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Computational Immune Monitoring Reveals Abnormal Double-Negative T Cells Present across Human Tumor Types.
Greenplate AR, McClanahan DD, Oberholtzer BK, Doxie DB, Roe CE, Diggins KE, Leelatian N, Rasmussen ML, Kelley MC, Gama V, Siska PJ, Rathmell JC, Ferrell PB, Johnson DB, Irish JM
(2019) Cancer Immunol Res 7: 86-99
MeSH Terms: Adenoids, Adult, Aged, Antibodies, Monoclonal, Humanized, Antineoplastic Agents, Immunological, Female, Humans, Imidazoles, MAP Kinase Kinase Kinases, Male, Middle Aged, Monitoring, Immunologic, Neoplasms, Oximes, Palatine Tonsil, Proto-Oncogene Proteins B-raf, Pyridones, Pyrimidinones, T-Lymphocytes
Show Abstract · Added November 11, 2018
Advances in single-cell biology have enabled measurements of >40 protein features on millions of immune cells within clinical samples. However, the data analysis steps following cell population identification are susceptible to bias, time-consuming, and challenging to compare across studies. Here, an ensemble of unsupervised tools was developed to evaluate four essential types of immune cell information, incorporate changes over time, and address diverse immune monitoring challenges. The four complementary properties characterized were (i) systemic plasticity, (ii) change in population abundance, (iii) change in signature population features, and (iv) novelty of cellular phenotype. Three systems immune monitoring studies were selected to challenge this ensemble approach. In serial biopsies of melanoma tumors undergoing targeted therapy, the ensemble approach revealed enrichment of double-negative (DN) T cells. Melanoma tumor-resident DN T cells were abnormal and phenotypically distinct from those found in nonmalignant lymphoid tissues, but similar to those found in glioblastoma and renal cell carcinoma. Overall, ensemble systems immune monitoring provided a robust, quantitative view of changes in both the system and cell subsets, allowed for transparent review by human experts, and revealed abnormal immune cells present across multiple human tumor types.
©2018 American Association for Cancer Research.
3 Communities
2 Members
0 Resources
19 MeSH Terms
Systems immune monitoring in cancer therapy.
Greenplate AR, Johnson DB, Ferrell PB, Irish JM
(2016) Eur J Cancer 61: 77-84
MeSH Terms: Biomarkers, Tumor, Flow Cytometry, Humans, Immune System, Monitoring, Immunologic, Neoplasms, Single-Cell Analysis
Show Abstract · Added May 8, 2016
Treatments that successfully modulate anti-cancer immunity have significantly improved outcomes for advanced stage malignancies and sparked intense study of the cellular mechanisms governing therapy response and resistance. These responses are governed by an evolving milieu of cancer and immune cell subpopulations that can be a rich source of biomarkers and biological insight, but it is only recently that research tools have developed to comprehensively characterize this level of cellular complexity. Mass cytometry is particularly well suited to tracking cells in complex tissues because >35 measurements can be made on each of hundreds of thousands of cells per sample, allowing all cells detected in a sample to be characterized for cell type, signalling activity, and functional outcome. This review focuses on mass cytometry as an example of systems level characterization of cancer and immune cells in human tissues, including blood, bone marrow, lymph nodes, and primary tumours. This review also discusses the state of the art in single cell tumour immunology, including tissue collection, technical and biological quality controls, computational analysis, and integration of different experimental and clinical data types. Ex vivo analysis of human tumour cells complements both in vivo monitoring, which generally measures far fewer features or lacks single cell resolution, and laboratory models, which incur cell type losses, signalling alterations, and genomic changes during establishment. Mass cytometry is on the leading edge of a new generation of cytomic tools that work with small tissue samples, such as a fine needle aspirates or blood draws, to monitor changes in rare or unexpected cell subsets during cancer therapy. This approach holds great promise for dissecting cellular microenvironments, monitoring how treatments affect tissues, revealing cellular biomarkers and effector mechanisms, and creating new treatments that productively engage the immune system to fight cancer and other diseases.
Copyright © 2016 Elsevier Ltd. All rights reserved.
3 Communities
1 Members
0 Resources
7 MeSH Terms