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Comprehensive mutational scanning of EGFR reveals TKI sensitivities of extracellular domain mutants (Vol 15, 2742, 2024)

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NATURE COMMUNICATIONS 2024年第1期15卷 1-18页

作者： Hayes, Tikvah K. Aquilanti, Elisa Persky, Nicole S. Yang, Xiaoping Kim, Erica E. Brenan, Lisa Goodale, Amy B. Alan, Douglas Sharpe, Ted Shue, Robert E. Westlake, Lindsay Golomb, Lior Silverman, Brianna R. Morris, Myshal D. Fisher, Ty Running Beyene, Eden Li, Yvonne Y. Cherniack, Andrew D. Piccioni, Federica Hicks, J. Kevin Chi, Andrew S. Cahill, Daniel P. Dietrich, Jorg Batchelor, Tracy T. Root, David E. Johannessen, Cory M. Meyerson, Matthew Department of Medical Oncology Dana-Farber Cancer Institute & Harvard Medical School Boston MA USA Cancer Program The Broad Institute of M.I.T. and Harvard Cambridge MA USA Genetic Perturbation Platform The Broad Institute of M.I.T. and Harvard Cambridge MA USA Data Science Platform The Broad Institute of M.I.T. and Harvard Cambridge Cambridge MA USA Summer Honors Undergraduate Research Program Harvard Medical School Boston MA USA Department of Pathology H. Lee Moffitt Cancer Center and Research Institute Tampa FL USA Center for Neuro-Oncology Division of Neuro-Oncology Massachusetts General Hospital Boston MA USA Department of Neurology Division of Neuro-Oncology Massachusetts General Hospital Boston MA USA Department of Neurology Brigham and Women’s Hospital & Harvard Medical School Boston MA USA

The epidermal growth factor receptor, EGFR, is frequently activated in lung cancer and glioblastoma by genomic alterations including missense mutations. The different mutation spectra in these diseases are reflected in divergent responses to EGFR inhibition: significant patient benefit in lung cancer, but limited in glioblastoma. Here, we report a comprehensive mutational analysis of EGFR function. We perform saturation mutagenesis of EGFR and assess function of ~22,500 variants in a human EGFR-dependent lung cancer cell line. This approach reveals enrichment of erlotinib-insensitive variants of known and unknown significance in the dimerization, transmembrane, and kinase domains. Multiple EGFR extracellular domain variants, not associated with approved targeted therapies, are sensitive to afatinib and dacomitinib in vitro. Two glioblastoma patients with somatic EGFR G598V dimerization domain mutations show responses to dacomitinib treatment followed by within-pathway resistance mutation in one case. In summary, this comprehensive screen expands the landscape of functional EGFR variants and suggests broader clinical investigation of EGFR inhibition for cancers harboring extracellular domain mutations.

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Development,validation,and transportability of several machine-learned,non-exercise-based VO_(2max)prediction models for older adults

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Journal of Sport and Health science 2024年第5期13卷 611-620页

作者： Benjamin T.Schumacher Michael J.LaMonte Andrea Z.LaCroix Eleanor M.Simonsick Steven P.Hooker Humberto Parada Jr. John Bellettiere Arun Kumar Herbert Wertheim School of Public Health and Human Longevity Science University of California San DiegoLa JollaCA 92093USA Department of Epidemiology and Environmental Health School of Public Health and Health ProfessionsUniversity at BuffaloState University of New YorkBuffaloNY 14214USA Translational Gerontology Branch Intramural Research ProgramNational Institute on AgingNational Institutes of HealthBaltimoreMD 21225USA College of Health and Human Services San Diego State UniversitySan DiegoCA 92182USA Division of Epidemiology and Biostatistics School of Public HealthSan Diego State UniversitySan DiegoCA 92182USA University of California San Diego Moores Cancer Center La JollaCA 92093USA Computer Science and Engineering and Halicioglu Data Science Institute University of California San DiegoLa JollaCA 92093USA

Background:There exist few maximal oxygen uptake(VO_(2max))non-exercise-based prediction equations,fewer using machine learning(ML),and none specifically for older *** direct measurement of VO_(2max)is infeasible in large epidemiologic cohort studies,we sought to develop,validate,compare,and assess the transportability of several ML VO_(2max)prediction ***:The Baltimore Longitudinal Study of Aging(BLSA)participants with valid VO2_(max)tests were included(n=1080).Least absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine(SVM)algorithms were trained to predict VO_(2max)*** developed these algorithms for:(a)the overall BLSA,(b)by sex,(c)using all BLSA variables,and(d)variables common in aging ***,we quantified the associations between measured and predicted VO_(2max)and ***:The age was 69.0±10.4 years(mean±SD)and the measured VO_(2max)was 21.6±5.9 mL/kg/*** absolute shrinkage and selection operator,linear-and tree-boosted extreme gradient boosting,random forest,and support vector machine yielded root mean squared errors of 3.4 mL/kg/min,3.6 mL/kg/min,3.4 mL/kg/min,3.6 mL/kg/min,and 3.5 mL/kg/min,*** quartiles of measured VO_(2max)showed an inverse gradient in mortality *** VO_(2max)variables yielded similar effect estimates but were not robust to ***:Measured VO_(2max)is a strong predictor of *** ML can improve the accuracy of prediction as compared to simpler approaches but estimates of association with mortality remain sensitive to *** studies should seek to reproduce these results so that VO_(2max),an important vital sign,can be more broadly studied as a modifiable target for promoting functional resiliency and healthy aging.

关键词： Cardiorespiratory fitness Prediction algorithms Epidemiology Mortality

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ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis (vol 53, pg 403, 2021)

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NATURE GENETICS 2021年第6期53卷 935-935页

作者： Granja, Jeffrey M. Corces, M. Ryan Pierce, Sarah E. Bagdatli, S. Tansu Choudhry, Hani Chang, Howard Y. Greenleaf, William J. Department of Genetics Stanford University School of Medicine Stanford CA USA Program in Biophysics Stanford University Stanford CA USA Center for Personal Dynamic Regulomes Stanford University Stanford CA USA Department of Pathology Stanford University School of Medicine Stanford CA USA Gladstone Institute of Neurological Disease Gladstone Institute of Data Science and Biotechnology San Francisco CA USA Department of Neurology University of California San Francisco San Francisco CA USA Program in Cancer Biology Stanford University School of Medicine Stanford CA USA Department of Biochemistry Faculty of Science Cancer and Mutagenesis Unit King Fahd Center for Medical Research King Abdulaziz University Jeddah Saudi Arabia Howard Hughes Medical Institute Stanford University Stanford CA USA Department of Applied Physics Stanford University Stanford CA USA Chan Zuckerberg Biohub San Francisco CA USA

The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://***/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells.

关键词： Epigenetics Epigenomics Gene regulation

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Fast and scalable image search for histology

arXiv

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arXiv 2021年

作者： Chen, Chengkuan Lu, Ming Y. Williamson, Drew F.K. Chen, Tiffany Y. Schaumberg, Andrew J. Mahmood, Faisal Department of Pathology Brigham and Women's Hospital 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

The expanding adoption of digital pathology has enabled the curation of large repositories of histology whole slide images (WSIs), which contain a wealth of information. Similar pathology image search offers the opportunity to comb through large historical repositories of gigapixel WSIs to identify cases with similar morphological features and can be particularly useful for diagnosing rare diseases, identifying similar cases for predicting prognosis, treatment outcomes and potential clinical trial success. A critical challenge in developing a WSI search and retrieval system is scalability, which is uniquely challenging given the need to search a growing number of slides that each can consist of billions of pixels and are several gigabytes in size. Such systems are typically slow and retrieval speed often scales with the size of the repository they search through, making their clinical adoption tedious and are not feasible for repositories that are constantly growing. Here we present Fast Image Search for Histopathology (FISH), a histology image search pipeline that is infinitely scalable and achieves constant search speed that is independent of the image database size, while being interpretable and without requiring detailed annotations. FISH uses self-supervised deep learning to encode meaningful representations from WSIs and a Van Emde Boas tree for fast search, followed by an uncertainty-based ranking algorithm to retrieve similar WSIs. We evaluated FISH on multiple tasks and datasets with over 22,000 patient cases spanning 56 disease subtypes. We additionally demonstrate that FISH can be used to assist with the diagnosis of rare cancer types where sufficient cases may not be available to train traditional supervised deep models. FISH is available as an easy-to-use, open source software package (https://***/mahmoodlab/FISH). © 2021, CC BY-NC-ND.

关键词： Diseases

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Impact of Molecular Ontogeny on Hematological Recovery in AML Patients Treated with Hypomethylating Agent Plus Venetoclax Therapy

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Blood 2024年 144卷 5175-5175页

作者： Josh Zhang Shai O. Shimony Yiwen Liu Eno Inyang Evan C. Chen Luis E. Aguirre Rebecca P. Bystrom Benjamin Rolles Richard M. Stone Daniel J. DeAngelo Emily Tiao Maximilian Stahl Department of Pharmacy Dana-Farber Cancer Institute Boston MA Adult Leukemia Program Dana-Farber Cancer Institute Boston MA Data Science Dana-Farber Cancer Institute Boston MA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Dana-Farber Cancer Institute Boston MA

Introduction : Therapy with hypomethylating agents (HMA) plus venetoclax (VEN) is approved for patients (pts) unfit to receive intensive chemotherapy in newly diagnosed acute myeloid leukemia (AML). However, many pts experience treatment delays due to hematologic toxicity, necessitating shortened days of VEN or HMA administration and extending cycle lengths. AML ontogeny can be classified by molecular characteristics into de-novo, secondary and TP53 mutated AML (Lindsley Blood 2015). Secondary ontogeny and TP53 mutated AML are associated with prior history of myeloid malignancy or prior cytotoxic therapy. We sought to determine the impact of AML ontogeny on treatment and hematologic outcomes in pts treated with HMA+VEN. Methods : We conducted a retrospective study of pts with newly diagnosed AML treated with at least one cycle of HMA+VEN at Dana-Farber cancer Institute between 2016 and 2023. To exclude the impact of persistent disease on blood counts, we only included those who achieved clearance of leukemia blasts including complete remission (CR), complete remission with incomplete count recovery (CRi), or morphological leukemia-free state (MLFS). We defined three ontogeny groups in a hierarchical pattern based on presence of TP 53 mutation (“ TP 53 mt”), >1 secondary ontogeny mutation ( ASXL1 , BCOR , EZH2 , SF3B1 , SRSF2 , STAG2 , U2AF1 or ZRSR2 ) without TP 53 mutation (“secondary”), and all others (“de novo”). We collected data for a maximum of five cycles of treatment including cycle lengths, days of venetoclax administration, hematologic counts and recovery times, and transfusion requirements. Results : A total of 135 pts were included: 37 (27%) were in the TP 53 mt group, 61 (45%) in the secondary group, and 37 (27%) in the de novo group. The median age differed between de novo (77 years), secondary (75 years), and TP53 mt (63 years) groups (p < 0.001). The median baseline hemoglobin (hgb) was higher in the de novo group (9.0 g/dL) than secondary (8.2 g/dL,

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Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data:a multi-phased study of prostate cancer

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cancer Communications 2021年第12期41卷 1387-1397页

作者： Chong Wu Jingjing Zhu Austin King Xiaoran Tong Qing Lu Jong Y.Park Liang Wang Guimin Gao Hong-Wen Deng Yaohua Yang Karen E.Knudsen Timothy R.Rebbeck Jirong Long Wei Zheng Wei Pan David V.Conti Christopher A Haiman Lang Wu Department of Statistics Florida State UniversityTallahasseeFL 32304USA Cancer Epidemiology Division Population Sciences in the Pacific ProgramUniversity of Hawaii Cancer CenterUniversity of Hawaii at ManoaHonoluluHI 96813USA Department of Epidemiology and Biostatistics Michigan State UniversityEast LansingMI 48824USA Department of Biostatistics University of FloridaGainesvilleFL 32603USA Department of Cancer Epidemiology H.Lee Moffitt Cancer Center and Research InstituteTampaFL 33612USA Department of Tumor Biology H.Lee Moffitt Cancer Center and Research InstituteTampaFL 33612USA Department of Public Health Sciences University of ChicagoChicagoIL 60637USA Center of Bioinformatics and Genomics Department of Global Biostatistics and Data ScienceTulane UniversityNew OrleansLA 70112USA Division of Epidemiology Department of MedicineVanderbilt Epidemiology CenterVanderbilt-Ingram Cancer CenterVanderbilt University Medical CenterNashvilleTN 37203USA Department of Cancer Biology Sidney Kimmel Cancer CenterThomas Jefferson UniversityPhiladelphiaPA 19107USA Department of Medical Oncology Dana-Farber Cancer InstituteBostonMA 02215USA Department of Epidemiology Harvard TH Chan School of Public HealthBostonMA 02115USA Division of Biostatistics University of MinnesotaMinneapolisMN 55455USA Department of Preventive Medicine Keck School of MedicineUniversity of Southern California/Norris Comprehensive Cancer CenterLos AngelesCA 90033USA

Background:DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer(PCa).However,it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores(PRSs).Here,we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation,and other genomic information using an integrative ***:Using data from the PRACTICAL consortium,we derived multiple sets of genetic scores,including those based on available single-nucleotide polymorphisms through widely used methods of pruning and thresholding,LDpred,LDpred-funt,AnnoPred,and EBPRS,as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding *** the tuning step,using the UK Biobank data(1458 prevalent cases and 1467 controls),we selected PRSs with the best *** an independent set of data from the UK Biobank,we developed an integrative PRS combining information from individual ***,in the testing step,we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and ***:Our constructed PRS had improved performance(C statistics:76.1%)over PRSs constructed by individual benchmark methods(from 69.6%to 74.7%).Furthermore,our new PRS had much higher risk assessment power than family *** overall net reclassification improvement was 69.0%by adding PRS to the baseline model compared with 12.5%by adding family ***:We developed and validated a new PRS which may improve the utility in predicting the risk of developing *** innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.

关键词： risk prediction polygenic risk scores predicted gene expression predicted DNA methylation integrative models prostate cancer

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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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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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Wait, but Tylenol is Acetaminophen… Investigating and Improving Language Models' Ability to Resist Requests for Misinformation

arXiv

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arXiv 2024年

作者： Chen Gao, Mingye Sasse, Kuleen Hartvigsen, Tom Anthony, Brian Fan, Lizhou Aerts, Hugo Gallifant, Jack Bitterman, Danielle Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Brigham and Women’s Hospital Dana-Farber Cancer Institute BostonMA United States Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School BostonMA United States Massachusetts Institute of Technology CambridgeMA United States Johns Hopkins University BaltimoreMD United States Department of Data Science University of Virginia CharlottesvilleVA United States Radiology and Nuclear Medicine GROW & CARIM Maastricht University Netherlands

Background: Large language models (LLMs) are trained to follow directions, but this introduces a vulnerability to blindly comply with user requests even if they generate wrong information. In medicine, this could accelerate the generation of misinformation that impacts human well-being. Objectives/Methods: We analyzed compliance to requests to generate misleading content about medications in settings where models know the request is illogical. We investigated whether in-context directions and instruction-tuning of LLMs to prioritize logical reasoning over compliance reduced misinformation risk. Results: While all frontier LLMs complied with misinformation requests, both prompt-based and parameter-based approaches can improve the detection of logic flaws in requests and prevent the dissemination of medical misinformation. Conclusion: Shifting LLMs to prioritize logic over compliance could reduce risks of exploitation for medical misinformation. © 2024, CC BY-NC-ND.

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Correction: Spatial organization of B lymphocytes and prognosis prediction in patients with gastric cancer

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Gastric cancer : official journal of the International Gastric cancer Association and the Japanese Gastric cancer Association 2025年 2025 May 16页

作者： Ryan Yong Kiat Tay Manavi Sachdeva Haoran Ma Young-Woo Kim Myeong-Cherl Kook Hyunki Kim Jae-Ho Cheong Lindsay C Hewitt Katharina Nekolla Günter Schmidt Takaki Yoshikawa Takashi Oshima Tomio Arai Supriya Srivastava Ming Teh Xuewen Ong Su Ting Tay Taotao Sheng Joseph J Zhao Patrick Tan Heike I Grabsch Raghav Sundar Yong Loo Lin School of Medicine National University of Singapore 1E Kent Ridge Road Singapore 119228 Singapore. Department of Haematology-Oncology National University Cancer Institute National University Hospital Singapore Singapore. Cancer and Stem Cell Biology Program Duke-NUS Medical School Singapore Singapore. Department of Cancer Policy and Population Health National Cancer Center Graduate School of Cancer Science and Policy and Center for Gastric Cancer and Department of Surgery National Cancer Center Goyang Republic of Korea. Center for Gastric Cancer Department of Pathology National Cancer Center Goyang Republic of Korea. Department of Pathology Yonsei University College of Medicine Seoul Republic of Korea. Department of Surgery Yonsei University College of Medicine Seoul Republic of Korea. Department of Pathology GROW Research Institute for Oncology and Reproduction Maastricht University Medical Center+ Maastricht The Netherlands. Department of Precision Medicine GROW School for Oncology and Reproduction Maastricht University Center+ Maastricht The Netherlands. Computational Pathology Oncology R&D AstraZeneca Munich Germany. Department of Surgery Kanagawa Cancer Center Yokohama Japan. Department of Surgery Yokohama City University Yokohama Japan. Department of Surgery Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital Tokyo Japan. Department of Pathology Tokyo Metropolitan Institute for Geriatrics and Gerontology Tokyo Japan. Department of Medicine National University of Singapore Singapore Singapore. Department of Pathology National University Hospital Singapore Singapore. Genome Institute of Singapore Agency for Science Technology and Research Singapore Singapore. Cancer Science Institute of Singapore National University of Singapore Singapore Singapore. Cellular and Molecular Research National Cancer Centre Singapore Singapore. Singhealth/Duke-NUS Institute of Precision Medicine National Heart Centre Singapore

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