Targeting metabolism to overcome cancer drug resistance: A promising therapeutic strategy for diffuse large B cell lymphoma

CCL8 as a promising prognostic factor in diffuse large B-cell lymphoma via M2 macrophage interactions: A bioinformatic analysis of the tumor microenvironment

BACKGROUNDS

Prior investigations of the tumor microenvironment (TME) of diffuse large B-cell lymphoma (DLBCL) have shown that immune and stromal cells are key contributing factors to patients' outcome. However, challenges remain in finding reliable prognostic biomarkers based on cell infiltration. In this study, we attempted to shed some light on chemokine C-C motif chemokine ligand 8 (CCL8) in DLBCL via interaction with M2 macrophages.

METHODS

The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was applied to evaluate immune and stromal scores from transcriptomic profiles of 443 DLBCL samples from The Cancer Genome Atlas (TCGA) and GSE10846 datasets. Immune cell infiltration (ICI) clusters were obtained based on different immune cell infiltrations of each sample, and gene clusters were derived through differentially expressed genes (DEGs) between the distinct ICI clusters. Five immune-related hub genes related to overall survival (OS) and clinical stages were obtained by COX regression analysis and protein-protein interaction (PPI) network construction then verified by quantitative real-time PCR (qPCR) and immunofluorescence staining in the FFPE tissues. The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and TIMER websites were employed to explore the biological functions of CCL8-related DEGs. Uni- and multivariable Cox regression analyses were performed to analyze CCL8 as an independent prognostic risk factor in GSE10846 and were verified in other independent GEO cohorts.

RESULTS

A higher stromal score was associated with favorable prognosis in DLBCL. Patients in the ICI B cluster and gene B clusters had a better follow-up status with a higher programmed death ligand 1 (PD-L1) and cytotoxic T-lymphocyte antigen 4 (CTLA4) expression. Most of ICI-related DEGs were enriched for immune-related signaling pathways. Five hub genes with a distinct prognosis association were identified, including CD163, which is a biomarker of M2 macrophages, and CCL8. Abundant M2 macrophages were discovered in the high-CCL8 expression group. The functional analysis indicated that CCL8 is a key component of immune-related processes and secretory granule groups. Cox regression analysis and data from other GSE datasets yielded additional evidence of the prognostic value of CCL8 in DLBCL.

CONCLUSIONS

CCL8 has been implicated in macrophage recruitment in several solid tumors, and only a few reports have been published on the role of CCL8 in the pathogenesis of hematological malignancies. This article attempted to find out TME-related genes that associated with the survival in DLBCL patients. CCL8 was identified to be involved in immune activities. Importantly, a series of bioinformatics analysis indicated that CCL8 might become an effective target for DLBCL, which interacts with M2 macrophage and immune checkpoint. The potential related mechanisms need to be further elucidated.

Nanomedicines for Overcoming Cancer Drug Resistance

Clinically, cancer drug resistance to chemotherapy, targeted therapy or immunotherapy remains the main impediment towards curative cancer therapy, which leads directly to treatment failure along with extended hospital stays, increased medical costs and high mortality. Therefore, increasing attention has been paid to nanotechnology-based delivery systems for overcoming drug resistance in cancer. In this respect, novel tumor-targeting nanomedicines offer fairly effective therapeutic strategies for surmounting the various limitations of chemotherapy, targeted therapy and immunotherapy, enabling more precise cancer treatment, more convenient monitoring of treatment agents, as well as surmounting cancer drug resistance, including multidrug resistance (MDR). Nanotechnology-based delivery systems, including liposomes, polymer micelles, nanoparticles (NPs), and DNA nanostructures, enable a large number of properly designed therapeutic nanomedicines. In this paper, we review the different mechanisms of cancer drug resistance to chemotherapy, targeted therapy and immunotherapy, and discuss the latest developments in nanomedicines for overcoming cancer drug resistance.

Metabolic Adaptation-Mediated Cancer Survival and Progression in Oxidative Stress

Undue elevation of ROS levels commonly occurs during cancer evolution as a result of various antitumor therapeutics and/or endogenous immune response. Overwhelming ROS levels induced cancer cell death through the dysregulation of ROS-sensitive glycolytic enzymes, leading to the catastrophic depression of glycolysis and oxidative phosphorylation (OXPHOS), which are critical for cancer survival and progression. However, cancer cells also adapt to such catastrophic oxidative and metabolic stresses by metabolic reprograming, resulting in cancer residuality, progression, and relapse. This adaptation is highly dependent on NADPH and GSH syntheses for ROS scavenging and the upregulation of lipolysis and glutaminolysis, which fuel tricarboxylic acid cycle-coupled OXPHOS and biosynthesis. The underlying mechanism remains poorly understood, thus presenting a promising field with opportunities to manipulate metabolic adaptations for cancer prevention and therapy. In this review, we provide a summary of the mechanisms of metabolic regulation in the adaptation of cancer cells to oxidative stress and the current understanding of its regulatory role in cancer survival and progression.

New insights into antiangiogenic therapy resistance in cancer: Mechanisms and therapeutic aspects

Angiogenesis is a hallmark of cancer and is required for tumor growth and progression. Antiangiogenic therapy has been revolutionarily developing and was approved for the treatment of various types of cancer for nearly two decades, among which bevacizumab and sorafenib continue to be the two most frequently used antiangiogenic drugs. Although antiangiogenic therapy has brought substantial survival benefits to many cancer patients, resistance to antiangiogenic drugs frequently occurs during clinical treatment, leading to poor outcomes and treatment failure. Cumulative evidence has demonstrated that the intricate interplay among tumor cells, bone marrow-derived cells, and local stromal cells critically allows for tumor escape from antiangiogenic therapy. Currently, drug resistance has become the main challenge that hinders the therapeutic efficacies of antiangiogenic therapy. In this review, we describe and summarize the cellular and molecular mechanisms conferring tumor drug resistance to antiangiogenic therapy, which was predominantly associated with redundancy in angiogenic signaling molecules (e.g., VEGFs, GM-CSF, G-CSF, and IL17), alterations in biological processes of tumor cells (e.g., tumor invasiveness and metastasis, stemness, autophagy, metabolic reprogramming, vessel co-option, and vasculogenic mimicry), increased recruitment of bone marrow-derived cells (e.g., myeloid-derived suppressive cells, tumor-associated macrophages, and tumor-associated neutrophils), and changes in the biological functions and features of local stromal cells (e.g., pericytes, cancer-associated fibroblasts, and endothelial cells). We also review potential biomarkers to predict the response to antiangiogenic therapy in cancer patients, which mainly consist of imaging biomarkers, cellular and extracellular proteins, a certain type of bone marrow-derived cells, local stromal cell content (e.g., pericyte coverage) as well as serum or plasma biomarkers (e.g., non-coding RNAs). Finally, we highlight the recent advances in combination strategies with the aim of enhancing the response to antiangiogenic therapy in cancer patients and mouse models. This review introduces a comprehensive understanding of the mechanisms and biomarkers associated with the evasion of antiangiogenic therapy in cancer, providing an outlook for developing more effective approaches to promote the therapeutic efficacy of antiangiogenic therapy.

A Metabolic Gene Signature to Predict Breast Cancer Prognosis

Background: The existing metabolic gene signatures for predicting breast cancer outcomes only focus on gene expression data without considering clinical characteristics. Therefore, this study aimed to establish a predictive risk model combining metabolic enzyme genes and clinicopathological characteristics to predict the overall survival in patients with breast cancer.

Methods: Transcriptomics and corresponding clinical data for patients with breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed metabolic genes between tumors and normal tissues were identified in the TCGA dataset (training dataset). A prognostic model was then built using univariate and multifactorial Cox proportional hazards regression analyses in the training dataset. The capability of the predictive model was then assessed using the receiver operating characteristic in both datasets. Pathway enrichment analysis and immune cell infiltration were performed using Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Ontology (GO) enrichment and CIBERSORT algorithm, respectively.

Results: In breast cancer and normal tissues, 212 metabolic enzyme genes were differentially expressed. The predictive model included four factors: age, stage, and expression of SLC35A2 and PLA2G10. Patients with breast cancer were classified into high- and low-risk groups based on the model; the high-risk group had a significantly poorer overall survival rate than the low-risk group. Furthermore, the two risk groups showed different activation of pathways and alterations in the properties of tumor microenvironment-infiltrating immune cells.

Conclusion: We developed a powerful model to predict prognosis in patients with breast cancer by combining the gene expression of metabolic enzymes with clinicopathological characteristics.