In considering key events of genomic disorders in the development and progression of cancer, the correlation between genomic instability and carcinogenesis is cur-rently under investigation. In this work, we propose an inductive logic program-ming approach to the problem of modeling evolution patterns for breast cancer. Using this approach, it is possible to extract fingerprints of stages of the disease that can be used in order to develop and deliver the most adequate therapies to patients. Furthermore, such a model can help physicians and biologists in the elu-cidation of molecular dynamics underlying the aberrations-waterfall model behind carcinogenesis. By showing results obtained on a real-world dataset, we try to give some hints about further approach to the knowledge-driven validations of such hypotheses. In considering key events of genomic disorders in the development and progression of cancer, the correlation between genomic instability and carcinogenesis is cur-rently under investigation. In this work, we propose an inductive logic program-ming approach to the problem of modeling evolution patterns for Using this approach, it is possible to extract fingerprints of stages of the disease that can be used in order to develop and deliver the most adequate therapies to patients. Furthermore, such a model can help physicians and biologists in the elu- cidation of molecular dynamics underlying the aberrations-waterfall model behind carcinogenesis. By showing results obtained on a real-world dataset, we try to give some hints about further approach to the knowledge-driven validations of such hypotheses.