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Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets
Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558123/ https://www.ncbi.nlm.nih.gov/pubmed/37801493 http://dx.doi.org/10.1126/sciadv.adf0837 |
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author | Su, Guan-Hua Xiao, Yi You, Chao Zheng, Ren-Cheng Zhao, Shen Sun, Shi-Yun Zhou, Jia-Yin Lin, Lu-Yi Wang, He Shao, Zhi-Ming Gu, Ya-Jia Jiang, Yi-Zhou |
author_facet | Su, Guan-Hua Xiao, Yi You, Chao Zheng, Ren-Cheng Zhao, Shen Sun, Shi-Yun Zhou, Jia-Yin Lin, Lu-Yi Wang, He Shao, Zhi-Ming Gu, Ya-Jia Jiang, Yi-Zhou |
author_sort | Su, Guan-Hua |
collection | PubMed |
description | Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset (n = 1474) encompassing radiomic features extracted from dynamic contrast–enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH. |
format | Online Article Text |
id | pubmed-10558123 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-105581232023-10-07 Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets Su, Guan-Hua Xiao, Yi You, Chao Zheng, Ren-Cheng Zhao, Shen Sun, Shi-Yun Zhou, Jia-Yin Lin, Lu-Yi Wang, He Shao, Zhi-Ming Gu, Ya-Jia Jiang, Yi-Zhou Sci Adv Biomedicine and Life Sciences Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset (n = 1474) encompassing radiomic features extracted from dynamic contrast–enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH. American Association for the Advancement of Science 2023-10-06 /pmc/articles/PMC10558123/ /pubmed/37801493 http://dx.doi.org/10.1126/sciadv.adf0837 Text en Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Biomedicine and Life Sciences Su, Guan-Hua Xiao, Yi You, Chao Zheng, Ren-Cheng Zhao, Shen Sun, Shi-Yun Zhou, Jia-Yin Lin, Lu-Yi Wang, He Shao, Zhi-Ming Gu, Ya-Jia Jiang, Yi-Zhou Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets |
title | Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets |
title_full | Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets |
title_fullStr | Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets |
title_full_unstemmed | Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets |
title_short | Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets |
title_sort | radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets |
topic | Biomedicine and Life Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558123/ https://www.ncbi.nlm.nih.gov/pubmed/37801493 http://dx.doi.org/10.1126/sciadv.adf0837 |
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