CAB uses a Support Vector Machine (SVM) trained algorithm which utilizes expression levels of five biomarkers (CD44, ABCC4, ABCC11, N-Cadherin, and Pan-Cadherin) and three clinical parameters such as tumor size, grade, and node status as inputs to generate a risk score and categorizes patients as low- or high-risk for distant recurrence within 5 years of diagnosis.
We have shown, for the first time, that ABCC1, ABCC11, and ABCG2 are highly expressed in aggressive breast cancer subtypes, and that tumorABCC11 expression is associated with poor prognosis.
We aimed to confirm/refute the association of the c.538G>A variant in ABCC11 with breast cancer risk and/or histo-pathological tumor characteristics in an independent population-based breast cancer case-control study from Germany comprising 1021 cases and 1015 age-matched controls.
ABCC11 expression was positively correlated with ER-alpha expression in both breast cell lines, and two independent series of tumors from postmenopausal patients.