Defining Diffuse Large B-Cell Lymphoma Immunotypes by CD8+ T Cells and Natural Killer Cells

Prediction significance of autophagy-related genes in survival probability and drug resistance in diffuse large B-cell lymphoma

BACKGROUND/AIMS

Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin lymphoma, has significant prognostic heterogeneity. This study aimed to generate a prognostic prediction model based on autophagy-related genes for DLBCL patients.

METHODS

Utilizing bioinformatics techniques, we analyzed the clinical information and transcriptome data of DLBCL patients from the Gene Expression Omnibus (GEO) database. Through unsupervised clustering, we identified new autophagy-related molecular subtypes and pinpointed differentially expressed genes (DEGs) between these subtypes. Based on these DEGs, a prognostic model was constructed using Cox and Lasso regression. The effectiveness, accuracy, and clinical utility of this prognostic model were assessed using numerous independent validation cohorts, survival analyses, receiver operating characteristic (ROC) curves, multivariate Cox regression analysis, nomograms, and calibration curves. Moreover, functional analysis, immune cell infiltration, and drug sensitivity analysis were performed.

RESULTS

DLBCL patients with different clinical characterizations (age, molecular subtypes, ECOG scores, and stages) showed different expression features of autophagy-related genes. The prediction model was constructed based on the eight autophagy-related genes (ADD3, IGFBP3, TPM1, LYZ, AFDN, DNAJC10, GLIS3, and CCDC102A). The prognostic nomogram for overall survival of DLBCL patients incorporated risk level, stage, ECOG scores, and molecular subtypes, showing excellent agreement between observed and predicted outcomes. Differences were noted in the proportions of immune cells (native B cells, Treg cells, CD8+ T cell, CD4+ memory activated T cells, gamma delta T cells, macrophages M1, and resting mast cells) between high-risk and low-risk groups. LYZ and ADD3 exhibited correlations with drug resistance to most chemotherapeutic drugs.

CONCLUSIONS

This study established a novel prognostic assessment model based on the expression profile of autophagy-related genes and clinical characteristics of DLBCL patients, explored immune infiltration and predicted drug resistance, which may guide precise and individualized immunochemotherapy regimens.

Artificial intelligence for prediction of response to cancer immunotherapy

Artificial intelligence (AI) indicates the application of machines to imitate intelligent behaviors for solving complex tasks with minimal human intervention, including machine learning and deep learning. The use of AI in medicine improves health-care systems in multiple areas such as diagnostic confirmation, risk stratification, analysis, prognosis prediction, treatment surveillance, and virtual health support, which has considerable potential to revolutionize and reshape medicine. In terms of immunotherapy, AI has been applied to unlock underlying immune signatures to associate with responses to immunotherapy indirectly as well as predict responses to immunotherapy responses directly. The AI-based analysis of high-throughput sequences and medical images can provide useful information for management of cancer immunotherapy considering the excellent abilities in selecting appropriate subjects, improving therapeutic regimens, and predicting individualized prognosis. In present review, we aim to evaluate a broad framework about AI-based computational approaches for prediction of response to cancer immunotherapy on both indirect and direct manners. Furthermore, we summarize our perspectives about challenges and opportunities of further AI applications on cancer immunotherapy relating to clinical practicability.

Harnessing Unconventional T Cells and Innate Lymphoid Cells to Prevent and Treat Hematological Malignancies: Prospects for New Immunotherapy

Unconventional T cells and innate lymphoid cells (ILCs) make up a heterogeneous set of cells that characteristically show prompt responses toward specific antigens. Unconventional T cells recognize non-peptide antigens, which are bound and presented by diverse non-polymorphic antigen-presenting molecules and comprise γδ T cells, MR1-restricted mucosal-associated invariant T cells (MAITs), and natural killer T cells (NKTs). On the other hand, ILCs lack antigen-specific receptors and act as the innate counterpart to the T lymphocytes found in the adaptive immune response. The alteration of unconventional T cells and ILCs in frequency and functionality is correlated with the onset of several autoimmune diseases, allergy, inflammation, and tumor. However, depending on the physio-pathological framework, unconventional T cells may exhibit either protective or pathogenic activity in a range of neoplastic diseases. Nonetheless, experimental models and clinical studies have displayed that some unconventional T cells are potential therapeutic targets, as well as prognostic and diagnostic markers. In fact, cell-mediated immune response in tumors has become the focus in immunotherapy against neoplastic disease. This review concentrates on the present knowledge concerning the function of unconventional T cell sets in the antitumor immune response in hematological malignancies, such as acute and chronic leukemia, multiple myeloma, and lymphoproliferative disorders. Moreover, we discuss the possibility that modulating the activity of unconventional T cells could be useful in the treatment of hematological neoplasms, in the prevention of specific conditions (such as graft versus host disease), and in the formulation of an effective anticancer vaccine therapy. The exact knowledge of the role of these cells could represent the prerequisite for the creation of a new form of immunotherapy for hematological neoplasms.