Racial Disparities in Patient Survival and Tumor Mutation Burden, and the Association Between Tumor Mutation Burden and Cancer Incidence Rate

Abstract

The causes underlying racial disparities in cancer are multifactorial. In addition to socioeconomic issues, biological factors may contribute to these inequities, especially in disease incidence and patient survival. To date, there have been few studies that relate the disparities in these aspects to genetic aberrations. In this work, we studied the impacts of race on the patient survival and tumor mutation burden using the data released by the Cancer Genome Atlas (TCGA). The potential relationship between mutation burden and disease incidence is further inferred by an integrative analysis of TCGA data and the data from the Surveillance, Epidemiology, and End Results (SEER) Program. The results show that disparities are present (p < 0.05) in patient survival of five cancers, such as head and neck squamous cell carcinoma. The numbers of tumor driver mutations are differentiated (p < 0.05) over the racial groups in five cancers, such as lung adenocarcinoma. By treating a specific cancer type and a racial group as an experimental unit, driver mutation numbers demonstrate a significant (r = 0.46, p < 0.002) positive correlation with cancer incidence rates, especially when the five cancers with mutational disparities are exclusively focused (r = 0.88, p < 0.00002). These results enrich our understanding of racial disparities in cancer and carcinogenic process.

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Document Details

Document Type
Technical Report
Publication Date
Oct 20, 2017
Accession Number
AD1071851

Entities

People

  • Andrea Edwards
  • Erik K Flemington
  • Kun Zhang
  • Wensheng Zhang

Organizations

  • Xavier University

Tags

DTIC Thesaurus Topics

  • African Americans
  • Cancer
  • Carcinoma
  • Caucasians
  • Cell Division
  • Computer Science
  • Data Science
  • Databases
  • Diseases And Disorders
  • Ethnic Groups
  • Genetics
  • Information Science
  • Minority Groups
  • Neoplasms
  • Regression Analysis
  • Statistical Analysis
  • Stem Cells

Fields of Study

  • Biology

Readers

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Technology Areas

  • AI & ML
  • Biotechnology