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arXiv 2025年

作者： Viswanath, Siddharth Madhu, Hiren Bhaskar, Dhananjay Kovalic, Jake Johnson, Dave Ying, Rex Tape, Christopher Adelstein, Ian Perlmutter, Michael Krishnaswamy, Smita Department of Computer Science Yale University New Haven United States Department of Genetics Yale University New Haven United States Department of Applied Physics Yale University New Haven United States Program in Computing Boise State University Idaho United States Cell Communication Lab Department of Oncology University College London Cancer Institute London United Kingdom Department of Mathematics Yale University New Haven United States Department of Mathematics Boise State University Idaho United States Computational Biology and Bioinformatics Program Yale University New Haven United States Wu-Tsai Institute Yale University New Haven United States Program for Applied Math Yale University New Haven United States

In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Single-cell data can have high dimensionality exceeding the capabilities of existing methods for point clouds which are tailored for 3D data. Moreover, modern single-cell and spatial experiments now yield entire cohorts of datasets (i.e. one on every patient), necessitating models that can process large, high-dimensional point clouds at scale. Most current approaches build a single nearest-neighbor graph, discarding important geometric information. In contrast, HiPoNet forms higher-order simplicial complexes through learnable feature reweighting, generating multiple data views that disentangle distinct biological processes. It then employs simplicial wavelet transforms to extract multiscale features—capturing both local and global topology. We empirically show that these components preserve topological information in the learned representations, and that HiPoNet significantly outperforms state-of-the-art point-cloud and graph-based models on single cell. We also show an application of HiPoNet on spatial transcriptomics datasets using spatial co-ordinates as one of the views. Overall, HiPoNet offers a robust and scalable solution for high-dimensional data analysis. © 2025, CC BY.

关键词： Network topology

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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computational methods to dissect gene regulatory networks in cancer

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Current Opinion in systems biology 2017年 2卷 115-122页

作者： Iyer, Archana S. Osmanbeyoglu, Hatice U. Leslie, Christina S. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York 10065 NY United States

cancer is a disease of gene dysregulation, where cells acquire genetic alterations that drive aberrant signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Large international consortia have generated massive tumor profiling data sets across many cancer types, collecting mutation and copy number variation, mRNA expression, and in some cases epigenomic and proteomic profiles. An overarching goal of these tumor-profiling efforts is to identify genes that are essential drivers of cellular processes in cancer. Here we review diverse computational methodologies that have sought to interpret somatic alterations and gene expression data through models of gene regulatory networks. Early work in the field used expression data alone to infer regulatory networks, and expression-only network inference continues to be an active area of research. Once catalogs of somatic mutations and copy number variations became available, another class of methods tried to interpret these alterations either in terms of a prior interaction network or an inferred regulatory network. More recently, the tools of regulatory genomics have been applied to cancer data sets, integrating regulatory sequence information and epigenomic data with gene expression, often through supervised methods. Finally, newer cross-cutting algorithms link upstream signaling changes - through a prior network or proteomics data - to downstream transcriptional programs and interpret somatic alterations in terms an integrative model. Currently, clinical trials use specific somatic alterations alone to direct patients towards pathway-targeted therapies. Ultimately, methods that interpret patient mutations profiles through the lens of gene regulatory networks may be better able to identify patient populations who will benefit from therapy. © 2017 Elsevier Ltd.

关键词： cancer genomics Machine learning Transcriptional regulatory networks

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Convergent Usage of Amino Acids in Human cancers as A Reversed Process of Tissue Development

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Genomics, Proteomics & Bioinformatics 2022年第1期20卷 147-162页

作者： Yikai Luo Han Liang Graduate Program in Quantitative and Computational Biosciences Baylor College of MedicineHoustonTX 77030USA Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA Department of Systems Biology The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA

Genome-and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution,but its characteristics and implication in cancer evolution and therapy remain largely ***,we analyzed large-scale transcriptome/proteome profiles,such as The cancer Genome Atlas(TCGA),the Genotype-Tissue Expression(GTEx),and the Clinical Proteomic Tumor Analysis Consortium(CPTAC),and found that compared to normal tissues,different cancer types showed a convergent pattern toward using biosynthetically low-cost amino *** a pattern can be accurately captured by a single index based on the average biosynthetic energy cost of amino acids,termed energy cost per amino acid(ECPA).With this index,we further compared the trends of amino acid usage and the contributing genes in cancer and tissue development,and revealed their reversed ***,focusing on the liver,a tissue with a dramatic increase in ECPA during development,we found that ECPA represents a powerful biomarker that could distinguish liver tumors from normal liver samples consistently across 11 independent patient cohorts and outperforms any index based on single *** study reveals an important principle underlying cancer evolution and suggests the global amino acid usage as a system-level biomarker for cancer diagnosis.

关键词： Amino acid usage Tissue development Biosynthetic energy Diagnostic biomarker

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Interferon-γ orchestrates leptomeningeal anti-tumour response

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Nature 2025年 1-10页

作者： Remsik, Jan Tong, Xinran Kunes, Russell Z. Li, Min Jun Estrera, Rachel Snyder, Jenna Thomson, Clark Osman, Ahmed M. Chabot, Kiana Sener, Ugur T. Wilcox, Jessica A. Isakov, Danielle Wang, Helen Bale, Tejus A. Chaligné, Ronan Sun, Joseph C. Brown, Chrysothemis Pe’er, Dana Boire, Adrienne Human Oncology & Pathogenesis Program Memorial Sloan Kettering Cancer Center New York NY United States Laboratory for Immunology of Metastatic Ecosystems Center for Cancer Biology VIB Leuven Belgium Department of Oncology KU Leuven Leuven Belgium BCMB Allied Program Graduate School of Medical Sciences Weill Cornell Medicine New York NY United States Program for Computational and Systems Biology Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY United States Department of Statistics Columbia University New York NY United States Gerstner Sloan Kettering Graduate School of Biomedical Sciences Memorial Sloan Kettering Cancer Center New York NY United States Department of Neurology Memorial Sloan Kettering Cancer Center New York NY United States Department of Pathology and Laboratory Medicine Memorial Sloan Kettering Cancer Center New York NY United States Immunology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY United States Department of Pediatrics Memorial Sloan Kettering Cancer Center New York NY United States Immunology and Microbial Pathogenesis Program Weill Cornell Medical College New York NY United States Howard Hughes Medical Institute Memorial Sloan Kettering Cancer Center New York NY United States Medical Scientist Training Program University of California San Diego La Jolla CA United States Department of Women’s and Children’s Health Karolinska Institutet Solna Sweden College of Osteopathic Medicine New York Institute of Technology Glen Head NY United States Department of Neurology & Department of Medical Oncology Mayo Clinic Rochester MN United States Tri-Institutional MD-PhD Program Weill Cornell Medicine New York NY United States

Metastasis to the cerebrospinal-fluid-filled leptomeninges, or leptomeningeal metastasis, represents a fatal complication of solid tumours1. Multimodal analyses of clinical specimens reveal substantial inflammatory in...

Metastasis to the cerebrospinal-fluid-filled leptomeninges, or leptomeningeal metastasis, represents a fatal complication of solid tumours¹. Multimodal analyses of clinical specimens reveal substantial inflammatory infiltrate in leptomeningeal metastases with enrichment of IFNγ and resulting downstream signalling. Here, to investigate and overcome this futile anti-tumour response within the leptomeninges, we developed syngeneic lung cancer, breast cancer and melanoma leptomeningeal-metastasis mouse models. We show that transgenic host mice lacking IFNγ or its receptor fail to control the growth of leptomeningeal metastases growth. Leptomeningeal overexpression of Ifng through a targeted adeno-associated-virus-based system controls cancer cell growth independent of adaptive immunity. Using a suite of transgenic hosts, we demonstrate that leptomeningeal T cells generate IFNγ to actively recruit and activate peripheral myeloid cells, generating a diverse spectrum of dendritic cell subsets. Independent of antigen presentation, migratory CCR7⁺ dendritic cells orchestrate the influx, proliferation and cytotoxic action of natural killer cells to control cancer cell growth in the leptomeninges. This study identifies unique, leptomeninges-specific IFNγ signalling and suggests an immune-therapeutic approach against tumours within this space. © The Author(s) 2025.

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Author Correction: ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

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Nature communications 2025年第1期16卷 684页

作者： Vianne R Gao Rui Yang Arnav Das Renhe Luo Hanzhi Luo Dylan R McNally Ioannis Karagiannidis Martin A Rivas Zhong-Min Wang Darko Barisic Alireza Karbalayghareh Wilfred Wong Yingqian A Zhan Christopher R Chin William S Noble Jeff A Bilmes Effie Apostolou Michael G Kharas Wendy Béguelin Aaron D Viny Danwei Huangfu Alexander Y Rudensky Ari M Melnick Christina S Leslie Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA. Tri-Institutional Program in Computational Biology and Medicine New York NY USA. University of Washington Seattle WA USA. Developmental Biology Program Sloan Kettering Institute New York NY USA. Molecular Pharmacology Program Experimental Therapeutics Center and Center for Stem Cell Biology Memorial Sloan Kettering Cancer Center New York NY USA. Caryl and Israel Englander Institute for Precision Medicine Institute for Computational Biomedicine Weill Cornell Medicine Cornell University New York NY USA. Division of Hematology and Medical Oncology Department of Medicine Weill Cornell Medical College New York NY USA. Department of Biochemistry & Molecular Biology Sylvester Comprehensive Cancer Center University of Miami Miller School of Medicine Miami FL USA. Howard Hughes Medical Institute and Immunology Program Sloan Kettering Institute and Ludwig Center at Memorial Sloan Kettering Cancer Center New York NY USA. Center for Epigenetics Research Memorial Sloan Kettering Cancer Center New York NY USA. Joan and Sanford I. Weill Department of Medicine Sandra and Edward Meyer Cancer Center Weill Cornell Medicine New York NY USA. Departments of Medicine Division of Hematology & Oncology and of Genetics & Development Columbia Stem Cell Initiative Herbert Irving Comprehensive Cancer Center Columbia University Irving Medical Center New York NY USA. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA. lesliec@***.

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KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection

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Science (New York, N.Y.) 2025年第6735期387卷 eadn2337页

作者： Fagerberg, Eric Attanasio, John Dien, Christine Singh, Jaiveer Kessler, Emily A. Abdullah, Leena Shen, Jian Hunt, Brian G. Connolly, Kelli A. De Brouwer, Edward He, Jiaming Iyer, Nivedita R. Buck, Jessica Borr, Emily R. Damo, Martina Foster, Gena G. Giles, Josephine R. Huang, Yina H. Tsang, John S. Krishnaswamy, Smita Cui, Weiguo Joshi, Nikhil S. Department of Immunobiology Yale University School of Medicine New Haven CT United States Program in Computational Biology & Bioinformatics Yale University New Haven CT United States Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth NH United States Department of Pathology Feinberg School of Medicine at Northwestern University Chicago IL United States Department of Genetics and Computer Science Yale University School of Medicine New Haven CT United States Department of Medicine University of Chicago Chicago IL United States Section of Hematology Department of Internal Medicine Yale University School of Medicine New Haven CT United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Philadelphia PA United States Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Parker Institute for Cancer Immunotherapy Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Yale Center for Systems and Engineering Immunology Yale University School of Medicine New Haven CT United States New Haven CT United States Department of Biomedical Engineering Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University CT USA New Haven United States Applied Math Program Yale University New Haven CT United States

Naïve CD8 T cells have the potential to differentiate into a spectrum of functional states during an immune response. How these developmental decisions are made and what mechanisms exist to suppress differentiation toward alternative fates remains unclear. We employed in vivo CRISPR-Cas9-based perturbation sequencing to assess the role of ~40 transcription factors (TFs) and epigenetic modulators in T cell fate decisions. Unexpectedly, we found that knockout of the TF Klf2 resulted in aberrant differentiation to exhausted-like CD8 T cells during acute infection. KLF2 was required to suppress the exhaustion-promoting TF TOX and to enable the TF TBET to drive effector differentiation. KLF2 was also necessary to maintain a polyfunctional tumor-specific progenitor state. Thus, KLF2 provides effector CD8 T cell lineage fidelity and suppresses the exhaustion program.

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Target specificity of the CRISPR-Cas9 system

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Quantitative biology 2014年第2期2卷 59-70页

作者： Xuebing Wu Andrea J.Kriz Phillip A.Sharp David H.Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Computational and Systems Biology Graduate Program Massachusetts Institute of Technology Department of Biology Massachusetts Institute of Technology

The CRISPR-Cas9 system, naturally a defense mechanism in prokaryotes, has been repurposed as an RNA-guided DNA targeting platform. It has been widely used for genome editing and transcriptome modulation, and has shown great promise in correcting mutations in human genetic diseases. Off-target effects are a critical issue for all of these applications. Here we review the current status on the target specificity of the CRISPR-Cas9 system.

关键词： CRISPR Cas9 genome engineering off-targets target specificity

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Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria

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genetics in Medicine 2025年第6期27卷 101402页

作者： Bergquist, Timothy Stenton, Sarah L. Nadeau, Emily A.W. Byrne, Alicia B. Greenblatt, Marc S. Harrison, Steven M. Tavtigian, Sean V. Biesecker, Leslie G. Radivojac, Predrag Brenner, Steven E. Pejaver, Vikas Tayoun, Ahmad A. Cutting, Garry R. Rehm, Heidi L. Karbassi, Izabela Mester, Jessica Berg, Jonathan S. Biesecker, Leslie G. Greenblatt, Marc S. Strande, Natasha T. Kang, Peter Karchin, Rachel Topper, Scott Tavtigian, Sean V. Plon, Sharon E. Ellard, Sian Brenner, Steven E. Harrison, Steven M. Pesaran, Tina Institute for Genomic Health Icahn School of Medicine at Mount Sinai New York NY United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA United States Division of Genetics and Genomics Boston Children's Hospital Harvard Medical School Boston MA United States Department of Medicine and University of Vermont Cancer Center University of Vermont Larner College of Medicine Burlington VT United States Ambry Genetics Aliso Viejo CA United States Department of Oncological Sciences Huntsman Cancer Institute University of Utah School of Medicine Salt Lake City UT United States Center for Precision Health Research National Human Genome Research Institute National Institutes of Health Bethesda MD United States Khoury College of Computer Sciences Northeastern University Boston MA United States Department of Plant and Microbial Biology and Center for Computational Biology University of California Berkeley CA United States Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY United States

Purpose: We previously developed an approach to calibrate computational tools for clinical variant classification, updating recommendations for the reliable use of variant impact predictors to provide evidence strength up to Strong. A new generation of tools using distinctive approaches has since been released, and these methods must be independently calibrated for clinical application. Methods: Using our local posterior probability-based calibration and our established data set of ClinVar pathogenic and benign variants, we determined the strength of evidence provided by 3 new tools (AlphaMissense, ESM1b, and VARITY) and calibrated scores meeting each evidence strength. Results: All 3 tools reached the Strong level of evidence for variant pathogenicity and Moderate for benignity, although sometimes for few variants. Compared with previously recommended tools, these yielded at best only modest improvements in the trade-offs between evidence strength and false-positive predictions. Conclusion: At calibrated thresholds, 3 new computational predictors provided evidence for variant pathogenicity at similar strength to the 4 previously recommended predictors (and comparable with functional assays for some variants). This calibration broadens the scope of computational tools for application in clinical variant classification. Their new approaches offer promise for future advancement of the field. © 2025 The Authors

关键词： ACMG/AMP classification AlphaMissense Calibration ESM1b VARITY

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Lymphatic endothelial mTORC1 instructs metabolic and developmental signaling during lymphangiogenesis

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Developmental Cell 2025年

作者： Han, Fei Simeroth, Summer Zhu, Jie Gryniuk, Irma Pranay, Atul Chen, Weiqing Wang, Yuan Cai, Yuanyuan Shen, Zhiyuan Wang, Guangyu Griffin, Courtney T. Xia, Lijun Yu, Pengchun Cardiovascular Biology Research Program Oklahoma Medical Research Foundation Oklahoma City 73104 OK United States Department of Cell Biology University of Oklahoma Health Sciences Center Oklahoma City 73104 OK United States Aging and Metabolism Research Program Oklahoma Medical Research Foundation Oklahoma City 73104 OK United States Center for Bioinformatics and Computational Biology Houston Methodist Research Institute Houston TX United States Department of Physiology Biophysics & Systems Biology Weill Cornell Graduate School of Medical Science Weill Cornell Medicine Cornell University New York NY United States Department of Radiation Oncology Rutgers Cancer Institute and Robert Wood Johnson Medical School Rutgers University New Brunswick NJ United States Center for Cardiovascular Regeneration Houston Methodist Research Institute Houston TX United States Center for RNA Therapeutics Houston Methodist Research Institute Houston TX United States Department of Cardiothoracic Surgery Weill Cornell Medicine Cornell University New York NY United States Department of Biochemistry and Physiology University of Oklahoma Health Sciences Center Oklahoma City OK United States

The lymphatic vasculature comprises lymphatic capillaries and collecting vessels. To support lymphatic development, lymphatic endothelial cells (LECs) utilize nutrients to fuel lymphangiogenic processes. Meanwhile, LECs maintain constant prospero homeobox 1 (PROX1) expression critical for lymphatic specification. However, molecular mechanisms orchestrating nutrient metabolism while sustaining PROX1 levels in LECs remain unclear. Here, we show that loss of RAPTOR, an indispensable mechanistic target of rapamycin complex 1 (mTORC1) component, downregulates PROX1 and impairs lymphatic capillary growth and differentiation of collecting lymphatics in mice. Mechanistically, mTORC1 inhibition in mouse and human LECs causes Myc reduction, which decreases hexokinase 2 (HK2) and glutaminase (GLS), inhibiting glycolysis and glutaminolysis. Myc or HK2/GLS ablation impedes lymphatic capillary and collecting vessel formation. Interestingly, mTORC1 regulation of PROX1 is independent of Myc-HK2/GLS signaling. Moreover, genetic interaction analysis indicates that Myc and PROX1 play crucial roles in mTORC1-regulated lymphatic development. Collectively, our findings identify mTORC1 as a key regulator of metabolic programs and PROX1 expression during lymphangiogenesis. © 2025 Elsevier Inc.

关键词： glutaminase glutaminolysis glycolysis hexokinase 2 lymphangiogenesis lymphatic endothelial cell lymphatic vessel mTORC1 Myc PROX1

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