Skip to main content
  • AACR Journals
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

AACR logo

  • Register
  • Log in
  • My Cart
Advertisement

Main menu

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Meeting Abstracts
    • Collections
      • COVID-19 & Cancer Resource Center
      • Focus on Radiation Oncology
      • Novel Combinations
      • Reviews
      • Editors' Picks
      • "Best of" Collection
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

  • AACR Journals
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

User menu

  • Register
  • Log in
  • My Cart

Search

  • Advanced search
Molecular Cancer Therapeutics
Molecular Cancer Therapeutics
  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Meeting Abstracts
    • Collections
      • COVID-19 & Cancer Resource Center
      • Focus on Radiation Oncology
      • Novel Combinations
      • Reviews
      • Editors' Picks
      • "Best of" Collection
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

Companion Diagnostic, Pharmacogenomic, and Cancer Biomarkers

A Qualitative Transcriptional Signature for Predicting Prognosis and Response to Bevacizumab in Metastatic Colorectal Cancer

Jing Yang, Kai Song, Wenbing Guo, Hailong Zheng, Yelin Fu, Tianyi You, Kai Wang, Lishuang Qi, Wenyuan Zhao and Zheng Guo
Jing Yang
1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kai Song
1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Wenbing Guo
1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hailong Zheng
1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yelin Fu
1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tianyi You
1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kai Wang
1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lishuang Qi
1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Lishuang Qi
Wenyuan Zhao
1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: zhaowenyuan@ems.hrbmu.edu.cn guoz@ems.hrbmu.edu.cn
Zheng Guo
1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
2Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
3Key Laboratory of Medical Bioinformatics, Fujian Province, Fuzhou, China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: zhaowenyuan@ems.hrbmu.edu.cn guoz@ems.hrbmu.edu.cn
DOI: 10.1158/1535-7163.MCT-19-0864 Published July 2020
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

This article requires a subscription to view the full text. You may purchase access to this article or login to access your subscription using the links below.

Abstract

Bevacizumab is the molecular-targeted agent used for the antiangiogenic therapy of metastatic colorectal cancer. But some patients are resistant to bevacizumab, it needs an effective biomarker to predict the prognosis and responses of metastatic colorectal cancer (mCRC) to bevacizumab therapy. In this work, we developed a qualitative transcriptional signature to individually predict the response of bevacizumab in patients with mCRC. First, using mCRC samples treated with bevacizumab, we detected differentially expressed genes between response and nonresponse groups. Then, the gene pairs, consisting of at least one differentially expressed gene, with stable relative expression orderings in the response samples but reversal stable relative expression orderings in the nonresponse samples were identified, denoted as pairs-bevacizumab. Similarly, we screened the gene pairs significantly associated with primary tumor locations, donated as pairs-LR. Among the overlapped gene pairs between the pairs-bevacizumab and pairs-LR, we adopted a feature selection process to extract gene pairs that reached the highest F-score for predicting bevacizumab response status in mCRC as the final gene pair signature (GPS), denoted as 64-GPS. In two independent datasets, the predicted response group showed significantly better overall survival than the nonresponse group (P = 6.00e−4 in GSE72970; P = 0.04 in TCGA). Genomic analyses showed that the predicted response group was characterized by frequent copy number alternations, whereas the nonresponse group was characterized by hypermutation. In conclusion, 64-GPS was an objective and robust predictive signature for patients with mCRC treated with bevacizumab, which could effectively assist in the decision of clinical therapy.

Footnotes

  • Note: Supplementary data for this article are available at Molecular Cancer Therapeutics Online (http://mct.aacrjournals.org/).

  • Mol Cancer Ther 2020;19:1497–505

  • Received September 10, 2019.
  • Revision received December 17, 2019.
  • Accepted May 1, 2020.
  • Published first May 5, 2020.
  • ©2020 American Association for Cancer Research.
View Full Text

Log in using your username and password

Forgot your user name or password?

Purchase access

You may purchase access to this article. This will require you to create an account if you don't already have one.
PreviousNext
Back to top
Molecular Cancer Therapeutics: 19 (7)
July 2020
Volume 19, Issue 7
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Editorial Board (PDF)

Sign up for alerts

View this article with LENS

Open full page PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for sharing this Molecular Cancer Therapeutics article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
A Qualitative Transcriptional Signature for Predicting Prognosis and Response to Bevacizumab in Metastatic Colorectal Cancer
(Your Name) has forwarded a page to you from Molecular Cancer Therapeutics
(Your Name) thought you would be interested in this article in Molecular Cancer Therapeutics.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
A Qualitative Transcriptional Signature for Predicting Prognosis and Response to Bevacizumab in Metastatic Colorectal Cancer
Jing Yang, Kai Song, Wenbing Guo, Hailong Zheng, Yelin Fu, Tianyi You, Kai Wang, Lishuang Qi, Wenyuan Zhao and Zheng Guo
Mol Cancer Ther July 1 2020 (19) (7) 1497-1505; DOI: 10.1158/1535-7163.MCT-19-0864

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
A Qualitative Transcriptional Signature for Predicting Prognosis and Response to Bevacizumab in Metastatic Colorectal Cancer
Jing Yang, Kai Song, Wenbing Guo, Hailong Zheng, Yelin Fu, Tianyi You, Kai Wang, Lishuang Qi, Wenyuan Zhao and Zheng Guo
Mol Cancer Ther July 1 2020 (19) (7) 1497-1505; DOI: 10.1158/1535-7163.MCT-19-0864
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Disclosure of Potential Conflicts of Interest
    • Authors' Contributions
    • Acknowledgments
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF
Advertisement

Related Articles

Cited By...

More in this TOC Section

  • Predictive ERBB Mutations
  • BRCA1/MAD2L1 Expression Predicts Response to Vinorelbine
  • HLA Polymorphisms Predict TFR in CML
Show more Companion Diagnostic, Pharmacogenomic, and Cancer Biomarkers
  • Home
  • Alerts
  • Feedback
  • Privacy Policy
Facebook  Twitter  LinkedIn  YouTube  RSS

Articles

  • Online First
  • Current Issue
  • Past Issues
  • Meeting Abstracts

Info for

  • Authors
  • Subscribers
  • Advertisers
  • Librarians

About MCT

  • About the Journal
  • Editorial Board
  • Permissions
  • Submit a Manuscript
AACR logo

Copyright © 2021 by the American Association for Cancer Research.

Molecular Cancer Therapeutics
eISSN: 1538-8514
ISSN: 1535-7163

Advertisement