Literature DB >> 29444438

Commonly Occurring Cell Subsets in High-Grade Serous Ovarian Tumors Identified by Single-Cell Mass Cytometry.

Veronica D Gonzalez1, Nikolay Samusik1, Tiffany J Chen1, Erica S Savig1, Nima Aghaeepour1, David A Quigley2, Ying-Wen Huang1, Valeria Giangarrà1, Alexander D Borowsky3, Neil E Hubbard3, Shih-Yu Chen1, Guojun Han1, Alan Ashworth4, Thomas J Kipps5, Jonathan S Berek6, Garry P Nolan1, Wendy J Fantl7.   

Abstract

We have performed an in-depth single-cell phenotypic characterization of high-grade serous ovarian cancer (HGSOC) by multiparametric mass cytometry (CyTOF). Using a CyTOF antibody panel to interrogate features of HGSOC biology, combined with unsupervised computational analysis, we identified noteworthy cell types co-occurring across the tumors. In addition to a dominant cell subset, each tumor harbored rarer cell phenotypes. One such group co-expressed E-cadherin and vimentin (EV), suggesting their potential role in epithelial mesenchymal transition, which was substantiated by pairwise correlation analyses. Furthermore, tumors from patients with poorer outcome had an increased frequency of another rare cell type that co-expressed vimentin, HE4, and cMyc. These poorer-outcome tumors also populated more cell phenotypes, as quantified by Simpson's diversity index. Thus, despite the recognized genomic complexity of the disease, the specific cell phenotypes uncovered here offer a focus for therapeutic intervention and disease monitoring.
Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  CyTOF; HE4; cMyc; heterogeneity; hierarchical clustering; high grade serious ovarian cancer; mass cytometry; phenotypic characterization; relapse; single cell

Mesh:

Substances:

Year:  2018        PMID: 29444438     DOI: 10.1016/j.celrep.2018.01.053

Source DB:  PubMed          Journal:  Cell Rep            Impact factor:   9.423


  32 in total

1.  Phase I Trial: Cirmtuzumab Inhibits ROR1 Signaling and Stemness Signatures in Patients with Chronic Lymphocytic Leukemia.

Authors:  Michael Y Choi; George F Widhopf; Emanuela M Ghia; Reilly L Kidwell; Md Kamrul Hasan; Jian Yu; Laura Z Rassenti; Liguang Chen; Yun Chen; Emily Pittman; Minya Pu; Karen Messer; Charles E Prussak; Januario E Castro; Catriona Jamieson; Thomas J Kipps
Journal:  Cell Stem Cell       Date:  2018-06-01       Impact factor: 24.633

Review 2.  A Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry.

Authors:  Timothy J Keyes; Pablo Domizi; Yu-Chen Lo; Garry P Nolan; Kara L Davis
Journal:  Cytometry A       Date:  2020-06-30       Impact factor: 4.355

3.  Mass synaptometry: High-dimensional multi parametric assay for single synapses.

Authors:  Chandresh R Gajera; Rosemary Fernandez; Nadia Postupna; Kathleen S Montine; Edward J Fox; Dmitry Tebaykin; Michael Angelo; Sean C Bendall; C Dirk Keene; Thomas J Montine
Journal:  J Neurosci Methods       Date:  2018-11-20       Impact factor: 2.390

4.  Training Novices in Generation and Analysis of High-Dimensional Human Cell Phospho-Flow Cytometry Data.

Authors:  Caroline E Roe; Madeline J Hayes; Sierra M Barone; Jonathan M Irish
Journal:  Curr Protoc Cytom       Date:  2020-03

Review 5.  Proteomics advances for precision therapy in ovarian cancer.

Authors:  Marilyne Labrie; Nicholas D Kendsersky; Hongli Ma; Lydia Campbell; Jennifer Eng; Koei Chin; Gordon B Mills
Journal:  Expert Rev Proteomics       Date:  2019-09-13       Impact factor: 3.940

Review 6.  Beyond the message: advantages of snapshot proteomics with single-cell mass cytometry in solid tumors.

Authors:  Akshitkumar M Mistry; Allison R Greenplate; Rebecca A Ihrie; Jonathan M Irish
Journal:  FEBS J       Date:  2019-01-07       Impact factor: 5.542

7.  Identification of NK Cell Subpopulations That Differentiate HIV-Infected Subject Cohorts with Diverse Levels of Virus Control.

Authors:  Christopher W Pohlmeyer; Veronica D Gonzalez; Alivelu Irrinki; Ricardo N Ramirez; Li Li; Andrew Mulato; Jeffrey P Murry; Aaron Arvey; Rebecca Hoh; Steven G Deeks; George Kukolj; Tomas Cihlar; Stefan Pflanz; Garry P Nolan; Gundula Min-Oo
Journal:  J Virol       Date:  2019-03-21       Impact factor: 5.103

Review 8.  The Spatial and Genomic Hierarchy of Tumor Ecosystems Revealed by Single-Cell Technologies.

Authors:  Eric A Smith; H Courtney Hodges
Journal:  Trends Cancer       Date:  2019-06-18

9.  Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells.

Authors:  Nalin Leelatian; Justine Sinnaeve; Akshitkumar M Mistry; Sierra M Barone; Asa A Brockman; Kirsten E Diggins; Allison R Greenplate; Kyle D Weaver; Reid C Thompson; Lola B Chambless; Bret C Mobley; Rebecca A Ihrie; Jonathan M Irish
Journal:  Elife       Date:  2020-06-23       Impact factor: 8.140

10.  An Automatic Platform Based on Nanostructured Microfluidic Chip for Isolating and Identification of Circulating Tumor Cells.

Authors:  Hei-Jen Jou; Li-Yun Chou; Wen-Chun Chang; Hsin-Cheng Ho; Wan-Ting Zhang; Pei-Ying Ling; Ko-Hsin Tsai; Szu-Hua Chen; Tze-Ho Chen; Pei-Hsuan Lo; Ming Chen; Heng-Tung Hsu
Journal:  Micromachines (Basel)       Date:  2021-04-21       Impact factor: 2.891

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.