addition, indels in the MMRd-ins genomes are enriched with subclonal alterations that provide clues about a distinct evolutionary relationship between the MMRd-ins and MMRd-del genomes. Notably, the transcriptome analysis indicated that MMRd-ins cancers upregulate immune-related genes, show a high level of immune cell infiltration, and display an elevated neoantigen burden. The genomic and transcriptomic distinctions between the two types of MMRd genomes highlight the heterogeneity of genetic mechanisms and resulting genomic footprints and transcriptomic changes in cancers, which has potential clinical implications.

Global research landscape and trends of cancer radiotherapy plus immunotherapy: A bibliometric analysis

The aim of this study was to present current research trends on the synergistic use of radiotherapy and immunotherapy (IRT) for cancer treatment. On March 1, 2023, we conducted a literature search for IRT papers using the Web of Science database. We extracted information and constructed two databases - the Core Database (CD) with 864 papers and Generalized Database (GD) with 6344 papers. A bibliometric analysis was performed to provide insights into the research landscape, to identify emerging trends and highly cited papers and journals in the field of IRT. The CD contained 864 papers that were collectively cited 31,818 times. Prominent journals in this area included the New England Journal of Medicine, Lancet Oncology, and the Journal of Clinical Oncology. Corresponding authors from the USA contributed the most publications. In recent years, lung cancer, melanoma, stereotactic radiotherapy, immune checkpoint inhibitors, and the tumor microenvironment emerged as hot research areas. This bibliometric analysis presented quantitative insights into research concerning IRT and proposed potential avenues for further exploration. Moreover, researchers can use our findings to select appropriate journals for publication or identify prospective collaborators. In summary, this bibliometric analysis provides a comprehensive overview of the historical progression and recent advancements in IRT research that may serve as inspiration for future investigations.

Enhancing the quality of panel-based tumor mutation burden assessment: a comprehensive study of real-world and in-silico outcomes

Targeted panel-based tumor mutation burden (TMB) assays are widely employed to guide immunotherapy for patients with solid tumors. However, the accuracy and consistency of this method can be compromised due to the variability in technical details across different laboratories, particularly in terms of panel size, somatic mutation detection and TMB calculation rules. Currently, systematic evaluations of the impact of these technical factors on existing assays and best practice recommendations remain lacking. We assessed the performance of 50 participating panel-based TMB assays involving 38 unique methods using cell line samples. In silico experiments utilizing TCGA MC3 datasets were performed to further dissect the impact of technical factors. Here we show that the panel sizes beyond 1.04 Mb and 389 genes are necessary for the basic discrete accuracy, as determined by over 40,000 synthetic panels. The somatic mutation detection should maintain a reciprocal gap of recall and precision less than 0.179 for reliable psTMB calculation results. The inclusion of synonymous, nonsense and hotspot mutations could enhance the accuracy of panel-based TMB assay. A 5% variant allele frequency cut-off is suitable for TMB assays using tumor samples with at least 20% tumor purity. In conclusion, this multicenter study elucidates the major technical factors as sources of variability in panel-based TMB assays and proposed comprehensive recommendations for the enhancement of accuracy and consistency. These findings will assist clinical laboratories in optimizing the methodological details through bioinformatic experiments to enhance the reliability of panel-based methods.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.