Using Computational Homology to Predict Recurrence on Early Breast Cancer Patients
Breast Cancer is the leading cause of cancer for women in the Western world. The most common presentation of the disease in Western populations is as lymph node-negative. Despite clear guidelines on how to proceed when treating these patients, based on clinical and histological criteria, approximately one fourth of patients with breast cancer who have negative lymph nodes will have recurrence and succumb to the disease. DNA copy number changes, such as amplifications and deletions, are the signature of genomic instability and have been associated with a number of clinical parameters (including metastasis, recurrence and response to treatment). Here we present a new computational method, based on computational algebraic topology, to perform associations between clinical parameters and profiles of copy number variation. We find that patients with deletions in chromosomes 8p and 6q have higher chances of recurrence.
Dr. Javier Arsuaga works in mathematical and computational modeling of chromosomes. In this talk he will mainly focus on our computational efforts to understand chromosome aberrations in cancer. It has been estimated than 1 in 3 people will developed cancer during their life time. One key problem is to identify patients at an early stage of cancer development and with aggressive tumors. He focuses how one can use computational methods to interrogate chromosomes to infer properties of the tumor and will discuss how this can be used for personalized treatments. However these methods are in their infancy and there is an enormous need for new computational tools (data base management, data mining, imaging, etc…) to analyze the data.