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The CCE welcomes all collaboration opportunities focusing on understanding tumor evolution. We are currently working on projects focusing on somatic tumor evolution and on somatic evolution specifically in breast cancer. However, we are interested in any collaboration in a variety of cancer fields. Please This email address is being protected from spambots. You need JavaScript enabled to view it. if you are interested in collaborating with us.


Evolution of Breast Cancer

Breast cancer is the most commonly diagnosed cancer and is the main cause of cancer-related mortality in women worldwide. Achieving a meaningful global impact on breast cancer-associated morbidity and mortality requires a better understanding of drivers of metastatic progression and therapeutic resistance, increased diagnostic recognition of these features, and early intervention and treatment strategies that prevent the emergence of metastatic and treatment-resistant tumors. The goals of this project are to tackle these key clinical problems in breast cancer through a combined mathematical modeling and experimental approach.


Evolutionary Dynamics of Mutation Accumulation in Cell Populations

Cancer is a genetic disease fuelled by somatic evolution. During somatic evolution, genetic and epigenetic alterations can spread through a population of premalignant or cancer cells. As cell populations accumulate progressively more changes over time, they acquire characteristics that enable them to persist within tissues. Therefore, an understanding of cancer development and progression requires the elucidation of collective properties of cells within a tissue and their interaction with the microenvironment. In this project we will combine novel experimental evolution systems with quantitative analysis and modeling to understand mechanisms of cancer evolution.


Optimal Scheduling of Cancer Therapy to Delay Resistance

Resistance to anticancer drugs is a major obstacle for the treatment of cancers. Clinical responses to therapies may sometimes be temporary as tumors acquire resistance as somatic mutations continue to occur even in the presence of drug. Resistance could even provide a therapy-induced selection of resistant subpopulations of cell that were already present in the original tumor. Our work explores evolutionary dynamics of heterogeneous tumor populations while taking into account pharmacokinetics and drug interactions to provide a mathematical description of the changes in cellular dynamics over time. We use this framework to design and optimize treatment schedules for patients with the goal of minimizing the probability of or time to resistance. We are working on projects to better estimate cellular dynamics from previous data, understand how to integrate drug interactions, and account for changes in pharmacokinetics due to clinical covariates in the population in order to create personalized or robust schedules.