APPLICATIONS OF TECHNOLOGY:
- Targeted cancer therapies
- Prediction of relapse-free survival
- Determines most effective treatment options for patient quality of life
- Standardizes breast cancer prognoses
A Berkeley Lab research team led by Antoine Snijders has identified a 12-gene signature that can be used to predict relapse-free survival (RFS) rates in breast cancer patients.
The research team combined independent databases and identified a gene expression signature of differentially expressed genes in breast tissue. Then, researchers used gene co-expression network analysis to validate the 12-gene signature that predicts breast cancer survival. A prognostic scoring system was created based on the signature, and results indicated that the scoring system robustly predicted breast cancer patient RFS in 60 sampling test sets with validation from TCGA breast tumor data and METABRIC breast cancer data.
Traditional prognostic factors include tumor size, lymph node involvement, histological grade, age, race, estrogen receptor, progesterone receptor, and epidermal growth factor receptor status. Although breast cancer patients have the same apparent histopathological disease, patients exhibit a wide range in clinical outcome of disease susceptibility, progression, treatment response, and relapse. Currently, oncologists lack easily assessed prognostic factors that indicate which patients will benefit from adjuvant therapy (i.e. chemotherapy) and which patients should be spared the toxic effects of such therapies. The Berkeley Lab approach provides a method to standardize patient prognosis without regard to conventional prognostic factors and predict survival among breast cancer patients.
DEVELOPMENT STAGE: Proven principle.
STATUS: Patent pending. Available for licensing or collaborative research.
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