Stage I non-small cell lung cancer stratification by using a model-based clustering algorithm with covariates

• Main Authors: Relvas, Carlos, Fujita, André
• Format: Journal Article
• Language: English
• Published: 05.04.2020
• Subjects: Computer Science - Learning; Quantitative Biology - Quantitative Methods; Statistics - Applications
• DOI: 10.48550/arxiv.2004.02333

Lung cancer is currently the leading cause of cancer deaths. Among various subtypes, the number of patients diagnosed with stage I non-small cell lung cancer (NSCLC), particularly adenocarcinoma, has been increasing. It is estimated that 30 - 40\% of stage I patients will relapse, and 10 - 30\% will die due to recurrence, clearly suggesting the presence of a subgroup that could be benefited by additional therapy. We hypothesize that current attempts to identify stage I NSCLC subgroup failed due to covariate effects, such as the age at diagnosis and differentiation, which may be masking the results. In this context, to stratify stage I NSCLC, we propose CEM-Co, a model-based clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process. We applied CEM-Co on a gene expression data set composed of 129 subjects diagnosed with stage I NSCLC and successfully identified a subgroup with a significantly different phenotype (poor prognosis), while standard clustering algorithms failed.