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Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer
BACKGROUND: Molecular heterogeneity of tumors suggests the presence of multiple different subclones that may limit response to targeted therapies and contribute to acquisition of drug resistance, but its quantification has remained challenging. RESULTS: We performed simulations to evaluate statistic...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4197225/ https://www.ncbi.nlm.nih.gov/pubmed/25294321 http://dx.doi.org/10.1186/1471-2164-15-876 |
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author | Jiang, Tingting Shi, Weiwei Natowicz, René Ononye, Sophia N Wali, Vikram B Kluger, Yuval Pusztai, Lajos Hatzis, Christos |
author_facet | Jiang, Tingting Shi, Weiwei Natowicz, René Ononye, Sophia N Wali, Vikram B Kluger, Yuval Pusztai, Lajos Hatzis, Christos |
author_sort | Jiang, Tingting |
collection | PubMed |
description | BACKGROUND: Molecular heterogeneity of tumors suggests the presence of multiple different subclones that may limit response to targeted therapies and contribute to acquisition of drug resistance, but its quantification has remained challenging. RESULTS: We performed simulations to evaluate statistical measures that best capture the molecular diversity within a group of tumors for either continuous (gene expression) or discrete (mutations, copy number alterations) molecular data. Dispersion based metrics in the principal component space best captured the underlying heterogeneity. To demonstrate utility of these measures, we characterized the diversity in transcriptional and genomic profiles of different breast tumor subtypes, and showed that basal-like or triple-negative breast cancers (TNBC) are significantly more heterogeneous molecularly than other subtypes. Our analysis also suggests that transcriptional diversity is a global characteristic of the tumors observed across the majority of molecular pathways. Among basal-like tumors, those that were resistant to multi-agent chemotherapy showed greater transcriptional diversity compared to chemotherapy-sensitive tumors, suggesting that potentially multiple mechanisms may be contributing to chemotherapy resistance. CONCLUSIONS: We proposed and validated measures of transcriptional and genomic diversity that can quantify the molecular diversity of tumors. We applied the new measures to genomic data from breast tumors and demonstrated that basal-like breast cancers are significantly more diverse than other breast cancers. The observation that chemo-resistant tumors are significantly more diverse molecularly than chemosensitive tumors implies that multiple resistance mechanisms may be active, thus limiting the sensitivity and accuracy of predictive markers of chemotherapy response. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-876) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4197225 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41972252014-10-16 Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer Jiang, Tingting Shi, Weiwei Natowicz, René Ononye, Sophia N Wali, Vikram B Kluger, Yuval Pusztai, Lajos Hatzis, Christos BMC Genomics Methodology Article BACKGROUND: Molecular heterogeneity of tumors suggests the presence of multiple different subclones that may limit response to targeted therapies and contribute to acquisition of drug resistance, but its quantification has remained challenging. RESULTS: We performed simulations to evaluate statistical measures that best capture the molecular diversity within a group of tumors for either continuous (gene expression) or discrete (mutations, copy number alterations) molecular data. Dispersion based metrics in the principal component space best captured the underlying heterogeneity. To demonstrate utility of these measures, we characterized the diversity in transcriptional and genomic profiles of different breast tumor subtypes, and showed that basal-like or triple-negative breast cancers (TNBC) are significantly more heterogeneous molecularly than other subtypes. Our analysis also suggests that transcriptional diversity is a global characteristic of the tumors observed across the majority of molecular pathways. Among basal-like tumors, those that were resistant to multi-agent chemotherapy showed greater transcriptional diversity compared to chemotherapy-sensitive tumors, suggesting that potentially multiple mechanisms may be contributing to chemotherapy resistance. CONCLUSIONS: We proposed and validated measures of transcriptional and genomic diversity that can quantify the molecular diversity of tumors. We applied the new measures to genomic data from breast tumors and demonstrated that basal-like breast cancers are significantly more diverse than other breast cancers. The observation that chemo-resistant tumors are significantly more diverse molecularly than chemosensitive tumors implies that multiple resistance mechanisms may be active, thus limiting the sensitivity and accuracy of predictive markers of chemotherapy response. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-876) contains supplementary material, which is available to authorized users. BioMed Central 2014-10-08 /pmc/articles/PMC4197225/ /pubmed/25294321 http://dx.doi.org/10.1186/1471-2164-15-876 Text en © Jiang et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Jiang, Tingting Shi, Weiwei Natowicz, René Ononye, Sophia N Wali, Vikram B Kluger, Yuval Pusztai, Lajos Hatzis, Christos Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer |
title | Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer |
title_full | Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer |
title_fullStr | Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer |
title_full_unstemmed | Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer |
title_short | Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer |
title_sort | statistical measures of transcriptional diversity capture genomic heterogeneity of cancer |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4197225/ https://www.ncbi.nlm.nih.gov/pubmed/25294321 http://dx.doi.org/10.1186/1471-2164-15-876 |
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