Genetic Insights into Metastatic Breast Cancer Timing: Analysis of 63 Patients

63 female participants from the Count Me In Metastatic Breast Cancer Project were analyzed for associations between genetic variants and time to metastasis. The dataset includes whole-exome sequencing data from saliva samples linked to medical and self-reported patient data, focusing on 50 metastasis-related genes. It was authored by G. M. Gehling and last updated on 2026-05-29.

Use Cases

• Identify candidate genes associated with metastatic timing based on the analysis of 50 metastasis-related genes.
• Validate genetic risk models for breast cancer progression using the reported CXCL12 and GAPDH variants.
• Conduct secondary analyses on time-to-event outcomes in metastatic breast cancer cohorts.
• Explore associations between single nucleotide polymorphisms and clinical progression metrics.

Strengths

• Includes 63 patient records with fully observed time-to-metastasis data.
• Analysis is based on whole-exome sequencing data from a specific cohort (CMI-MBCProject).
• Results include specific genetic variants (CXCL12 rs266091, GAPDH rs1803622) meeting statistical significance thresholds.

Limitations

• Row count for the underlying data table is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The sample size of 63 participants is relatively small for genetic association studies.

Provenance

Source

Count Me In: The Metastatic Breast Cancer Project (CMI-MBCProject), available through dbGaP (phs001709.v1.p1).

Collection Method

Secondary analysis of retrospectively ascertained time-to-event outcomes within a cross-sectional genomic dataset linked to medical and self-reported data.

Freshness

Last updated 2026-05-29 06:21:28; freshness should be verified.