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Machine learning predicts cancer subtypes and progression from blood immune signatures

Clinical adoption of immune checkpoint inhibitors in cancer management has highlighted the interconnection between carcinogenesis and the immune system. Immune cells are integral to the tumour microenvironment and can influence the outcome of therapies. Better understanding of an individual’s immune...

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Autores principales: Simon Davis, David A., Mun, Sahngeun, Smith, Julianne M., Hammill, Dillon, Garrett, Jessica, Gosling, Katharine, Price, Jason, Elsaleh, Hany, Syed, Farhan M., Atmosukarto, Ines I., Quah, Benjamin J. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8884497/
https://www.ncbi.nlm.nih.gov/pubmed/35226704
http://dx.doi.org/10.1371/journal.pone.0264631
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author Simon Davis, David A.
Mun, Sahngeun
Smith, Julianne M.
Hammill, Dillon
Garrett, Jessica
Gosling, Katharine
Price, Jason
Elsaleh, Hany
Syed, Farhan M.
Atmosukarto, Ines I.
Quah, Benjamin J. C.
author_facet Simon Davis, David A.
Mun, Sahngeun
Smith, Julianne M.
Hammill, Dillon
Garrett, Jessica
Gosling, Katharine
Price, Jason
Elsaleh, Hany
Syed, Farhan M.
Atmosukarto, Ines I.
Quah, Benjamin J. C.
author_sort Simon Davis, David A.
collection PubMed
description Clinical adoption of immune checkpoint inhibitors in cancer management has highlighted the interconnection between carcinogenesis and the immune system. Immune cells are integral to the tumour microenvironment and can influence the outcome of therapies. Better understanding of an individual’s immune landscape may play an important role in treatment personalisation. Peripheral blood is a readily accessible source of information to study an individual’s immune landscape compared to more complex and invasive tumour bioipsies, and may hold immense diagnostic and prognostic potential. Identifying the critical components of these immune signatures in peripheral blood presents an attractive alternative to tumour biopsy-based immune phenotyping strategies. We used two syngeneic solid tumour models, a 4T1 breast cancer model and a CT26 colorectal cancer model, in a longitudinal study of the peripheral blood immune landscape. Our strategy combined two highly accessible approaches, blood leukocyte immune phenotyping and plasma soluble immune factor characterisation, to identify distinguishing immune signatures of the CT26 and 4T1 tumour models using machine learning. Myeloid cells, specifically neutrophils and PD-L1-expressing myeloid cells, were found to correlate with tumour size in both the models. Elevated levels of G-CSF, IL-6 and CXCL13, and B cell counts were associated with 4T1 growth, whereas CCL17, CXCL10, total myeloid cells, CCL2, IL-10, CXCL1, and Ly6C(intermediate) monocytes were associated with CT26 tumour development. Peripheral blood appears to be an accessible means to interrogate tumour-dependent changes to the host immune landscape, and to identify blood immune phenotypes for future treatment stratification.
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spelling pubmed-88844972022-03-01 Machine learning predicts cancer subtypes and progression from blood immune signatures Simon Davis, David A. Mun, Sahngeun Smith, Julianne M. Hammill, Dillon Garrett, Jessica Gosling, Katharine Price, Jason Elsaleh, Hany Syed, Farhan M. Atmosukarto, Ines I. Quah, Benjamin J. C. PLoS One Research Article Clinical adoption of immune checkpoint inhibitors in cancer management has highlighted the interconnection between carcinogenesis and the immune system. Immune cells are integral to the tumour microenvironment and can influence the outcome of therapies. Better understanding of an individual’s immune landscape may play an important role in treatment personalisation. Peripheral blood is a readily accessible source of information to study an individual’s immune landscape compared to more complex and invasive tumour bioipsies, and may hold immense diagnostic and prognostic potential. Identifying the critical components of these immune signatures in peripheral blood presents an attractive alternative to tumour biopsy-based immune phenotyping strategies. We used two syngeneic solid tumour models, a 4T1 breast cancer model and a CT26 colorectal cancer model, in a longitudinal study of the peripheral blood immune landscape. Our strategy combined two highly accessible approaches, blood leukocyte immune phenotyping and plasma soluble immune factor characterisation, to identify distinguishing immune signatures of the CT26 and 4T1 tumour models using machine learning. Myeloid cells, specifically neutrophils and PD-L1-expressing myeloid cells, were found to correlate with tumour size in both the models. Elevated levels of G-CSF, IL-6 and CXCL13, and B cell counts were associated with 4T1 growth, whereas CCL17, CXCL10, total myeloid cells, CCL2, IL-10, CXCL1, and Ly6C(intermediate) monocytes were associated with CT26 tumour development. Peripheral blood appears to be an accessible means to interrogate tumour-dependent changes to the host immune landscape, and to identify blood immune phenotypes for future treatment stratification. Public Library of Science 2022-02-28 /pmc/articles/PMC8884497/ /pubmed/35226704 http://dx.doi.org/10.1371/journal.pone.0264631 Text en © 2022 Simon Davis et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Simon Davis, David A.
Mun, Sahngeun
Smith, Julianne M.
Hammill, Dillon
Garrett, Jessica
Gosling, Katharine
Price, Jason
Elsaleh, Hany
Syed, Farhan M.
Atmosukarto, Ines I.
Quah, Benjamin J. C.
Machine learning predicts cancer subtypes and progression from blood immune signatures
title Machine learning predicts cancer subtypes and progression from blood immune signatures
title_full Machine learning predicts cancer subtypes and progression from blood immune signatures
title_fullStr Machine learning predicts cancer subtypes and progression from blood immune signatures
title_full_unstemmed Machine learning predicts cancer subtypes and progression from blood immune signatures
title_short Machine learning predicts cancer subtypes and progression from blood immune signatures
title_sort machine learning predicts cancer subtypes and progression from blood immune signatures
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8884497/
https://www.ncbi.nlm.nih.gov/pubmed/35226704
http://dx.doi.org/10.1371/journal.pone.0264631
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