Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Jiang, Tingting, Shi, Weiwei, Natowicz, René, Ononye, Sophia N, Wali, Vikram B, Kluger, Yuval, Pusztai, Lajos, Hatzis, Christos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
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
_version_ 1782339581174087680
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
work_keys_str_mv AT jiangtingting statisticalmeasuresoftranscriptionaldiversitycapturegenomicheterogeneityofcancer
AT shiweiwei statisticalmeasuresoftranscriptionaldiversitycapturegenomicheterogeneityofcancer
AT natowiczrene statisticalmeasuresoftranscriptionaldiversitycapturegenomicheterogeneityofcancer
AT ononyesophian statisticalmeasuresoftranscriptionaldiversitycapturegenomicheterogeneityofcancer
AT walivikramb statisticalmeasuresoftranscriptionaldiversitycapturegenomicheterogeneityofcancer
AT klugeryuval statisticalmeasuresoftranscriptionaldiversitycapturegenomicheterogeneityofcancer
AT pusztailajos statisticalmeasuresoftranscriptionaldiversitycapturegenomicheterogeneityofcancer
AT hatzischristos statisticalmeasuresoftranscriptionaldiversitycapturegenomicheterogeneityofcancer