Identification and validation of expressed HLA-binding breast cancer neoepitopes for potential use in individualized cancer therapy

Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies

Therapeutic cancer vaccines have been considered in recent decades as important immunotherapeutic strategies capable of leading to tumor regression. In the development of these vaccines, the identification of neoepitopes plays a critical role, and different computational methods have been proposed and employed to direct and accelerate this process. In this context, this review identified and systematically analyzed the most recent studies published in the literature on the computational prediction of epitopes for the development of therapeutic vaccines, outlining critical steps, along with the associated program's strengths and limitations. A scoping review was conducted following the PRISMA extension (PRISMA-ScR). Searches were performed in databases (Scopus, PubMed, Web of Science, Science Direct) using the keywords: neoepitope, epitope, vaccine, prediction, algorithm, cancer, and tumor. Forty-nine articles published from 2012 to 2024 were synthesized and analyzed. Most of the identified studies focus on the prediction of epitopes with an affinity for MHC I molecules in solid tumors, such as lung carcinoma. Predicting epitopes with class II MHC affinity has been relatively underexplored. Besides neoepitope prediction from high-throughput sequencing data, additional steps were identified, such as the prioritization of neoepitopes and validation. Mutect2 is the most used tool for variant calling, while NetMHCpan is favored for neoepitope prediction. Artificial/convolutional neural networks are the preferred methods for neoepitope prediction. For prioritizing immunogenic epitopes, the random forest algorithm is the most used for classification. The performance values related to the computational models for the prediction and prioritization of neoepitopes are high; however, a large part of the studies still use microbiome databases for training. The in vitro/in vivo validations of the predicted neoepitopes were verified in 55% of the analyzed studies. Clinical trials that led to successful tumor remission were identified, highlighting that this immunotherapeutic approach can benefit these patients. Integrating high-throughput sequencing, sophisticated bioinformatics tools, and rigorous validation methods through in vitro/in vivo assays as well as clinical trials, the tumor neoepitope-based vaccine approach holds promise for developing personalized therapeutic vaccines that target specific tumor cancers.

Tumor-Derived Antigenic Peptides as Potential Cancer Vaccines

Peptide antigens derived from tumors have been observed to elicit protective immune responses, categorized as either tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs). Subunit cancer vaccines incorporating these antigens have shown promise in inducing protective immune responses, leading to cancer prevention or eradication. Over recent years, peptide-based cancer vaccines have gained popularity as a treatment modality and are often combined with other forms of cancer therapy. Several clinical trials have explored the safety and efficacy of peptide-based cancer vaccines, with promising outcomes. Advancements in techniques such as whole-exome sequencing, next-generation sequencing, and in silico methods have facilitated the identification of antigens, making it increasingly feasible. Furthermore, the development of novel delivery methods and a deeper understanding of tumor immune evasion mechanisms have heightened the interest in these vaccines among researchers. This article provides an overview of novel insights regarding advancements in the field of peptide-based vaccines as a promising therapeutic avenue for cancer treatment. It summarizes existing computational methods for tumor neoantigen prediction, ongoing clinical trials involving peptide-based cancer vaccines, and recent studies on human vaccination experiments.

A prognostic model related to necrotizing apoptosis of breast cancer based on biorthogonal constrained depth semi-supervised nonnegative matrix decomposition and single-cell sequencing analysis

Breast cancer (BC) is one of the most common malignant tumours in women, and its prognosis is poor. The prognosis of BC patients can be improved by immunotherapy. However, due to the heterogeneity of BC, the identification of new biomarkers is urgently needed to improve the prognosis of BC patients. Necrotic apoptosis has been shown to play an essential role in many cancers. First, this study proposed a novel clustering algorithm called biorthogonal constrained depth semisupervised nonnegative matrix factorization (DO-DSNMF). The DO-DSNMF algorithm added multilayer nonlinear transformation to the coefficient matrix obtained after decomposition, which was used to mine the nonlinear relationship between samples. In addition, we also added orthogonal constraints on the basis matrix and coefficient matrix to reduce the influence of redundant features and samples on the results. We applied the DO-DSNMF algorithm and analysed the differences in survival and immunity between the subtypes. Then, we used prognosis analysis to construct the prognosis model. Finally, we analysed single cells using single-cell sequencing (scRNA-seq) data from the GSE75688 dataset in the GEO database. We identified two BC subtypes based on the BC transcriptome data in the TCGA database. Immune infiltration analysis showed that the necrotizing apoptosis-related genes of BC were related to various immune cells and immune functions. Necrotizing apoptosis was found to play a role in BC progression and immunity. The role of prognosis-related NRGs in BC was also verified by cell experiments. This study proposed a novel clustering algorithm to analyse BC subtypes and constructed an NRG prognostic model for BC. The prognosis and immune landscape of BC patients were evaluated by this model. The cell experiment supported its role in BC, which provides a potential therapeutic target for the treatment of BC.

Identification and characterization of T-cell receptors with therapeutic potential showing conserved specificity against all SARS-CoV 2 strains

INTRODUCTION

Treatment of severe COVID-19 disease can be challenging in immunocompromized patients due to newly emerging virus variants of concern (VOC) escaping the humoral response. Thus, T cells recognizing to date unmutated epitopes are not only relevant for patients' immune responses against VOC, but might also serve as a therapeutic option for patients with severe COVID-19 disease in the future, e.g. following allogenic stem cell transplantation.

METHODS

To this purpose, the activation, cytokine profile and specificity of T-cell clones against unmutated and omicron Spike (S)-protein was analyzed, HLA restriction was determined and most promising T-cell receptor (TCR) was introduced into allogeneic T cells via CRISPR/Cas9-mediated orthotopic TCR replacement. Finally, T-cell responses of engineered T cells was determined and durability of the TCR replacement measured.

PERSPECTIVE

SARS-CoV-2 specific engineered T cells recognizing a genomically stable region of the S-protein of all SARS-CoV 2 variants were successfully generated. Such transgenic T cells exhibit favorable effector functions and provide a treatment option of immunocompromised COVID-19 patients.

Update Breast Cancer 2022 Part 5 - Early Stage Breast Cancer

The treatment of patients with early stage breast cancer has changed in recent years due to the introduction of pembrolizumab, olaparib, and abemaciclib. These and other drugs with the same class of active ingredient are currently in trial for various indications. This review article summarizes the latest results that have either been presented at major conferences such as the ESMO 2022 or published recently in international journals. This includes reports on newly discovered breast cancer genes, atezolizumab in neoadjuvant therapy in HER2-positive patients, long-term data from the APHINITY study, and on how preoperative peritumoral application of local anesthetics can influence the prognosis. We also present solid data on dynamic Ki-67 from the ADAPT studies.

Identification of HLA class I-restricted immunogenic neoantigens in triple negative breast cancer

Immune checkpoint inhibitor (ICI)-based immunotherapy in triple negative breast cancer (TNBC) is achieving limited therapeutic results, requiring the development of more potent strategies. Combination of ICI with vaccination strategies would enhance antitumor immunity and response rates to ICI in patients having poorly infiltrated tumors. In heavily mutated tumors, neoantigens (neoAgs) resulting from tumor mutations have induced potent responses when used as vaccines. Thus, our aim was the identification of immunogenic neoAgs suitable as vaccines in TNBC patients. By using whole exome sequencing, RNAseq and HLA binding algorithms of tumor samples from a cohort of eight TNBC patients, we identified a median of 60 mutations/patient, which originated a putative median number of 98 HLA class I-restricted neoAgs. Considering a group of 27 predicted neoAgs presented by HLA-A*02:01 allele in two patients, peptide binding to HLA was experimentally confirmed in 63% of them, whereas 55% were immunogenic in vivo in HLA-A*02:01⁺ transgenic mice, inducing T-cells against the mutated but not the wild-type peptide sequence. Vaccination with peptide pools or DNA plasmids expressing these neoAgs induced polyepitopic T-cell responses, which recognized neoAg-expressing tumor cells. These results suggest that TNBC tumors harbor neoAgs potentially useful in therapeutic vaccines, opening the way for new combined immunotherapies.

Update Breast Cancer 2022 Part 1 - Early Stage Breast Cancer

Evidence relating to the treatment of breast cancer patients with early-stage disease has increased significantly in the past year. Abemaciclib, olaparib, and pembrolizumab are new drugs with good efficacy in the relevant patient groups. However, some questions remain unanswered. In particular, it remains unclear which premenopausal patients with hormone receptor-positive breast cancer should be spared unnecessary treatment. The question of the degree to which chemotherapy exerts a direct cytotoxic effect on the tumor or reduces ovarian function through chemotherapy could be of key importance. This group of patients could potentially be spared chemotherapy. New, previously experimental biomarker analysis methods, such as spatial analysis of gene expression (spatial transcriptomics) are gradually finding their way into large randomized phase III trials, such as the NeoTRIP trial. This in turn leads to a better understanding of the predictive factors of new therapies, for example immunotherapy. This review summarizes the scientific innovations from recent congresses such as the San Antonio Breast Cancer Symposium 2021 but also from recent publications.