Not provided
Not provided
| ID | Type | Description | Link |
|---|---|---|---|
| NCI-2021-07536 | Registry Identifier | CTRP (Clinical Trial Reporting Program) |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
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.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Observational (questionnaire, biospecimen collection) | Participants complete questionnaires over 10 minutes and undergo blood, urine, saliva, and fecal samples collection. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Biospecimen Collection | Procedure | Undergo biospecimen collection |
|
| Measure | Description | Time Frame |
|---|---|---|
| Utilization of multiscale omics to build a cohort database for breast cancer survivors | Up to 5 years |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | Up to 5 years |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Patients diagnosed with breast cancer stages 0 through 3 at time of diagnosis
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Brent A. Bauer, M.D. | Mayo Clinic in Rochester | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Mayo Clinic in Rochester | Rochester | Minnesota | 55905 | United States |
Not provided
| Label | URL |
|---|---|
| Mayo Clinic Clinical Trials | View source |
Not provided
Not provided
Not provided
Not provided
Not provided
Blood, urine, saliva, stool
| Quality-of-Life Assessment | Other | Ancillary studies |
|
|
| Questionnaire Administration | Other | Complete questionnaires |
|
| 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. | 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. | Up to 5 years |
| ID | Term |
|---|---|
| D000071960 | Breast Carcinoma In Situ |
| ID | Term |
|---|---|
| D002278 | Carcinoma in Situ |
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
| D001943 | Breast Neoplasms |
| D009371 | Neoplasms by Site |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
Not provided
Not provided