Data Mining on Carcinogen Rearrangements (CR) attempts to address the problem of finding patterns and relevant/useful information and knowledge from CR graph representations, involving pattern mining and sub-graph isomorphism and coloring problems, known to be NP-hard. The problem consists in identifying CR patterns across samples, with statistical relevance as tumor root cause or consequence in front of random noise, which would allow identifying potential markers with clinical value. DM-CANCER addresses the mining problem by representing data as graphs to apply heuristic methods form mining data, enhanced by Machine Learning methods like Neural Networks, used as generative models for prediction and forecasting of pattern components, also for learning multi-dimensional structured data. In addition, DM-CANCER proposes the use of computational distributed solutions for the mining process leveraging the most advanced supercomputing facilities.