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OncoThreads: visualization of large-scale longitudinal cancer molecular data

MOTIVATION: Molecular profiling of patient tumors and liquid biopsies over time with next-generation sequencing technologies and new immuno-profile assays are becoming part of standard research and clinical practice. With the wealth of new longitudinal data, there is a critical need for visualizatio...

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Autores principales: Harbig, Theresa A, Nusrat, Sabrina, Mazor, Tali, Wang, Qianwen, Thomson, Alexander, Bitter, Hans, Cerami, Ethan, Gehlenborg, Nils
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275328/
https://www.ncbi.nlm.nih.gov/pubmed/34252935
http://dx.doi.org/10.1093/bioinformatics/btab289
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author Harbig, Theresa A
Nusrat, Sabrina
Mazor, Tali
Wang, Qianwen
Thomson, Alexander
Bitter, Hans
Cerami, Ethan
Gehlenborg, Nils
author_facet Harbig, Theresa A
Nusrat, Sabrina
Mazor, Tali
Wang, Qianwen
Thomson, Alexander
Bitter, Hans
Cerami, Ethan
Gehlenborg, Nils
author_sort Harbig, Theresa A
collection PubMed
description MOTIVATION: Molecular profiling of patient tumors and liquid biopsies over time with next-generation sequencing technologies and new immuno-profile assays are becoming part of standard research and clinical practice. With the wealth of new longitudinal data, there is a critical need for visualizations for cancer researchers to explore and interpret temporal patterns not just in a single patient but across cohorts. RESULTS: To address this need we developed OncoThreads, a tool for the visualization of longitudinal clinical and cancer genomics and other molecular data in patient cohorts. The tool visualizes patient cohorts as temporal heatmaps and Sankey diagrams that support the interactive exploration and ranking of a wide range of clinical and molecular features. This allows analysts to discover temporal patterns in longitudinal data, such as the impact of mutations on response to a treatment, for example, emergence of resistant clones. We demonstrate the functionality of OncoThreads using a cohort of 23 glioma patients sampled at 2–4 timepoints. AVAILABILITY AND IMPLEMENTATION: Freely available at http://oncothreads.gehlenborglab.org. Implemented in Java Script using the cBioPortal web API as a backend. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-82753282021-07-13 OncoThreads: visualization of large-scale longitudinal cancer molecular data Harbig, Theresa A Nusrat, Sabrina Mazor, Tali Wang, Qianwen Thomson, Alexander Bitter, Hans Cerami, Ethan Gehlenborg, Nils Bioinformatics Biomedical Informatics MOTIVATION: Molecular profiling of patient tumors and liquid biopsies over time with next-generation sequencing technologies and new immuno-profile assays are becoming part of standard research and clinical practice. With the wealth of new longitudinal data, there is a critical need for visualizations for cancer researchers to explore and interpret temporal patterns not just in a single patient but across cohorts. RESULTS: To address this need we developed OncoThreads, a tool for the visualization of longitudinal clinical and cancer genomics and other molecular data in patient cohorts. The tool visualizes patient cohorts as temporal heatmaps and Sankey diagrams that support the interactive exploration and ranking of a wide range of clinical and molecular features. This allows analysts to discover temporal patterns in longitudinal data, such as the impact of mutations on response to a treatment, for example, emergence of resistant clones. We demonstrate the functionality of OncoThreads using a cohort of 23 glioma patients sampled at 2–4 timepoints. AVAILABILITY AND IMPLEMENTATION: Freely available at http://oncothreads.gehlenborglab.org. Implemented in Java Script using the cBioPortal web API as a backend. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-07-12 /pmc/articles/PMC8275328/ /pubmed/34252935 http://dx.doi.org/10.1093/bioinformatics/btab289 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Biomedical Informatics
Harbig, Theresa A
Nusrat, Sabrina
Mazor, Tali
Wang, Qianwen
Thomson, Alexander
Bitter, Hans
Cerami, Ethan
Gehlenborg, Nils
OncoThreads: visualization of large-scale longitudinal cancer molecular data
title OncoThreads: visualization of large-scale longitudinal cancer molecular data
title_full OncoThreads: visualization of large-scale longitudinal cancer molecular data
title_fullStr OncoThreads: visualization of large-scale longitudinal cancer molecular data
title_full_unstemmed OncoThreads: visualization of large-scale longitudinal cancer molecular data
title_short OncoThreads: visualization of large-scale longitudinal cancer molecular data
title_sort oncothreads: visualization of large-scale longitudinal cancer molecular data
topic Biomedical Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275328/
https://www.ncbi.nlm.nih.gov/pubmed/34252935
http://dx.doi.org/10.1093/bioinformatics/btab289
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