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Optimizing Postneoadjuvant Treatment of Residual Breast cancer With Adjuvant Bevacizumab Alone, With Metronomic or Standard-Dose Chemotherapy: A Combined Analysis of DFCI 05-055 and DFCI 09-134/TBCRC 012/ABCDE Clinical Trials

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Clinical Breast cancer 2025年第4期25卷 e419-e430.e5页

作者： Trapani, Dario Jin, Qingchun Miller, Kathy D. Rugo, Hope S. Reeder-Hayes, Katherine E. Traina, Tiffany Abdou, Yara Falkson, Carla Abramson, Vandana Ligibel, Jennifer Chen, Wendy Come, Steven Nohria, Anju Ryabin, Nicole Tayob, Nabihah Tolaney, Sara M Burstein, Harold J. Mayer, Erica L. Medical Oncology Dana-Farber Cancer Institute Boston MA United States Breast Oncology Program Dana-Farber Brigham Cancer Center Boston MA United States Harvard Medical School Boston MA United States Data Science Dana-Farber Cancer Institute Boston MA United States Indiana University Melvin and Bren Simon Comprehensive Cancer Center Indianapolis IN United States University of California at San Francisco San Francisco CA United States University of North Carolina Lineberger Comprehensive Cancer Institute Chapel Hill NC United States Memorial Sloan Kettering Cancer Center New York NY United States University of Alabama Birmingham AL United States Vanderbilt University Nashville TN United States Beth Israel Deaconess Medical Center Boston MA United States Division of Cardiovascular Medicine Brigham and Women's Hospital Boston MA United States

Background: Breast cancer patients with residual disease after neoadjuvant therapy have increased risk of recurrence. Novel therapies to decrease this risk are urgently needed. Methods: Two clinical trials (05-055 and 09-134) offered adjuvant bevacizumab-based therapy to stage I–III breast cancer patients with residual disease after neoadjuvant chemotherapy. Study 05-055 evaluated four treatment regimens: bevacizumab (cohort A);bevacizumab with metronomic cyclophosphamide and methotrexate (CM) (cohort B);and bevacizumab with body surface area-dosed capecitabine (cohorts C);or flat-dosed capecitabine (cohort D). The primary endpoint was feasibility and tolerability. In 09-134, patients were randomized to bevacizumab with or without CM;the primary endpoint was recurrence-free survival (RFS). Study 09-134 closed prematurely for lack of accrual. A pooled survival analysis with participants from 05-055 and 09-134 was conducted. Results: Among 213 total patients (05-055, n = 163;09-134, n = 50), the most common adverse events (AEs) of any grade were headache (49.3%) and fatigue (57.3%). Grade 3-4 AEs were highest in cohorts C (71.4%) and D (72.5%). The 36-month RFS was 58.0% with bevacizumab monotherapy, 62.3% with bevacizumab plus CM, and 72.7%-75.0% with bevacizumab plus capecitabine (depending on schedule). Treatment with capecitabine was independently associated with improved RFS in triple-negative breast cancer (TNBC) (HR: 0.47;95% CI, 0.23-0.96). Conclusion: This pooled analysis demonstrates that postneoadjuvant bevacizumab plus capecitabine may be associated with improved RFS, especially in TNBC. Each regimen carries moderate toxicity, and despite these treatments, patients with residual disease after neoadjuvant therapy still experience high rates of recurrence, indicating that new strategies are warranted. Clinical Trial Registration: ***, NCT00121134 (DFCI Protocol Number: 05-055);NCT00925652 (DFCI Protocol Number: 09-134). © 2025 Elsevier Inc.

关键词： Anti-angiogenesis Capecitabine Preoperative TNBC VEGF

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Multimodal Whole Slide Foundation Model for Pathology

arXiv

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arXiv 2024年

作者： Ding, Tong Wagner, Sophia J. Song, Andrew H. Chen, Richard J. Lu, Ming Y. Zhang, Andrew Vaidya, Anurag J. Jaume, Guillaume Shaban, Muhammad Kim, Ahrong Williamson, Drew F.K. Chen, Bowen Almagro-Perez, Cristina Doucet, Paul Sahai, Sharifa Chen, Chengkuan Komura, Daisuke Kawabe, Akihiro Ishikawa, Shumpei Gerber, Georg Peng, Tingying Le, Long Phi Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Harvard Data Science Initiative Harvard University CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Department of Preventive Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Pathology National Cancer Center Exploratory Oncology Research & Clinical Trial Center Chiba Japan

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://***/mahmoodlab/TITAN © 2024, CC BY-NC-ND.

关键词： Self-supervised learning

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Pan-cancer integrative histology-genomic analysis via interpretable multimodal deep learning

arXiv

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arXiv 2021年

作者： Chen, Richard J. Lu, Ming Y. Williamson, Drew F.K. Chen, Tiffany Y. Lipkova, Jana Shaban, Muhammad Shady, Maha Williams, Mane Joo, Bumjin Noor, Zahra Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Computer Science Harvard University CambridgeMA United States

The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. However, most prognostic models are either based on histology or genomics alone and do not address how histology and genomics can be integrated to develop joint image-omic prognostic models. Additionally identifying explainable morphological and molecular descriptors from these models that govern such prognosis is of interest. We used multimodal deep learning to integrate gigapixel whole slide pathology images, RNA-seq abundance, copy number variation, and mutation data from 5,720 patients across 14 major cancer types. Our interpretable, weakly-supervised, multimodal deep learning algorithm is able to fuse these heterogeneous modalities for predicting outcomes and discover prognostic features from these modalities that corroborate with poor and favorable outcomes via multimodal interpretability. We compared our model with unimodal deep learning models trained on histology slides and molecular profiles alone, and demonstrate performance increase in risk stratification on 9 out of 14 cancers. In addition, we analyze morphologic and molecular markers responsible for prognostic predictions across all cancer types. All analyzed data, including morphological and molecular correlates of patient prognosis across the 14 cancer types at a disease and patient level are presented in an interactive open-access database (http://***) to allow for further exploration and prognostic biomarker discovery. To validate that these model explanations are prognostic, we further analyzed high attention morphological regions in WSIs, which indicates that tumor-infiltrating lymphocyte presence corroborates with favorable cancer prognosis on 9 out of 14 cancer types studied. Copyright © 2021, The Authors. All rights reserved.

关键词： Diseases

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Genetic Perturbation of CD70/CD27 Co-Stimulation Promotes the Development of Bcl6-Driven Diffuse Large B-Cell Lymphoma

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BLOOD 2021年 138卷 713-713页

作者： Mandato, Elisa Sun, Yanbo Shanmugam, Vignesh Choi, Il-Kyu Wright, Kyle T. Redd, Robert A. Lawton, Lee N. Neuberg, Donna S. Rodig, Scott J. Zhang, Baochun Shipp, Margaret A. Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Department of Pathology Brigham and Women's Hospital Boston MA Broad Institute of MIT and Harvard Cancer Program Cambridge MA Department of Pathology University of Oklahoma Health Sciences Center Oklahoma City OK Department of Data Science Dana-Farber Cancer Institute Boston MA Department of Data Science Dana-Farber Cancer Institute Boston Department of Pathology Brigham & Women's Hospital Boston MA Department of Medical Oncology Dana Farber Cancer Institute Boston MA

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Phase I Study of Ixazomib Added to Chemotherapy in the Treatment of Acute Lymphoblastic Leukemia in Older Adults

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BLOOD 2020年 136卷 41-42页

作者： Amrein, Philip C. Ballen, Karen K. Stevenson, Kristen E. Blonquist, Traci M. Brunner, Andrew M. Hobbs, Gabriela S. Hock, Hanno R. McAfee, Steven L. Moran, Jenna A. Bergeron, Meghan Foster, Julia E. Bertoli, Christina McGregor, Kristin Macrae, Molly Burke, Meghan Behnan, Tanya T. Som, Tina T. Ramos, Aura Y. Vartanian, Megan K. Story, Jennifer Lombardi Connolly, Christine Graubert, Timothy A. Neuberg, Donna S. Fathi, Amir T. Leukemia Center/MGH Cancer Center Massachusetts General Hospital Belmont MA Division of Hematology/Oncology University of Virginia Health Center Charlottesville VA Department of Data Science Dana-Farber Cancer Institute Boston MA Data Science Dana-Farber Cancer Institute Boston MA Center for Leukemia Massachusetts General Hospital Boston MA Leukemia Center Massachusetts General Hospital Boston MA Leukemia Center Massachusetts General Hospital / Harvard Medical School Boston MA Massachusetts General Hospital Blood and Marrow Transplant Program Boston MA Leukemia Center Massachusetts General Hospital Center for Leukemia Cambridge MA

Introduction: While progress has been made in the treatment of childhood leukemia, the outlook for patients >60 years of age with acute lymphoblastic leukemia (ALL) is poor with complete remission rates (CR) of approximately 60% and 3-year survivals (OS) of less than 15%. Intensified treatment in a later CALGB trial showed little improvement with a CR=61% and 5-year OS=6% (Stock, cancer 2013). Ixazomib is an oral proteasome inhibitor, which has shown single agent activity and promising combination activity in pediatric ALL patients (Messinger, Blood 2012). We sought to assess the safety and tolerability, as well as early efficacy of adding ixazomib to a current MGH-DFCI/HCC multi-agent regimen for older adults with ALL. Methods: Patients aged 51 to 75 years of age with newly diagnosed B-ALL and T-ALL were screened for eligibility. Patients with mature ALL (including Burkitt's) were excluded. Patients with Philadelphia chromosome positive ALL (BCR-ABL1+) were eligible, and dasatinib was added to the chemotherapy on Day 10 for these patients. The chemotherapy treatment schedule from induction through maintenance is outlined in Table 1. A standard 3 + 3 patient cohort dose escalation design was used to determine the maximum tolerated dose (MTD) of ixazomib during induction for these patients, the primary objective of the trial. After consolidation I, patients in complete remission (CR) with a suitable donor were offered a hematopoietic stem cell transplantation (HSCT) as per institutional guidelines. Those not going to HSCT continued therapy as noted in the table. Results: There were 19 patients with B-ALL enrolled, none with T-ALL. Among these patients, 7 harbored BCR-ABL1 rearrangements. The median age was 65 years, 74% were male, and 90% had a performance status 0 or 1. The MTD was 2.3 mg of ixazomib, as 2 patients at 3.0 mg developed DLT's: a grade 3 peripheral neuropathy and a grade 5 acute kidney injury (Table 2). Grade 3 and 4 toxicities encountered at any time

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Macrophages direct location-dependent recall of B cell memory to vaccination

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Cell 2025年

作者： Dhenni, Rama Hoppé, Alexandra Carey Reynaldi, Arnold Kyaw, Wunna Handoko, Nathalie Tricia Grootveld, Abigail K. Keith, Yuki Honda Bhattacharyya, Nayan Deger Ahel, Holly I. Telfser, Aiden Josiah McCorkindale, Andrew N. Yazar, Seyhan Bui, Christina H.T. Smith, James T. Khoo, Weng Hua Boyd, Mollie Obeid, Solange Milner, Brad Starr, Mitchell Brilot, Fabienne Milogiannakis, Vanessa Akerman, Anouschka Aggarwal, Anupriya Davenport, Miles P. Deenick, Elissa K. Chaffer, Christine L. Croucher, Peter I. Brink, Robert Goldstein, Leonard D. Cromer, Deborah Turville, Stuart G. Kelleher, Anthony D. Venturi, Vanessa Munier, C. Mee Ling Phan, Tri Giang Precision Immunology Program Garvan Institute of Medical Research Sydney NSW Australia St. Vincent's Healthcare Clinical Campus School of Clinical Medicine Faculty of Medicine and Health UNSW Sydney Kensington Sydney NSW Australia Immunovirology and Pathogenesis Program Kirby Institute UNSW Sydney Sydney NSW Australia Infection Analytics Program Kirby Institute UNSW Sydney Sydney NSW Australia Data Science Platform Garvan Institute of Medical Research Sydney 2010 NSW Australia St. Vincent's Hospital Sydney Sydney NSW Australia Cancer Plasticity and Dormancy Program The Kinghorn Cancer Centre Garvan Institute of Medical Research Sydney 2010 NSW Australia St. Vincent's Centre for Applied Medical Research Sydney NSW Australia Immune Biotherapies Program Garvan Institute of Medical Research Sydney 2010 NSW Australia Brain Autoimmunity Group Kids Neuroscience Centre The Children's Hospital at Westmead Faculty of Medicine and Health School of Medical Sciences Sydney NSW Australia School of Medical Science Faculty of Medicine and Health University of Sydney Sydney NSW Australia

Vaccines generate long-lived plasma cells and memory B cells (Bmems) that may re-enter secondary germinal centers (GCs) to further mutate their B cell receptor upon boosting and re-exposure to antigen. We show in mouse models that lymph nodes draining the site of primary vaccination harbor a subset of Bmems that reside in the subcapsular niche, generate larger recall responses, and are more likely to re-enter GCs compared with circulating Bmems in non-draining lymph nodes. This location-dependent recall of Bmems into the GC in the draining lymph node was dependent on CD169⁺ subcapsular sinus macrophages (SSMs) in the subcapsular niche. In human participants, boosting of the BNT162b2 vaccine in the same arm generated more rapid secretion of broadly neutralizing antibodies, GC participation, and clonal expansion of SARS-CoV-2-specific B cells than boosting of the opposite arm. These data reveal an unappreciated role for primed draining lymph node SSMs in Bmem cell fate determination. © 2025 The Author(s)

关键词： affinity maturation broadly neutralizing antibodies germinal center immune imprinting memory original antigenic sin plasma cells SARS-CoV-2 subcapsular sinus macrophage vaccine

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EMBEDDING SPACE AUGMENTATION FOR WEAKLY SUPERVISED LEARNING IN WHOLE-SLIDE IMAGES

arXiv

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arXiv 2022年

作者： Zaffar, Imaad Jaume, Guillaume Rajpoot, Nasir Mahmood, Faisal Department of Computer Science University College London United Kingdom Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Tissue Image Analytics Centre Department of Computer Science University of Warwick Coventry United Kingdom Department of Pathology University Hospitals Coventry and Warwickshire NHS Trust Coventry United Kingdom The Alan Turing Institute London United Kingdom

Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelines for WSI-level analysis, the WSIs are often divided into patches and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall computational cost and cope with the GPUs' limited RAM. To overcome this limitation, we present EMBAUGMENTER, a data augmentation generative adversarial network (DA-GAN) that can synthesize data augmentations in the embedding space rather than in the pixel space, thereby significantly reducing the computational requirements. Experiments on the SICAPv2 dataset show that our approach outperforms MIL without augmentation and is on par with traditional patch-level augmentation for MIL training while being substantially faster. © 2022, CC BY.

关键词： Generative adversarial networks

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Molecular-driven Foundation Model for Oncologic Pathology

arXiv

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arXiv 2025年

作者： Vaidya, Anurag Zhang, Andrew Jaume, Guillaume Song, Andrew H. Ding, Tong Wagner, Sophia J. Lu, Ming Y. Doucet, Paul Robertson, Harry Almagro-Pérez, Cristina Chen, Richard J. ElHarouni, Dina Ayoub, Georges Bossi, Connor Ligon, Keith L. Gerber, Georg Le, Long Phi Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich – German Research Center for Environment and Health Munich Germany School of Computation Information and Technology TUM Munich Germany CambridgeMA United States Sydney Precision Data Science Center The University of Sydney CamperdownNSW Australia Department of Oncologic Pathology Dana-Farber Cancer Institute BostonMA02215 United States Department of Pathology Boston Children’s Hospital BostonMA02115 United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks. Despite these advances, foundation models are still limited in their ability to encode the entire gigapixel whole-slide images without additional training and often lack complementary multimodal data. Here, we introduce THREADS, a slide-level foundation model capable of generating universal representations of whole-slide images of any size. THREADS was pretrained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections, paired with corresponding genomic and transcriptomic profiles—the largest such paired dataset to be used for foundation model development to date. This unique training paradigm enables THREADS to capture the tissue’s underlying molecular composition, yielding powerful representations applicable to a wide array of downstream tasks. In extensive benchmarking across 54 oncology tasks, including clinical subtyping, grading, mutation prediction, immunohistochemistry status determination, treatment response prediction and survival prediction THREADS outperformed all baselines while demonstrating remarkable generalizability and label efficiency. It is particularly well-suited for predicting rare events, further emphasizing its clinical utility. We intend to make the model publicly available for the broader community. © 2025, CC BY-NC-ND.

关键词： Transfer learning

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The exposome and pancreatic cancer, lifestyle and environmental risk factors for PDAC

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Seminars in cancer Biology 2025年 113卷 100-129页

作者： Peduzzi, Giulia Archibugi, Livia Farinella, Riccardo de Leon Pisani, Ruggero Ponz Vodickova, Ludmila Vodicka, Pavel Kraja, Bledar Sainz, Juan Bars-Cortina, David Daniel, Neil Silvestri, Roberto Uysal-Onganer, Pinar Landi, Stefano Dulińska-Litewka, Joanna Comandatore, Annalisa Campa, Daniele Hughes, David J. Rizzato, Cosmeri Department of Biology University of Pisa Pisa Italy Pancreato-Biliary Endoscopy and Endosonography Division Pancreas Translational and Clinical Research Center IRCCS Ospedale San Raffaele Milan Italy Biomedical Center Martin Bioinformatic Center Comenius University in Bratislava Jessenius Faculty of Medicine in Martin Slovakia Faculty of Medicine and Biomedical Center in Pilsen Charles University Pilsen Czech Republic University Clinic of Gastrohepatology University Hospital Center Mother Teresa Tirana Albania Department of Biochemistry and Molecular Biology University of Granada Granada Spain Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP) Madrid 28029 Spain GENYO. Centre for Genomics and Oncological Research. Genomic Oncology department Granada Spain Instituto de Investigación Biosanitaria Ibs.Granada Granada Spain Institut Català d'Oncologia (ICO) IDIBELL Unit of Biomarkers and Susceptibility (UBS) Oncology Data Analytics Program (ODAP) Catalan Institute of Oncology (ICO) L′Hospitalet del Llobregat Barcelona Spain Institut Català d'Oncologia (ICO) IDIBELL ONCOBELL Program Bellvitge Biomedical Research Institute (IDIBELL) L′Hospitalet de Llobregat Barcelona Spain Molecular Epidemiology of Cancer Group UCD Conway Institute School of Biomolecular and Biomedical Science University College Dublin Dublin Ireland Cancer Mechanisms and Biomarkers Research Group School of Life Sciences University of Westminster London United Kingdom Chair of Medical Biochemistry Medical College Jagiellonian University Krakow Poland General Surgery Unit Department of Translational Research and New Technologies in Medicine and Surgery University of Pisa Italy

Pancreatic cancer (PC), particularly pancreatic ductal adenocarcinoma (PDAC), is a significant global health issue with high mortality rates. PDAC, though only 3 % of cancer diagnoses, causes 7 % of cancer deaths due to its severity and asymptomatic early stages. Risk factors include lifestyle choices, environmental exposures, and genetic predispositions. Conditions like new-onset type 2 diabetes and chronic pancreatitis also contribute significantly. Modifiable risk factors include smoking, alcohol consumption, non-alcoholic fatty pancreatic disease (NAFPD), and obesity. Smoking and heavy alcohol consumption increase PC risk, while NAFPD and obesity, particularly central adiposity, contribute through chronic inflammation and insulin resistance. Refined sugar and sugar-sweetened beverages (SSBs) are also linked to increased PC risk, especially among younger individuals. Hormonal treatments and medications like statins, aspirin, and metformin have mixed results on PC risk, with some showing protective effects. The gut microbiome influences PC through the gut-pancreas axis, with disruptions leading to inflammation and carcinogenesis. Exposure to toxic substances, including heavy metals and chemicals, is associated with increased PC risk. Glycome changes, such as abnormal glycosylation patterns, are significant in PDAC development and offer potential for early diagnosis. Interactions between environmental and genetic factors are crucial in PDAC susceptibility. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) linked to PDAC, but gene-environment interactions remain largely unexplored. Future research should focus on polygenic risk scores (PRS) and large-scale studies to better understand these interactions and their impact on PDAC risk. © 2025 The Authors

关键词： Exposome Pancreatic cancer Risk factors

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