Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression.

ABSTRACT: Ductal carcinoma in situ (DCIS) is a preinvasive form of breast cancer with a highly variable potential of becoming invasive and affecting mortality of the patients. Due to the lack of accurate markers of disease progression, many women with detected DCIS are currently overtreated. To distinguish those DCIS cases who are likely to require therapy from those who should be left untreated, there is a need for robust and predictive biomarkers extracted from molecular or genetic profiles. We developed a supervised machine learning approach that implements multi-omics feature selection and model regularization for the identification of biomarker combinations that could be used to distinguish low-risk DCIS lesions from those with a higher likelihood of progression. To investigate the genet

SUBMITTER: Xu H 

PROVIDER: S-EPMC8209521 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature