Breast Cancer Pathways
Improved Breast Cancer diagnosis and treatment by combining Computational and Personalized Medicine
Molecular diagnosis of Breast Cancer (BC) is based on the identification of pathway alterations that frequently occur in the population. This approach has limitations as it considers molecular pathways as closed boxes, largely ignoring the highly interconnected nature of proteins in the cell, and it restricts diagnosis to a single set of genes that might not be valid for patients with individualized alterations. We propose the use of a computational approach to i) portrait all cellular players of BC, ii) identify patient-specific molecular alterations iii) find possible causal genes of these alterations and iv) suggest drugs targeting these genes. The invention starts by the creation of a Global Breast Cancer Molecular Map (b-map) at the cellular level that collects all documented BC-related genes and connects them based on protein-protein interaction data into one global network of molecular connections associated to cancer. This can be seen as a roadmap where proteins are cities and protein interactions the roads connecting them. Next, gene expression analysis is used to reveal the Set of BC Genes (SoBCG) particularly affected in a patient. The SoBCG is then mapped into the b-map to unravel patient-specific affected pathways and protein connections (the “active” cities and roads for that patient). Once the patient’s data are placed onto the b-map, a network search algorithm will identify possible causal genes for that molecular profile. We find, by escalating the network, those upstream genes that explain the obtained SoBCG. These genes are potential targets for drug discovery. Finally, by searching into specialized databases that relate chemical compounds to specific proteins or SoBCGs, the computational framework will propose molecules to be tested as drugs for the personalized BC profile. This system combines the power of computational analysis and the potential of personal data to provide an improved tool for breast cancer diagnosis.
Nov 20, 2011