Single-cell mass cytometry and machine learning predict relapse in childhood leukemia

Improved insight into cancer cell populations responsible for treatment failure will lead to better outcomes for patients. We herein highlight a single-cell study of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) at diagnosis that revealed hidden developmentally dependent cell signaling sta...

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Detalles Bibliográficos
Autores principales: Sarno, Jolanda, Davis, Kara L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2018
Materias:
Author’s Views
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154835/
https://www.ncbi.nlm.nih.gov/pubmed/30263942
http://dx.doi.org/10.1080/23723556.2018.1472057
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author Sarno, Jolanda
Davis, Kara L.
author_facet Sarno, Jolanda
Davis, Kara L.
author_sort Sarno, Jolanda
collection PubMed
description Improved insight into cancer cell populations responsible for treatment failure will lead to better outcomes for patients. We herein highlight a single-cell study of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) at diagnosis that revealed hidden developmentally dependent cell signaling states uniquely associated with relapse.
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spelling pubmed-61548352019-09-12 Single-cell mass cytometry and machine learning predict relapse in childhood leukemia Sarno, Jolanda Davis, Kara L. Mol Cell Oncol Author’s Views Improved insight into cancer cell populations responsible for treatment failure will lead to better outcomes for patients. We herein highlight a single-cell study of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) at diagnosis that revealed hidden developmentally dependent cell signaling states uniquely associated with relapse. Taylor & Francis 2018-09-12 /pmc/articles/PMC6154835/ /pubmed/30263942 http://dx.doi.org/10.1080/23723556.2018.1472057 Text en © 2018 The Author(s). Published by Taylor & Francis. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
spellingShingle Author’s Views
Sarno, Jolanda
Davis, Kara L.
Single-cell mass cytometry and machine learning predict relapse in childhood leukemia
title Single-cell mass cytometry and machine learning predict relapse in childhood leukemia
title_full Single-cell mass cytometry and machine learning predict relapse in childhood leukemia
title_fullStr Single-cell mass cytometry and machine learning predict relapse in childhood leukemia
title_full_unstemmed Single-cell mass cytometry and machine learning predict relapse in childhood leukemia
title_short Single-cell mass cytometry and machine learning predict relapse in childhood leukemia
title_sort single-cell mass cytometry and machine learning predict relapse in childhood leukemia
topic Author’s Views
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154835/
https://www.ncbi.nlm.nih.gov/pubmed/30263942
http://dx.doi.org/10.1080/23723556.2018.1472057
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AT daviskaral singlecellmasscytometryandmachinelearningpredictrelapseinchildhoodleukemia

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