Genetic Modeling of Radiation Injury in Prostate Cancer Patients Treated with Radiotherapy

Abstract

An expanded meta-analysis was accomplished that identified three highly significant genomic signals (Pmeta<5x10 (exp -8)) with low Bayesian false discovery probability (<2%): single nucleotide polymorphism (SNP) rs17055178 with rectal bleeding (Pmeta=6.2x10 (exp -10)), rs10969913 with decreased urinary stream (Pmeta=2.9x10 (exp -10)) and rs11122573 with hematuria (Pmeta=1.8x10 (exp -8)). Fine scale mapping of these three regions identified a second independent signal (rs147121532) associated with hematuria (Pconditional=4.69x10 (exp-6)). Credible causal variants at these four signals lie in gene-regulatory regions and some modulate expression of nearby genes. Previously identified variants (rs17599026, rs7720298, and rs1801516) showed consistent associations in the new cohorts. In addition, we developed and tested TaqMan quantitative polymerase chain reaction (qPCR) assays for SNPs that were shown to be significant in the GWAS meta analyses.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2019
Accession Number
AD1086311

Entities

People

  • Barry S. Rosenstein
  • Harry Ostrer

Organizations

  • Icahn School of Medicine at Mount Sinai

Tags

DTIC Thesaurus Topics

  • Biomedical Research
  • Cardiovascular Diseases
  • Chain Reactions
  • Chemical Reactions
  • Data Management
  • Genetics
  • Information Science
  • Machine Learning
  • Medical Personnel
  • Neoplasms
  • Nucleotides
  • Polymerase Chain Reaction
  • Probability
  • Prostate Cancer
  • Radiotherapy
  • Statistical Analysis
  • Three Dimensional

Fields of Study

  • Biology

Readers

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  • Neural Network Machine Learning.
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • AI & ML
  • Biotechnology