Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death
in American women. Lumpectomy followed by radiotherapy (RT) has significantly improved survival. However,
about 30% of patients develop a Grade 2 or worse early or late skin reaction, pain, breast edema and poor
cosmetic results that impact quality of life. Inter-individual variability in the development of RT-induced adverse
reactions in normal tissue is well-documented for both acute and late effects. African-American (AA) and
underserved populations are less likely than Whites to receive the recommended adjuvant RT, if treated, have
a higher risk for developing RT-related side effects and worse clinical outcome. To achieve our long-term goals
in improving quality of life, clinical outcome, and overcoming breast cancer disparities, we will use a genome-
wide approach to test genomic prediction models for RT-induced adverse reactions and recurrence in three
racial/ethnic populations. We will test a new paradigm that multiple genetic variations and functional
phenotypes contribute to radiation sensitivity that may predict RT-induced side effects and clinical outcome.
Investigating this new paradigm will develop powerful tools in identifying high-risk populations and targets for
personalized intervention and treatment. Aim 1 will evaluate polygenic models of RT-induced early adverse
skin reactions (EASRs) in 1000 breast cancer patients with a comprehensive evaluation of genome-wide
nonsynonymous single nucleotide polymorphisms (nsSNPs; n=21,877). Aim 2 will evaluate the association
between RT-induced EASRs and three functional DNA damage/repair phenotypes. Aim 3 will develop
polygenic models of genome-wide nsSNPs in predicting RT-induced late side effects and/or recurrence in a
breast cancer cohort of 850 women with a median follow up of 8 years (range 4-12 years). The outcome of the
proposed research will advance our scientific knowledge in the accurate assessment of prognosis in cancer
patients, which is crucial to controlling the suffering and death due to breast cancer. Prediction models provide
an important approach to assessing cancer risk, progression, quality of life, and prognosis. These prediction
models may identify individuals at high risk of developing adverse reactions or recurrence who may benefit
from targeted treatment or other interventions. They also may enable the development of benefit-risk indices
that will aid in the design and planning of clinical treatment. The proposed research will use a hypothesis-
driven approach to integrate genetic and functional biomarkers in developing optimal prediction models of RT-
induced adverse reactions and recurrence. The outcome will target effective intervention and treatment
strategies, and ultimately improve quality of life and progression-free survival in breast cancer patients,
particularly in minority and underserved populations with more aggressive disease and worse clinical outcome.
This will be the largest and most complete genetic analysis of RT-related clinical outcome to date, and will
move the field significantly towards the goal of more effective, personalized therapy for breast cancer patients.
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