Seminars in cancer biology最新文献_第8页

Multifaceted effects of obesity on cancer immunotherapies: Bridging preclinical models and clinical data 肥胖对癌症免疫治疗的多方面影响:连接临床前模型和临床数据

IF 14.5 1区医学

Seminars in cancer biology Pub Date : 2023-10-01 DOI: 10.1016/j.semcancer.2023.07.004

Logan V. Vick , Robert J. Canter , Arta M. Monjazeb , William J. Murphy

{"title":"Multifaceted effects of obesity on cancer immunotherapies: Bridging preclinical models and clinical data","authors":"Logan V. Vick , Robert J. Canter , Arta M. Monjazeb , William J. Murphy","doi":"10.1016/j.semcancer.2023.07.004","DOIUrl":"10.1016/j.semcancer.2023.07.004","url":null,"abstract":"<div><p>Obesity, defined by excessive body fat, is a highly complex condition affecting numerous physiological processes, such as metabolism, proliferation, and cellular homeostasis. These multifaceted effects impact cells and tissues throughout the host, including immune cells as well as cancer biology. Because of the multifaceted nature of obesity, common parameters used to define it (such as body mass index in humans) can be problematic, and more nuanced methods are needed to characterize the pleiotropic metabolic effects of obesity. Obesity is well-accepted as an overall negative prognostic factor for cancer incidence, progression, and outcome. This is in part due to the meta-inflammatory and immunosuppressive effects of obesity. Immunotherapy is increasingly used in cancer therapy, and there are many different types of immunotherapy approaches. The effects of obesity on immunotherapy have only recently been studied with the demonstration of an “obesity paradox”, in which some immune therapies have been demonstrated to result in greater efficacy in obese subjects despite the direct adverse effects of obesity and excess body fat acting on the cancer itself. The multifactorial characteristics that influence the effects of obesity (age, sex, lean muscle mass, underlying metabolic conditions and drugs) further confound interpretation of clinical data and necessitate the use of more relevant preclinical models mirroring these variables in the human scenario. Such models will allow for more nuanced mechanistic assessment of how obesity can impact, both positively and negatively, cancer biology, host metabolism, immune regulation, and how these intersecting processes impact the delivery and outcome of cancer immunotherapy.</p></div>","PeriodicalId":21594,"journal":{"name":"Seminars in cancer biology","volume":"95 ","pages":"Pages 88-102"},"PeriodicalIF":14.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10520413","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

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives 应用人工智能加强头颈部肿瘤管理:整合与展望

IF 14.5 1区医学

Seminars in cancer biology Pub Date : 2023-10-01 DOI: 10.1016/j.semcancer.2023.07.002

Nian-Nian Zhong , Han-Qi Wang , Xin-Yue Huang , Zi-Zhan Li , Lei-Ming Cao , Fang-Yi Huo , Bing Liu , Lin-Lin Bu

{"title":"Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives","authors":"Nian-Nian Zhong , Han-Qi Wang , Xin-Yue Huang , Zi-Zhan Li , Lei-Ming Cao , Fang-Yi Huo , Bing Liu , Lin-Lin Bu","doi":"10.1016/j.semcancer.2023.07.002","DOIUrl":"10.1016/j.semcancer.2023.07.002","url":null,"abstract":"<div><p>Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI’s indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.</p></div>","PeriodicalId":21594,"journal":{"name":"Seminars in cancer biology","volume":"95 ","pages":"Pages 52-74"},"PeriodicalIF":14.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10166293","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: 2

Proactive and reactive roles of TGF-β in cancer 转化生长因子-β在癌症中的主动和反应作用。

IF 14.5 1区医学

Seminars in cancer biology Pub Date : 2023-10-01 DOI: 10.1016/j.semcancer.2023.08.002

Nick A. Kuburich , Thiru Sabapathy , Breanna R. Demestichas , Joanna Joyce Maddela , Petra den Hollander , Sendurai A. Mani

{"title":"Proactive and reactive roles of TGF-β in cancer","authors":"Nick A. Kuburich , Thiru Sabapathy , Breanna R. Demestichas , Joanna Joyce Maddela , Petra den Hollander , Sendurai A. Mani","doi":"10.1016/j.semcancer.2023.08.002","DOIUrl":"10.1016/j.semcancer.2023.08.002","url":null,"abstract":"<div><p>Cancer cells adapt to varying stress conditions to survive through plasticity. Stem cells exhibit a high degree of plasticity, allowing them to generate more stem cells or differentiate them into specialized cell types to contribute to tissue development, growth, and repair. Cancer cells can also exhibit plasticity and acquire properties that enhance their survival. TGF-β is an unrivaled growth factor exploited by cancer cells to gain plasticity. TGF-β-mediated signaling enables carcinoma cells to alter their epithelial and mesenchymal properties through epithelial-mesenchymal plasticity (EMP). However, TGF-β is a multifunctional cytokine; thus, the signaling by TGF-β can be detrimental or beneficial to cancer cells depending on the cellular context. Those cells that overcome the anti-tumor effect of TGF-β can induce epithelial-mesenchymal transition (EMT) to gain EMP benefits. EMP allows cancer cells to alter their cell properties and the tumor immune microenvironment (TIME), facilitating their survival. Due to the significant roles of TGF-β and EMP in carcinoma progression, it is essential to understand how TGF-β enables EMP and how cancer cells exploit this plasticity. This understanding will guide the development of effective TGF-β-targeting therapies that eliminate cancer cell plasticity.</p></div>","PeriodicalId":21594,"journal":{"name":"Seminars in cancer biology","volume":"95 ","pages":"Pages 120-139"},"PeriodicalIF":14.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10166819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application 基于人工智能的骨肿瘤放射组学:技术进展与临床应用

IF 14.5 1区医学

Seminars in cancer biology Pub Date : 2023-10-01 DOI: 10.1016/j.semcancer.2023.07.003

Yichen Meng , Yue Yang , Miao Hu, Zheng Zhang, Xuhui Zhou

{"title":"Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application","authors":"Yichen Meng , Yue Yang , Miao Hu, Zheng Zhang, Xuhui Zhou","doi":"10.1016/j.semcancer.2023.07.003","DOIUrl":"10.1016/j.semcancer.2023.07.003","url":null,"abstract":"<div><p>Radiomics<span> is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.</span></p></div>","PeriodicalId":21594,"journal":{"name":"Seminars in cancer biology","volume":"95 ","pages":"Pages 75-87"},"PeriodicalIF":14.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10538699","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

Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects 用人工智能加速抗体的发现和设计:最新进展和前景

IF 14.5 1区医学

Seminars in cancer biology Pub Date : 2023-10-01 DOI: 10.1016/j.semcancer.2023.06.005

Ganggang Bai , Chuance Sun , Ziang Guo , Yangjing Wang , Xincheng Zeng , Yuhong Su , Qi Zhao , Buyong Ma

引用次数: 2

The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics 单细胞和空间转录组学在癌症细胞-细胞相互作用分析中的应用前景。

IF 14.5 1区医学

Seminars in cancer biology Pub Date : 2023-10-01 DOI: 10.1016/j.semcancer.2023.07.001

Xinyi Wang , Axel A. Almet , Qing Nie

引用次数: 2

Harnessing computational spatial omics to explore the spatial biology intricacies 利用计算空间组学探索空间生物学的复杂性

IF 14.5 1区医学

Seminars in cancer biology Pub Date : 2023-10-01 DOI: 10.1016/j.semcancer.2023.06.006

Zhiyuan Yuan , Jianhua Yao

{"title":"Harnessing computational spatial omics to explore the spatial biology intricacies","authors":"Zhiyuan Yuan , Jianhua Yao","doi":"10.1016/j.semcancer.2023.06.006","DOIUrl":"10.1016/j.semcancer.2023.06.006","url":null,"abstract":"<div><p>Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.</p></div>","PeriodicalId":21594,"journal":{"name":"Seminars in cancer biology","volume":"95 ","pages":"Pages 25-41"},"PeriodicalIF":14.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10167065","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: 2

Mediterranean diet and olive oil, microbiota, and obesity-related cancers. From mechanisms to prevention 地中海饮食和橄榄油，微生物群，和肥胖相关的癌症。从机制到预防

IF 14.5 1区医学

Seminars in cancer biology Pub Date : 2023-10-01 DOI: 10.1016/j.semcancer.2023.08.001

Enrique Almanza-Aguilera , Ainara Cano , Mercedes Gil-Lespinard , Nerea Burguera , Raul Zamora-Ros , Antonio Agudo , Marta Farràs

{"title":"Mediterranean diet and olive oil, microbiota, and obesity-related cancers. From mechanisms to prevention","authors":"Enrique Almanza-Aguilera , Ainara Cano , Mercedes Gil-Lespinard , Nerea Burguera , Raul Zamora-Ros , Antonio Agudo , Marta Farràs","doi":"10.1016/j.semcancer.2023.08.001","DOIUrl":"10.1016/j.semcancer.2023.08.001","url":null,"abstract":"<div><p>Olive oil (OO) is the main source of added fat in the Mediterranean diet (MD). It is a mix of bioactive compounds, including monounsaturated fatty acids, phytosterols, simple phenols, secoiridoids, flavonoids, and terpenoids. There is a growing body of evidence that MD and OO improve obesity-related factors. In addition, obesity has been associated with an increased risk for several cancers: endometrial, oesophageal adenocarcinoma, renal, pancreatic, hepatocellular, gastric cardia, meningioma, multiple myeloma, colorectal, postmenopausal breast, ovarian, gallbladder, and thyroid cancer. However, the epidemiological evidence linking MD and OO with these obesity-related cancers, and their potential mechanisms of action, especially those involving the gut microbiota, are not clearly described or understood. The goals of this review are 1) to update the current epidemiological knowledge on the associations between MD and OO consumption and obesity-related cancers, 2) to identify the gut microbiota mechanisms involved in obesity-related cancers, and 3) to report the effects of MD and OO on these mechanisms.</p></div>","PeriodicalId":21594,"journal":{"name":"Seminars in cancer biology","volume":"95 ","pages":"Pages 103-119"},"PeriodicalIF":14.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10158127","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: 3

Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity 癌症的动力学特征：黑色素瘤表型转换和上皮-间充质可塑性。

IF 14.5 1区医学

Seminars in cancer biology Pub Date : 2023-10-01 DOI: 10.1016/j.semcancer.2023.09.007

Paras Jain , Maalavika Pillai , Atchuta Srinivas Duddu , Jason A. Somarelli , Yogesh Goyal , Mohit Kumar Jolly

{"title":"Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity","authors":"Paras Jain , Maalavika Pillai , Atchuta Srinivas Duddu , Jason A. Somarelli , Yogesh Goyal , Mohit Kumar Jolly","doi":"10.1016/j.semcancer.2023.09.007","DOIUrl":"10.1016/j.semcancer.2023.09.007","url":null,"abstract":"<div><p>Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.</p></div>","PeriodicalId":21594,"journal":{"name":"Seminars in cancer biology","volume":"96 ","pages":"Pages 48-63"},"PeriodicalIF":14.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41139637","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

Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology 基于人工智能的妇科肿瘤风险分层、准确诊断和治疗预测。

IF 14.5 1区医学

Seminars in cancer biology Pub Date : 2023-09-30 DOI: 10.1016/j.semcancer.2023.09.005

Yuting Jiang , Chengdi Wang , Shengtao Zhou

{"title":"Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology","authors":"Yuting Jiang , Chengdi Wang , Shengtao Zhou","doi":"10.1016/j.semcancer.2023.09.005","DOIUrl":"10.1016/j.semcancer.2023.09.005","url":null,"abstract":"<div><p>As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.</p></div>","PeriodicalId":21594,"journal":{"name":"Seminars in cancer biology","volume":"96 ","pages":"Pages 82-99"},"PeriodicalIF":14.5,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41149728","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: 2