Cancer Communications最新文献

Copper, one of the essential nutrients for the human body, acts as an electron relay in multiple pathways due to its redox properties. Both deficiencies and excesses of copper lead to cellular fragility. Therefore, it can manifest pro- and anti-cancer properties in tumors. Therefore, it is crucial to clarify the copper activity within the cell. We have thoughtfully summarized the metabolic activities of copper from a macro and micro perspective. Cuproptosis, as well as other forms of cell death, is directly or indirectly interfered with by Cu²⁺, causing cancer cell death. Meanwhile, we did pan-cancer analysis of cuproptosis-related genes to further clarify the roles of these genes. In addition, copper has been found to be involved in multiple pathways within the metastasis of cancer cells. Given the complexity of copper's role, we are compelled to ask: is copper a friend or a foe? Up to now, copper has been used in various clinical applications, including protocols for measurement of copper concentration and bioimaging of radioactive ⁶⁴Cu. But therapeutically it is still a continuation of the old medicine, and new possibilities need to be explored, such as the use of nanomaterials. Some studies have also shown that copper has considerable interventional power in metabolic cancers, which provides the great applications potential of copper therapy in specific cancer types. This paper reviews the dual roles played by cuproptosis in cancer from the new perspectives of oxidative stress, cell death, and tumor metastasis, and points out the value of its application in specific cancer types, summarizes the value of its testing and imaging from the perspective of clinical application as well as the current feasible options for the new use of the old drugs, and emphasizes the prospects for the application of nano-copper.

Renal cell carcinoma (RCC) is responsible for an estimated 434,419 new cases and 155,702 deaths, which amounts to 2.2% of all new cancer globally [1]. Despite the advent of new combination therapies, the 3-year overall survival (OS) in metastatic RCC (mRCC) remains around 60% [2-4]. To stratify the patients according to their prognosis, the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model and the Memorial Sloan Kettering Cancer Center (MSKCC) model, are well developed and used before the initiation of the treatment. Sunitinib was the standard of care after showing superiority over interferon alfa in mRCC until immunotherapy (IO) combinations gained approval [5]. While IO and anti-vascular endothelial growth factor (VEGF) tyrosine kinase inhibitor (TKI) combinations are recommended first-line, they have not shown a significant OS benefit in patients with IMDC favorable risk and remain costly, limiting access in resource-constrained settings. This study evaluates clinical outcomes of mRCC patients treated with first-line sunitinib and aims to identify factors impacting these outcomes.

We conducted a retrospective cohort study using data from the Turkish Kidney Cancer Consortium (TKCC) database. The study included patients aged 18 and older diagnosed with mRCC and treated with sunitinib as a first-line therapy. The primary objectives were to evaluate time to treatment failure (TTF) across risk groups and identify factors affecting TTF. Secondary objectives included assessing OS by risk group, factors affecting OS, and objective response rate (ORR). TTF was defined as the time from treatment start to discontinuation due to any cause, while OS was defined as the time from treatment start to death. ORR represented the percentage achieving complete response (CR) or partial response (PR).

Statistical analyses were performed using IBM SPSS Statistics Version 24.0. Survival curves were estimated with the Kaplan-Meier method, and multivariate analyses included variables with P ≤ 0.20 in univariate analyses. Cox regression calculated hazard ratios (HRs) with 95% confidence intervals (CIs), with P < 0.05 considered statistically significant.

A total of 531 patients with median age of 57.3 (interquartile range = 12.5) years were included the study. Baseline characteristics and of patients were summarized in Supplementary Table S1. Median TTF was 10.25 (95% CI = 8.94-11.55) months (Figure 1A). Median TTF was 10.12 (95% CI = 8.55-11.69) months for clear cell histology and 10.86 (95% CI = 8.14-13.21) months for non-clear cell histology (P = 0.653). TTF was 15.70 (95% CI = 11.76-19.64), 11.36 (95% CI = 9.89-12.84), and 4.89 (95% CI = 3.83-5.95) months for IMDC favorable, intermediate and poor risk groups, respectively. There was no difference in TTF between patients with IMDC favorable risk and IMDC intermediate risk with 1 risk factor (15.70 months vs

Despite significant advancements in cancer treatment, current therapies often fail to completely eradicate malignant cells. This shortfall underscores the urgent need to explore alternative approaches such as cancer vaccines. Leveraging the immune system's natural ability to target and kill cancer cells holds great therapeutic potential. However, the development of cancer vaccines is hindered by several challenges, including low stability, inadequate immune response activation, and the immunosuppressive tumor microenvironment, which limit their efficacy. Recent progress in various fields, such as click chemistry, nanotechnology, exosome engineering, and neoantigen design, offer innovative solutions to these challenges. These achievements have led to the emergence of smart vaccine platforms (SVPs), which integrate protective carriers for messenger ribonucleic acid (mRNA) with functionalization strategies to optimize targeted delivery. Click chemistry further enhances SVP performance by improving the encapsulation of mRNA antigens and facilitating their precise delivery to target cells. This review highlights the latest developments in SVP technologies for cancer therapy, exploring both their opportunities and challenges in advancing these transformative approaches.

Copper is an essential micronutrient in the human body, mainly acting as a crucial cofactor required for a wide range of physiological processes across nearly all cell types. Recent advances revealed that tumor cells seize copper to fulfill their rapid proliferation, metastasis, immune evasion, and so on by reprogramming the copper regulatory network, defined as cuproplasia. Thus, targeting copper chelation to reduce copper levels has been considered a rational tumor therapy strategy. However, overloaded copper ions could be toxic, which leads to the aggregation of lipoylated mitochondrial proteins and the depletion of iron-sulfur clusters, ultimately resulting in cell death, termed cuproptosis. Upon its discovery, cuproptosis has attracted great interest from oncologists, and targeting cuproptosis by copper ionophores exhibits as a potential anti-tumor therapy. In this review, we present the underlying mechanisms involved in cuproplasia and cuproptosis. Additionally, we sum up the chemicals targeting either cuproplasia or cuproptosis for cancer therapy. Further attention should be paid to distinguishing cancer patients who are suitable for targeting cuproplasia or cuproptosis.

The cover image is based on the article Effect of neutrophils on tumor immunity and immunotherapy resistance with underlying mechanisms by Jiali Yao et al., https://doi.org/10.1002/cac2.12613.

Epithelioid sarcoma (EpS) is a high-grade malignancy of unknown histogenesis first described in 1970 [1], characterized by high rates of relapse and metastasis, with 5-year survival rates of 60%-75% [2]. The only Food and Drug Administration (FDA)-approved targeted therapy, the enhancer of zeste homology 2 (EZH2) inhibitor tazemetostat, achieved transient responses in only 15% of patients [2]. To establish a solid mechanistic basis, we investigated the role of SWI/SNF related BAF chromatin remodeling complex subunit B1 (SMARCB1) via multi-omics analyses. We engineered isogenic cell line models single-cell-cloned to minimize genetic variability, featuring doxycycline-(DOX)-inducible SMARCB1 expression systems alongside respective empty vector controls. The cell lines (FU-EPS-1; HS-ES-1, -2M, -2R; NEPS; VA-ES-BJ) exhibited homozygous SMARCB1 deletion and represented proximal and distal subtypes, with prominent SMARCB1 re-expression upon DOX-treatment (Figure 1A). DOX concentrations were adjusted to achieve SYBR/TaqMan-qPCR-controlled SMARCB1 levels comparable to SMARCB1-proficient Ewing sarcoma (EwS) cell lines, minimizing experimental artefacts associated with supraphysiological expression (Supplementary Figure S1A-B). Western blots demonstrated that SMARCB1 underwent nuclear translocation and re-incorporation into the SWI/SNF complex (Figure 1B). Transcriptome profiling using Affymetrix Clariom D microarrays (GEO: GSE276634) and Weighted Gene Correlation Network Analysis (WGCNA) based on Gene Set Enrichment Analysis (GSEA) revealed downregulated signatures related to DNA-repair and epigenetic regulation, alongside upregulated developmental pathways upon SMARCB1 re-expression (Figure 1C). These findings were accompanied by dose-dependent reductions in clonogenicity (Figure 1D, Supplementary Figure S1C), while propidium-iodide-(PI)-based flow-cytometric cell-cycle-analysis showed delayed G1-to-S-phase transition (Supplementary Figure S1D). Orthotopic subcutaneous (s.c.) xenotransplantation experiments using VA-ES-BJ in immunocompromised Nod/Scid/gamma (NSG) mice recapitulated the typical EpS morphology (Supplementary Figure S1E). After tumors became palpable, SMARCB1 re-expression via DOX supplementation in drinking water resulted in significantly reduced tumor growth (Figure 1E).

Since these findings underscored significant SMARCB1-associated epigenetic regulation (Figure 1C) [3], we next investigated SWI/SNF chromatin-remodeling functionality via Assay for Transposase-Accessible Chromatin using Sequencing (ATAC-Seq; GEO: GSE281434) in FU-EPS-1, HS-ES-2M, NEPS and VA-ES-BJ to compare the effects of SMARCB1-deficient and physiological SWI/SNF assemblies. SMARCB1 re-expression increased chromatin accessibility at putative enhancer sites (box 1) and gene bodies (box 2) (Figure 1F). Conversely, SWI/SNF-inhibi