A twovariate phenotypetargeted test for detection of phenotypic biomarkers on breast cancer Traditional pipeline for the task of detecting phenotypic biomarkers is a twostage implementation, i.e., differentially expressed candidates are identified by NT tests, and then a subset of the candidates are further detected by phenotypetargeted tests (PT test) for significant phenotypic features, where N is short for Normal data and T is for TreatmentTrouble data. Such a twostage 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 twovariate PT test which jointly considers tumoradjacent data and tumor data for improving the detection power. We investigate its performance by experiments on realworld 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 tumoradjacent 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.

Pseudotime Based Discovery of Breast Cancer Heterogeneity Breast cancer is highly sporadic and heterogeneous in nature. Even the patients with same clinical stage do not cluster together in terms of genomic profiles such as mRNA expression. In order to prevent and cure breast cancer completely, it is essential to decipher the detailed heterogeneity of breast cancer at genomic level. Putting the cancer patients on a time scale, which represents the trajectory of cancer development, may help discover the detailed heterogeneity. This in turn would help establish the mechanisms for prevention and complete cure of breast cancer. The goal of this study is to discover the heterogeneity of breast cancer by ordering the cancer patients using pseudotime. This is achieved through two objectives: First, a computational framework is developed to place the cancer patients on a time scale, meaning construct a trajectory of cancer development, by inferring pseudotime from static mRNA expression data; Second, discovering breast cancer heterogeneity at different time periods of the trajectory using statistical and machine learning techniques. In this study, the trajectory of breast cancer progression was constructed using static mRNA expression profiles of 1072 breast cancer patients by inferring pseudotime. Three sets of key genes discovered using supervised machine learning techniques are used to develop the trajectories. The first set of genes are PAM50 genes which is available in literature. The second and third sets of genes were discovered in the present study using the clinical stages of breast cancer (StageI, StageII, StageIII, and StageIV). The proposed computational framework has the capability of deciphering heterogeneity in breast cancer at a granular level. The results also show the existence of multiple parallel trajectories at different time periods of cancer development or progression.