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Carlson RJ, Patten JJ, Stefanakis G, Soong BY, Radhakrishnan A, Singh A, Thakur N, Amarasinghe GK, Hacohen N, Basler CF, Leung D, Uhler C, Davey RA, Blainey PC. Single-cell image-based genetic screens systematically identify regulators of Ebola virus subcellular infection dynamics. bioRxiv 2024:2024.04.06.588168. [PMID: 38617272 PMCID: PMC11014611 DOI: 10.1101/2024.04.06.588168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Ebola virus (EBOV) is a high-consequence filovirus that gives rise to frequent epidemics with high case fatality rates and few therapeutic options. Here, we applied image-based screening of a genome-wide CRISPR library to systematically identify host cell regulators of Ebola virus infection in 39,085,093 million single cells. Measuring viral RNA and protein levels together with their localization in cells identified over 998 related host factors and provided detailed information about the role of each gene across the virus replication cycle. We trained a deep learning model on single-cell images to associate each host factor with predicted replication steps, and confirmed the predicted relationship for select host factors. Among the findings, we showed that the mitochondrial complex III subunit UQCRB is a post-entry regulator of Ebola virus RNA replication, and demonstrated that UQCRB inhibition with a small molecule reduced overall Ebola virus infection with an IC50 of 5 μM. Using a random forest model, we also identified perturbations that reduced infection by disrupting the equilibrium between viral RNA and protein. One such protein, STRAP, is a spliceosome-associated factor that was found to be closely associated with VP35, a viral protein required for RNA processing. Loss of STRAP expression resulted in a reduction in full-length viral genome production and subsequent production of non-infectious virus particles. Overall, the data produced in this genome-wide high-content single-cell screen and secondary screens in additional cell lines and related filoviruses (MARV and SUDV) revealed new insights about the role of host factors in virus replication and potential new targets for therapeutic intervention.
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Affiliation(s)
- Rebecca J Carlson
- Massachusetts Institute of Technology, Department of Health Sciences and Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - J J Patten
- Department of Virology, Immunology, and Microbiology, Boston University School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | - George Stefanakis
- Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brian Y Soong
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adityanarayanan Radhakrishnan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Avtar Singh
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Naveen Thakur
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gaya K Amarasinghe
- Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA
| | - Nir Hacohen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts General Hospital, Cancer Center, Boston, MA, USA
| | - Christopher F Basler
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daisy Leung
- Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA
| | - Caroline Uhler
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Robert A Davey
- Department of Virology, Immunology, and Microbiology, Boston University School of Medicine, Boston, MA, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | - Paul C Blainey
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Department of Biological Engineering, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
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3
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Gulamali F, Jayaraman P, Sawant AS, Desman J, Fox B, Chang A, Soong BY, Arivazaghan N, Reynolds AS, Duong SQ, Vaid A, Kovatch P, Freeman R, Hofer IS, Sakhuja A, Dangayach NS, Reich DS, Charney AW, Nadkarni GN. Derivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements. medRxiv 2024:2024.01.30.24301974. [PMID: 38352556 PMCID: PMC10863000 DOI: 10.1101/2024.01.30.24301974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Importance Increased intracranial pressure (ICP) is associated with adverse neurological outcomes, but needs invasive monitoring. Objective Development and validation of an AI approach for detecting increased ICP (aICP) using only non-invasive extracranial physiological waveform data. Design Retrospective diagnostic study of AI-assisted detection of increased ICP. We developed an AI model using exclusively extracranial waveforms, externally validated it and assessed associations with clinical outcomes. Setting MIMIC-III Waveform Database (2000-2013), a database derived from patients admitted to an ICU in an academic Boston hospital, was used for development of the aICP model, and to report association with neurologic outcomes. Data from Mount Sinai Hospital (2020-2022) in New York City was used for external validation. Participants Patients were included if they were older than 18 years, and were monitored with electrocardiograms, arterial blood pressure, respiratory impedance plethysmography and pulse oximetry. Patients who additionally had intracranial pressure monitoring were used for development (N=157) and external validation (N=56). Patients without intracranial monitors were used for association with outcomes (N=1694). Exposures Extracranial waveforms including electrocardiogram, arterial blood pressure, plethysmography and SpO2. Main Outcomes and Measures Intracranial pressure > 15 mmHg. Measures were Area under receiver operating characteristic curves (AUROCs), sensitivity, specificity, and accuracy at threshold of 0.5. We calculated odds ratios and p-values for phenotype association. Results The AUROC was 0.91 (95% CI, 0.90-0.91) on testing and 0.80 (95% CI, 0.80-0.80) on external validation. aICP had accuracy, sensitivity, and specificity of 73.8% (95% CI, 72.0%-75.6%), 99.5% (95% CI 99.3%-99.6%), and 76.9% (95% CI, 74.0-79.8%) on external validation. A ten-percentile increment was associated with stroke (OR=2.12; 95% CI, 1.27-3.13), brain malignancy (OR=1.68; 95% CI, 1.09-2.60), subdural hemorrhage (OR=1.66; 95% CI, 1.07-2.57), intracerebral hemorrhage (OR=1.18; 95% CI, 1.07-1.32), and procedures like percutaneous brain biopsy (OR=1.58; 95% CI, 1.15-2.18) and craniotomy (OR = 1.43; 95% CI, 1.12-1.84; P < 0.05 for all). Conclusions and Relevance aICP provides accurate, non-invasive estimation of increased ICP, and is associated with neurological outcomes and neurosurgical procedures in patients without intracranial monitoring.
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Affiliation(s)
- Faris Gulamali
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Pushkala Jayaraman
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ashwin S. Sawant
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Jacob Desman
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Benjamin Fox
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Annie Chang
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Brian Y. Soong
- Department of Medical Education, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Naveen Arivazaghan
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Alexandra S. Reynolds
- Department of Neurosurgery and Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Son Q Duong
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Patricia Kovatch
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Robert Freeman
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ira S. Hofer
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ankit Sakhuja
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Neha S. Dangayach
- Department of Neurosurgery and Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - David S. Reich
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Alexander W Charney
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish N. Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
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Thrane K, Winge MCG, Wang H, Chen L, Guo MG, Andersson A, Abalo XM, Yang X, Kim DS, Longo SK, Soong BY, Meyers JM, Reynolds DL, McGeever A, Demircioglu D, Hasson D, Mirzazadeh R, Rubin AJ, Bae GH, Karkanias J, Rieger K, Lundeberg J, Ji AL. Single-Cell and Spatial Transcriptomic Analysis of Human Skin Delineates Intercellular Communication and Pathogenic Cells. J Invest Dermatol 2023; 143:2177-2192.e13. [PMID: 37142187 PMCID: PMC10592679 DOI: 10.1016/j.jid.2023.02.040] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 01/26/2023] [Accepted: 02/16/2023] [Indexed: 05/06/2023]
Abstract
Epidermal homeostasis is governed by a balance between keratinocyte proliferation and differentiation with contributions from cell-cell interactions, but conserved or divergent mechanisms governing this equilibrium across species and how an imbalance contributes to skin disease are largely undefined. To address these questions, human skin single-cell RNA sequencing and spatial transcriptomics data were integrated and compared with mouse skin data. Human skin cell-type annotation was improved using matched spatial transcriptomics data, highlighting the importance of spatial context in cell-type identity, and spatial transcriptomics refined cellular communication inference. In cross-species analyses, we identified a human spinous keratinocyte subpopulation that exhibited proliferative capacity and a heavy metal processing signature, which was absent in mouse and may account for species differences in epidermal thickness. This human subpopulation was expanded in psoriasis and zinc-deficiency dermatitis, attesting to disease relevance and suggesting a paradigm of subpopulation dysfunction as a hallmark of the disease. To assess additional potential subpopulation drivers of skin diseases, we performed cell-of-origin enrichment analysis within genodermatoses, nominating pathogenic cell subpopulations and their communication pathways, which highlighted multiple potential therapeutic targets. This integrated dataset is encompassed in a publicly available web resource to aid mechanistic and translational studies of normal and diseased skin.
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Affiliation(s)
- Kim Thrane
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden
| | - Mårten C G Winge
- Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, California, USA
| | - Hongyu Wang
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Black Family Stem Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China
| | - Larry Chen
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Black Family Stem Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Margaret G Guo
- Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, California, USA; Biomedical Informatics Program, Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA
| | - Alma Andersson
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden
| | - Xesús M Abalo
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden
| | - Xue Yang
- Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, California, USA
| | - Daniel S Kim
- Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, California, USA; Biomedical Informatics Program, Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA
| | - Sophia K Longo
- Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, California, USA
| | - Brian Y Soong
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Black Family Stem Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jordan M Meyers
- Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, California, USA
| | - David L Reynolds
- Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, California, USA
| | - Aaron McGeever
- Chan Zuckerberg Biohub San Francisco, San Francisco, California, USA
| | - Deniz Demircioglu
- Bioinformatics for Next Generation Sequencing Core, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Dan Hasson
- Bioinformatics for Next Generation Sequencing Core, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Reza Mirzazadeh
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden
| | - Adam J Rubin
- Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, California, USA
| | - Gordon H Bae
- Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, California, USA
| | - Jim Karkanias
- Chan Zuckerberg Biohub San Francisco, San Francisco, California, USA
| | - Kerri Rieger
- Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, California, USA
| | - Joakim Lundeberg
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden
| | - Andrew L Ji
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Black Family Stem Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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5
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Magen A, Hamon P, Fiaschi N, Soong BY, Park MD, Mattiuz R, Humblin E, Troncoso L, D'souza D, Dawson T, Kim J, Hamel S, Buckup M, Chang C, Tabachnikova A, Schwartz H, Malissen N, Lavin Y, Soares-Schanoski A, Giotti B, Hegde S, Ioannou G, Gonzalez-Kozlova E, Hennequin C, Le Berichel J, Zhao Z, Ward SC, Fiel I, Kou B, Dobosz M, Li L, Adler C, Ni M, Wei Y, Wang W, Atwal GS, Kundu K, Cygan KJ, Tsankov AM, Rahman A, Price C, Fernandez N, He J, Gupta NT, Kim-Schulze S, Gnjatic S, Kenigsberg E, Deering RP, Schwartz M, Marron TU, Thurston G, Kamphorst AO, Merad M. Intratumoral dendritic cell-CD4 + T helper cell niches enable CD8 + T cell differentiation following PD-1 blockade in hepatocellular carcinoma. Nat Med 2023; 29:1389-1399. [PMID: 37322116 PMCID: PMC11027932 DOI: 10.1038/s41591-023-02345-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 04/10/2023] [Indexed: 06/17/2023]
Abstract
Despite no apparent defects in T cell priming and recruitment to tumors, a large subset of T cell rich tumors fail to respond to immune checkpoint blockade (ICB). We leveraged a neoadjuvant anti-PD-1 trial in patients with hepatocellular carcinoma (HCC), as well as additional samples collected from patients treated off-label, to explore correlates of response to ICB within T cell-rich tumors. We show that ICB response correlated with the clonal expansion of intratumoral CXCL13+CH25H+IL-21+PD-1+CD4+ T helper cells ("CXCL13+ TH") and Granzyme K+ PD-1+ effector-like CD8+ T cells, whereas terminally exhausted CD39hiTOXhiPD-1hiCD8+ T cells dominated in nonresponders. CD4+ and CD8+ T cell clones that expanded post-treatment were found in pretreatment biopsies. Notably, PD-1+TCF-1+ (Progenitor-exhausted) CD8+ T cells shared clones mainly with effector-like cells in responders or terminally exhausted cells in nonresponders, suggesting that local CD8+ T cell differentiation occurs upon ICB. We found that these Progenitor CD8+ T cells interact with CXCL13+ TH within cellular triads around dendritic cells enriched in maturation and regulatory molecules, or "mregDC". These results suggest that discrete intratumoral niches that include mregDC and CXCL13+ TH control the differentiation of tumor-specific Progenitor exhasuted CD8+ T cells following ICB.
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Affiliation(s)
- Assaf Magen
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pauline Hamon
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nathalie Fiaschi
- Department of Oncology & Angiogenesis, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Brian Y Soong
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew D Park
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Raphaël Mattiuz
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Etienne Humblin
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Leanna Troncoso
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Darwin D'souza
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Travis Dawson
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joel Kim
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven Hamel
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mark Buckup
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Christie Chang
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexandra Tabachnikova
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hara Schwartz
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nausicaa Malissen
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yonit Lavin
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alessandra Soares-Schanoski
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bruno Giotti
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samarth Hegde
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Giorgio Ioannou
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Gonzalez-Kozlova
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Clotilde Hennequin
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jessica Le Berichel
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zhen Zhao
- The Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephen C Ward
- The Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Isabel Fiel
- The Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Baijun Kou
- Department of Oncology & Angiogenesis, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Michael Dobosz
- Department of Oncology & Angiogenesis, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Lianjie Li
- Department of Oncology & Angiogenesis, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Christina Adler
- Molecular Profiling & Data Science, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Min Ni
- Molecular Profiling & Data Science, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Yi Wei
- Molecular Profiling & Data Science, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Wei Wang
- Molecular Profiling & Data Science, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Gurinder S Atwal
- Molecular Profiling & Data Science, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Kunal Kundu
- VI NEXT, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Kamil J Cygan
- VI NEXT, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Alexander M Tsankov
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adeeb Rahman
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | | | - Namita T Gupta
- Molecular Profiling & Data Science, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Seunghee Kim-Schulze
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sacha Gnjatic
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ephraim Kenigsberg
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Raquel P Deering
- Department of Oncology & Angiogenesis, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA
| | - Myron Schwartz
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Division of Hematology/Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Thomas U Marron
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Division of Hematology/Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Gavin Thurston
- Department of Oncology & Angiogenesis, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA.
| | - Alice O Kamphorst
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Miriam Merad
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Institute for Thoracic Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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