Limitations in prognostication contribute to many women with early stage breast cancer BCa receiving unnecessary aggressive therapy while others receive insufficient treatment. We hypothesize that tumor genomic changes, assessed by measures of DNA copy number and allelic imbalance, contribute to the progression and treatment responsiveness of early stage breast cancer and as such will add strong predictive power to current methods of prognostication to improve patient care. We propose to take advantage of a unique data and tissue repository, the """"""""Early Stage Breast Cancer Repository"""""""" built from a retrospective molecular epidemiologic cohort study of 3,673 women with early stage BCa treated at M.D. Anderson between 1985 and 2000. We will apply a high throughput SNP-based platform for DNA copy number and allelic imbalance (LOH) analysis in a cost effective manner to characterize somatic genomic alterations and their relationship with BCa outcomes. We have demonstrated that the SNP arrays can be used to provide high resolution, robust data on DNA isolated from formalin fixed paraffin embedded samples from MD Anderson.
Specific Aims 1. To determine whether Stage I and II breast cancers are intrinsically different diseases or represent a continuum of the same disease by assessing: ? Whether changes in DNA copy number as single loci and/or co-occurring allele-specific imbalances differ by stage and correlate with known prognostic histopathologic and clinical variables. ? Whether ethnicity affects the pattern of copy number aberrations, loci involved or degree of allelic imbalance in stage matched patients. 2. To test whether changes in DNA copy number at specific loci and/or co-occurring allele specific imbalances independently predict recurrence after treatment with tamoxifen and/'or anthracycline-based chemotherapy. 3. To develop and test new statistical approaches to determine 'robust sample size for future training and test sets to generate high level evidence for use of genomic or other high dimensional data obtained from tumors in prognostication and treatment planning. 4. To construct statistical models to determine whether tumor DNA copy number, known prognostic markers and/or epidemiologic factors combined are better predictors of recurrence} and treatment response than the standard histopathologic and clinical parameters.
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