Li, Haiyin; Chen, Yeqing; Kaster, Jessica; Dunne, Maggie; Xiao, Min; Li, Ling; Thomas, Monzy; Promi, Nazifa; Fingerman, Dylan; Brown, Gregory Schuyler; Zheng, Qiuxian; Zhu, Xingyue; Reale, McKenna; Patterson, Andrew; Gao, Le; Zhang, Xuxiang; Jiang, Siqi; Hu, Tianxing; Fang, Hanzhang; Ren, Jianlan;
doi: 10.1186/s12943-026-02622-9pmid: 41776501
BackgroundMost advanced melanomas initially respond to targeted therapy but eventually relapse. Increasing evidence suggests that drug-tolerant persister cells can adopt a reversible drug-refractory state and represent a key driver of therapeutic resistance.MethodsWe developed MeRLin, a lineage tracing platform that integrates cellular barcoding, single-cell transcriptomic profiling, RNA fluorescence in situ hybridization, and computational analyses to track clonal and transcriptional dynamics in a patient-derived melanoma model during prolonged targeted therapy. Longitudinal analyses enabled the characterization of clonal fates, transcriptional states, and spatial organization of persister populations.ResultsClonal dynamics showed that persister subpopulations initially responded to therapy, persisted through minimal residual disease, and expanded during tumor recurrence. Four persister-associated transcriptional states characterized by stress-like, lipid metabolism, PI3K signaling, and extracellular matrix remodeling programs were associated with persister populations arising from minor pre-treatment subpopulations under sustained drug pressure. Spatial transcriptomic analyses revealed structured spatial organization of these programs and suggested coordinated autocrine and paracrine interactions among persister states. Targeted barcode RNA fluorescence in situ hybridization enabled spatial mapping of clonal identity and gene expression, revealing in situ co-localization of a dominant resistant clone marked by SLC2A1 expression.ConclusionsTogether, MeRLin provides a robust framework for dissecting cancer heterogeneity and characterizing persister subpopulations. Our findings demonstrate that melanoma recurrence is associated with diverse, spatially organized persister states linked to adaptive transcriptional programs.
Zhang, Weimin; Hong, Ruoxi; Li, Lin; Wang, Yan; Du, Peina; Ou, Yunwei; Zhao, Zitong; Liu, Xuefeng; Xiao, Wenchang; Dong, Dezuo; Wu, Qingnan; Chen, Jie; Song, Yongmei; Zhan, Qimin
doi: 10.1186/s12943-026-02586-wpmid: 41656277
Zhou, Ran; Li, Yuejun; Li, Guanghui; Li, Yan; Luo, Lie; Wang, Bin; Wang, Liping
doi: 10.1186/s12943-025-02492-7pmid: 41501789
Hepatocellular carcinoma (HCC) is a common type of primary liver cancer and is considered the third leading cause of cancer-related deaths worldwide. The high aggressiveness and resistance to therapies exhibited by HCC present significant challenges to global public health. As the primary metabolic organ in the human body, the liver undergoes substantial metabolic reprogramming during carcinogenesis, affecting various metabolic pathways including those involved in carbohydrates, lipids, and amino acids. Notably, disruptions in amino acid metabolism play a critical role in the initiation and progression of HCC, helping to sustain its malignant characteristics. This review aims to provide an in-depth analysis of the alterations observed in aromatic amino acids metabolism, branched chain amino acids (BCAAs) metabolism, glutamine metabolism, and other amino acid metabolism processes, including serine, arginine, and methionine, along with the expression patterns of associated metabolic enzymes. Furthermore, it discusses potential therapeutic approaches and their clinical relevance, offering insights and strategies for improving HCC diagnosis and treatment in the future.
Hao, Qing; Zhou, Tao; Yan, Huayun; Ren, Zhixiang; Mao, Wei; Huang, Li-Bin; Chen, Yue-yun; Wang, Yuelan; Xiao, Minqin; He, Yajiao; Xia, Xuyang; Wu, Jing; Zhang, Wei-Han; Chen, Hai-Ning; Qian, Maoxiang; Zhang, Feng; Yang, Li; Dai, Lunzhi; Zhang, Huiyuan; Huang, Canhua; Shu, Yang; Xu, Yu; Luo, Yang; Peng, Yong; Xu, Heng
doi: 10.1186/s12943-025-02548-8pmid: 41484771
BackgroundDouble-Negative T (DNT) cells, lacking both CD4 and CD8 expression, play critical roles in cancer immunology, and have garnered increasing attention in cancer research. However, their heterogeneity and functional diversity within the tumor microenvironment (TME) remain underexplored.MethodsIn-house and publicly available single-cell RNA sequencing (scRNA-seq) data for different cancer types were integrated after quality control and batch effect correction, followed by DNT cells separation from CD3+ T cells subtypes. Functional characteristics, intercellular communication, differentiation trajectories, regulatory networks, and clinical relevance were analyzed among different DNT subsets. Key findings were validated using multiplex immunofluorescence and spatial transcriptomics to investigate the spatial localization of DNT subsets and their interactions within the TME. Impact of γδ T cells on immunotherapy response was also assessed using MC38-based murine tumor model.ResultsBy integrating scRNA-seq data from 2,369 samples across 23 cancer types, we established a comprehensive single-cell atlas of 157,025 high-quality DNT cells. Fourteen distinct DNT subsets (6 αβ DNT and 8 γδ T cell subsets) were identified, demonstrating tumor both type-specific and shared distribution patterns, as well as unique cell-cell interaction network within the TME. These subsets displayed specialized functional profiles, including cytotoxicity, antigen presentation, and immune modulation, indicating that the functional diversity of DNT cells is largely subset-specific rather than a manifestation of multifunctionality within a single population. We also delineated divergent trajectories for αβ DNT and γδ T cell subsets, including the functional plasticity of gut-resident γδ T cells transitioning between cytotoxic and immunosuppressive states. Notably, several DNT subsets were significantly associated with favorable clinical treatment outcomes, including improved responses to cancer immunotherapy. Consistently, depletion of γδ T cells in the murine tumor model significantly decreased the efficacy of PD-1 blockade, underscoring their critical role in therapeutic response.ConclusionsOur study uncovers the previously underappreciated heterogeneity and functional diversity of DNT cells in the TME and demonstrates their profound impact on tumor progression and immunotherapy outcomes.
Sun, Rui; Huang, Yong; He, Hao; Li, Qiuchen; Wang, Linfeng; Zhang, Gaojie; Wei, Ziling; Cao, Yang; Li, Jing; Wang, Xianmin; Yang, Fan; Chen, Wenjun; Li, Xiang; Yu, Jiang; Liu, Siyuan; Lei, Congfeng; Jiang, Yu; Peng, Yueqiang; Su, Huiyi; Gao, Yingying; He, Weiyang; Yang, Lei; Liu, Jiayu
doi: 10.1186/s12943-026-02602-zpmid: 41787526
AimsResistance to enzalutamide (Enza) in castration-resistant prostate cancer (CRPC) is linked to poor prognosis. While KDM5B is highly expressed in Enza-resistant CRPC, the mechanisms of resistance remain poorly understood.MethodsWe applied an integrated approach to study KDM5B using bioinformatics analyses of single-cell and multi-omics data, along with in vitro and in vivo validation. We explored mechanisms through lactylation proteomics, CRISPR/Cas9 editing, ChIP, and dual-luciferase reporter assays.ResultsKDM5B induces Enza resistance by epigenetically suppressing PTEN, which in turn activates the PI3K/Akt signaling pathway to upregulate PGK1 and drive metabolic reprogramming and lactate production. Lactate acts as a substrate for p300-mediated lactylation of hnRNPA1 at lysine 179 (K179), stabilizing hnRNPA1 by blocking NEDD4L-mediated ubiquitination and promoting AR-V7 splicing. A potential positive feedback loop enhances this effect: KDM5B activates AR, and AR, in turn, increases KDM5B expression. Inhibiting KDM5B or p300 can reverse Enza resistance in vivo.ConclusionsWe identify a mechanism linking metabolism, epigenetics, and a KDM5B/AR feedback loop in drug resistance. These findings suggest that multi-target strategies may represent a promising approach to overcome Enza resistance in CRPC.
Tiwari, Ashutosh; Widodo, ; Krisnawati, Dyah Ika; Chen, Chih-Yu; Kuo, Tsung-Rong
doi: 10.1186/s12943-025-02563-9pmid: 41680778
Nanomedicine's merging with artificial intelligence (AI) is fundamentally changing cancer theranostics through precise creation of multifunctional nanoparticles which can simultaneously diagnose and treat diseases. Traditional cancer treatments today face issues with imprecise delivery and general body toxicity as well as late-stage disease recognition which theranostic nanoplatforms address through precision drug transport and live imaging functions. In this analysis, we examine the existing state and forthcoming developments of AI applications in cancer theranostics through nanoparticles. Our study first examines the three primary classes of theranostic nanomaterials that show clinical significance: liposomes, gold nanoparticles, iron oxide nanoparticles, and quantum dots. AI is being applied to nanoparticle development through machine learning, deep learning, reinforcement learning, and generative models that support physicochemical predictions, synthesis optimization, biodistribution modeling, and inverse design. We analyze clinical applications of AI solutions which support patient identification, response predictions, and implementation of virtual patient models for individualized cancer treatment. The paper evaluates major difficulties which are nanotoxicity, AI explainability, data limitations, and regulatory concerns while addressing ethical dilemmas. Rather than a broad overview of nanomedicine, we center on AI methods that directly improve theranostic decisions and support this with worked exemplars reporting datasets, baselines, metrics, and clinical tie-ins. This Task–Data–Method–Metric lens replaces generic background and grounds claims in reproducible evidence.Graphical abstract[graphic not available: see fulltext]
Angelopoulou, Andriani; Theocharous, Giorgos; Valakos, Dimitrios; Polyzou, Aikaterini; Magkouta, Sophia; Myrianthopoulos, Vassilios; Havaki, Sophia; Fiorillo, Marco; Tremi, Ioanna; Vachlas, Konstantinos; Nisotakis, Theodoros; Thanos, Dimitris-Foivos; Pantazaki, Anastasia; Kletsas, Dimitris; Bartek, Jiri; Petty, Russell; Thanos, Dimitris; McCrimmon, Rory J; Papaspyropoulos, Angelos; Gorgoulis, Vassilis G
doi: 10.1186/s12943-025-02561-xpmid: 41526943
Fei, Siqi; Xu, Xiayun; Wang, Tingrui; Jiang, Xinyue; Zhu, Hanrong; Chen, Yingji; Yuan, Renjie; Lv, Zeheng; Gao, Zhijian; Liu, Huanxin; Chen, Xiaojun; Wang, Chenji; Gao, Kun
doi: 10.1186/s12943-026-02605-wpmid: 41691251
The progression of endometrial cancer (EC) involves substantial metabolic reprogramming, frequently driven by mutations in tumor suppressors and oncogenes. In this study, we identify a previously unrecognized pathway through which FBXW7 mutations rewire glucose metabolism in EC cells. Specifically, we demonstrate that loss-of-function mutations in FBXW7 disrupt the SCFFBXW7 E3 ubiquitin ligase complex, resulting in the stabilization of the transcription factor ETV6 by preventing its ubiquitin-mediated degradation. Accumulated ETV6 subsequently enhances the transcriptional activation of glucose transporter 1 (GLUT1), elevating its expression and localization to the plasma membrane. This increased GLUT1 expression significantly enhances glucose uptake, fueling both aerobic glycolysis and oxidative phosphorylation, which collectively accelerate EC cell proliferation and tumor growth. Importantly, targeting GLUT1 pharmacologically partially reverses the proliferative advantage conferred by ETV6 overexpression, highlighting a promising therapeutic vulnerability. Our findings establish the FBXW7-ETV6-GLUT1 regulatory axis as a critical driver of metabolic adaptation and tumor progression, offering potential strategies for targeted therapy in FBXW7-mutant EC.
Yan, Man; Liu, Kai; Xu, Jing; Liu, Yandi; Ji, Liechen; Zhang, Shiwu
doi: 10.1186/s12943-026-02659-wpmid: 41964004
KRAS is a critical proto-oncogene that encodes a protein functioning as a pivotal molecular switch in intracellular signaling. Both KRAS mutations and MYC dysregulation are key drivers of tumor progression and have historically been regarded as “undruggable” targets. Emerging evidence underscores that the coordinated activation of KRAS and MYC cooperatively fuels tumorigenesis, suggesting that dual inhibition of these oncogenes may constitute a synergistic therapeutic approach for KRAS-mutant cancers. However, the mechanistic basis underlying the effective combined targeting of KRAS and MYC remains poorly defined, largely due to the complexity of their functional interplay. This review examines their collaborative roles in metabolic reprogramming, epigenetic remodeling, and shaping an immunosuppressive tumor microenvironment through crosstalk with immune cells. It also surveys current and emerging anti-KRAS strategies and discusses the challenge of therapy resistance, particularly in the setting of MYC dysregulation. Since resistant tumors often circumvent KRAS inhibition by reactivating MYC to sustain proliferation and survival, interventions that concurrently target these adaptive pathways may hold promise for overcoming resistance in KRAS-driven malignancies.
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