Stage I non-small cell lung cancer stratification by using a model-based clustering algorithm with covariates
This work addresses the challenge of improving prognosis and treatment for stage I NSCLC patients, where 30-40% relapse and 10-30% die, by enabling better stratification, though it is incremental as it builds on existing clustering methods with covariate adjustments.
The researchers tackled the problem of identifying high-risk subgroups in stage I non-small cell lung cancer patients by developing CEM-Co, a model-based clustering algorithm that accounts for covariate effects like age and differentiation, and successfully identified a poor-prognosis subgroup in a dataset of 129 patients where standard methods failed.
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.