Improvement of Neoantigen Identification Through Convolution Neural Network

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Resistance mechanisms of immune checkpoint inhibition in lymphoma: Focusing on the tumor microenvironment

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic strategies of multiple types of malignancies including lymphoma. However, efficiency of ICIs varies dramatically among different lymphoma subtypes, and durable response can only be achieved in a minority of patients, thus requiring unveiling the underlying mechanisms of ICI resistance to optimize the individualized regimens and improve the treatment outcomes. Recently, accumulating evidence has identified potential prognostic factors for ICI therapy, including tumor mutation burden and tumor microenvironment (TME). Given the distinction between solid tumors and hematological malignancies in terms of TME, we here review the clinical updates of ICIs for lymphoma, and focus on the underlying mechanisms for resistance induced by TME, which play important roles in lymphoma and remarkably influence its sensitivity to ICIs. Particularly, we highlight the value of multiple cell populations (e.g., tumor infiltrating lymphocytes, M2 tumor-associated macrophages, and myeloid-derived suppressor cells) and metabolites (e.g., indoleamine 2, 3-dioxygenase and adenosine) in the TME as prognostic biomarkers for ICI response, and also underline additional potential targets in immunotherapy, such as EZH2, LAG-3, TIM-3, adenosine, and PI3Kδ/γ.

Engineering neoantigen vaccines to improve cancer personalized immunotherapy

Immunotherapy treatments harnessing the immune system herald a new era of personalized medicine, offering considerable benefits for cancer patients. Over the past years, tumor neoantigens emerged as a rising star in immunotherapy. Neoantigens are tumor-specific antigens arising from somatic mutations, which are proceeded and presented by the major histocompatibility complex on the cell surface. With the advancement of sequencing technology and bioinformatics engineering, the recognition of neoantigens has accelerated and is expected to be incorporated into the clinical routine. Currently, tumor vaccines against neoantigens mainly encompass peptides, DNA, RNA, and dendritic cells, which are extremely specific to individual patients. Due to the high immunogenicity of neoantigens, tumor vaccines could activate and expand antigen-specific CD4+ and CD8+ T cells to intensify anti-tumor immunity. Herein, we introduce the origin and prediction of neoantigens and compare the advantages and disadvantages of multiple types of neoantigen vaccines. Besides, we review the immunizations and the current clinical research status in neoantigen vaccines, and outline strategies for enhancing the efficacy of neoantigen vaccines. Finally, we present the challenges facing the application of neoantigens.

Neoantigens and their potential applications in tumor immunotherapy

The incidence of malignant tumors is increasing, the majority of which are associated with high morbidity and mortality rates worldwide. The traditional treatment method for malignant tumors is surgery, coupled with radiotherapy or chemotherapy. However, these therapeutic strategies are frequently accompanied with adverse side effects. Over recent decades, tumor immunotherapy shown promise in demonstrating notable efficacy for the treatment of cancer. With the development of sequencing technology and bioinformatics algorithms, neoantigens have become compelling targets for cancer immunotherapy due to high levels of immunogenicity. In addition, neoantigen-based vaccines have demonstrated potential for cancer therapy, primarily by augmenting T-cell responses. Neoantigens have also been shown to be effective in immune checkpoint blockade therapy. Therefore, neoantigens may serve to be predictive biomarkers and synergistic treatment targets in cancer immunotherapy. The aim of the present review was to provide an overview of the recent progress in the classification, screening and clinical application of neoantigens for cancer therapy.