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The CLL-1100 Project: Towards Complete Genomic Characterization and Improved Prognostics for CLL

BLOOD 2020年 136卷 3-4页

作者： Knisbacher, Binyamin A. Lin, Ziao Hahn, Cynthia K. Nadeu, Ferran Duran-Ferrer, Marti Stevenson, Kristen E. Tausch, Eugen Barbera-Mourelle, Alex Dunford, Andrew Anand, Shankara Delgado, Julio Hess, Julian M. Taylor-Weiner, Amaro Kretzmer, Helene Munoz, Pablo Bousquets Shaughnessy, Conner J. Fernandes, Stacey M. Sivina, Mariela Rassenti, Laura Z. Meissner, Alexander Wiestner, Adrian Burger, Jan A. Kipps, Thomas J. Brown, Jennifer R. Hallek, Michael Aguet, Francois Neuberg, Donna S. Puente, Xose S. Stewart, Chip Martin-Subero, Jose I. Stilgenbauer, Stephan Wu, Catherine J. Campo, Elias Getz, Gad Broad Institute of MIT and Harvard Cambridge MA Harvard Medical School Boston MA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Barcelona Spain Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Madrid Spain Department of Data Science Dana-Farber Cancer Institute Boston MA Department of Internal Medicine III Ulm University Ulm Germany Center for Cancer Research Massachusetts General Hospital Boston MA Servicio de Hematología Hospital Clínic IDIBAPS Barcelona Spain Department of Genome Regulation Max Planck Institute for Molecular Genetics Berlin Germany Departamento de Bioquímica y Biología Molecular Instituto Universitario de Oncología Universidad de Oviedo Oviedo Spain Dana-Farber Cancer Institute Boston MA Cancer Program Broad Institute of MIT and Harvard Cambridge MA Department of Leukemia UT M.D. Anderson Cancer Center Houston TX Moores Cancer Center University of California San Diego La Jolla CA Department of Stem Cell and Regenerative Biology Harvard University Cambridge MA Department of Pathology Massachusetts General Hospital Boston MA

Although chronic lymphocytic leukemia (CLL) has been consistently at the forefront of genomic discovery, complete characterization of its genomic landscape has been limited by sample size and cohort diversity. To address this challenge, we assembled and generated new genomic data from CLL samples from ~1100 patients (‘CLL-1100‘). In total, we analyzed 984 whole-exome sequences (WES), 177 whole-genome sequences (WGS), RNA-sequencing of 717 cases, and 758 methylome profiles. With our large dataset, we had increased sensitivity to detect candidate drivers even with frequency less than 1%. By applying MutSig2CV to the WES data, we identified 89 putative cancer driver genes (q<0.1), 46 of which were novel. New candidate driver genes highlighted cellular pathways including metabolism (e.g. GPS2, CHKB, CD36 ) and protein synthesis/stability (e.g. RPS16, EEF1A1, USP8 ), thus expanding our knowledge of the processes contributing to CLL leukemogenesis. Analysis of somatic copy number alterations (CNAs) using GISTIC2 confirmed both high frequency and rarely reported arm-level events, as well as identified 6 novel focal amplifications and 54 deletions (35 new). Many of these genomic regions primarily contained known CLL drivers but also novel drivers found based on somatic single nucleotide variants (e.g. DDX5 ), providing further evidence that these likely contribute to pathogenesis. Thus, by approximately doubling the previously reported number of driver genes in CLL, we are able to assign a putative driving event to >92% of CLL samples within the cohort. The gain in power was most evident in our characterization of the two major molecular subtypes of CLL, those with mutated (M-CLL) or unmutated (U-CLL) IGHV. Separate WES analyses of 513 M-CLLs and 459 U-CLLs revealed numerous differences between these two subtypes: (i) Mutation analysis revealed 24 and 59 candidate driver genes in M-CLL and U-CLL, respectively (q<0.1). Only a minority of genes were significant in both subgro

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Federated learning for computational pathology on gigapixel whole slide images

arXiv

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arXiv 2020年

作者： Lu, Ming Y. Kong, Dehan Lipkova, Jana Chen, Richard J. Singh, Rajendra Williamson, Drew F.K. Chen, Tiffany Y. 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 and MIT CambridgeMA United States Cancer Data Science Dana-Farber Cancer Institute BostonMA United States Department of Pathology Northwell Health NY United States

Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morphological phenotypes to predicting non human-identifiable features from histology such as molecular alterations. However, the development of robust, adaptable and accurate deep learning-based models often rely on the collection and time-costly curation large high-quality annotated training data that should ideally come from diverse sources and patient populations to cater for the heterogeneity that exists in such datasets. Multi-centric and collaborative integration of medical data across multiple institutions can naturally help overcome this challenge and boost the model performance but is limited by privacy concerns amongst other difficulties that may arise in the complex data sharing process as models scale towards using hundreds of thousands of gigapixel whole slide images. In this paper, we introduce privacy preserving federated learning for gigapixel whole slide images in computational pathology using weakly-supervised attention multiple instance learning and differential privacy. We evaluated our approach on two different diagnostic problems using thousands of histology whole slide images with only slide-level labels. Additionally, we present a weakly-supervised learning framework for survival prediction and patient stratification from whole slide images and demonstrate its effectiveness in a federated setting. Our results show that using federated learning, we can effectively develop accurate weakly supervised deep learning models from distributed data silos without direct data sharing and its associated complexities, while also preserving differential privacy using randomized noise generation. Copyright © 2020, The Authors. All rights reserved.

关键词： Pathology

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Worldwide Examination of Patients with CLL Hospitalized for COVID-19

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BLOOD 2020年 136卷 45-49页

作者： Roeker, Lindsey E. Scarfo, Lydia Chatzikonstantinou, Thomas Abrisqueta, Pau Eyre, Toby A. Cordoba, Raul Prat, Ana Muntanola Villacampa, Guillermo Leslie, Lori A. Koropsak, Michael Quaresmini, Giulia Allan, John N. Furman, Richard R. Bhavsar, Erica B. Pagel, John M. Hernandez-Rivas, Jose Angel Patel, Krish Motta, Marina Bailey, Neil Miras, Fatima Lamanna, Nicole Alonso, Rosalia Osorio-Prendes, Santiago Vitale, Candida Kamdar, Manali Baltasar, Patricia Osterborg, Anders Hanson, Lotta Baile, Monica Rodriguez-Hernandez, Ines Valenciano, Susana Popov, Viola Maria Garcia, Abelardo Barez Alfayate, Ana Oliveira, Ana C. Eichhorst, Barbara Quaglia, Francesca M. Reda, Gianluigi Jimenez, Javier Lopez Varettoni, Marzia Marchetti, Monia Romero, Pilar Grau, Rosalia Riaza Munir, Talha Zabalza, Amaya Janssens, Ann Niemann, Carsten U. Perini, Guilherme Fleury Delgado, Julio San Segundo, Lucrecia Yanez Roncero, Ma Isabel Gomez Wilson, Matthew Patten, Piers Marasca, Roberto Iyengar, Sunil Seddon, Amanda Torres, Ana Ferrari, Angela Cuellar-Garcia, Carolina Wojenski, Daniel El-Sharkawi, Dima Itchaki, Gilad Parry, Helen Mateos-Mazon, Juan Jose Martinez-Calle, Nicolas Ma, Shuo Naya, Daniel Van der Spek, Ellen Seymour, Erlene K. Vazquez, Eva Gimeno Rigolin, Gian Matteo Mauro, Francesca Romana Walter, Harriet S. Labrador, Jorge De Paoli, Lorenzo Laurenti, Luca Ruiz, Elena Levin, Mark-David Simkovic, Martin Spacek, Martin Andreu, Rafa Walewska, Renata Perez-Gonzalez, Sonia Sundaram, Suchitra Wiestner, Adrian Cuesta, Amalia Broom, Angus Kater, Arnon P. Muina, Begona Velasquez, Cesar A. Ujjani, Chaitra S. Seri, Cristina Antic, Darko Bron, Dominique Vandenberghe, Elisabeth Chong, Elise A. Lista, Enrico Garcia, Fiz Campoy Del Poeta, Giovanni Ahn, Inhye Pu, Jeffrey J. Brown, Jennifer R. Campos, Juan Alfonso Soler Malerba, Lara Trentin, Livio Orsucci, Lorella Farina, Lucia Villalon, Lucia Vidal, Maria Jesus Sanchez, Maria Jose Terol, Maria Jose De Paolis, Maria Rosaria Gentile, Massimo Davids, Matthew S. Shadman, Mazyar Yassin, Mohamed A. Foglie CLL Program Leukemia Service Memorial Sloan Kettering Cancer Center New York NY Università Vita-Salute San Raffaele and IRCCS Ospedale San Raffaele Milano Italy Hematology Department and HCT Unit George Papanicolaou Hospital Thessaloniki Greece Hospital Universitario Vall d’Hebrón Barcelona Spain Churchill Cancer Center Oxford University Hospitals NHS Foundation Trust Old Road United Kingdom Department of Hematology Fundación Jiménez Díaz Madrid Spain Hematology Department Hospital Mutua de Terrasa Barcelona Spain Oncology Data Science (ODysSey) Group Vall d´Hebron Institute of Oncology Barcelona Spain Division of Lymphoma John Theurer Cancer Center Hackensack University Medical Center Hackensack NJ John Theurer Cancer Center at Hackensack University Medical Center Hackensack NJ USC Ematologia ASST Papa Giovanni XXIII Bergamo Italy Weill Cornell Medicine New York NY Morton Coleman M.D. Distinguished Professor of Medicine Weill Cornell Medical College New York NY Clinical Research Division Fred Hutchinson Cancer Research Center Seattle WA University Hospital Infanta Leonor Madrid Spain Center for Blood Disorders and Stem Cell Transplantation Swedish Cancer Institute Seattle WA Department of Hematology ASST Spedali Civili di Brescia Brescia Italy University Hospital Doce De Octubre Madrid ESP Beatson West of Scotland Cancer Centre Glasgow United Kingdom Karmanos Cancer Institute Wayne State University Detroit

Introduction: Patients (pts) with CLL may be at particular risk of severe COVID-19 given advanced age and immune dysregulation. Two large series with limited follow-up have reported outcomes for pts with CLL and COVID-19 (Scarfò, et al. Leukemia 2020; Mato, et al. Blood 2020). To provide maximal clarity on outcomes for pts with CLL and COVID-19, we partnered in a worldwide effort to describe the clinical experience and validate predictors of survival, including potential treatment effects. Methods : This international collaboration represents a partnership between investigators at 141 centers. data are presented in two cohorts. Cohort 1 (Co1) includes pts captured through efforts by European Research Initiative on CLL (ERIC), Italian CAMPUS CLL program, and Grupo Español de Leucemia Linfática Crónica. The validation cohort, Cohort 2 (Co2), includes pts from US (66%), UK (23%), EU (7%), and other countries (4%). There is no overlap in cases between cohorts. CLL pts were included if COVID-19 was diagnosed by PCR detection of SARS-CoV-2 and they required inpatient hospitalization. data were collected retrospectively 2/2020 - 5/2020 using standardized case report forms. Baseline characteristics, preexisting comorbidities (including cumulative illness rating scale (CIRS) score ≥6 vs. <6), CLL treatment history, details regarding COVID-19 course, management, and therapy, and vital status were collected. The primary endpoint of this study was to estimate the case fatality rate (CFR), defined as the proportion of pts who died among all pts hospitalized with COVID-19. Chi-squared test was used to compare frequencies; univariable and multivariable analyses utilized Cox regression. Predictors of inferior OS in both Co1 and Co2 were included in multivariable analyses. Kaplan-Meier method was used to estimate overall survival (OS) from time of COVID-19 diagnosis (dx). Results : 411 hospitalized, COVID-19 positive CLL pts were analyzed (Co1 n=281, Co2 n=130). Table 1 describes ba

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Clinical response to azacitidine in MDS is associated with distinct DNA methylation changes in HSPCs

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Nature Communications 2025年第1期16卷 1-20页

作者： Thoms, Julie A. I. Yan, Feng Hampton, Henry R. Davidson, Sarah Joshi, Swapna Saw, Jesslyn Sarowar, Chowdhury H. Lim, Xin Ying Nunez, Andrea C. Kakadia, Purvi M. Bhuyan, Golam Sarower Zou, Xiaoheng Nguyen, Mary Ghodousi, Elaheh S. Koch, Forrest C. Vafaee, Fatemeh Thompson, I. Richard Karimi, Mohammad M. Pickford, Russell Raftery, Mark J. Hough, Sally Buckland, Griselda Bailey, Michelle Ghodke, Yuvaraj Absar, Noorul Vaughan, Lachlin Pasalic, Leonardo Fong, Chun Y. Kenealy, Melita Hiwase, Devendra K. Stoddart, Rohanna I. Mohammed, Soma Lee, Linda Passam, Freda H. Larsen, Stephen R. Spring, Kevin J. Skarratt, Kristen K. Rebeiro, Patricia Presgrave, Peter Stevenson, William S. Ling, Silvia Tiley, Campbell Fuller, Stephen J. Roncolato, Fernando Enjeti, Anoop K. Hoenemann, Dirk Lemech, Charlotte Jolly, Christopher J. Bohlander, Stefan K. Curtis, David J. Wong, Jason W. H. Unnikrishnan, Ashwin Hertzberg, Mark Olivier, Jake Polizzotto, Mark N. Pimanda, John E. School of Biomedical Sciences University of New South Wales Sydney NSW Australia Bioinformatics Division Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia ANU Clinical Hub for Interventional Research (CHOIR) John Curtin School of Medical Research Canberra ACT Australia Kirby Institute University of New South Wales Sydney NSW Australia School of Clinical Medicine University of New South Wales Sydney NSW Australia Australian Centre for Blood Diseases Monash University Melbourne VIC Australia Leukaemia & Blood Cancer Research Unit Department of Molecular Medicine and Pathology Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand School of Biotechnology and Biomolecular Sciences University of New South Wales Sydney NSW Australia UNSW Data Science Hub University of New South Wales Sydney NSW Australia Comprehensive Cancer Centre School of Cancer and Pharmaceutical Sciences King’s College London London United Kingdom Bioanalytical Mass Spectrometry Facility University of New South Wales Sydney NSW Australia Westmead Hospital Sydney NSW Australia ICPMR Department of Haematology Westmead Hospital Sydney NSW Australia Department of Haematology Austin Health Melbourne VIC Australia Cabrini Hospital Melbourne VIC Australia Department of Haematology Royal Adelaide Hospital Adelaide SA Australia University of New South Wales Sydney NSW Australia Royal North Shore Hospital Sydney NSW Australia Haematology Research Group Heart Research Institute Sydney NSW Australia Institute of Haematology Royal Prince Alfred Hospital Sydney NSW Australia Medical Oncology Group Liverpool Clinical School School of Medicine Western Sydney University and Ingham Institute for Applied Medical Research Liverpool NSW Australia South-West Sydney Clinical Campuses UNSW Medicine & Health Sydney NSW Australia Sydney Medical School Nepean Clinical School Faculty of Medicine and Health University of Sydney

Hypomethylating agents are frontline therapies for myelodysplastic neoplasms (MDS), yet clinical responses remain unpredictable. We conducted a phase 2 trial comparing injectable and oral azacitidine (AZA) administered over one or three weeks per four-week cycle, with the primary objective of investigating whether response is linked to in vivo drug incorporation or DNA hypomethylation. Our findings show that injection results in higher drug incorporation, but lower DNA demethylation per cycle, while global DNA methylation levels in mononuclear cells are comparable between responders and non-responders. However, hematopoietic stem and progenitor cells (HSPCs) from responders exhibit distinct baseline and early treatment-induced CpG methylation changes at regulatory regions linked to tissue patterning, cell migration, and myeloid differentiation. By cycle six—when clinical responses typically emerge—further differential hypomethylation in responder HSPCs suggests marrow adaptation as a driver of improved hematopoiesis. These findings indicate that intrinsic baseline and early drug-induced epigenetic differences in HSPCs may underlie the variable clinical response to AZA in MDS. © The Author(s) 2025.

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Polygenic risk modeling for prediction of epithelial ovarian cancer risk (vol 30, pg 349, 2021)

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EUROPEAN JOURNAL OF HUMAN GENETICS 2022年第5期30卷 630-631页

作者： Dareng, Eileen O. Tyrer, Jonathan P. Barnes, Daniel R. Jones, Michelle R. Yang, Xin Aben, Katja K. H. Adank, Muriel A. Agata, Simona Andrulis, Irene L. Anton-Culver, Hoda Antonenkova, Natalia N. Aravantinos, Gerasimos Arun, Banu K. Augustinsson, Annelie Balmana, Judith Bandera, Elisa V. Barkardottir, Rosa B. Barrowdale, Daniel Beckmann, Matthias W. Beeghly-Fadiel, Alicia Benitez, Javier Bermisheva, Marina Bernardini, Marcus Q. Bjorge, Line Black, Amanda Bogdanova, Natalia V. Bonanni, Bernardo Borg, Ake Brenton, James D. Budzilowska, Agnieszka Butzow, Ralf Buys, Saundra S. Cai, Hui Caligo, Maria A. Campbell, Ian Cannioto, Rikki Cassingham, Hayley Chang-Claude, Jenny Chanock, Stephen J. Chen, Kexin Chiew, Yoke-Eng Chung, Wendy K. Claes, Kathleen B. M. Colonna, Sarah Cook, Linda S. Couch, Fergus J. Daly, Mary B. Dao, Fanny Davies, Eleanor de la Hoya, Miguel de Putter, Robin Dennis, Joe DePersia, Allison Devilee, Peter Diez, Orland Ding, Yuan Chun Doherty, Jennifer A. Domchek, Susan M. Dork, Thilo du Bois, Andreas Durst, Matthias Eccles, Diana M. Eliassen, Heather A. Engel, Christoph Evans, Gareth D. Fasching, Peter A. Flanagan, James M. Fortner, Renee T. Machackova, Eva Friedman, Eitan Ganz, Patricia A. Garber, Judy Gensini, Francesca Giles, Graham G. Glendon, Gord Godwin, Andrew K. Goodman, Marc T. Greene, Mark H. Gronwald, Jacek Hahnen, Eric Haiman, Christopher A. Hakansson, Niclas Hamann, Ute Hansen, Thomas V. O. Harris, Holly R. Hartman, Mikael Heitz, Florian Hildebrandt, Michelle A. T. Hogdall, Estrid Hogdall, Claus K. Hopper, John L. Huang, Ruea-Yea Huff, Chad Hulick, Peter J. Huntsman, David G. Imyanitov, Evgeny N. Isaacs, Claudine Jakubowska, Anna James, Paul A. Janavicius, Ramunas Jensen, Allan Johannsson, Oskar Th. John, Esther M. Jones, Michael E. Kang, Daehee Karlan, Beth Y. Karnezis, Anthony Kelemen, Linda E. Khusnutdinova, Elza Kiemeney, Lambertus A. Kim, Byoung-Gie Kjaer, Susanne K. Komenaka, Ian Kupryjanczyk, Jolanta Kurian, Allison W. Kwong, Ava Lambrechts, Diether Larson, Melissa C. Lazaro, Conxi L University of Cambridge Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care Cambridge UK University of Cambridge Centre for Cancer Genetic Epidemiology Department of Oncology Cambridge UK Center for Bioinformatics and Functional Genomics Cedars-Sinai Medical Center Los Angeles CA USA Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen The Netherlands Netherlands Comprehensive Cancer Organisation Utrecht The Netherlands The Netherlands Cancer Institute—Antoni van Leeuwenhoek hospital Family Cancer Clinic Amsterdam The Netherlands Veneto Institute of Oncology IOV—IRCCS Immunology and Molecular Oncology Unit Padua Italy Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Fred A. Litwin Center for Cancer Genetics Toronto ON Canada University of Toronto Department of Molecular Genetics Toronto ON Canada University of California Irvine Department of Epidemiology Genetic Epidemiology Research Institute Irvine CA USA N.N. Alexandrov Research Institute of Oncology and Medical Radiology Minsk Belarus ‘Agii Anargiri’ Cancer Hospital Athens Greece University of Texas MD Anderson Cancer Center Department of Breast Medical Oncology Houston TX USA Lund University Department of Cancer Epidemiology Clinical Sciences Lund Sweden Vall d’Hebron Institute of Oncology Hereditary cancer Genetics Group Barcelona Spain University Hospital of Vall d’Hebron Department of Medical Oncology Barcelona Spain Rutgers Cancer Institute of New Jersey Cancer Prevention and Control Program New Brunswick NJ USA Landspitali University Hospital Department of Pathology Reykjavik Iceland University of Iceland BMC (Biomedical Centre) Faculty of Medicine Reykjavik Iceland University Hospital Erlangen Friedrich-Alexander-University Erlangen-Nuremberg Department of Gynecology and Obstetrics Comprehensive Cancer Center ER-EMN Erlangen Germany Vanderbilt University School of Medicine Division of Epidemiology Department

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

关键词： Clinical genetics Genetic markers Risk factors Biomedicine general Human Genetics Bioinformatics Gene Expression Cytogenetics

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Algorithm Fairness in AI for Medicine and Healthcare

arXiv

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arXiv 2021年

作者： Chen, Richard J. Chen, Tiffany Y. Lipkova, Jana Wang, Judy J. Williamson, Drew F.K. Lu, Ming Y. Sahai, Sharifa Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School 60 Fenwood Road Hale Building for Transformative Medicine BostonMA02445 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 Boston University School of Medicine BostonMA United States CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Harvard Medical School BostonMA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent evaluation of AI models stratified across race sub-populations have revealed inequalities in how patients are diagnosed, given treatments, and billed for healthcare costs. In this perspective article, we summarize the intersectional field of fairness in machine learning through the context of current issues in healthcare, outline how algorithmic biases (e.g.-image acquisition, genetic variation, intra-observer labeling variability) arise in current clinical workflows and their resulting healthcare disparities. Lastly, we also review emerging technology for mitigating bias via federated learning, disentanglement, and model explainability, and their role in AI-SaMD development. Copyright © 2021, The Authors. All rights reserved.

关键词： Health care

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graph-GPA 2.0: A Graphical Model for Multi-disease Analysis of GWAS Results with Integration of Functional Annotation data

arXiv

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arXiv 2022年

作者： Deng, Qiaolan Nam, Jin Hyun Yilmaz, Ayse Selen Chang, Won Pietrzak, Maciej Li, Lang Kim, Hang J. Chung, Dongjun The Interdisciplinary PhD program in Biostatistics The Ohio State University ColumbusOH United States Division of Big Data Science Korea University Sejong Campus Sejong Korea Republic of Department of Biomedical Informatics The Ohio State University ColumbusOH United States Division of Statistics and Data Science University of Cincinnati CincinnatiOH United States Pelotonia Institute for Immuno-Oncology The James Comprehensive Cancer Center The Ohio State University ColumbusOH United States

Motivation: Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a novel statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Results: First, we conducted simulation studies to evaluate GGPA 2.0. The results indicate that incorporating functional annotation data using GGPA 2.0 does not only improve detection of disease-associated variants, but also allows to identify more accurate relationships among diseases. Second, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus and the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood, especially B cells and regulatory T cells across multiple diseases. Psychiatric disorders were enriched for brain, especially prefrontal cortex and inferior temporal lobe for bipolar disorder (BIP) and schizophrenia (SCZ), respectively. Finally, GGPA 2.0 successfully identified the pleiotropy between BIP and SCZ. These results demonstrate that GGPA 2.0 can be a powerful tool to identify associated variants associated with each phenotype or those shared across multiple phenotypes, while also promoting understanding of functional mechanisms underlying the associated variants. Availability: R package ‘GGPA2’ is available at https://***/GGPA2/. © 2022, CC BY.

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data gaps and opportunities for modeling cancer health equity

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Journal of the National cancer Institute - Monographs 2023年第62期2023卷 246-254页

作者： Trentham-Dietz, Amy Corley, Douglas A. Del Vecchio, Natalie J. Greenlee, Robert T. Haas, Jennifer S. Hubbard, Rebecca A. Hughes, Amy E. Kim, Jane J. Kobrin, Sarah Li, Christopher I. Meza, Rafael Neslund-Dudas, Christine M. Tiro, Jasmin A. Department of Population Health Sciences and Carbone Cancer Center School of Medicine and Public Health University of Wisconsin-Madison Madison WI United States Division of Research Kaiser Permanente Northern California Oakland CA United States Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle WA United States Marshfield Clinic Research Institute Marshfield WI United States Division of General Internal Medicine Massachusetts General Hospital Boston MA United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Population and Data Sciences University of Texas Southwestern Medical Center Dallas TX United States Department of Health Policy and Management Center for Health Decision Science Harvard T.H. Chan School of Public Health Boston MA United States Healthcare Delivery Research Program Division of Cancer Control & Population Sciences National Cancer Institute National Institutes of Health Rockville MD United States Department of Integrative Oncology British Columbia (BC) Cancer Research Institute Vancouver BC Canada Department of Public Health Sciences and Henry Ford Cancer Henry Ford Health Detroit MI United States Department of Public Health Sciences University of Chicago Biological Sciences Division University of Chicago Medicine Comprehensive Cancer Center Chicago IL United States

Population models of cancer reflect the overall US population by drawing on numerous existing data resources for parameter inputs and calibration targets. Models require data inputs that are appropriately representative, collected in a harmonized manner, have minimal missing or inaccurate values, and reflect adequate sample sizes. data resource priorities for population modeling to support cancer health equity include increasing the availability of data that 1) arise from uninsured and underinsured individuals and those traditionally not included in health-care delivery studies, 2) reflect relevant exposures for groups historically and intentionally excluded across the full cancer control continuum, 3) disaggregate categories (race, ethnicity, socioeconomic status, gender, sexual orientation, etc.) and their intersections that conceal important variation in health outcomes, 4) identify specific populations of interest in clinical databases whose health outcomes have been understudied, 5) enhance health records through expanded data elements and linkage with other data types (eg, patient surveys, provider and/or facility level information, neighborhood data), 6) decrease missing and misclassified data from historically underrecognized populations, and 7) capture potential measures or effects of systemic racism and corresponding intervenable targets for change. © The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: ***@***.

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Author Correction: Multiplexed inhibition of immunosuppressive genes with Cas13d for combinatorial cancer immunotherapy

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Nature biotechnology 2025年 2025 Feb 3页

作者： Feifei Zhang Ryan D Chow Emily He Chuanpeng Dong Shan Xin Daniyal Mirza Yanzhi Feng Xiaolong Tian Nipun Verma Medha Majety Yueqi Zhang Guangchuan Wang Sidi Chen Department of Genetics Yale University School of Medicine New Haven CT USA. System Biology Institute Yale University West Haven CT USA. Center for Cancer Systems Biology Yale University West Haven CT USA. M.D.-Ph.D. Program Yale University West Haven CT USA. Molecular Cell Biology Genetics and Development Program Yale University New Haven CT USA. Yale College New Haven CT USA. Immunobiology Program Yale University New Haven CT USA. Department of Therapeutic Radiology Yale University School of Medicine New Haven CT USA. Department of Genetics Yale University School of Medicine New Haven CT USA. guangchuan.wang@***. System Biology Institute Yale University West Haven CT USA. guangchuan.wang@***. Center for Cancer Systems Biology Yale University West Haven CT USA. guangchuan.wang@***. Key Laboratory of Multi-Cell Systems Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China. guangchuan.wang@***. Department of Genetics Yale University School of Medicine New Haven CT USA. sidi.chen@yale.edu. System Biology Institute Yale University West Haven CT USA. sidi.chen@yale.edu. Center for Cancer Systems Biology Yale University West Haven CT USA. sidi.chen@yale.edu. M.D.-Ph.D. Program Yale University West Haven CT USA. sidi.chen@yale.edu. Molecular Cell Biology Genetics and Development Program Yale University New Haven CT USA. sidi.chen@yale.edu. Yale College New Haven CT USA. sidi.chen@yale.edu. Immunobiology Program Yale University New Haven CT USA. sidi.chen@yale.edu. Department of Neurosurgery Yale University School of Medicine New Haven CT USA. sidi.chen@yale.edu. Yale Comprehensive Cancer Center Yale University School of Medicine New Haven CT USA. sidi.chen@yale.edu. Yale Stem Cell Center Yale University School of Medicine New Haven CT USA. sidi.chen@yale.edu. Yale Center for Biomedical Data Science Yale University School of Medicine New Have

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Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images

Embedding Space Augmentation for Weakly Supervised Learning ...

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IEEE International Symposium on Biomedical Imaging

作者： Imaad Zaffar Guillaume Jaume Nasir Rajpoot Faisal Mahmood Department of Computer Science University College London UK Department of Pathology Harvard Medical School Brigham and Women’s Hospital Boston MA USA Department of Pathology Harvard Medical School Massachusetts General Hospital Boston MA USA Cancer Program Broad Institute of Harvard and MIT Cambridge MA USA Data Science Program Dana-Farber Cancer Institute Boston MA USA Department of Computer Science Tissue Image Analytics Centre University of Warwick Coventry UK Department of Pathology University Hospitals Coventry and Warwickshire NHS Trust Coventry UK The Alan Turing Institute London UK

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 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. Because of this bottleneck, incorporating patch-level data augmentations during training adds an extra computational burden. 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.

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