We introduce an interpretable methodology to cluster subjects suffering from cancer, based on features extracted from their biopsies. We capture patterns in cell distributions using histograms, and compare subjects based on these. We describe here a workflow to define these histograms and use them for clustering purposes. We illustrate our approach on a database of hematoxylin and eosin-stained tissues of subjects with Stage I lung adenocarcinoma, where our results match existing knowledge with high confidence.