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arXiv 2021年

作者： Ozyoruk, Kutsev Bengisu Can, Sermet Gokceler, Guliz Irem Başak, Kayhan Demir, Derya Serin, Gurdeniz Hacisalihoglu, Uguray Payam Kurtuluş, Emirhan Darbaz, Berkan Lu, Ming Y. Chen, Tiffany Y. Williamson, Drew F.K. Yilmaz, Funda Mahmood, Faisal Turan, Mehmet Institute of Biomedical Engineering Bogazici University Istanbul Turkey Uk Biocentre Tilbrook United Kingdom Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and Mit CambradgeMA United States Saǧlik Bilimleri University Kartal Dr.Lütfi Kirdar City Hospital Department of Pathology Istanbul Turkey Faculty of Medicine Department of Pathology Ege University Izmir Turkey Istanbul Yeni Yuzyil University Medical Faculty Gaziosmanpasa Hospital Pathology Department Turkey

Frozen sectioning (FS) is the preparation method of choice for microscopic evaluation of tissues during surgical operations. The high speed of procedure allows pathologists to rapidly assess the key microscopic features, such as tumor margins and malignant status to guide surgical decision-making and minimise disruptions to the course of the operation. However, FS is prone to introducing many misleading artificial structures (histological artefacts), such as nuclear ice crystals, compression, and cutting artefacts, hindering timely and accurate diagnostic judgement of the pathologist. Additional training and prolonged experience is often required to make highly effective and time critical diagnosis on frozen sections. On the other hand, the gold standard tissue preparation technique of formalin-fixation and paraffin-embedding (FFPE) provides significantly superior image quality, but is a very time-consuming process (12-48 hours), making it unsuitable for intra-operative use. In this paper, we propose an artificial intelligence (AI) method that improves FS image quality by computationally transforming frozen-sectioned whole-slide images (FS-WSIs) into whole-slide FFPE-style images in minutes. AI-FFPE rectifies FS artefacts with the guidance of an attention-mechanism that puts a particular emphasis on artefacts while utilising a self-regularization mechanism established between FS input image and synthesized FFPE-style image that preserves clinically relevant features. As a result, AI-FFPE method successfully generates FFPE-style images without significantly extending tissue processing time and consequently improves diagnostic accuracy. We demonstrate the efficacy of AI-FFPE on lung and brain frozen sections using a variety of different qualitative and quantitative metrics including visual Turing tests from 20 board certified pathologists. Additionally, we demonstrate that deep models trained on FFPE tissue can adapt to frozen sections which a much higher efficacy whe

关键词： Surgery

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Predicting drug response through tumor deconvolution by cancer cell lines

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Patterns 2024年第4期5卷 100949页

作者： Hsu, Yu-Ching Chiu, Yu-Chiao Lu, Tzu-Pin Hsiao, Tzu-Hung Chen, Yidong Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei115 Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Health Data Analytics and Statistics Department of Public Health College of Public Health National Taiwan University Taipei100 Taiwan Greehey Children's Cancer Research Institute University of Texas Health San Antonio San AntonioTX78229 United States Department of Medicine School of Medicine University of Pittsburgh PittsburghPA15261 United States UPMC Hillman Cancer Center University of Pittsburgh PittsburghPA15232 United States Department of Medical Research Taichung Veterans General Hospital Taichung40705 Taiwan Department of Population Health Sciences University of Texas Health San Antonio San AntonioTX78229 United States

Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing. © 2024 The Authors

关键词： Tumors

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The power of specialty palliative care: moving towards a systems perspective

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The Lancet Haematology 2021年第5期8卷 e376-e381页

作者： Barnato, Amber E Khayal, Inas S The Dartmouth Institute for Health Policy and Clinical Practice Geisel School of Medicine at Dartmouth Lebanon NH United States Department of Biomedical Data Science Geisel School of Medicine at Dartmouth Lebanon NH United States Section of Palliative Care Department of Medicine Dartmouth-Hitchcock Medical Center Lebanon NH United States Department of Computer Science Dartmouth College Hanover NH United States Cancer Population Sciences Program Norris Cotton Cancer Center Lebanon NH United States

Three palliative care clinical trials were presented at the 2020 American Society for Clinical Oncology Annual Meeting. The heterogeneity in populations, models of care, study design, and assessment of clinical outcomes across these three studies show the broad opportunities for research into interventions for palliative care. In this Viewpoint, we summarise the characteristics of these studies, discuss their novel features and lingering questions, and offer a suggestion for further expanding the focus of clinical trials for delivery of palliative care in the future. We particularly argue that the propensity to characterise palliative care as if it was a clinical or biomedical intervention hampers the design and evaluation of complex clinical interventions that influence clinicians, systems for health-care delivery, individual patients, and their families. © 2021 Elsevier Ltd

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cancer Genomic Rearrangements and Copy Number Alterations from Errors in Cell Division

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cancer Biology 1000年第1期6卷 245-268页

作者： Cheng-Zhong Zhang David Pellman 3Cancer Program Broad Institute of MIT and Harvard Cambridge Massachusetts USA 2Department of Biomedical Informatics Blavatnik Institute of Harvard Medical School Boston Massachusetts USA 1Department of Data Science Dana-Farber Cancer Institute Boston Massachusetts USA email: cheng-zhong_zhang@dfci.harvard.edu 4Howard Hughes Medical Institute Chevy Chase Maryland USA 5Department of Pediatric Oncology Dana-Farber Cancer Institute Boston Massachusetts USA email: david_pellman@dfci.harvard.edu 6Department of Cell Biology Blavatnik Institute of Harvard Medical School Boston Massachusetts USA

Analysis of cancer genomes has shown that a large fraction of chromosomal changes originate from catastrophic events including whole-genome duplication, chromothripsis, breakage-fusion-bridge cycles, and chromoplexy. Through sophisticated computational analysis of cancer genomes and experimental recapitulation of these catastrophic alterations, we have gained significant insights into the origin, mechanism, and evolutionary dynamics of cancer genome complexity. In this review, we summarize this progress and survey the major unresolved questions, with particular emphasis on the relative contributions of chromosome fragmentation and DNA replication errors to complex chromosomal alterations.

关键词： somatic copy number alterations chromosome rearrangement cancer evolution whole-genome duplication micronuclei chromosome bridge

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Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model

arXiv

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arXiv 2024年

作者： Gilson, Aidan Ai, Xuguang Xie, Qianqian Srinivasan, Sahana Pushpanathan, Krithi Singer, Maxwell B. Huang, Jimin Kim, Hyunjae Long, Erping Wan, Peixing Del Priore, Luciano V. Ohno-Machado, Lucila Xu, Hua Liu, Dianbo Adelman, Ron A. Tham, Yih-Chung Chen, Qingyu Department of Ophthalmology Massachusetts Eye and Ear Harvard Medical School BostonMA United States Department of Biomedical Informatics and Data Science Yale School of Medicine Yale University New Haven United States Singapore Eye Research Institute Singapore National Eye Centre Singapore Centre for Innovation and Precision Eye Health Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore Department of Ophthalmology and Visual Science Yale School of Medicine Yale University New Haven United States Department of Computer Science Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Division of Cancer Epidemiology and Genetics National Cancer Institute National Institutes of Health BethesdaMD United States Center for Cancer Research National Cancer Institute National Institutes of Health BethesdaMD United States Ophthalmology and Visual Science Academic Clinical Program Duke-NUS Medical School Singapore Singapore

Large Language Models (LLMs) are poised to revolutionize healthcare. Ophthalmology-specific LLMs remain scarce and underexplored. We introduced an open-source, specialized LLM for ophthalmology, termed Language Enhanced Model for Eye (LEME). LEME was initially pre-trained on the Llama2 70B framework and further fine-tuned with a corpus of ~127,000 non-copyrighted training instances curated from ophthalmology-specific case reports, abstracts, and open-source study materials. We benchmarked LEME against eight other LLMs, namely, GPT-3.5, GPT-4, three Llama2 models (7B, 13B, 70B), PMC-LLAMA 13B, Meditron 70B, and EYE-Llama (another ophthalmology-specific LLM). Evaluations included four internal validation tasks: abstract completion, fill-in-the-blank, multiple-choice questions (MCQ), and short-answer QA. External validation tasks encompassed long-form QA, MCQ, patient EHR summarization, and clinical QA. Evaluation metrics included Rouge-L scores, accuracy, and expert evaluation of correctness, completeness, and readability. In internal validations, LEME consistently outperformed its counterparts, achieving Rouge-L scores of 0.20 ± 0.03 in abstract completion (all p © 2024, CC BY.

关键词： Ophthalmology

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Locla: A Novel Local Optimization Tool for Chromosome-Level Assembly of Multiple Sequencing Technologies

SSRN

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SSRN 2024年

作者： Chuang, Wei-Hsuan Cheng, Hsueh-Chien Chang, Yu-Jung Fu, Pao-Ying Huang, Yi-Chen Hsieh, Ping-Heng Lin, Chung-Yen Ho, Jan-Ming Chen, Shu-Hwa Wellcome Genome Campus United Kingdom Institute of Information Science Academia Sinica Taipei115 Taiwan Wellcome Sanger Institute Hinxton United Kingdom Ocean Data Bank Institute of Oceanography National Taiwan University Taipei10663 Taiwan Genome and Systems Biology Degree Program National Taiwan University Taipei10663 Taiwan Institute of Fisheries Science National Taiwan University Taipei10663 Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei11031 Taiwan

LOCLA (Local Optimization for Chromosome-Level Assembly) is an innovative tool designed to enhance genome assemblies by addressing challenges in repetitive regions and alignment errors through iterative gap-filling. It improves draft assemblies by utilizing original raw reads and new sequencing data from various technologies, such as PacBio HiFi, Bionano, 10x Genomics Linked-Reads, Oxford Nanopore, and Illumina. LOCLA can optimize genomes using the original reads or by integrating new data from different platforms to achieve superior *** LOCLA algorithm consists of four key modules: (1) local-contig-based (LCB) gap filling, (2) global-contig-based (GCB) gap filling, (3) LCB scaffolding, and (4) GCB scaffolding. LCB gap filling utilizes high-quality NGS reads aligned on gap flanks to create local contigs (L-contigs), while GCB gap filling uses global contigs (G-contigs) from TGS reads or assembled scaffolds. The scaffolding modules link scaffold pairs based on barcode distribution or align G-contigs to scaffold ends for assembly *** experiments, LOCLA significantly improved genome assembly quality across multiple datasets. For example, in the de novo assembly of the LLD0021C human genome, LOCLA added 23.3 million base pairs, enhancing the resolution of noncoding transcripts and pseudogenes. The CHM13 human genome increased the N50 value and added 51.8 million base pairs, improving the resolution of functional genes related to complex diseases. LOCLA also enhanced the RagTag reference-guided assemblies of the HG002 human genome by 63 million bases and filled 41.4% of gaps in the Bruguiera sexangula genome, raising its BUSCO score from 97.90% to 98.10%.Overall, LOCLA consistently improves the quantity and quality of genome assemblies, demonstrating its potential to advance personalized medicine and our understanding of complex biological pathways. © 2024, The Authors. All rights reserved.

关键词： Genome

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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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Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

arXiv

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arXiv 2021年

作者： Ezhov, Ivan Scibilia, Kevin Franitza, Katharina Steinbauer, Felix Shit, Suprosanna Zimmer, Lucas Lipkova, Jana Kofler, Florian Paetzold, Johannes Canalini, Luca Waldmannstetter, Diana Menten, Martin J. Metz, Marie Wiestler, Benedikt Menze, Bjoern Department of Informatics TUM Munich Germany TranslaTUM - Central Institute for Translational Cancer Research TUM Munich Germany Department of Mechanical Engineering TUM Munich Germany Department of Quantitative Biomedicine UZH Zurich Switzerland Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Broad Institute of Harvard MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Fraunhofer MEVIS Bremen Germany Neuroradiology Department of Klinikum Rechts der Isar TUM Munich Germany

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer, the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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SYNTHETIC data IN MACHINE LEARNING FOR PATHOLOGY

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Journal of Pathology Informatics 2022年 13卷 100044-100044页

作者： Chen, Richard J. Lu, Ming Y. Chen, Tiffany Y. Williamson, Drew F.K. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Cancer Program Broad Institute of Harvard and MIT Cambridge MA USA Cancer Data Science Program Dana-Farber Cancer Institute Boston MA USA

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