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Article ; Online: The Genome of the Human Pathogen

Wang, Joshua M / Bennett, Richard J / Anderson, Matthew Z

2018 Volume 9, Issue 5

Abstract: The opportunistic fungal ... ...

Abstract	The opportunistic fungal pathogen
MeSH term(s)	Candida albicans/classification ; Candida albicans/genetics ; Candida albicans/isolation & purification ; Candidiasis/microbiology ; Cluster Analysis ; Evolution, Molecular ; Genetic Variation ; Genome, Fungal ; Genome, Mitochondrial ; Humans ; Mutagenesis, Insertional ; Mutation ; Phylogeny ; Polymorphism, Single Nucleotide ; Recombination, Genetic ; Sequence Analysis, DNA ; Sequence Deletion
Language	English
Publishing date	2018-09-18
Publishing country	United States
Document type	Comparative Study ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2557172-2
ISSN	2150-7511 ; 2161-2129
ISSN (online)	2150-7511
ISSN	2161-2129
DOI	10.1128/mBio.01205-18
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Article: Predictive modeling of morbidity and mortality in COVID-19 hospitalized patients and its clinical implications.

Wang, Joshua M / Liu, Wenke / Chen, Xiaoshan / McRae, Michael P / McDevitt, John T / Fenyö, David

medRxiv : the preprint server for health sciences

2021

Abstract: Clinical activity of 3740 de-identified COVID-19 positive patients treated at NYU Langone Health (NYULH) were collected between January and August 2020. XGBoost model trained on clinical data from the final 24 hours excelled at predicting mortality (AUC= ... ...

Abstract	Clinical activity of 3740 de-identified COVID-19 positive patients treated at NYU Langone Health (NYULH) were collected between January and August 2020. XGBoost model trained on clinical data from the final 24 hours excelled at predicting mortality (AUC=0.92, specificity=86% and sensitivity=85%). Respiration rate was the most important feature, followed by SpO2 and age 75+. Performance of this model to predict the deceased outcome extended 5 days prior with AUC=0.81, specificity=70%, sensitivity=75%. When only using clinical data from the first 24 hours, AUCs of 0.79, 0.80, and 0.77 were obtained for deceased, ventilated, or ICU admitted, respectively. Although respiration rate and SpO2 levels offered the highest feature importance, other canonical markers including diabetic history, age and temperature offered minimal gain. When lab values were incorporated, prediction of mortality benefited the most from blood urea nitrogen (BUN) and lactate dehydrogenase (LDH). Features predictive of morbidity included LDH, calcium, glucose, and C-reactive protein (CRP). Together this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.
Language	English
Publishing date	2021-03-29
Publishing country	United States
Document type	Preprint
DOI	10.1101/2020.12.02.20235879
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Article ; Online: Intraspecies Transcriptional Profiling Reveals Key Regulators of Candida albicans Pathogenic Traits.

Wang, Joshua M / Woodruff, Andrew L / Dunn, Matthew J / Fillinger, Robert J / Bennett, Richard J / Anderson, Matthew Z

mBio

2021 Volume 12, Issue 2

Abstract: The human commensal and opportunistic fungal ... ...

Abstract	The human commensal and opportunistic fungal pathogen
MeSH term(s)	Candida albicans/genetics ; Candida albicans/pathogenicity ; Candidiasis/microbiology ; Gene Expression Profiling ; Gene Expression Regulation, Fungal/genetics ; Genetic Variation ; Genome, Fungal ; Genotype ; Humans ; Phenotype ; Phylogeny ; Sequence Analysis, RNA ; Virulence
Language	English
Publishing date	2021-04-20
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2557172-2
ISSN	2150-7511 ; 2161-2129
ISSN (online)	2150-7511
ISSN	2161-2129
DOI	10.1128/mBio.00586-21
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Article ; Online: Predictive Modeling of Morbidity and Mortality in Patients Hospitalized With COVID-19 and its Clinical Implications: Algorithm Development and Interpretation.

Wang, Joshua M / Liu, Wenke / Chen, Xiaoshan / McRae, Michael P / McDevitt, John T / Fenyö, David

Journal of medical Internet research

2021 Volume 23, Issue 7, Page(s) e29514

Abstract: Background: The COVID-19 pandemic began in early 2021 and placed significant strains on health care systems worldwide. There remains a compelling need to analyze factors that are predictive for patients at elevated risk of morbidity and mortality.: ... ...

Abstract	Background: The COVID-19 pandemic began in early 2021 and placed significant strains on health care systems worldwide. There remains a compelling need to analyze factors that are predictive for patients at elevated risk of morbidity and mortality. Objective: The goal of this retrospective study of patients who tested positive with COVID-19 and were treated at NYU (New York University) Langone Health was to identify clinical markers predictive of disease severity in order to assist in clinical decision triage and to provide additional biological insights into disease progression. Methods: The clinical activity of 3740 patients at NYU Langone Hospital was obtained between January and August 2020; patient data were deidentified. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to the intensive care unit (ICU). Results: The XGBoost (eXtreme Gradient Boosting) model that was trained on clinical data from the final 24 hours excelled at predicting mortality (area under the curve [AUC]=0.92; specificity=86%; and sensitivity=85%). Respiration rate was the most important feature, followed by SpO Conclusions: Together, this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.
MeSH term(s)	Adolescent ; Adult ; Aged ; Algorithms ; Area Under Curve ; COVID-19/diagnosis ; COVID-19/mortality ; Child ; Child, Preschool ; Diabetes Mellitus ; Female ; Hospitalization ; Hospitals ; Humans ; Infant ; Infant, Newborn ; Intensive Care Units ; Male ; Middle Aged ; Morbidity ; New York City/epidemiology ; Pandemics ; Retrospective Studies ; SARS-CoV-2 ; Triage ; Young Adult
Language	English
Publishing date	2021-07-09
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/29514
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Article ; Online: Deep learning integrates histopathology and proteogenomics at a pan-cancer level.

Cell reports. Medicine

2023 Volume 4, Issue 9, Page(s) 101173

Abstract: We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides ...

Abstract	We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models.
MeSH term(s)	Humans ; Proteogenomics ; Deep Learning ; Neoplasms/genetics ; Proteomics ; Machine Learning
Language	English
Publishing date	2023-08-14
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	2666-3791
ISSN (online)	2666-3791
DOI	10.1016/j.xcrm.2023.101173
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Article ; Online: Predictive modeling of morbidity and mortality in COVID-19 hospitalized patients and its clinical implications.

Wang, Joshua M. / Liu, Wenke / Chen, Xiaoshan / McRae, Michael P. / McDevitt, John T. / Fenyo, David

medRxiv

Abstract: ... Objective: ... Retrospective study of COVID-19 positive patients treated at NYU Langone Health (NYULH) to identify clinical markers predictive of disease severity to assist in clinical decision triage and provide additional biological insights into ... ...

Abstract	Objective: Retrospective study of COVID-19 positive patients treated at NYU Langone Health (NYULH) to identify clinical markers predictive of disease severity to assist in clinical decision triage and provide additional biological insights into disease progression. Materials and Methods: Clinical activity of 3740 de-identified patients at NYULH between January and August 2020. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to ICU. Results: XGBoost model trained on clinical data from the final 24 hours excelled at predicting mortality (AUC=0.92, specificity=86% and sensitivity=85%). Respiration rate was the most important feature, followed by SpO2 and age 75+. Performance of this model to predict the deceased outcome extended 5 days prior with AUC=0.81, specificity=70%, sensitivity=75%. When only using clinical data from the first 24 hours, AUCs of 0.79, 0.80, and 0.77 were obtained for deceased, ventilated, or ICU admitted, respectively. Although respiration rate and SpO2 levels offered the highest feature importance, other canonical markers including diabetic history, age and temperature offered minimal gain. When lab values were incorporated, prediction of mortality benefited the most from blood urea nitrogen (BUN) and lactate dehydrogenase (LDH). Features predictive of morbidity included LDH, calcium, glucose, and C-reactive protein (CRP). Conclusion: Together this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.
Keywords	covid19
Language	English
Publishing date	2020-12-04
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.12.02.20235879
Database	COVID19

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Article ; Online: Hemizygosity Enables a Mutational Transition Governing Fungal Virulence and Commensalism.

Liang, Shen-Huan / Anderson, Matthew Z / Hirakawa, Matthew P / Wang, Joshua M / Frazer, Corey / Alaalm, Leenah M / Thomson, Gregory J / Ene, Iuliana V / Bennett, Richard J

Cell host & microbe

2019 Volume 25, Issue 3, Page(s) 418–431.e6

Abstract: Candida albicans is a commensal fungus of human gastrointestinal and reproductive tracts, but also causes life-threatening systemic infections. The balance between colonization and pathogenesis is associated with phenotypic plasticity, with alternative ... ...

Abstract	Candida albicans is a commensal fungus of human gastrointestinal and reproductive tracts, but also causes life-threatening systemic infections. The balance between colonization and pathogenesis is associated with phenotypic plasticity, with alternative cell states producing different outcomes in a mammalian host. Here, we reveal that gene dosage of a master transcription factor regulates cell differentiation in diploid C. albicans cells, as EFG1 hemizygous cells undergo a phenotypic transition inaccessible to "wild-type" cells with two functional EFG1 alleles. Notably, clinical isolates are often EFG1 hemizygous and thus licensed to undergo this transition. Phenotypic change corresponds to high-frequency loss of the functional EFG1 allele via de novo mutation or gene conversion events. This phenomenon also occurs during passaging in the gastrointestinal tract with the resulting cell type being hypercompetitive for commensal and systemic infections. A "two-hit" genetic model therefore underlies a key phenotypic transition in C. albicans that enables adaptation to host niches.
MeSH term(s)	Candida albicans/genetics ; Candida albicans/growth & development ; Candida albicans/pathogenicity ; Candidiasis/microbiology ; DNA-Binding Proteins/genetics ; Fungal Proteins/genetics ; Gastrointestinal Tract/microbiology ; Gene Dosage ; Gene Expression Regulation, Fungal ; Humans ; Mutation ; Symbiosis ; Transcription Factors/genetics ; Virulence
Chemical Substances	DNA-Binding Proteins ; EFG1 protein, Candida albicans ; Fungal Proteins ; Transcription Factors
Language	English
Publishing date	2019-02-26
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2278004-X
ISSN	1934-6069 ; 1931-3128
ISSN (online)	1934-6069
ISSN	1931-3128
DOI	10.1016/j.chom.2019.01.005
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Article ; Online: Pan-cancer proteogenomics characterization of tumor immunity.

Petralia, Francesca / Ma, Weiping / Yaron, Tomer M / Caruso, Francesca Pia / Tignor, Nicole / Wang, Joshua M / Charytonowicz, Daniel / Johnson, Jared L / Huntsman, Emily M / Marino, Giacomo B / Calinawan, Anna / Evangelista, John Erol / Selvan, Myvizhi Esai / Chowdhury, Shrabanti / Rykunov, Dmitry / Krek, Azra / Song, Xiaoyu / Turhan, Berk / Christianson, Karen E /

Lewis, David A / Deng, Eden Z / Clarke, Daniel J B / Whiteaker, Jeffrey R / Kennedy, Jacob J / Zhao, Lei / Segura, Rossana Lazcano / Batra, Harsh / Raso, Maria Gabriela / Parra, Edwin Roger / Soundararajan, Rama / Tang, Ximing / Li, Yize / Yi, Xinpei / Satpathy, Shankha / Wang, Ying / Wiznerowicz, Maciej / González-Robles, Tania J / Iavarone, Antonio / Gosline, Sara J C / Reva, Boris / Robles, Ana I / Nesvizhskii, Alexey I / Mani, D R / Gillette, Michael A / Klein, Robert J / Cieslik, Marcin / Zhang, Bing / Paulovich, Amanda G / Sebra, Robert / Gümüş, Zeynep H / Hostetter, Galen / Fenyö, David / Omenn, Gilbert S / Cantley, Lewis C / Ma'ayan, Avi / Lazar, Alexander J / Ceccarelli, Michele / Wang, Pei

Cell

2024 Volume 187, Issue 5, Page(s) 1255–1277.e27

Abstract: Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies ... ...

Abstract	Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.
MeSH term(s)	Humans ; Combined Modality Therapy ; Genomics ; Neoplasms/genetics ; Neoplasms/immunology ; Neoplasms/therapy ; Proteogenomics ; Proteomics ; Tumor Escape
Language	English
Publishing date	2024-02-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	187009-9
ISSN	1097-4172 ; 0092-8674
ISSN (online)	1097-4172
ISSN	0092-8674
DOI	10.1016/j.cell.2024.01.027
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Article ; Online: Genetic and phenotypic intra-species variation in Candida albicans.

Hirakawa, Matthew P / Martinez, Diego A / Sakthikumar, Sharadha / Anderson, Matthew Z / Berlin, Aaron / Gujja, Sharvari / Zeng, Qiandong / Zisson, Ethan / Wang, Joshua M / Greenberg, Joshua M / Berman, Judith / Bennett, Richard J / Cuomo, Christina A

Genome research

2015 Volume 25, Issue 3, Page(s) 413–425

Abstract: Candida albicans is a commensal fungus of the human gastrointestinal tract and a prevalent opportunistic pathogen. To examine diversity within this species, extensive genomic and phenotypic analyses were performed on 21 clinical C. albicans isolates. ... ...

Abstract	Candida albicans is a commensal fungus of the human gastrointestinal tract and a prevalent opportunistic pathogen. To examine diversity within this species, extensive genomic and phenotypic analyses were performed on 21 clinical C. albicans isolates. Genomic variation was evident in the form of polymorphisms, copy number variations, chromosomal inversions, subtelomeric hypervariation, loss of heterozygosity (LOH), and whole or partial chromosome aneuploidies. All 21 strains were diploid, although karyotypic changes were present in eight of the 21 isolates, with multiple strains being trisomic for Chromosome 4 or Chromosome 7. Aneuploid strains exhibited a general fitness defect relative to euploid strains when grown under replete conditions. All strains were also heterozygous, yet multiple, distinct LOH tracts were present in each isolate. Higher overall levels of genome heterozygosity correlated with faster growth rates, consistent with increased overall fitness. Genes with the highest rates of amino acid substitutions included many cell wall proteins, implicating fast evolving changes in cell adhesion and host interactions. One clinical isolate, P94015, presented several striking properties including a novel cellular phenotype, an inability to filament, drug resistance, and decreased virulence. Several of these properties were shown to be due to a homozygous nonsense mutation in the EFG1 gene. Furthermore, loss of EFG1 function resulted in increased fitness of P94015 in a commensal model of infection. Our analysis therefore reveals intra-species genetic and phenotypic differences in C. albicans and delineates a natural mutation that alters the balance between commensalism and pathogenicity.
MeSH term(s)	Aneuploidy ; Candida albicans/classification ; Candida albicans/genetics ; Candidiasis/microbiology ; Chromosomes, Fungal ; DNA Copy Number Variations ; Evolution, Molecular ; Genetic Variation ; Genome, Fungal ; Genotype ; Humans ; Phenotype ; Phylogeny ; Polymorphism, Single Nucleotide ; Selection, Genetic ; Sequence Analysis, DNA
Language	English
Publishing date	2015-03
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1284872-4
ISSN	1549-5469 ; 1088-9051 ; 1054-9803
ISSN (online)	1549-5469
ISSN	1088-9051 ; 1054-9803
DOI	10.1101/gr.174623.114
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Article ; Online: Proteogenomic characterization of pancreatic ductal adenocarcinoma.

Cao, Liwei / Huang, Chen / Cui Zhou, Daniel / Hu, Yingwei / Lih, T Mamie / Savage, Sara R / Krug, Karsten / Clark, David J / Schnaubelt, Michael / Chen, Lijun / da Veiga Leprevost, Felipe / Eguez, Rodrigo Vargas / Yang, Weiming / Pan, Jianbo / Wen, Bo / Dou, Yongchao / Jiang, Wen / Liao, Yuxing / Shi, Zhiao /

Terekhanova, Nadezhda V / Cao, Song / Lu, Rita Jui-Hsien / Li, Yize / Liu, Ruiyang / Zhu, Houxiang / Ronning, Peter / Wu, Yige / Wyczalkowski, Matthew A / Easwaran, Hariharan / Danilova, Ludmila / Mer, Arvind Singh / Yoo, Seungyeul / Wang, Joshua M / Liu, Wenke / Haibe-Kains, Benjamin / Thiagarajan, Mathangi / Jewell, Scott D / Hostetter, Galen / Newton, Chelsea J / Li, Qing Kay / Roehrl, Michael H / Fenyö, David / Wang, Pei / Nesvizhskii, Alexey I / Mani, D R / Omenn, Gilbert S / Boja, Emily S / Mesri, Mehdi / Robles, Ana I / Rodriguez, Henry / Bathe, Oliver F / Chan, Daniel W / Hruban, Ralph H / Ding, Li / Zhang, Bing / Zhang, Hui

Cell

2021 Volume 184, Issue 19, Page(s) 5031–5052.e26

Abstract: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic ... ...

Abstract	Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.
MeSH term(s)	Adenocarcinoma/diagnosis ; Adenocarcinoma/genetics ; Adult ; Aged ; Aged, 80 and over ; Algorithms ; Carcinoma, Pancreatic Ductal/diagnosis ; Carcinoma, Pancreatic Ductal/genetics ; Cohort Studies ; Endothelial Cells/metabolism ; Epigenesis, Genetic ; Female ; Gene Dosage ; Genome, Human ; Glycolysis ; Glycoproteins/biosynthesis ; Humans ; Male ; Middle Aged ; Molecular Targeted Therapy ; Pancreatic Neoplasms/diagnosis ; Pancreatic Neoplasms/genetics ; Phenotype ; Phosphoproteins/metabolism ; Phosphorylation ; Prognosis ; Protein Kinases/metabolism ; Proteogenomics ; Proteome/metabolism ; Substrate Specificity ; Transcriptome/genetics
Chemical Substances	Glycoproteins ; Phosphoproteins ; Proteome ; Protein Kinases (EC 2.7.-)
Language	English
Publishing date	2021-09-14
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	187009-9
ISSN	1097-4172 ; 0092-8674
ISSN (online)	1097-4172
ISSN	0092-8674
DOI	10.1016/j.cell.2021.08.023
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