Cancer Communications最新文献

Dear Editor,

Hereditary prostate cancer (PC) comprises 5%-10% of all PC cases. The increased risk of PC in men with a family history of the disease is well known and is commonly caused by germline mutations, leading to clinical guidelines mentioning various genes for identifying high-risk individuals. However, the complex inheritance patterns involving multiple single nucleotide polymorphisms (SNPs) make it a genetically heterogeneous disease, with genetic testing still in its early stages. Current guidelines, such as those from the National Comprehensive Cancer Network (NCCN), are insufficient to identify and stratify all PC patients [1]. To improve testing and screening for familial PC, we report a multi-omic analysis (Supplementary Figures S1-S2) in a PC multi-case family of seven members (two healthy, four PC, and one breast cancer) (Figure 1A, Supplementary Table S1) combining exome, transcriptome and epigenomic analyses (whole-DNA methylation and small-RNA sequencing), offering a unique perspective on the understanding of hereditary PC to date. Each family is a small genetic unit that differs significantly from others with the same pathology but different genetic origins. Therefore, individualized studies may be the key to unravel the heterogeneity of this disease. However, we need to consider that conducting futuremetabolomic analysis would be next steps to reinforce present data, as well as reproducible analysis in other PC families.

We selected 34 genes based on NCCN (v1.2023) and European Association of Urology (EAU, v2.0) clinical guidelines and literature [2, 3] (Supplementary Table S2). We found 268 variants in 26 of these genes (APC, ATM, AXIN2, BARD1, BMPR1A, BRCA1/2, CDH1, CDK4, CHEK2, DICER1, MLH1, MSH2/3/6, MUTYH, NF1, PMS2, POLD1, POLE, PTEN, RAD51C/D, SMAD4, STK11 and TP53), most of which were intronic (91.4%) and/or unreported (84.3%) (Supplementary Figure S3 and Supplementary Table S3). In addition, genome-wide analysis of high-impact variants revealed only four mutations affecting the major isoforms of the ANAPC1, HIBCH, and MOK, but none of these genes have been previously reported in PC (Supplementary Table S4). Interestingly, despite being high-risk cancer patients, the individuals in the present study's family did not show any pathogenic mutations in the genes specified by clinical guidelines. Furthermore, this is added to the growing evidence for the potential of non-coding mutations, both near-exonic and deep-intronic mutations, in carcinogenesis. There is already evidence of how known tumor suppressor genes are affected by intronic mutations [4]. Exome analysis also reported ten identical mutations in three genes, one in AXIN2, two in DICER1 and seven in BARD1, in all PC patients (Supplementary Table S3), suggesting that these mutations may be responsible for the development of cancer in this family. Among th

Lung cancer is the second most common and the deadliest type of cancer worldwide. Clinically, non-small cell lung cancer (NSCLC) is the most common pathological type of lung cancer; approximately one-third of affected patients have locally advanced NSCLC (LA-NSCLC, stage III NSCLC) at diagnosis. Because of its heterogeneity, LA-NSCLC often requires multidisciplinary assessment. Moreover, the prognosis of affected patients is much below satisfaction, and the efficacy of traditional therapeutic strategies has reached a plateau. With the emergence of targeted therapies and immunotherapies, as well as the continuous development of novel radiotherapies, we have entered an era of novel treatment paradigm for LA-NSCLC. Here, we reviewed the landscape of relevant therapeutic modalities, including adjuvant, neoadjuvant, and perioperative targeted and immune strategies in patients with resectable LA-NSCLC with/without oncogenic alterations; as well as novel combinations of chemoradiation and immunotherapy/targeted therapy in unresectable LA-NSCLC. We addressed the unresolved challenges that remain in the field, and examined future directions to optimize clinical management and increase the cure rate of LA-NSCLC.

The cover image is based on the Original Article Targeting histone deacetylase suppresses tumor growth through eliciting METTL14-modified m⁶A RNA methylation in ocular melanoma by Ai Zhuang et al., https://doi.org/10.1002/cac2.12471.

The cover image is based on the Correspondence Combining methylated SEPTIN9 and RNF180 plasma markers for diagnosis and early detection of gastric cancer by Yongzhan Nie et al., https://doi.org/10.1002/cac2.12478.

Atovaquone (ATO), a mitochondrial inhibitor, has anti-cancer effects [1]. Based on ATO, we developed mitochondria-targeted atovaquone (Mito-ATO) that had even stronger anti-tumor efficacy than ATO [2]. We synthesized Mito-ATO by attaching the bulky triphenylphosphonium (TPP) group to ATO via a ten-carbon alkyl chain (Supplementary file of methods; Supplementary Figure S1). To assess the effects of Mito-ATO on tumor microenvironment, we conducted single-cell RNA-sequencing (scRNA-seq) on treated immune cells from mice having lung tumors either treated with or without Mito-ATO. Seurat was used for clustering and annotation of CD45⁺ immune cells [3]. The detected lymphoid cell populations were CD8⁺ T cells, CD4⁺ T cells, regulatory T cells (Tregs), gamma-delta T (Tgd) cells, B cells, and natural killer (NK) cells; and the myeloid cells identified were macrophages, neutrophils, plasmacytoid dendritic cells (pDCs), conventional dendritic cells (cDCs) and mast cells (Figure 1A-C). Clustering of CD4⁺ T cells into seven subpopulations, the separation of neutrophils and granulocytic myeloid-derived suppressor cells (G-MDSCs), and the division of macrophages into M1 and M2 subtypes were described in our previous publication [2]. In this study, we further divided CD8⁺ T cells into four subpopulations, i.e., exhausted CD8⁺ T (CD8T_Exhausted) cells, memory like CD8⁺ T (CD8T_MemoryLike) cells, effector memory like CD8⁺ T (CD8T_EffectorMemory) cells and naive CD8⁺ T (CD8T_Naive) cells, using the tumor-infiltrating CD8⁺ lymphocyte state predictor (TILPRED) method [4] (Figure 1D-E). Probability scores computed with TILPRED could discriminate CD8T_Exhausted from CD8T_MemoryLike cells despite overlap between the two subsets on UMAP representation (Supplementary Figure S2). Mito-ATO treatment significantly decreased the proportion of the CD8T_Exhausted cells (7.3% vs. 32.5%, P < 0.001) but increased the proportion of anti-tumor CD8T_EffectorMemory cells as compared with vehicle treatment (37.3% vs. 11.9%, P < 0.001) (Figure 1F). In comparison, the percentages of CD8⁺ T cells out of total T cells were not different between the two groups (Supplementary Table S1). For validation, we verified that Mito-ATO treatment induced changes in CD8⁺ T cell repartition by conducting flow cytometry. Mito-ATO treatment significantly increased the percentage of cytotoxic tumor necrosis factor-alpha (TNF-α)⁺CD8⁺ T cells and decreased the percentage of programmed cell death protein-1 (PD-1)⁺ T cell immunoglobulin and mucin domain-containing protein 3 (TIM3)⁺CD8⁺ T cells (Supplementary Figure S3). These matched the scRNA-seq results. We also observed a slight trend toward the upregulation of genes involved in CD8