This study gathers information to create a database to improve the ability to diagnose cancer, predict long term health of breast cancer patients, and help develop future treatment products. This study will provide a foundational platform for therapeutic development and intervention studies in the future and may for therapeutic development and intervention studies in the future, and may advance researchers understanding of the contribution gut bacteria to altered circulating estrogens in breast cancer survivors.
PRIMARY OBJECTIVE: I. To use multiscale omics to build a cohort database that can be used as a reference population in support of multivariate analysis, predictive modeling, and development of natural product therapeutics and precision medicine applications for breast cancer survivors. SECONDARY OBJECTIVE: I. To detect unique patterns of variance between 1) targeted serum metabolites, 2) plasma metabolome, 3) gut microbiome community structure, 4) gut microbiome metabolome, 5) urine metabolome, 6) quality of life measures, and 7) breast cancer survivors (BCS) symptoms by using multivariate analysis, machine learning tools, and artificial intelligence applied to the large data sets developed in this trial. OUTLINE: Participants complete questionnaires over 10 minutes and undergo blood, urine, saliva, and fecal samples collection.
Study Type
OBSERVATIONAL
Enrollment
105
Undergo biospecimen collection
Ancillary studies
Complete questionnaires
Mayo Clinic in Rochester
Rochester, Minnesota, United States
Utilization of multiscale omics to build a cohort database for breast cancer survivors
Time frame: Up to 5 years
Detection of unique patterns of variance
Will be detected between 1) serum metabolites, 2) plasma metabolome, 3) gut microbiome community structure, 4) gut microbiome metabolome, 5) urine metabolome, 6) quality of life measures, and 7) BCS symptoms by using multivariate analysis applied to the large data sets developed in this trial.
Time frame: Up to 5 years
Detection of unique patterns of variance
Will be detected between 1) serum metabolites, 2) plasma metabolome, 3) gut microbiome community structure, 4) gut microbiome metabolome, 5) urine metabolome, 6) quality of life measures, and 7) BCS symptoms by using machine learning tools applied to the large data sets developed in this trial.
Time frame: Up to 5 years
Detection of unique patterns of variance
Will be detected between 1) serum metabolites, 2) plasma metabolome, 3) gut microbiome community structure, 4) gut microbiome metabolome, 5) urine metabolome, 6) quality of life measures, and 7) BCS symptoms by using artificial intelligence applied to the large data sets developed in this trial.
Time frame: Up to 5 years
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