A Study of Gene Polymorphisms and Normal Tissue Radiation Injury in Patients Treated for Breast, Prostate, Brain, Lung, and Head and Neck Cancers

Overview

This study will examine, for the first time, the independent contribution of a patient's own genetic makeup to the development of post-radiation complications, permitting the future development of predictive tests to avoid radiation injury. To do this, the investigators will examine gene markers in a series of breast, prostate, brain and lung cancer survivors who have received conformal radiotherapy between 1996 and 2003 at the Cross Cancer Institute and Tom Baker Cancer Centre.

Major innovations in radiotherapy (RT) delivery (3D conformal RT, intensity modulated RT) now permit RT dose escalation to be tested as a means of improving disease control in many tumour sites. With delivery innovations, life-threatening toxicity occurs rarely, but significant clinical toxicity is common. In previous work the investigators have studied a cohort of 98 prostate patients who received dose-escalated 3D-CRT and have obtained evidence of genetic and dosimetric factors underlying rectal/bladder toxicity. They posit that the late radiation toxicity disease state has significant genetic determinants in other malignancies. These determinants are neither understood nor accounted for in selection of treatment, and the investigators propose to study additional well-characterized cohorts, who are clinically well from a disease control perspective, given that comprehensive dosimetric and outcome information is available on all. For a thorough understanding of the molecular processes underlying tissue responses to radiation damage, the investigators propose a genomic analysis. Their working hypothesis is that normal organ toxicity will be associated with patient genetics as measured by single nucleotide polymorphisms (SNPs) in a select group of genes. The criteria for selecting SNPs will be based on a candidate gene approach, choosing genes implicated or demonstrated in DNA repair pathways and radiation-induced tissue damage/tissue homeostasis. Analysis of these data will use both statistically based bioinformatics approaches.

Conditions