Cancer Cell最新文献_第7页

Easing cisplatin’s toll in nasopharyngeal carcinoma 缓解顺铂在鼻咽癌中的作用

IF 50.3 1区医学

Cancer Cell Pub Date : 2025-04-24 DOI: 10.1016/j.ccell.2025.03.034

Michael J. Dennis, Ravindra Uppaluri

引用次数: 0

Stromal lipid species dictate melanoma metastasis and tropism 基质脂质种类决定黑色素瘤的转移和趋向性

IF 50.3 1区医学

Cancer Cell Pub Date : 2025-04-24 DOI: 10.1016/j.ccell.2025.04.001

Shilpa Gurung, Timothy Budden, Karthik Mallela, Benjamin Jenkins, Alex von Kriegsheim, Esperanza Manrique, David Millán-Esteban, Isabel Romero-Camarero, Fabio Amaral, Sarah Craig, Pedro Durao, Joanna Pozniak, Laura Stennett, Duncan Smith, Garry Ashton, Alex Baker, Kang Zeng, Gilbert Fruhwirth, Victoria Sanz-Moreno, Jair Marques, Amaya Virós

{"title":"Stromal lipid species dictate melanoma metastasis and tropism","authors":"Shilpa Gurung, Timothy Budden, Karthik Mallela, Benjamin Jenkins, Alex von Kriegsheim, Esperanza Manrique, David Millán-Esteban, Isabel Romero-Camarero, Fabio Amaral, Sarah Craig, Pedro Durao, Joanna Pozniak, Laura Stennett, Duncan Smith, Garry Ashton, Alex Baker, Kang Zeng, Gilbert Fruhwirth, Victoria Sanz-Moreno, Jair Marques, Amaya Virós","doi":"10.1016/j.ccell.2025.04.001","DOIUrl":"https://doi.org/10.1016/j.ccell.2025.04.001","url":null,"abstract":"Cancer cells adapt to signals in the tumor microenvironment (TME), but the TME cues that impact metastasis and tropism are still incompletely understood. We show that abundant stromal lipids from young subcutaneous adipocytes, including phosphatidylcholines, are taken up by melanoma cells, where they upregulate melanoma PI3K-AKT signaling, fatty acid oxidation, oxidative phosphorylation (OXPHOS) leading to oxidative stress, resulting in decreased metastatic burden. High OXPHOS melanoma cells predominantly seed the lung and brain; decreasing oxidative stress with antioxidants shifts tropism from the lung to the liver. By contrast, the aged TME provides fewer total lipids but is rich in ceramides, leading to lower OXPHOS and high metastatic burden. Aged TME ceramides taken up by melanoma cells activate the S1P-STAT3-IL-6 signaling axis and promote liver tropism. Inhibiting OXPHOS in the young TME or blocking the IL-6 receptor in the aged TME reduces the age-specific patterns of metastasis imposed by lipid availability.","PeriodicalId":9670,"journal":{"name":"Cancer Cell","volume":"14 1","pages":""},"PeriodicalIF":50.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

KMT2C deficiency drives transdifferentiation of double-negative prostate cancer and confer resistance to AR-targeted therapy KMT2C缺陷驱动双阴性前列腺癌转分化并赋予对ar靶向治疗的抗性

IF 50.3 1区医学

Cancer Cell Pub Date : 2025-04-24 DOI: 10.1016/j.ccell.2025.04.002

Jiacheng Guo, Ni Li, Qiuli Liu, Zongyao Hao, Guanghui Zhu, Xuege Wang, Hanling Wang, Qiang Pan, Beitao Xu, Ying Han, Guoying Zhang, Yannan Lian, Wei Zhang, Yongqiang Gu, Naiheng Lin, Xin Zeng, Zige Jin, Weihua Lan, Jun Jiang, Dong Gao, Jun Qin

{"title":"KMT2C deficiency drives transdifferentiation of double-negative prostate cancer and confer resistance to AR-targeted therapy","authors":"Jiacheng Guo, Ni Li, Qiuli Liu, Zongyao Hao, Guanghui Zhu, Xuege Wang, Hanling Wang, Qiang Pan, Beitao Xu, Ying Han, Guoying Zhang, Yannan Lian, Wei Zhang, Yongqiang Gu, Naiheng Lin, Xin Zeng, Zige Jin, Weihua Lan, Jun Jiang, Dong Gao, Jun Qin","doi":"10.1016/j.ccell.2025.04.002","DOIUrl":"https://doi.org/10.1016/j.ccell.2025.04.002","url":null,"abstract":"Double-negative prostate cancer (DNPC), characterized by an androgen receptor (AR)- and neuroendocrine-null phenotype, frequently emerges following androgen deprivation therapy (ADT). However, our understanding of the origins and regulatory mechanisms of DNPC remains limited. Here, we discover that tumors with <em>KMT2C</em> mutation or loss are highly susceptible to transitioning into DNPC following ADT. We clarify that DNPC primarily stems from luminal cell transdifferentiation rather than basal cell transformation. Antiandrogen treatment induces KMT2C binding at enhancers of a subset of AR-regulated genes, preserving the adenocarcinoma lineage. KMT2C maintains <em>ASPP2</em> expression via enhancer-promoter communication post-AR inhibition, while its inactivation reduces ASPP2, triggering ΔNp63-dependent transdifferentiation. This DNPC transition maintains fatty acid (FA) synthesis through ΔNp63-mediated SREBP1c transactivation, fueling DNPC growth via HRAS palmitoylation and MAPK signaling activation. These findings highlight KMT2C as an epigenetic checkpoint against DNPC development and suggest the therapeutic potential of targeting fatty acid synthesis.","PeriodicalId":9670,"journal":{"name":"Cancer Cell","volume":"7 1","pages":""},"PeriodicalIF":50.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-driven predictive biomarker discovery with contrastive learning to improve clinical trial outcomes 人工智能驱动的预测性生物标志物发现与对比学习，以改善临床试验结果

IF 50.3 1区医学

Cancer Cell Pub Date : 2025-04-17 DOI: 10.1016/j.ccell.2025.03.029

Gustavo Arango-Argoty, Damian E. Bikiel, Gerald J. Sun, Elly Kipkogei, Kaitlin M. Smith, Sebastian Carrasco Pro, Elizabeth Y. Choe, Etai Jacob

{"title":"AI-driven predictive biomarker discovery with contrastive learning to improve clinical trial outcomes","authors":"Gustavo Arango-Argoty, Damian E. Bikiel, Gerald J. Sun, Elly Kipkogei, Kaitlin M. Smith, Sebastian Carrasco Pro, Elizabeth Y. Choe, Etai Jacob","doi":"10.1016/j.ccell.2025.03.029","DOIUrl":"https://doi.org/10.1016/j.ccell.2025.03.029","url":null,"abstract":"Modern clinical trials can capture tens of thousands of clinicogenomic measurements per individual. Discovering predictive biomarkers, as opposed to prognostic markers, remains challenging. To address this, we present a neural network framework based on contrastive learning—the Predictive Biomarker Modeling Framework (PBMF)—that explores potential predictive biomarkers in an automated, systematic, and unbiased manner. Applied retrospectively to real clinicogenomic datasets, particularly for immuno-oncology (IO) trials, our algorithm identifies biomarkers of IO-treated individuals who survive longer than those treated with other therapies. We demonstrate how our framework retrospectively contributes to a phase 3 clinical trial by uncovering a predictive, interpretable biomarker based solely on early study data. Patients identified with this predictive biomarker show a 15% improvement in survival risk compared to those in the original trial. The PBMF offers a general-purpose, rapid, and robust approach to inform biomarker strategy, providing actionable outcomes for clinical decision-making.","PeriodicalId":9670,"journal":{"name":"Cancer Cell","volume":"263 1","pages":""},"PeriodicalIF":50.3,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning the language of T cell receptors through large-scale screening 通过大规模筛选学习 T 细胞受体的语言

IF 50.3 1区医学

Cancer Cell Pub Date : 2025-04-17 DOI: 10.1016/j.ccell.2025.03.032

Živa Moravec, John B.A.G. Haanen, Ton N. Schumacher, Wouter Scheper

引用次数: 0

Complexity of insulin resistance in early-onset colorectal cancer 早发性结直肠癌胰岛素抵抗的复杂性

IF 50.3 1区医学

Cancer Cell Pub Date : 2025-04-17 DOI: 10.1016/j.ccell.2025.03.033

Samson Mathews Samuel, Elizabeth Varghese, Dietrich Büsselberg

引用次数: 0

Many paths, one destination: Bridging the gap in cancer care 许多途径，一个目标：弥合癌症治疗的差距

IF 50.3 1区医学

Cancer Cell Pub Date : 2025-04-14 DOI: 10.1016/j.ccell.2025.03.026

Erik Sahai, Hongxia Wang, Marleen Kok, Catherine J. Wu, Lin Shen, John Carpten, César Serrano, Varun Venkataramani

引用次数: 0

New horizons at the interface of artificial intelligence and translational cancer research 人工智能与转化性癌症研究的新视野

IF 50.3 1区医学

Cancer Cell Pub Date : 2025-04-14 DOI: 10.1016/j.ccell.2025.03.018

Josephine Yates, Eliezer M. Van Allen

{"title":"New horizons at the interface of artificial intelligence and translational cancer research","authors":"Josephine Yates, Eliezer M. Van Allen","doi":"10.1016/j.ccell.2025.03.018","DOIUrl":"https://doi.org/10.1016/j.ccell.2025.03.018","url":null,"abstract":"Artificial intelligence (AI) is increasingly being utilized in cancer research as a computational strategy for analyzing multiomics datasets. Advances in single-cell and spatial profiling technologies have contributed significantly to our understanding of tumor biology, and AI methodologies are now being applied to accelerate translational efforts, including target discovery, biomarker identification, patient stratification, and therapeutic response prediction. Despite these advancements, the integration of AI into clinical workflows remains limited, presenting both challenges and opportunities. This review discusses AI applications in multiomics analysis and translational oncology, emphasizing their role in advancing biological discoveries and informing clinical decision-making. Key areas of focus include cellular heterogeneity, tumor microenvironment interactions, and AI-aided diagnostics. Challenges such as reproducibility, interpretability of AI models, and clinical integration are explored, with attention to strategies for addressing these hurdles. Together, these developments underscore the potential of AI and multiomics to enhance precision oncology and contribute to advancements in cancer care.","PeriodicalId":9670,"journal":{"name":"Cancer Cell","volume":"25 1","pages":""},"PeriodicalIF":50.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143827141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bridging the gap: The future of cancer research and clinical oncology in Cancer Cell 弥合差距：癌症研究和临床肿瘤学的未来

IF 50.3 1区医学

Cancer Cell Pub Date : 2025-04-14 DOI: 10.1016/j.ccell.2025.03.024

Steve Mao

引用次数: 0

CAR-T therapy in solid tumors CAR-T疗法在实体瘤中的应用

IF 50.3 1区医学

Cancer Cell Pub Date : 2025-04-14 DOI: 10.1016/j.ccell.2025.03.019

Bing Du, Juliang Qin, Boxu Lin, Jiqin Zhang, Dali Li, Mingyao Liu

引用次数: 0