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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Infection after CD19 chimeric antigen receptor T cell therapy for large B cell lymphoma: Real-world analysis from CIBMTR

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Blood Advances 2025年

作者： Kitsada Wudhikarn Megan M. Herr Min Chen Michael J. Martens John H. Baird Lohith Gowda Hemalatha G. Rangarajan Muhammad Bilal Abid Mohamed A. Kharfan-Dabaja Kirsten M. Williams Siddhartha Ganguly Jo-Anne H. Young Akshay Sharma Giancarlo Fatobene Tania Jain Christopher G. Kanakry Dipenkumar Modi Natalie S. Grover Baheyeldin Salem Marjorie Vieira Batista Jeffery J. Auletta Center of Excellence in Translational Hematology and Division of Hematology Faculty of Medicine Chulalongkorn University Bangkok Thailand Roswell Park Comprehensive Cancer Center Buffalo NY CIBMTR® (Center for International Blood and Marrow Transplant Research) Medical College of Wisconsin Milwaukee WI Division of Biostatistics Data Science Institute Medical College of Wisconsin Milwaukee WI Division of Lymphoma Department of Hematology & Hematopoietic Cell Transplantation City of Hope Duarte CA Yale Cancer Center and Yale School of Medicine New Haven CT Department of Pediatric Hematology Oncology Blood and Marrow Transplantation Nationwide Children’s hospital Columbus OH Divisions of Hematology/Oncology & Infectious Diseases BMT & Cellular Therapy Program Medical College of Wisconsin Milwaukee WI Division of Hematology-Oncology Blood and Marrow Transplantation Program Mayo Clinic Jacksonville FL Aflac Cancer & Blood Disorders Center at Children’s Healthcare of Atlanta Emory University Atlanta GA Houston Methodist Hospital and Neal Cancer Center Houston TX Division of Infectious Disease and International Medicine Department of Medicine University of Minnesota Minneapolis MN Department of Bone Marrow Transplantation and Cellular Therapy St. Jude Children's Research Hospital Memphis TN Hospital das Clínicas and LIM31 Faculty of Medicine University of São Paulo Brazil / Hospital Vila Nova Star - Rede D'Or Sao Paulo Brazil Division of Hematological Malignancies and Bone Marrow Transplantation Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD Center for Immuno-Oncology Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD Division of Oncology Karmanos Cancer Center Wayne State University Detroit MI Lineberger Comprehensive Cancer Center Division of Hematology Department of Medicine University of North Carolina Chapel Hill NC Center for Cell and Gene Therapy Texas Children's Hospital

Infection is increasingly recognized as a significant cause of morbidity and mortality in patients with relapsed/refractory (R/R) large B-cell lymphoma (LBCL) receiving CD19 chimeric antigen receptor (CAR) T-cell therapy. The current study analyzed the natural history, risk factors, and outcomes of infection in 3350 R/R LBCL patients receiving commercial CD19 CAR T-cell (n=2804 axicabtagene ciloleucel, n=546 tisagenlecleucel) from December 2017 to June 2022. Infection developed in 834 (24.9%) patients within 100 days post-infusion, resulting in an infection density of 0.43 per 100 patient-days and a 100-day cumulative incidence of 22%. Bacterial, viral, and fungal infections were recorded in 527 (15.7%), 374 (11.2%), and 108 (3.2%) patients, respectively, with corresponding infection densities of 0.23, 0.15, and 0.04 per 100 patient-days. After a 24-month median follow-up, 1482 (44%) patients had died, with infection as the primary cause in 173 cases (12%). The 100-day infection-related mortality (IRM) was 1.6% (95% confidence interval, 1.2-2.0%). Patients with Karnofsky score ≤80, infection history pre-CAR-T, axicabtagene ciloleucel therapy, severe cytokine release syndrome (grade ≥3), and severe immune effector cell-associated neurotoxicity syndrome (grade ≥3) had increased infection risk. Infections within 100 days were an independent risk factor for inferior overall survival beyond day 100 after CD19 CAR T-cell therapy. In conclusion, study results show a significant incidence of infection and IRM in patients with R/R LBCL treated with CD19 CAR T-cell. Furthermore, results identify patients at heightened risk for infection, offering insights to guide potential interventions aimed at mitigating infection and improving patient outcomes after CAR T-cell therapy.

关键词： Infection CD19 Chimeric Antigen Receptor T cell Lymphoma LBCL Relapse

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Chronic radiation-associated dysphagia in oropharyngeal cancer survivors: Towards age-adjusted dose constraints for deglutitive muscles

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CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY 2019年 18卷 16-22页

作者： Christopherson, Kaitlin M. Ghosh, Alokananda Sherif, Abdallah Mohamed, Radwan Kamal, Mona Fuller, David Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston TX United States Department of Biostatistics and Data Science University of Texas School of Public Health Houston TX United States Department of Clinical Oncology University of Alexandria Alexandria Egypt Department of Emergency Medicine The University of Texas MD Anderson Cancer Center Houston TX United States Baylor College of Medicine Houston TX United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA United States Department of Radiation Oncology The University of Texas Medical Branch Galveston TX United States Department of Head and Neck Surgery The University of Texas MD Anderson Cancer Center Houston TX United States Department of Thoracic & Head and Neck Oncology The University of Texas MD Anderson Cancer Center Houston TX United States Medical Physics Program The University of Texas Graduate School of Biomedical Sciences Houston TX United States Department of Computer Science University of Illinois at Chicago Chicago IL United States Department of Electrical and Computer Engineering The University of Iowa Iowa City IA United States

Objectives: We sought to model chronic radiation-associated dysphagia (RAD) in patients given intensity-modulated radiation therapy (IMRT) for oropharyngeal squamous cell cancer (OPSCC) as a function of age and dose to non-target swallowing muscles. Methods: We reviewed 300 patients with T1-T4 N0-3 M0 OPSCC given definitive IMRT with concurrent chemotherapy. Chronic RAD was defined as aspiration or stricture on videoflouroscopy/endoscopy, gastrostomy tube, or aspiration pneumonia at >= 12 months after IMRT. Doses to autosegmented regions of interest (ROIs;inferior, middle and superior constrictors, anterior and posterior digastrics, mylo/geniohyoid complex, intrinsic tongue, and gengioglossus) were obtained from DICOM-RT plans and dose-volume histograms. The probability of chronic RAD as a function of mean ROI dose, stratified by age (<50, 50-59, 60-69, or >70 years), was estimated with logistic probability models and subsequent unsupervised nonlinear curves. Results: Chronic RAD was observed in 34 patients (11%). Age was a significant correlate of chronic RAD, both independently and with dose for all muscle groups examined. Distinct muscle-specific dose-response profiles were observed as a function of age (e.g., 5% of patients in their 50 s [but 20% of those 70 +] who received 60 Gy to the superior constrictor had chronic RAD). This effect was stable across all observed muscle ROIs, with a false discovery rate-corrected p < 0.05, for all dose/muscle/age models, suggesting that including age as a covariate improves modeling of chronic RAD. Conclusions: Age at treatment moderates the probability of chronic RAD after chemo-IMRT for OPSCC, with aging muscles showing lower dose thresholds. Uniform dose constraints may not predict toxicity in older patients. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of European Society for Radiotherapy and Oncology.

关键词： Oropharynx IMRT Radiation Toxicity Presbyphagia

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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415O Predictive factors of fertility in patients with breast cancer interrupting adjuvant endocrine therapy to attempt pregnancy in the POSITIVE trial

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ESMO Open 2025年 10卷 104987-104987页

作者： I. Demeestere S.M. Niman A.H. Partridge D. Diego R. Kammler M. Ruggeri M.A. Colleoni C. Shimizu C. Saura Manich K.A. Gelmon A. Satersdal J.R. Kroep A. Mailliez F. Amant T. Spanic S. El-Abed R.D. Gelber O. Pagani H.A. Azim F.A. Peccatori Research Laboratory on Human Reproduction ULB - Université Libre de Bruxelles Brussels Belgium Department of Data Science Dana-Farber Cancer Institute Boston MA United States of America Medical Oncology Dana-Farber Cancer Institute Boston United States of America Gynecology and Obstetrics Emory University Hospital Midtown Atlanta United States of America Translational Research Coordination Department ETOP IBCSG Partners Foundation Bern Switzerland IBCSG International Breast Cancer Study Group Bern Switzerland Division of Medical Senology IEO - Istituto Europeo di Oncologia Milan Italy Department of Breast and Medical Oncology NCGM - National Center for Global Health and Medicine Shinjuku-ku Japan Breast Cancer Program Vall d'Hebron University Hospital Barcelona Spain Department of Medical Oncology BC Cancer Agency - Vancouver Vancouver Canada Breast Cancer Unit Department of Oncology Oslo University Hospital Oslo Norway Medical Oncology Department LUMC-Leiden University Medical Center Leiden Netherlands Department of Medical Oncology Centre Oscar Lambret Lille France Gynaecology Unit UZ Leuven - University Hospitals Leuven - Campus Gasthuisberg Leuven Belgium Europa Donna Slovenia Europa Donna - Slovensko zdruzenje za boj proti raku dojk Ljubljana Slovenia Breast International Group Breast International Group (BIG) - AISBL Brussels Belgium Oncology HRC - Hopital Riviera-Chablais Rennaz Switzerland Medical Cairo Cure Oncology Center Cairo Egypt Department of Gynaecological Oncology IEO - Istituto Europeo di Oncologia IRCCS Milan Italy

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JS08.6.A A connectivity signature for glioblastoma

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Neuro-Oncology 2022年第SUPPLEMENT_2期24卷 ii8-ii9页

作者： Hoffmann, D C Hai, L Wagener, R Mandelbaum, H Xie, R Hausmann, D Dominguez Azorín, D Weil, S Sievers, P Cebulla, G Ito, J Warnken, U Venkataramani, V Ernst, K Reibold, D Will, R Suvà, M L Herold-Mende, C Sahm, F Winkler, F Schlesner, M Wick, W Kessler, T Clinical Cooperation Unit Neurooncology German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Department of Neurology and Neurooncology Program National Center for Tumor Diseases Heidelberg University Hospital Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Bioinformatics and Omics Data Analytics DKFZ Heidelberg Germany Department of Neuropathology Institute of Pathology University Hospital Heidelberg Heidelberg Germany Clinical Cooperation Unit Neuropathology DKTK DKFZ Heidelberg Germany Department of Neuroanatomy Institute for Anatomy and Cell Biology Heidelberg University Heidelberg Germany Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ) Heidelberg Germany Division of Pediatric Neurooncology DKTK DKFZ Heidelberg Germany Genomics and Proteomics Core Facility DKTK DKFZ Heidelberg Germany Broad Institute of Harvard and MIT Cambridge MA United States Department of Pathology and Center for Cancer Research Massachusetts General Hospital and Harvard Medical School Boston MA United States Department of Neurosurgery Heidelberg University Hospital Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany

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PTEN pathogenic variants are associated with poor prognosis in patients with advanced soft tissue sarcoma

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BJC reports 2024年第1期2卷 9页

作者： Minggui Pan Maggie Y Zhou Chen Jiang Zheyang Zhang Nam Bui Jeffrey Bien Amanda Siy Ninah Achacoso Aleyda V Solorzano Pam Tse Elaine Chung Wenwei Hu Sachdev Thomas Kristen Ganjoo Laurel A Habel Division of Research Kaiser Permanente Oakland CA 94612 USA. minggui@stanford.edu. Sarcoma Program Division of Oncology Stanford University School of Medicine Stanford CA 94305 USA. minggui@stanford.edu. Sarcoma Program Division of Oncology Stanford University School of Medicine Stanford CA 94305 USA. Division of Research Kaiser Permanente Oakland CA 94612 USA. State Key Laboratory of Cellular Stress Biology School of Life Sciences Faculty of Medicine and Life Sciences Xiamen University Xiamen Fujian 361102 China. National Institute for Data Science in Health and Medicine Xiamen University Xiamen Fujian 361102 China. Rutger's Cancer Institute of New Jersey New Brunswick NJ 08903 USA. Department of Oncology and Hematology Kaiser Permanente Vallejo CA 94589 USA.

BACKGROUND:We aimed to examine whether PTEN pathogenic variants (mutPTEN) were associated with overall survival (OS) in patients with advanced soft tissue sarcoma (STS) with the presence of one or more of the most common genomic alterations including p53, CDKN2A, RB1, and ATRX pathogenic variants. METHODS:This study included patients from Kaiser Permanente Northern California and Stanford cancer Center with grade 2 or higher locally advanced and metastatic STS. RESULTS:A total of 174 patients had leiomyosarcoma (LMS), 136 had undifferentiated pleomorphic sarcoma (UPS), 78 had Liposarcoma (LPS), and 214 had other histology subtypes (Others). Among all patients with STS, OS was worse for those with mutPTEN versus wild-type PTEN (wtPTEN, adjusted HR [aHR] = 1.58 [95% CI, 1.11-2.23]), mutCDKN2A vs wtCDKN2A (aHR = 1.33 [95% CI .99-1.80]), and mutRB1 vs wtRB1 (aHR = 1.26 [95% CI 0.93-1.70[), while OS was similar for mutp53 vs wtp53 and mutATRX vs wtATRX. MutPTEN versus wtPTEN was consistently associated with worse OS in histologic subtypes including LMS and UPS and molecular subgroups. CONCLUSION:MutPTEN vs wtPTEN was associated with worse OS in advanced STS. If confirmed, our findings could be helpful for prognostic stratification in clinical practice and for further understanding the molecular mechanisms of STS.

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A robust multiplex-DIA workflow profiles protein turnover regulations associated with cisplatin resistance and aneuploidy

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Nature communications 2025年第1期16卷 5034页

作者： Barbora Salovska Wenxue Li Oliver M Bernhardt Pierre-Luc Germain Qinyue Wang Tejas Gandhi Lukas Reiter Yansheng Liu Department of Pharmacology Yale University School of Medicine New Haven CT USA. Cancer Biology Institute Yale University School of Medicine West Haven CT USA. Biognosys AG Zurich-Schlieren Switzerland. Institute for Neuroscience Department of Health Sciences and Technology ETH Zurich Zurich Switzerland. Master Program of Public Health Department of Chronic Disease Epidemiology Yale School of Public Health New Haven CT USA. Department of Pharmacology Yale University School of Medicine New Haven CT USA. yansheng.liu@yale.edu. Cancer Biology Institute Yale University School of Medicine West Haven CT USA. yansheng.liu@yale.edu. Department of Biomedical Informatics & Data Science Yale University School of Medicine New Haven CT USA. yansheng.liu@yale.edu.

Quantifying protein turnover is fundamental to understanding cellular processes and advancing drug discovery. Multiplex-DIA mass spectrometry (MS), combined with dynamic SILAC labeling (pulse-SILAC, or pSILAC) reliably measures protein turnover and degradation kinetics. Previous multiplex-DIA-MS workflows have employed various strategies including leveraging the highest isotopic labeling channels to enhance the detection of isotopic signal pairs. Here we present a robust workflow that integrates a machine learning algorithm and channel-specific statistical filtering, enabling dynamic adaptation to channel ratio changes across multiplexed experiments and enhancing both coverage and accuracy of protein turnover profiling. We also introduce KdeggeR, a data analysis tool optimized for pSILAC-DIA experiments, which determines and visualizes peptide and protein degradation profiles. Our workflow is broadly applicable, as demonstrated on 2-channel and 3-channel DIA datasets and across two MS platforms. Applying this framework to an aneuploid cancer cell model before and after cisplatin resistance, we uncover strong proteome buffering of key protein complex subunits encoded by the aneuploid genome mediated by protein degradation. We identify resistance-associated turnover signatures, including mitochondrial metabolic adaptation via accelerated degradation of respiratory complexes I and IV. Our approach provides a powerful platform for high-throughput, quantitative analysis of proteome dynamics and stability in health and disease.

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Author Correction: The landscape of viral associations in human cancers

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Nature genetics 2023年第6期55卷 1077页

作者： Marc Zapatka Ivan Borozan Daniel S Brewer Murat Iskar Adam Grundhoff Malik Alawi Nikita Desai Holger Sültmann Holger Moch Colin S Cooper Roland Eils Vincent Ferretti Peter Lichter Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. Informatics and Bio-computing Program Ontario Institute for Cancer Research Toronto Ontario Canada. Norwich Medical School University of East Anglia Norwich UK. Earlham Institute Norwich UK. Heinrich-Pette-Institute Leibniz Institute for Experimental Virology Hamburg Germany. German Center for Infection Research (DZIF) Partner Site Hamburg-Borstel-Lübeck-Riems Hamburg Germany. Bioinformatics Core University Medical Center Hamburg-Eppendorf Hamburg Germany. Bioinformatics Group Department of Computer Science University College London London UK. Biomedical Data Science Laboratory Francis Crick Institute London UK. National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany. Division of Cancer Genome Research German Cancer Research Center (DKFZ) Heidelberg Germany. German Cancer Consortium (DKTK) Heidelberg Germany. Department of Pathology and Molecular Pathology University and University Hospital Zürich Zurich Switzerland. Institute of Cancer Research London UK. University of East Anglia Norwich UK. Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Bioinformatics and Functional Genomics Institute of Pharmacy and Molecular Biotechnology Heidelberg University and BioQuant Center Heidelberg Germany. Center for Digital Health Berlin Institute of Health and Charité Universitätsmedizin Berlin Berlin Germany. Ontario Institute for Cancer Research MaRS Centre Toronto Ontario Canada. Department of Biochemistry and Molecular Medicine University of Montreal Montreal Québec Canada. Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de. German Cancer Consortium (DKTK) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de.

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Analysis of more than 400,000 women provides case-control evidence for BRCA1 and BRCA2 variant classification

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Nature Communications 2025年第1期16卷 1-18页

作者： Maria Zanti Denise G. O’Mahony Kristia Yiangou Kyriaki Michailidou Michael T. Parsons Amanda B. Spurdle Leila Dorling Joe Dennis Marc Naven Antonis C. Antoniou Manjeet K. Bolla Qin Wang Douglas F. Easton Nicholas J. Boddicker Wenan Chen Chunling Hu Steven N. Hart Fergus J. Couch Thomas U. Ahearn Jonine D. Figueroa Montserrat García-Closas Christine B. Ambrosone Song Yao Irene L. Andrulis Gord Glendon Paul L. Auer Caroline Baynes Don M. Conroy Alison M. Dunning Craig Luccarini Clara Bodelon James M. Hodge Alpa V. Patel Lauren R. Teras Natalia V. Bogdanova Stig E. Bojesen Kristen D. Brantley Nicola J. Camp Archie Campbell Jose E. Castelao Melissa H. Cessna Jenny Chang-Claude Fei Chen Christopher A. Haiman Georgia Chenevix-Trench Kamila Czene Arcangela De Nicolo Susan M. Domchek Katherine L. Nathanson Thilo Dörk A. Heather Eliassen D. Gareth Evans Peter A. Fasching Henrik Flyger Manuela Gago-Dominguez Anna González-Neira Rocio Nuñez-Torres Felix Grassmann Andreas Hadjisavvas Mihalis I. Panayiotidis Ute Hamann Muhammad U. Rashid Diana Torres Mikael B. A. Hartman Weang-Kee Ho Reiner Hoppe Sacha J. Howell Anna Jakubowska Elza K. Khusnutdinova Yon-Dschun Ko Peter Kraft Vessela N. Kristensen James V. Lacey Jingmei Li Geok Hoon Lim Veronique Kiak-Mien Tan Sara Lindström Artitaya Lophatananon Kenneth Muir Arto Mannermaa Maria Elena Martinez Dimitrios Mavroudis Roger L. Milne Melissa C. Southey Nadia Obi Janet E. Olson Celine M. Vachon Julie R. Palmer Gary R. Zirpoli Paul D. P. Pharoah Eric C. Polley Kathryn J. Ruddy Siddhartha Yadav Emmanouil Saloustros Elinor J. Sawyer Marjanka K. Schmidt Soo Hwang Teo Amy Trentham-Dietz Thérèse Truong Jeffrey N. Weitzel Melissa S. Cline Peter Devilee Sean V. Tavtigian David E. Goldgar Biostatistics Unit The Cyprus Institute of Neurology & Genetics Nicosia Cyprus Department of Medical Genetics Oslo University Hospital Oslo Norway Population Health Program QIMR Berghofer Medical Research Institute Brisbane Queensland Australia Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care University of Cambridge Cambridge UK Centre for Cancer Genetic Epidemiology Department of Oncology University of Cambridge Cambridge UK Department of Quantitative Health Sciences Division of Computational Biology Mayo Clinic Rochester MN USA Department of Laboratory Medicine and Pathology Mayo Clinic Rochester MN USA Division of Cancer Epidemiology and Genetics National Cancer Institute National Institutes of Health Department of Health and Human Services Bethesda MD USA Usher Institute of Population Health Sciences and Informatics The University of Edinburgh Edinburgh UK Cancer Research UK Edinburgh Centre The University of Edinburgh Edinburgh UK Department of Cancer Prevention and Control Roswell Park Comprehensive Cancer Institute Buffalo NY USA Fred A. Litwin Center for Cancer Genetics Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Toronto Ontario Canada Department of Molecular Genetics University of Toronto Toronto Ontario Canada Division of Biostatistics Data Science Institute and Cancer Center Medical College of Wisconsin Milwaukee WI USA Department of Population Science American Cancer Society Atlanta GA USA Radiation Oncology Research Unit Hannover Medical School Hannover Germany Copenhagen General Population Study Herlev and Gentofte Hospital Copenhagen University Hospital Herlev Denmark Department of Clinical Biochemistry Herlev and Gentofte Hospital Copenhagen University Hospital Herlev Denmark Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Internal Medicine and Hu

Clinical genetic testing identifies variants causal for hereditary cancer, information that is used for risk assessment and clinical management. Unfortunately, some variants identified are of uncertain clinical significance (VUS), complicating patient management. Case-control data is one evidence type used to classify VUS. As an initiative of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) Analytical Working Group we analyze germline sequencing data ofBRCA1andBRCA2from 96,691 female breast cancer cases and 302,116 controls from three studies: the BRIDGES study of the Breast cancer Association Consortium, the cancer Risk Estimates Related to Susceptibility consortium, and the UK Biobank. We observe 11,207BRCA1andBRCA2variants, with 6909 being coding, covering 23.4% ofBRCA1andBRCA2VUS in ClinVar and 19.2% of ClinVar curated (likely) benign or pathogenic variants. Case-control likelihood ratio (ccLR) evidence is highly consistent with ClinVar assertions for (likely) benign or pathogenic variants; exhibiting 99.1% sensitivity and 95.3% specificity forBRCA1and 93.3% sensitivity and 86.6% specificity forBRCA2. This approach provides case-control evidence for 787 unclassified variants; these include 579 with strong or moderate benign evidence and 10 with strong pathogenic evidence for which ccLR evidence is sufficient to alter clinical classification.

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