The evolution of a patient's Tumor to predict progress very individualized cancer treatment

Researchers from the University of Miami (UM) and the University of Heidelberg in Germany have a mathematical model to understand and predict the progress of a tumor, from the early stages to metastasis, in hopes of creating highly personalized treatment strategies for patients who have cancer developed. The results are published in nature's new online journal scientific reports.

When a tumor in the landscape of the human body, can be one of two things happen: the tumor can stop growth and lie dormant or it can grow fed by a network of underlying vessels is extended when the tumour develops. The ships also offer a way to the cancer cells to travel to other parts of the body, to settle and grow-a process called metastasis.

The study reveals a hidden connection between the tumor and the nutrient supply vessels. The method outlines paths of future tumor expansion and identifies specific points in the vessels that can be focused on the control of growth, Neil Johnson, Ph.d., professor of physics, Director of the research group complexity to UM College of Arts and Sciences and co-principal investigator of the study explains.

"Cancer is a disease of many scales. There are the individual cells, cells that group together form the tumor, the therapies and finally metastasis. By including information about how the tumor grows in response to its nutrients, and how the growth of the tumor feeds back the nutrient delivery itself, our model moves us one step closer to predicting the future development of a patient's tumor, "says Johnson. "It opens a path toward personalised treatment and intervention."

An interesting aspect of the model is that it is based on the distribution of feeding vessels in a section of the tumor. As the ships both feed and be fed by the tumor, can estimates of growth characteristics of a patient's tumor. This type of estimate potential can be applied to a better design of treatment schedules for cancer patients, explains Joseph d. Rosenblatt, m.d., Director ad interim of Sylvester Comprehensive Cancer Center, on the UM Miller School of Medicine and co-principal investigator of the study.

"This method can be used to predict growth rates in the human institution and model effects of agents specifically focused its supporting processes that keep the tumor," said Rosenblatt. "Our model can be useful when designing treatment intervals and dosage schemes on the basis of an accurate assessment of the growth dynamics and the interdependence of the formation of tumour growth and blood vessel."

By analyzing images of tumour sections for distribution of tumor cells and tumor therapies, the researchers created a simple model that the most likely course of disease predicts, Sehyo Choe, post-doctoral research fellow at the Division of theoretical Bioinformatics at the German Cancer Research Center in Germany and at the Institute for Pharmacy and molecular biotechnology University of Heidelberg; and co-principal investigator of the study.

"Our model implements local differences of directly from a tumor in vivo imaging and the parameters are directly measurable for every cancer," says Choe. "By doing so, we believe we are one step closer to eventually build a model that able for a probable corridor of progression of cancer, will be based on real-time data for a specific patient images and other specific patient data describe."

Article reference:

"Model for in vivo progression of tumors based on co-evolving-cell population and therapies." Co-authors are Guannan Zhao, Ph.d. student and Zhenyuan Zhao, Ph.d., Assistant investigator in the Department of physics, on the UM College of Arts and Sciences; Hyun-Mi Cho Ph.d., and Seung-Shin Uon Ph.d., research associate professors, at Sylvester Comprehensive Cancer Center.

University of Miami