Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 
  • Users Online: 440
  • Home
  • Print this page
  • Email this page
Year : 2021  |  Volume : 8  |  Issue : 2  |  Page : 112-114

Topological Data Analysis: A New Method to Identify Genetic Alterations in Cancer

1 Foreign Languages College, Tianjin Normal University, Tianjin, China
2 Department of Breast Surgery, Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China

Correspondence Address:
MD Xinzhong Chang
Department of Breast Surgery, Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2347-5625.308301

Rights and Permissions

Cancer is the largest health problem worldwide. A number of targeted therapies are currently employed for the treatment of different cancers. Determining the molecular mechanisms that are necessary for cancer development and progression is the most critical step in targeted therapies. Currently, many studies have identified a large number of frequently mutated cancer-associated genes using recurrence-based methods. However, only the cancer-associated mutations with a mutation frequency >15% can be identified by these methods. In other words, they cannot be used to identify driver genes that have low mutation frequency but play a major role in tumorigenesis and development. Thus, there is an urgent need for a method for identifying cancer-associated genes that are not based on recurrence. In a study, recently published in Nature Communications, research team led by Prof. Raúl Rabadán from the Columbia University successfully devised a novel topological data analysis approach to identify low-prevalence cancer-associated gene mutations using expression data from multiple cancers.

Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)

 Article Access Statistics
    PDF Downloaded157    
    Comments [Add]    

Recommend this journal