Abstract
We present a computational-statistical algorithm that, from data on the staining degree of immunocytochemical markers: i) evaluates the ability of the considered immuno-panel in predicting the breast cancer stage; ii) makes the accurate identification of breast cancer stage possible; iii) provides the best stage prognosis compatible with the considered sample; and iv) does so through the use of the minimum number of markers minimizing time and resource costs. After running the algorithm on two data sets [triple-negative breast cancer, (TNBC), and estrogen receptor-negative breast cancer, (ERNBC)], we conclude that EpCAM and β1 integrin are enough to accurately predict TNBC stage, being ALDH1, CD24, CD61, and CK5 the necessary markers to exactly predict ERNBC stage.