检索结果-内蒙古大学图书馆

Five-Year Outcomes of the “Abatacept Combined with a Calcineurin Inhibitor and Methotrexate for Graft Versus Host Disease (GVHD) Prophylaxis: A Randomized Controlled Trial” (‘ABA2‘)

引用

Blood 2024年 144卷 3516-3516页

作者： Lev Gorfinkel Muna Qayed Brandi Bratrude Kayla Betz Kyle Hebert Sung W. Choi Jeffrey Davis Christine Duncan Roger H. Giller Michael S. Grimley Andrew Harris David A Jacobsohn Nahal Lalefar Nosha Farhadfar Michael A. Pulsipher Shalini Shenoy Aleksandra Petrovic Kirk R. Schultz Gregory Yanik Bruce R Blazar Benjamin Watkins Division of Hematology-Oncology Boston Children's Hospital Boston MA Harvard Medical School Boston MA Dana-Farber Cancer Institute Boston MA Division of Pediatric Hematology Oncology and Bone Marrow Transplantation Aflac Cancer and Blood Disorders Center Children's Healthcare of Atlanta Atlanta GA Boston Children's Hospital Boston Boston Children's Hospital boston MA Department of Data Science Dana-Farber Cancer Institute Boston MA Blood and Marrow Transplantation Program University of Michigan Ann Arbor MI BC Children's Hospital Oak Harbor WA Division of Pediatric Hematology/Oncology Boston Children's Hospital Boston MA Department of Pediatric Oncology Dana-Farber Cancer Institute Boston MA Department of Pediatrics Harvard Medical School Boston MA University of Colorado SOM / Childrens Hospital Colorado Denver CO Cancer and Blood Diseases Institute Cincinnati Children's Hospital Medical Center Cincinnati OH Pediatric Stem Cell Transplantation and Cellular Therapies Service Department of Pediatrics Memorial Sloan Kettering Cancer Center New York NY Children's National Medical Center Washington DC UCSF Benioff Children's Hospital Oakland Oakland CA University of Florida Gainesville FL Huntsman Cancer Institute Salt Lake City UT Department of Pediatrics Division of Hematology / Oncology Washington University Medical Center Saint Louis MO Fred Hutchinson Cancer Center Seattle WA B.C. Children's Hospital Vancouver Canada Rogel Cancer Center University of Michigan Ann Arbor MI Division of Pediatric Blood and Marrow Transplantation University of Minnesota Minneapolis MN Tulane University New Orleans

Background: GVHD remains a major cause of non-relapse mortality (NRM) after hematopoietic cell transplant (HCT). Abatacept, a costimulatory blockade agent targeting the CD28:CD80/86 pathway, was FDA-approved in 2021 for GVHD prophylaxis in patients undergoing an 8/8 HLA-matched unrelated donor (MUD), or 7/8-HLA mismatched unrelated donor (MMUD) HCT for a hematologic malignancy, in part based on the ABA2 trial (NCT01743131). Here, we present 5-yr follow-up data for ABA2. Methods: ABA2 patient eligibility criteria (described in detail in PMID: 33449816) included patients ≥ 6 yr with heme malignancies undergoing MUD or MMUD HCT after intensive conditioning. MUD patients were enrolled on the randomized, double-blind placebo-controlled arm, with patients randomized to either 4 doses of abatacept (10mg/kg/dose on Day -1, +5, +14 +28) or placebo. MMUD patients all received 4 doses of abatacept. 185 patients were enrolled: 69 MUD-placebo, 73 MUD-abatacept, 43 MMUD-abatacept. GVHD prophylaxis also included a calcineurin inhibitor (CNI, continued through Day +100 followed by a wean) and methotrexate (MTX, 15mg/m 2 on day +1 and 10mg/m 2 on Day +3, +6, +11). Both bone marrow and PBSC grafts were used. All patient data were independently monitored through 5 yr by the NMDP CRO. Here we report cumulative incidence (CI) data at 2-, 3- and 5-yr for overall survival (OS), relapse, relapse-free survival (RFS), non-relapse mortality (NRM), systemic therapy (ST)-requiring chronic GVHD (cGVHD), and grade 3-4 acute GVHD (aGVHD)-free/ST-requiring cGVHD-free/relapse-free survival (GRFS). Results: The median follow-up was 53.8 months, 56.1 months and 60 months for the MUD-placebo, MUD-abatacept, and MMUD-abatacept cohorts, respectively. The 2-, 3- and 5-yr OS was 65.2%, 62.3% and 54.3% for MUD-placebo, 75.2%, 68.0% and 61.0% for MUD-abatacept, and 78.8%, 74.0% and 71.7% for MMUD-abatacept cohorts, respectively. The 2-, 3- and 5-yr CI of relapse was 25.7%, 27.5% and 27.5% for MUD-placebo, 21

关键词：

来源：评论

学校读者我要写书评

暂无评论

NGS-Based Copy Number Analysis in 1,185 Patients with Myeloid Neoplasms

引用

BLOOD 2016年第22期128卷 955-955页

作者： Saiki, Ryunosuke Shiozawa, Yusuke Yoshizato, Tetsuichi Yoshida, Kenichi Shiraishi, Yuichi Tanaka, Hiroko Chiba, Kenichi Atsuta, Yoshiko Onizuka, Makoto Itonaga, Hidehiro Kanda, Yoshinobu Sekeres, Mikkael A. Guinta, Kathryn M. Miyawaki, Shuichi Nakamaki, Tsuyoshi Chiba, Shigeru Shih, Lee-Yung Miyazaki, Yasushi Miyano, Satoru Makishima, Hideki Maciejewski, Jaroslaw P. Ogawa, Seishi Department of Pathology and Tumor Biology Graduate School of Medicine Kyoto University Kyoto Japan Department of Pediatrics Graduate School of Medicine The University of Tokyo Tokyo Japan Human Genome Center Institute of Medical Science The University of Tokyo Tokyo Japan Laboratory of DNA information Analysis & Laboratory of Sequence Analysis Human Genome Center The University of Tokyo Tokyo Japan Japanese Data Center for Hematopoietic Cell Transplantation Nagoya Japan Nagoya University Graduate School of Medicine Nagoya Japan Department of Hematology and Oncology Tokai University School of Medicine Isehara Japan Department of Hematology Atomic Bomb Disease and Hibakusha Medicine Unit Atomic Bomb Disease Institute Nagasaki University Nagasaki Japan Division of Hematology Saitama Medical Center Jichi Medical University Saitama Japan Department of Translational Hematology and Oncology Research Taussig Cancer Institute Cleveland Clinic Cleveland OH Leukemia Program Department of Hematology and Medical Oncology Cleveland Clinic Cleveland OH Division of Hematology Tokyo Metropolitan Ohtsuka Hospital Tokyo Japan Department of Hematology Showa University School of Medicine Tokyo Japan Department of Hematology Faculty of Medicine University of Tsukuba Tsukuba Japan Division of Hematology-Oncology Department of Internal Medicine Chang Gung Memorial Hospital Chang Gung University Taoyuan Taiwan

955

关键词：

来源：评论

学校读者我要写书评

暂无评论

Correction to: Required concentration index quantifies effective drug combinations against hepatitis C virus infection

引用

Theoretical biology & medical modelling 2021年第1期18卷 5页

作者： Yusuke Kakizoe Yoshiki Koizumi Yukino Ikoma Hirofumi Ohashi Takaji Wakita Shingo Iwami Koichi Watashi Department of Biology Faculty of Sciences Kyushu University Fukuoka 812-8581 Japan. Present address: Data Science Group Advanced Technology Division INTAGE Inc Tokyo 101-8201 Japan. National Center for Global Health and Medicine Tokyo 162-8655 Japan. Department of Virology II National Institute of Infectious Diseases Tokyo 162-8640 Japan. Department of Applied Biological Science Tokyo University of Science Noda 278-8510 Japan. Department of Biology Faculty of Sciences Kyushu University Fukuoka 812-8581 Japan. siwami@***. Institute for the Advanced Study of Human Biology (ASHBi) Kyoto University Kyoto 606-8501 Japan. siwami@***. NEXT-Ganken Program Japanese Foundation for Cancer Research (JFCR) Tokyo 135-8550 Japan. siwami@***. Science Groove Inc Fukuoka Japan. siwami@***. Department of Virology II National Institute of Infectious Diseases Tokyo 162-8640 Japan. kwatashi@nih.go.jp. Department of Applied Biological Science Tokyo University of Science Noda 278-8510 Japan. kwatashi@nih.go.jp. Institute for the Advanced Study of Human Biology (ASHBi) Kyoto University Kyoto 606-8501 Japan. kwatashi@nih.go.jp. NEXT-Ganken Program Japanese Foundation for Cancer Research (JFCR) Tokyo 135-8550 Japan. kwatashi@nih.go.jp. Institute for Frontier Life and Medical Sciences Kyoto University Kyoto Japan. kwatashi@nih.go.jp.

An amendment to this paper has been published and can be accessed via the original article.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Classification of datasets with imputed missing values: does imputation quality matter?

arXiv

引用

arXiv 2022年

作者： Shadbahr, Tolou Roberts, Michael Stanczuk, Jan Gilbey, Julian Teare, Philip Dittmer, Sören Thorpe, Matthew Torné, Ramon Viñas Sala, Evis Lió, Pietro Patel, Mishal Rudd, James H.F. Mirtti, Tuomas Rannikko, Antti Sakari Aston, John A.D. Tang, Jing Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Preller, Jacobus Stanczuk, Jan Babar, Judith Sánchez, Lorena Escudero Wassin, Marcel Holzer, Markus Walton, Nicholas Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Department of Mathematics University of Manchester Manchester United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pathology University of Helsinki Helsinki University Hospital Finland iCAN-Digital Precision Cancer Medicine Flagship Helsinki Finland Department of Urology University of Helsinki Helsinki University Hospital Helsinki Finland Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Faculty of Mathematics University of Vienna Austria Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data. All code and data used in this paper are also released publicly at [inserted upon publication]. © 2022, CC BY.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Do Hematopoietic Cell Transplant Patients Treated on a Clinical Trial Do Better? Comparison of Characteristics and Outcomes of Patients Enrolled Versus Not Enrolled on Blood and Marrow Transplant Clinical Trials Network (BMT-CTN) 0201 Trial

引用

Blood 2014年第21期124卷 209-209页

作者： Nandita Khera Navneet S. Majhail Ruta Brazauskas Zhiwei Wang Mahmoud Aljurf Görgün Akpek Yoshiko Atsuta Sara Beattie Christopher N. Bredeson Linda J. Burns Allen R. Chen Jason Dehn César O Freytes Vikas Gupta Gregory A. Hale Yoshihiro Inamoto Hillard M. Lazarus Charles F. LeMaistre Jeanne M. Palmer Kristjan Paulson Stephanie J. Lee Mayo Clinic Arizona Phoenix AZ Cleveland Clinic Cleveland OH Medical College of Wisconsin Milwaukee WI King Faisal Specialist Hospital and Research Centre Riyadh Saudi Arabia Section of Hematology Oncology Gilbert AZ Japanese Data Center for Hematopoietic Cell Transplantation Nagoya Japan University of Ottawa Ottawa Canada The Ottawa Hospital Ottawa Canada University of Minnesota Minneapolis MN Johns Hopkins University Baltimore MD National Marrow Donor Program Minneapolis MN University of Texas Health Science Center at San Antonio San Antonio TX Princess Margaret Cancer Centre University of Toronto Toronto Canada All Children’s Hospital St. Petersburg FL Fred Hutchinson Cancer Research Center Seattle WA University Hospitals Seidman Cancer Center and Case Western Reserve University Cleveland OH Sarah Cannon Nashville TN Mayo Clinic Arizona and Phoenix Children’s Hospital Phoenix AZ CancerCare Manitoba University of Manitoba Winnipeg Canada

Background: The association of clinical trial participation with outcomes in cancer patients is controversial, with some studies reporting better outcomes for trial participants while others do not. It is possible that the difference in outcomes is due to differences in baseline characteristics of participants and non-participants. The Blood and Marrow Transplant Clinical Trials Network (BMT CTN) 0201 trial was a large randomized, multi-center study comparing peripheral blood (PB) with bone marrow (BM) for hematopoietic cell transplantation (HCT) from HLA-matched or 1 allele mismatched unrelated donors (URD) in patients with acute leukemia, chronic myeloid leukemia and myelodysplastic syndromes. The trial reported no significant survival differences between PB and BM; there was decreased risk of graft failure but increased risk of chronic graft-versus-host disease (GVHD) with PB grafts (Anasetti et al NEJM 2012). Methods: We compared characteristics of BMT CTN 0201 study participants (n=494) with HCT recipients who did not participate on the trial (n=1384) but were potentially eligible by virtue of known characteristics. These patients received similar conditioning and GVHD prophylaxis as trial participants at 38 United States trial centers during the study time-period. Centers that had no eligible non-trial participants were excluded. Outcomes between patients on and off trial were compared using Cox proportional hazards regression models. data were obtained from the Center for International Blood and Marrow Transplant Research (CIBMTR) that collects patient data on all trial and non-trial allogeneic HCT recipients through its registry function. Results: Approximately 29% [494/1878] of apparently eligible patients were randomized and analyzed as part of the trial. There were no significant differences between study participants and non-participants in age or sex distribution, disease types, HLA or sex matching, comorbidities, or interval from diagnosis to HCT. High

关键词：

来源：评论

学校读者我要写书评

暂无评论

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

引用

arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Africa-specific human genetic variation near CHD1L associates with HIV-1 load (vol 620, pg 1025, 2023)

引用

NATURE 2023年第7979期621卷 E42-E42页

作者： McLaren, Paul J. Porreca, Immacolata Iaconis, Gennaro Mok, Hoi Ping Mukhopadhyay, Subhankar Karakoc, Emre Cristinelli, Sara Pomilla, Cristina Bartha, Istvan Thorball, Christian W. Tough, Riley H. Angelino, Paolo Kiar, Cher S. Carstensen, Tommy Fatumo, Segun Porter, Tarryn Jarvis, Isobel Skarnes, William C. Bassett, Andrew DeGorter, Marianne K. Moorthy, Mohana Prasad Sathya Tuff, Jeffrey F. Kim, Eun-Young Walter, Miriam Simons, Lacy M. Bashirova, Arman Buchbinder, Susan Carrington, Mary Cossarizza, Andrea De Luca, Andrea Goedert, James J. Goldstein, David B. Haas, David W. Herbeck, Joshua T. Johnson, Eric O. Kaleebu, Pontiano Kilembe, William Kirk, Gregory D. Kootstra, Neeltje A. Kral, Alex H. Lambotte, Olivier Luo, Ma Mallal, Simon Martinez-Picado, Javier Meyer, Laurence Miro, Jose M. Moodley, Pravi Motala, Ayesha A. Mullins, James I. Nam, Kireem Obel, Niels Pirie, Fraser Plummer, Francis A. Poli, Guido Price, Matthew A. Rauch, Andri Theodorou, Ioannis Trkola, Alexandra Walker, Bruce D. Winkler, Cheryl A. Zagury, Jean-Francois Montgomery, Stephen B. Ciuffi, Angela Hultquist, Judd F. Wolinsky, Steven M. Dougan, Gordon Lever, Andrew M. L. Gurdasani, Deepti Groom, Harriet Sandhu, Manjinder S. Fellay, Jacques Sexually Transmitted and Blood-Borne Infections Division at JC Wilt Infectious Diseases Research Centre National Microbiology Laboratory Branch Public Health Agency of Canada Winnipeg Manitoba Canada Department of Medical Microbiology and Infectious Diseases University of Manitoba Winnipeg Manitoba Canada Vaccine and Therapeutics Laboratory Medical and Scientific Affairs National Microbiology Laboratory Branch Public Health Agency of Canada Winnipeg Manitoba Canada Wellcome Trust Sanger Institute Hinxton UK Department of Medicine University of Cambridge Cambridge UK Department of Medicine National University of Singapore Singapore Singapore Peter Gorer Department of Immunobiology School of Immunology and Microbial Sciences King’s College London London UK Institute of Microbiology Lausanne University Hospital and University of Lausanne Lausanne Switzerland Global Health Institute School of Life Sciences École Polytechnique Fédérale de Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland Precision Medicine Unit Biomedical Data Science Center Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland The African Computational Genomics (TACG) Research Group MRC/UVRI and LSHTM Uganda Research Unit Entebbe Uganda Department of Non-Communicable Disease Epidemiology Faculty of Epidemiology and Population Health London School of Hygiene and Tropical Medicine London UK The Jackson Laboratory for Genomic Medicine Farmington CT USA Department of Pathology Stanford University School of Medicine Stanford CA USA Department of Genetics Stanford University School of Medicine Stanford CA USA Division of Infectious Diseases Feinberg School of Medicine Northwestern University Chicago IL USA Basic Science Program Frederick National Laboratory for Cancer Research National Cancer Institute Frederick MD USA Laboratory of Integrative Cancer Immunology Center for Cancer Research National Cancer Institute Bethesda

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to "Identification of Differential Tumor Subtypes of T1 Bladder cancer" [Eur. Urol. 78 (2020) 533-537]

引用

European urology 2022年第2期81卷 e53页

作者： A Gordon Robertson Clarice S Groeneveld Brian Jordan Xiaoqi Lin Kimberly A McLaughlin Arighno Das Leigh Ann Fall Damiano Fantini Timothy J Taxter Lauren S Mogil Sia Viborg Lindskrog Lars Dyrskjøt David J McConkey Robert S Svatek Aurélien de Reyniès Mauro A A Castro Joshua J Meeks Dxige Research Inc. Courtenay BC Canada. Cartes d'Identité des Tumeurs Program Ligue Nationale Contre le Cancer Paris France Oncologie Moleculaire Institut Curie Equipe Labellisée Ligue Contre le Cancer Paris France. Department of Urology University of Washington Seattle WA USA. Department of Pathology Northwestern University Feinberg School of Medicine Chicago IL USA. Departments of Urology Biochemistry and Molecular Genetics Feinberg School of Medicine Northwestern University Chicago IL USA. Tempus Labs Inc. Chicago IL USA. Center for Translational Data Science University of Chicago Chicago IL USA. Department of Molecular Medicine Aarhus University Hospital Aarhus Denmark. Johns Hopkins Greenberg Bladder Cancer Institute Brady Urological Institute Johns Hopkins University Baltimore MD USA. Experimental Developmental Therapeutics Program UT Health MD Anderson San Antonio TX USA Department of Urology UT Health San Antonio San Antonio TX USA. Cartes d'Identité des Tumeurs Program Ligue Nationale Contre le Cancer Paris France. Bioinformatics and Systems Biology Laboratory Federal University of Paraná Curitiba Brazil. Departments of Urology Biochemistry and Molecular Genetics Feinberg School of Medicine Northwestern University Chicago IL USA Jesse Brown VA Medical Center Chicago IL USA. Electronic address: joshua.meeks@northwestern.edu.

来源：评论

学校读者我要写书评

暂无评论

SWEET: a single-sample network inference method for deciphering individual features in disease

引用

Briefings in bioinformatics 2023年第2期24卷 bbad032页

作者： Hsin-Hua Chen Chun-Wei Hsueh Chia-Hwa Lee Ting-Yi Hao Tzu-Ying Tu Lan-Yun Chang Jih-Chin Lee Chun-Yu Lin Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan. TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei 110 Taiwan. Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital National Defense Medical Center Taipei 110 Taiwan. Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. Center for Intelligent Drug Systems and Smart Bio-devices National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. School of Dentistry Kaohsiung Medical University Kaohsiung 807 Taiwan.

Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.

关键词： drug repurposing gene expression network inference network medicine precision medicine single-sample network

来源：评论

学校读者我要写书评

暂无评论

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

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：