Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Vathiotis, Ioannis A., Yang, Zhi, Reeves, Jason, Toki, Maria, Aung, Thazin Nwe, Wong, Pok Fai, Kluger, Harriet, Syrigos, Konstantinos N., Warren, Sarah, Rimm, David L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163775/
https://www.ncbi.nlm.nih.gov/pubmed/34050252
http://dx.doi.org/10.1038/s41698-021-00184-1
_version_ 1783700975461072896
author Vathiotis, Ioannis A.
Yang, Zhi
Reeves, Jason
Toki, Maria
Aung, Thazin Nwe
Wong, Pok Fai
Kluger, Harriet
Syrigos, Konstantinos N.
Warren, Sarah
Rimm, David L.
author_facet Vathiotis, Ioannis A.
Yang, Zhi
Reeves, Jason
Toki, Maria
Aung, Thazin Nwe
Wong, Pok Fai
Kluger, Harriet
Syrigos, Konstantinos N.
Warren, Sarah
Rimm, David L.
author_sort Vathiotis, Ioannis A.
collection PubMed
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 Online
Article
Text
id pubmed-8163775
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-81637752021-06-10 Models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality Vathiotis, Ioannis A. Yang, Zhi Reeves, Jason Toki, Maria Aung, Thazin Nwe Wong, Pok Fai Kluger, Harriet Syrigos, Konstantinos N. Warren, Sarah Rimm, David L. NPJ Precis Oncol Brief Communication 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. Nature Publishing Group UK 2021-05-28 /pmc/articles/PMC8163775/ /pubmed/34050252 http://dx.doi.org/10.1038/s41698-021-00184-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Brief Communication
Vathiotis, Ioannis A.
Yang, Zhi
Reeves, Jason
Toki, Maria
Aung, Thazin Nwe
Wong, Pok Fai
Kluger, Harriet
Syrigos, Konstantinos N.
Warren, Sarah
Rimm, David L.
Models that combine transcriptomic with spatial protein information exceed the predictive value for either single modality
title 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_short 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
topic Brief Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163775/
https://www.ncbi.nlm.nih.gov/pubmed/34050252
http://dx.doi.org/10.1038/s41698-021-00184-1
work_keys_str_mv AT vathiotisioannisa modelsthatcombinetranscriptomicwithspatialproteininformationexceedthepredictivevalueforeithersinglemodality
AT yangzhi modelsthatcombinetranscriptomicwithspatialproteininformationexceedthepredictivevalueforeithersinglemodality
AT reevesjason modelsthatcombinetranscriptomicwithspatialproteininformationexceedthepredictivevalueforeithersinglemodality
AT tokimaria modelsthatcombinetranscriptomicwithspatialproteininformationexceedthepredictivevalueforeithersinglemodality
AT aungthazinnwe modelsthatcombinetranscriptomicwithspatialproteininformationexceedthepredictivevalueforeithersinglemodality
AT wongpokfai modelsthatcombinetranscriptomicwithspatialproteininformationexceedthepredictivevalueforeithersinglemodality
AT klugerharriet modelsthatcombinetranscriptomicwithspatialproteininformationexceedthepredictivevalueforeithersinglemodality
AT syrigoskonstantinosn modelsthatcombinetranscriptomicwithspatialproteininformationexceedthepredictivevalueforeithersinglemodality
AT warrensarah modelsthatcombinetranscriptomicwithspatialproteininformationexceedthepredictivevalueforeithersinglemodality
AT rimmdavidl modelsthatcombinetranscriptomicwithspatialproteininformationexceedthepredictivevalueforeithersinglemodality