Models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality

Immunotherapy has reshaped the field of cancer therapeutics but the population that benefits are small in many tumor types, warranting a companion diagnostic test. While immunohistochemistry (IHC) for programmed death-ligand 1 (PD-L1) or mismatch repair (MMR) and polymerase chain reaction (PCR) for...

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Autores principales:	Ioannis A. Vathiotis, Zhi Yang, Jason Reeves, Maria Toki, Thazin Nwe Aung, Pok Fai Wong, Harriet Kluger, Konstantinos N. Syrigos, Sarah Warren, David L. Rimm
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/738251428549426680ef4b46005c18fe
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spelling	oai:doaj.org-article:738251428549426680ef4b46005c18fe2021-12-02T14:47:28ZModels that combine transcriptomic with spatial protein information exceed the predictive value for either single modality10.1038/s41698-021-00184-12397-768Xhttps://doaj.org/article/738251428549426680ef4b46005c18fe2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41698-021-00184-1https://doaj.org/toc/2397-768XImmunotherapy has reshaped the field of cancer therapeutics but the population that benefits are small in many tumor types, warranting a companion diagnostic test. While immunohistochemistry (IHC) for programmed death-ligand 1 (PD-L1) or mismatch repair (MMR) and polymerase chain reaction (PCR) for microsatellite instability (MSI) are the only approved companion diagnostics others are under consideration. An optimal companion diagnostic test might combine the spatial information of IHC with the quantitative information from RNA expression profiling. Here, we show proof of concept for combination of spatially resolved protein information acquired by the NanoString GeoMx® Digital Spatial Profiler (DSP) with transcriptomic information from bulk mRNA gene expression acquired using NanoString nCounter® PanCancer IO 360™ panel on the same cohort of immunotherapy treated melanoma patients to create predictive models associated with clinical outcomes. We show that the combination of mRNA and spatially defined protein information can predict clinical outcomes more accurately (AUC 0.97) than either of these factors alone.Ioannis A. VathiotisZhi YangJason ReevesMaria TokiThazin Nwe AungPok Fai WongHarriet KlugerKonstantinos N. SyrigosSarah WarrenDavid L. RimmNature PortfolioarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENnpj Precision Oncology, Vol 5, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Ioannis A. Vathiotis Zhi Yang Jason Reeves Maria Toki Thazin Nwe Aung Pok Fai Wong Harriet Kluger Konstantinos N. Syrigos Sarah Warren David L. Rimm Models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality
description	Immunotherapy has reshaped the field of cancer therapeutics but the population that benefits are small in many tumor types, warranting a companion diagnostic test. While immunohistochemistry (IHC) for programmed death-ligand 1 (PD-L1) or mismatch repair (MMR) and polymerase chain reaction (PCR) for microsatellite instability (MSI) are the only approved companion diagnostics others are under consideration. An optimal companion diagnostic test might combine the spatial information of IHC with the quantitative information from RNA expression profiling. Here, we show proof of concept for combination of spatially resolved protein information acquired by the NanoString GeoMx® Digital Spatial Profiler (DSP) with transcriptomic information from bulk mRNA gene expression acquired using NanoString nCounter® PanCancer IO 360™ panel on the same cohort of immunotherapy treated melanoma patients to create predictive models associated with clinical outcomes. We show that the combination of mRNA and spatially defined protein information can predict clinical outcomes more accurately (AUC 0.97) than either of these factors alone.
format	article
author	Ioannis A. Vathiotis Zhi Yang Jason Reeves Maria Toki Thazin Nwe Aung Pok Fai Wong Harriet Kluger Konstantinos N. Syrigos Sarah Warren David L. Rimm
author_facet	Ioannis A. Vathiotis Zhi Yang Jason Reeves Maria Toki Thazin Nwe Aung Pok Fai Wong Harriet Kluger Konstantinos N. Syrigos Sarah Warren David L. Rimm
author_sort	Ioannis A. Vathiotis
title	Models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality
title_short	Models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality
title_full	Models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality
title_fullStr	Models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality
title_full_unstemmed	Models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality
title_sort	models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/738251428549426680ef4b46005c18fe
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Models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality

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