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Decoding the evolutionary response to prostate cancer therapy by plasma genome sequencing

BACKGROUND: Investigating genome evolution in response to therapy is difficult in human tissue samples. To address this challenge, we develop an unbiased whole-genome plasma DNA sequencing approach that concurrently measures genomic copy number and exome mutations from archival cryostored plasma sam...

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Autores principales: Ramesh, Naveen, Sei, Emi, Tsai, Pei Ching, Bai, Shanshan, Zhao, Yuehui, Troncoso, Patricia, Corn, Paul G., Logothetis, Christopher, Zurita, Amado J., Navin, Nicholas E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336456/
https://www.ncbi.nlm.nih.gov/pubmed/32631448
http://dx.doi.org/10.1186/s13059-020-02045-9
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author Ramesh, Naveen
Sei, Emi
Tsai, Pei Ching
Bai, Shanshan
Zhao, Yuehui
Troncoso, Patricia
Corn, Paul G.
Logothetis, Christopher
Zurita, Amado J.
Navin, Nicholas E.
author_facet Ramesh, Naveen
Sei, Emi
Tsai, Pei Ching
Bai, Shanshan
Zhao, Yuehui
Troncoso, Patricia
Corn, Paul G.
Logothetis, Christopher
Zurita, Amado J.
Navin, Nicholas E.
author_sort Ramesh, Naveen
collection PubMed
description BACKGROUND: Investigating genome evolution in response to therapy is difficult in human tissue samples. To address this challenge, we develop an unbiased whole-genome plasma DNA sequencing approach that concurrently measures genomic copy number and exome mutations from archival cryostored plasma samples. This approach is applied to study longitudinal blood plasma samples from prostate cancer patients, where longitudinal tissue biopsies from the bone and other metastatic sites have been challenging to collect. RESULTS: A molecular characterization of archival plasma DNA from 233 patients and genomic profiling of 101 patients identifies clinical correlations of aneuploid plasma DNA profiles with poor survival, increased plasma DNA concentrations, and lower plasma DNA size distributions. Deep-exome sequencing and genomic copy number profiling are performed on 23 patients, including 9 patients with matched metastatic tissues and 12 patients with serial plasma samples. These data show a high concordance in genomic alterations between the plasma DNA and metastatic tissue samples, suggesting the plasma DNA is highly representative of the tissue alterations. Longitudinal sequencing of 12 patients with 2–5 serial plasma samples reveals clonal dynamics and genome evolution in response to hormonal and chemotherapy. By performing an integrated evolutionary analysis, minor subclones are identified in 9 patients that expanded in response to therapy and harbored mutations associated with resistance. CONCLUSIONS: This study provides an unbiased evolutionary approach to non-invasively delineate clonal dynamics and identify clones with mutations associated with resistance in prostate cancer.
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spelling pubmed-73364562020-07-08 Decoding the evolutionary response to prostate cancer therapy by plasma genome sequencing Ramesh, Naveen Sei, Emi Tsai, Pei Ching Bai, Shanshan Zhao, Yuehui Troncoso, Patricia Corn, Paul G. Logothetis, Christopher Zurita, Amado J. Navin, Nicholas E. Genome Biol Research BACKGROUND: Investigating genome evolution in response to therapy is difficult in human tissue samples. To address this challenge, we develop an unbiased whole-genome plasma DNA sequencing approach that concurrently measures genomic copy number and exome mutations from archival cryostored plasma samples. This approach is applied to study longitudinal blood plasma samples from prostate cancer patients, where longitudinal tissue biopsies from the bone and other metastatic sites have been challenging to collect. RESULTS: A molecular characterization of archival plasma DNA from 233 patients and genomic profiling of 101 patients identifies clinical correlations of aneuploid plasma DNA profiles with poor survival, increased plasma DNA concentrations, and lower plasma DNA size distributions. Deep-exome sequencing and genomic copy number profiling are performed on 23 patients, including 9 patients with matched metastatic tissues and 12 patients with serial plasma samples. These data show a high concordance in genomic alterations between the plasma DNA and metastatic tissue samples, suggesting the plasma DNA is highly representative of the tissue alterations. Longitudinal sequencing of 12 patients with 2–5 serial plasma samples reveals clonal dynamics and genome evolution in response to hormonal and chemotherapy. By performing an integrated evolutionary analysis, minor subclones are identified in 9 patients that expanded in response to therapy and harbored mutations associated with resistance. CONCLUSIONS: This study provides an unbiased evolutionary approach to non-invasively delineate clonal dynamics and identify clones with mutations associated with resistance in prostate cancer. BioMed Central 2020-07-06 /pmc/articles/PMC7336456/ /pubmed/32631448 http://dx.doi.org/10.1186/s13059-020-02045-9 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research
Ramesh, Naveen
Sei, Emi
Tsai, Pei Ching
Bai, Shanshan
Zhao, Yuehui
Troncoso, Patricia
Corn, Paul G.
Logothetis, Christopher
Zurita, Amado J.
Navin, Nicholas E.
Decoding the evolutionary response to prostate cancer therapy by plasma genome sequencing
title Decoding the evolutionary response to prostate cancer therapy by plasma genome sequencing
title_full Decoding the evolutionary response to prostate cancer therapy by plasma genome sequencing
title_fullStr Decoding the evolutionary response to prostate cancer therapy by plasma genome sequencing
title_full_unstemmed Decoding the evolutionary response to prostate cancer therapy by plasma genome sequencing
title_short Decoding the evolutionary response to prostate cancer therapy by plasma genome sequencing
title_sort decoding the evolutionary response to prostate cancer therapy by plasma genome sequencing
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336456/
https://www.ncbi.nlm.nih.gov/pubmed/32631448
http://dx.doi.org/10.1186/s13059-020-02045-9
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