  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.
