Computational Pathways Analysis and Personalized Medicine in HER2- Positive Breast Cancer

The heterogeneity of some diseases, such as cancer, makes the decisions on therapeutic strategy very challenging. In this context, pathway analysis can support the identification of the best treatment and indeed prevent the issues arising from the trial and error process, in terms of best overall efficacy and lowest toxicity, ultimately saving time and resources. In a pathway, each gene is represented by a node and the pathway analysis can be performed using algorithms that interpolate data from different sources (i.e. sequencing, microarray, drug efficacy and interactions). Objective The purpose of this study was to evaluate the effects of erbb2 amplification on HER2- positive breast cancer and to predict, with a pathway based computational approach, the efficacy of a therapy with Trastuzumab and Palbociclib, alone or in combination. Methods One of the available and most integrated algorithms is PHENSIM that was used in this study to evaluate the gene dysregulations caused by the erbb2 amplification on its related pathways and the effects of Trastuzumab and Palbociclib on these deregulations. The effects have been estimated considering the drugs alone or in a combination therapy. Results A reduction of the number of pro-proliferative signals has been observed for both drugs alone or in combination. Regarding genes involved in MAPK signaling pathway, a total of 69 nodes were activated by the erbb2 mutation. A simulated treatment with Palbociclib reduced the number of activated genes down to 60, while with Trastuzumab the activated nodes were only 53. The combined therapy revealed an intriguing result providing a significant and remarkable reduction of the activated genes from 69 to 33. Conclusion These results let us hypothesize that there could be an increased efficacy giving the combination therapy to subjects with HER2 positive breast cancer. Finally, pathway analysis could be specifically used to design clinical trials predicting the efficacy of combination therapies or untested drugs on a specific disease.