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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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Dish-trend: Intervention modeling simulator that accounts for trend influences

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arXiv 2021年

作者： Andjelkovic, Stefan Miskov-Zivanov, Natasa Computational and Systems Biology Joint Carnegie Mellon University University of Pittsburgh Computational Biology PhD Program University of Pittsburgh 4420 Bayard St PittsburghPA15213 United States Electrical and Computer Engineering Bioengineering Computational and Systems Biology University of Pittsburgh 4420 Bayard St PittsburghPA15213 United States

关键词： Stochastic systems

COULD DROPPING A FEW CELLS CHANGE THE TAKEAWAYS FROM DIFFERENTIAL EXPRESSION?

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arXiv 2023年

作者： Shiffman, Miriam Giordano, Ryan Broderick, Tamara MIT Computational & Systems Biology Program United States MIT Dept. of Electrical Engineering & Computer Science United States UC Berkeley Dept. of Statistics United States

Analysis of differential gene expression plays a fundamental role in biology toward illuminating the molecular mechanisms driving a difference between groups (e.g., due to treatment or disease). While any analysis is run on particular cells or samples, the intent is to generalize to future occurrences of the treatment or disease. Implicitly, generalization is justified under the assumption that present and future samples are independent and identically distributed from the same population. Though this assumption is always false, we might hope that any deviation from the assumption is small enough that A) fundamental conclusions of the analysis still hold, and B) standard tools like standard error, significance, and power still reflect generalizability. Conversely, we might worry about these deviations, and reliance on standard statistical tools, if conclusions could be substantively changed by dropping a very small fraction of observations. While checking every small fraction is computationally intractable, recent work develops an approximation to identify when such an influential subset exists. Building on this work, we develop a metric for dropping-data robustness of differential expression;namely, we cast the analysis in a differentiable form suitable to the approximation, extend the approximation to models whose hyperparameters depend on the full dataset, and extend the notion of a data point from a single cell to a pseudobulk observation. We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets. We use our tool to assess the robustness of differential expression for published single-cell RNA-seq data, and discover that thousands of genes can have their results flipped by dropping Copyright © 2023, The Authors. All rights reserved.

关键词： Diseases

COEM: Cross-Modal Embedding for MetaCell Identification

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arXiv 2022年

作者： Mao, Haiyi Jia, Minxue Dou, Jason Xiaotian Zhang, Haotian Benos, Panayiotis V. Department of Computational and Systems Biology University of Pittsburgh PA United States Joint Carnegie Mellon University University of Pittsburgh PhD Program in Computational Biology United States Department of Electrical and Computer Engineering University of Pittsburgh PA United States

Metacells are disjoint and homogeneous groups of single-cell profiles, representing discrete and highly granular cell states. Existing metacell algorithms tend to use only one modality to infer metacells, even though single-cell multi-omics datasets profile multiple molecular modalities within the same cell. Here, we present Cross-MOdal Embedding for MetaCell Identification (COEM), which utilizes an embedded space leveraging the information of both scATAC-seq and scRNA-seq to perform aggregation, balancing the trade-off between fine resolution and sufficient sequencing coverage. COEM outperforms the state-of-the-art method SEACells by efficiently identifying accurate and well-separated metacells across datasets with continuous and discrete cell types. Furthermore, COEM significantly improves peak-to-gene association analyses, and facilitates complex gene regulatory inference tasks. © 2022, CC BY.

关键词： Embeddings

The dynamic immune behavior of primary and metastatic ovarian carcinoma

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npj Precision Oncology 2025年第1期9卷 1-12页

作者： Stur, Elaine Peng, Fuduan Teng, Pang-Ning Bayraktar, Emine Hu, Min Corvigno, Sara Brown, David J. Lee, Sanghoon Moore, Kathleen N. Bateman, Nicholas W. Darcy, Kathleen M. Maxwell, George L. P. Conrads, Thomas Sahni, Nidhi Vázquez-García, Ignacio Shah, Sohrab P. Celestino, Joseph D. Fleming, Nicole Navin, Nicholas E. Wang, Linghua Sood, Anil K. Department of Gynecologic Oncology and Reproductive Medicine The University of Texas MD Anderson Cancer Center Houston TX United States Department of Genomic Medicine The University of Texas MD Anderson Cancer Center Houston TX United States Gynecologic Cancer Center of Excellence The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc. Walter Reed National Military Medical Center Bethesda 20889 MD United States Department of Gynecologic Surgery and Obstetrics Uniformed Services University of the Health Sciences Walter Reed National Military Medical Center Bethesda MD United States Department of Genetics The University of Texas MD Anderson Cancer Center Houston TX United States Obstetrics and Gynecology Stephenson Cancer Center Stephenson Cancer Center at the University of Oklahoma Health Sciences Center/Sarah Cannon Research Institute Oklahoma City OK United States Women’s Health Integrated Research Center Women’s Service Line Inova Health System Falls Church VA United States Department of Epigenetics and Molecular Carcinogenesis The University of Texas MD Anderson Cancer Center Baylor College of Medicine Houston TX United States Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX United States Quantitative and Computational Biosciences Program Baylor College of Medicine Houston TX United States Computational Oncology Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY United States The Halvorsen Center for Computational Oncology Memorial Sloan Kettering Cancer Center Boston MA United States Irving Institute for Cancer Dynamics Columbia University Cambridge 10027 MA United States Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston MA United States The James P. Allison Institute The University of Texas MD Anderson Cancer Center Houston 77030 TX United States The Institute for Data Sc

Patients with high-grade serous ovarian carcinoma (HGSC) are usually diagnosed with advanced-stage disease, and the tumors often have immunosuppressive characteristics. Together, these factors are important for disease progression, drug resistance, and mortality. In this study, we used a combination of single-cell sequencing and spatial transcriptomics to identify the molecular mechanisms that lead to immunosuppression in HGSC. Primary tumors consistently showed a more active immune microenvironment than did omental tumors. In addition, we found that untreated primary tumors were mostly populated by dysfunctional CD4 and CD8 T cells in later stages of differentiation;this, in turn, was correlated with expression changes in the interferon α and γ pathways in epithelial cells, showing that cross-communication between the epithelial and immune compartments is important for immune suppression in HGSC. These findings could have implications for the design of clinical trials with immune-modulating drugs. © The Author(s) 2025.

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Mixed graphical models for causal analysis of multi-modal variables

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arXiv 2017年

作者： Sedgewick, Andrew J. Ramsey, Joseph D. Spirtes, Peter Glymour, Clark Benos, Panayiotis V. Department of Computational and Systems Biology University of Pittsburgh School of Medicine PittsburghPA United States Joint Carnegie Mellon University University of Pittsburgh PhD Program in Computational Biology PittsburghPA United States Department of Bioengineering University of Pittsburgh PittsburghPA United States Department of Philosophy Carnegie Mellon University PittsburghPA United States

Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data. Once learned, causal graphs can be used for classification, feature selection and hypothesis generation, while revealing the underlying causal network structure and thus allowing for arbitrary likelihood queries over the data. However, current algorithms for learning sparse directed graphs are generally designed to handle only one type of data (continuous-only or discrete-only), which limits their applicability to a large class of multi-modal biological datasets that include mixed type variables. To address this issue, we developed new methods that modify and combine existing methods for finding undirected graphs with methods for finding directed graphs. These hybrid methods are not only faster, but also perform better than the directed graph estimation methods alone for a variety of parameter settings and data set sizes. Here, we describe a new conditional independence test for learning directed graphs over mixed data types and we compare performances of different graph learning strategies on synthetic data. Copyright © 2017, The Authors. All rights reserved.

关键词： Directed graphs

Adversarial attacks against medical deep learning systems

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arXiv 2018年

作者： Finlayson, Samuel G. Chung, Hyung Won Kohane, Isaac S. Beam, Andrew L. Department of Systems Biology and MD-PhD program Harvard Medical School MIT BostonMA Massachusetts Institute of Technology CambridgeMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States

The discovery of adversarial examples has raised concerns about the practical deployment of deep learning systems. In this paper, we demonstrate that adversarial examples are capable of manipulating deep learning systems across three clinical domains. For each of our representative medical deep learning classifiers, both white and black box attacks were highly successful. Our models are representative of the current state of the art in medical computer vision and, in some cases, directly reflect architectures already seeing deployment in real world clinical settings. In addition to the technical contribution of our paper, we synthesize a large body of knowledge about the healthcare system to argue that medicine may be uniquely susceptible to adversarial attacks, both in terms of monetary incentives and technical vulnerability. To this end, we outline the healthcare economy and the incentives it creates for fraud and provide concrete examples of how and why such attacks could be realistically carried out. We urge practitioners to be aware of current vulnerabilities when deploying deep learning systems in clinical settings, and encourage the machine learning community to further investigate the domain-specific characteristics of medical learning systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Health care

End-to-End Differentiable Molecular Mechanics Force Field Construction

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arXiv 2020年

作者： Wang, Yuanqing Fass, Josh Kaminow, Benjamin Herr, John E. Rufa, Dominic Zhang, Ivy Pulido, Iván Henry, Mike Chodera, John D. Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medical College Cornell University New YorkNY10065 United States Physiology Biophysics and System Biology Ph.D. Program Weill Cornell Medical College Cornell University New YorkNY10065 United States Program in Creative Writing Division of Humanities and Arts The City College of New York The City University of New York New YorkNY10031 United States

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process-spanning chemical perception to parameter assignment-is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with little inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field ("Parsley") openff-1.2.0 small molecule force field augmented with a peptide dataset

关键词： Atoms

Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods

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Current Opinion in Structural biology 2023年 78卷 102517页

作者： Banerjee, Anupam Saha, Satyaki Tvedt, Nathan C. Yang, Lee-Wei Bahar, Ivet Computational and Systems Biology University of Pittsburgh School of Medicine Pittsburgh 15261 PA United States Computational and Applied Mathematics and Statistics The College of William and Mary Williamsburg 23185 VA United States Institute of Bioinformatics and Structural Biology and PhD Program in Biomedical Artificial Intelligence National Tsing Hua University Hsinchu 300044 Taiwan Physics Division National Center for Theoretical Sciences Taipei 106319 Taiwan

Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in, if not determine, viable mechanisms of biological function. In recent years, machine learning frameworks have proven uniquely useful in structural biology, and recent studies further provide evidence to the utility and/or necessity of considering intrinsic dynamics for increasing their predictive ability. Efficient quantification of dynamics-based attributes by recently developed physics-based theories and models such as elastic network models provides a unique opportunity to generate data on dynamics for training ML models towards inferring mechanisms of protein function, assessing pathogenicity, or estimating binding affinities. © 2022 The Authors

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