This manuscript summarises two years of analyses, experiments and developments in the machine learning field. During that period, authors have collaborated in devising novel ideas and applying them to real world problems. The main application setting is related to the analysis of patient derived xenografts (PDXs) of metastatic colorectal cancer (mCRC). PDXs are obtained by propagating surgically derived tumor specimens in immunocompromised mice. Through this procedure, cancer cells remain viable ex-vivo and retain the typical characteristics of different tumors from different patients. Hence, they can effectively recapitulate the intra- and inter-tumor heterogeneity that is found in real patients. During the last decade, the Candiolo Cancer Institute (Italy, IRCC) has been assembling the largest collection of PDXs from mCRC available worldwide in an academic environment.