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The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping

Molecular signatures have been suggested as biomarkers to classify pancreatic ductal adenocarcinoma (PDAC) into two, three, four or five subtypes. Since the robustness of existing signatures is controversial, we performed a systematic evaluation of four established signatures for PDAC stratification...

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Autores principales: Lautizi, Manuela, Baumbach, Jan, Weichert, Wilko, Steiger, Katja, List, Markus, Pfarr, Nicole, Kacprowski, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575186/
https://www.ncbi.nlm.nih.gov/pubmed/36267208
http://dx.doi.org/10.1093/narcan/zcac030
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author Lautizi, Manuela
Baumbach, Jan
Weichert, Wilko
Steiger, Katja
List, Markus
Pfarr, Nicole
Kacprowski, Tim
author_facet Lautizi, Manuela
Baumbach, Jan
Weichert, Wilko
Steiger, Katja
List, Markus
Pfarr, Nicole
Kacprowski, Tim
author_sort Lautizi, Manuela
collection PubMed
description Molecular signatures have been suggested as biomarkers to classify pancreatic ductal adenocarcinoma (PDAC) into two, three, four or five subtypes. Since the robustness of existing signatures is controversial, we performed a systematic evaluation of four established signatures for PDAC stratification across nine publicly available datasets. Clustering revealed inconsistency of subtypes across independent datasets and in some cases a different number of PDAC subgroups than in the original study, casting doubt on the actual number of existing subtypes. Next, we built sixteen classification models to investigate the ability of the signatures for tumor subtype prediction. The overall classification performance ranged from ∼35% to ∼90% accuracy, suggesting instability of the signatures. Notably, permuted subtypes and random gene sets achieved very similar performance. Cellular decomposition and functional pathway enrichment analysis revealed strong tissue-specificity of the predicted classes. Our study highlights severe limitations and inconsistencies that can be attributed to technical biases in sample preparation and tumor purity, suggesting that PDAC molecular signatures do not generalize across datasets. How stromal heterogeneity and immune compartment interplay in the diverging development of PDAC is still unclear. Therefore, a more mechanistic or a cross-platform multi-omic approach seems necessary to extract more robust and clinically exploitable insights.
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spelling pubmed-95751862022-10-19 The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping Lautizi, Manuela Baumbach, Jan Weichert, Wilko Steiger, Katja List, Markus Pfarr, Nicole Kacprowski, Tim NAR Cancer Cancer Computational Biology Molecular signatures have been suggested as biomarkers to classify pancreatic ductal adenocarcinoma (PDAC) into two, three, four or five subtypes. Since the robustness of existing signatures is controversial, we performed a systematic evaluation of four established signatures for PDAC stratification across nine publicly available datasets. Clustering revealed inconsistency of subtypes across independent datasets and in some cases a different number of PDAC subgroups than in the original study, casting doubt on the actual number of existing subtypes. Next, we built sixteen classification models to investigate the ability of the signatures for tumor subtype prediction. The overall classification performance ranged from ∼35% to ∼90% accuracy, suggesting instability of the signatures. Notably, permuted subtypes and random gene sets achieved very similar performance. Cellular decomposition and functional pathway enrichment analysis revealed strong tissue-specificity of the predicted classes. Our study highlights severe limitations and inconsistencies that can be attributed to technical biases in sample preparation and tumor purity, suggesting that PDAC molecular signatures do not generalize across datasets. How stromal heterogeneity and immune compartment interplay in the diverging development of PDAC is still unclear. Therefore, a more mechanistic or a cross-platform multi-omic approach seems necessary to extract more robust and clinically exploitable insights. Oxford University Press 2022-10-17 /pmc/articles/PMC9575186/ /pubmed/36267208 http://dx.doi.org/10.1093/narcan/zcac030 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of NAR Cancer. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cancer Computational Biology
Lautizi, Manuela
Baumbach, Jan
Weichert, Wilko
Steiger, Katja
List, Markus
Pfarr, Nicole
Kacprowski, Tim
The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping
title The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping
title_full The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping
title_fullStr The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping
title_full_unstemmed The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping
title_short The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping
title_sort limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping
topic Cancer Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575186/
https://www.ncbi.nlm.nih.gov/pubmed/36267208
http://dx.doi.org/10.1093/narcan/zcac030
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