Machine learning characterization of a novel panel for metastatic prediction in breast cancer

Abstract

Metastasis is one of the most challenging problems in cancer diagnosis and treatment, as causal factors have yet to be fully disentangled. Prediction of the metastatic status of breast cancer is important for informing treatment protocols and reducing mortality. However, the systems biology behind metastasis is complex and driven by a variety of interacting factors. Furthermore, the prediction of cancer metastasis is a challenging task due to the variation in parameters and conditions specific to individual patients and mutation subtypes. In this paper, we apply tree-based machine learning algorithms for gene expression data analysis in the estimation of metastatic potentials within a group of 490 breast cancer patients. Tree-based machine learning algorithms including decision trees, gradient boosting, and extremely randomized trees are used to assess the variable importance of different genes in breast cancer metastasis. ighly accurate values were obtained from all three algorithms, with the gradient boosting method having the highest accuracy at 0.8901. The most significant ten genetic variables and fifteen gene functions in metastatic progression were identified. Respective importance scores and biological functions were also cataloged. Key genes in metastatic breast cancer progression include but are not limited to CD8, PB1, and THP-1.