Automated comparison of protein subcellular location patterns between images of normal and cancerous tissues

Early cancer diagnosis and evaluation of cancer progression during treatment are two important factors for clinical therapy. In this study we propose a novel approach which automatically compares the subcellular location of proteins between normal and cancerous tissues in order to identify proteins whose distribution is modified by oncogenesis. This study analyzes 258 proteins in 14 different cancer tissues and their corresponding normal tissues using images provided by the tissue microarray collection of the Human Protein Atlas. Using texture features automatically extracted from the tissue images, 14 machine classifiers were trained to recognize the patterns of eight major organelles in each tissue. For each tissue-protein combination, the results of the classifier for normal and cancerous tissues were compared. Eleven proteins were identified as showing differences in location; these proteins may have potential as biomarkers.