Measuring tumor heterogeneity for better, evolution-based cancer treatment strategies
By Joyce Dall'Acqua Peterson
Evolution is going on right now, in your body: Whenever a cell divides, a few mutations occur and get passed on to the new cells. This is also true of cancer cells, and in a tumor that has been treated with a chemotherapy drug, some cancer cells may accumulate mutations that allow them to evade the treatment and survive. These subclonal (offshoot) cell populations may eventually dominate the tumor and stop the effectiveness of chemotherapy.
Assessing the evolution rates of a tumor’s various (heterogeneous) cell populations is fundamental to developing treatment strategies that can minimize resistance, and thus morbidity and mortality in cancers. But current measurement approaches have not been up to the task.
JAX Associate Professor Jeffrey Chuang, Ph.D., has been awarded a five-year grant totaling $2,650,484 from the National Cancer Institute for research that could pave the way for the first evolution-based approaches to cancer treatment.
As a proof of concept, Chuang conducted a wide-ranging comparative study of triple-negative breast cancer tumors that had been treated with various chemotherapies. By combining computational data science approaches with DNA sequencing, he discovered the subclonal populations within a tumor and determined how they evolve differently in response to cisplatin, a platinum-based cancer treatment.
Chuang, together with JAX collaborators Edison Liu, M.D., Francesca Menghi, Ph.D., Olga Anczuków-Camarda, Ph.D., and Paul Robson, Ph.D., will leverage this system together with high-throughput sequencing and a powerful high-content confocal imaging technology on patient-derived cancer organoids to determine how tumor heterogeneity evolves in response to a wide range of therapies in triple-negative breast cancer. These studies will be used to develop evolution-based approaches to treatment for breast cancer and other solid tumors.
National Cancer Institute, Grant Number 1R01CA230031-01, “Quantitative Computational Methods to Accurately Measure Tumor Heterogeneity in Solid Tumors to Inform Development of Evolution-based Treatment Strategies”