A two-variate phenotype-targeted test for detection of phenotypic biomarkers on breast cancer

Traditional pipeline for the task of detecting phenotypic biomarkers is a two-stage implementation, i.e., differentially expressed candidates are identified by NT tests, and then a subset of the candidates are further detected by phenotype-targeted tests (PT test) for significant phenotypic features, where N is short for Normal data and T is for Treatment/Trouble data. Such a two-stage procedure has low detection power as they do not make full use of the information contained in the (T, N). In this paper, we apply the two-variate PT test which jointly considers tumor-adjacent data and tumor data for improving the detection power. We investigate its performance by experiments on real-world datasets of breast cancer, considering phenotypes including BMI, overall survival time, pathologic stage, and tumor size. The results show that the method has high detection power and is more reliable, and the tumor-adjacent normal data plays an important role in the detection of phenotypic biomarkers. Finally, we obtain a new finding that the gene TCTEX1D2 is significantly related to tumor size in breast cancer.