Explainable Machine Learning Unveils Novel Insights into Breast Cancer Metastases Sites Bio-Markers

Tumor metastasis is the major cause of cancer fatality. Taking this perspective into account, the examination of gene expressions within malignant cells and the alterations in their transcriptome hold significance in the investigation of the molecular mechanisms and cellular phenomena associated with tumor metastasis. Accurately assessing a patient’s cancer condition and predicting their prognosis constitutes the central hurdle in formulating an effective therapeutic schedule for them. In recent years, a variety of machine learning techniques have widely contributed to analyzing empirical gene expression data from actual biological contexts, predicting medical outcomes, and supporting decision-making processes. This paper focuses on extracting important genes linked with each of the most common metastasis sites for breast cancer. Furthermore, the implications of the expression levels of each of the identified sets of bio-markers on the probability of predicting the occurrence of a certain metastasis are illustrated using the Shapley values as a model’s explainability framework - an approach that has never been applied on this problem before, unveils novel insights and directions for future research. The pioneering advancements of this research lie in the application of specific feature selection methods and compatible evaluation metrics to produce a small set of bio-markers for targeting a specific metastasis site, and further performing explanatory analysis of the impact of gene expression values on each of the examined metastasis sites.