Computer Technology Poised to Revolutionize Cancer Therapy

New computer programs have been developed that accurately predict how a person reacts to different cancer therapies based on their genetic makeup.

Computer technology algorithms have dramatically changed the management of type-1 diabetes, and now it is on the cusp of doing the same for cancer therapy. Just as implantable insulin pumps are controlled by computer algorithms to control blood glucose levels, the same type of technology is about to change how some cancer patients are managed.

New computer programs have been developed that accurately predict how a person reacts to different cancer therapies based on their genetic makeup.

Recently, President Barack Obama unveiled the Precision Medicine Initiative, a program that fosters a research approach focusing on treating patients based on their genetic makeup. As part of this initiative, researchers at the University of Florida (UF) Health have launched a clinical trial that tests a new method of translating thousands of gene mutations into treatment options for patients. This approach involves computer simulation modeling. Not only will the software take the genes of a patient’s cancer into account, it will also examine the genes that govern how a person reacts to a particular medication.

Christopher Cogle, MD, lead study investigator, UF Health physician, and associate professor of medicine in the UF College of Medicine, Gainesville, Fla., said his team plans to study the effectiveness of a novel computer model and how it is able to guide treatment. Dr. Cogle and his team are mapping thousands of genes within each patient’s cancer that can drive aggressive growth. Cancer often involves hundreds to thousands of gene abnormalities and researchers are trying to decode the numerous DNA misspellings that drive disease.

Focusing on the diagnosis and treatment of patients with the myelodysplastic syndromes (MDS), acute leukemias, and bone marrow failure syndromes, Dr. Cogle treats and studies different types of blood cancers and typically sees patients whose cancer has relapsed.

The goal of this current study is to examine 91 genes involved in the movement of drugs within the body. These so-called "pharma-genes" will then be tested to identify which treatments are safest and most effective for the patient. "The cancer genes are genes we believe will give us prognostic and treatment information, and the pharma-genes will tell us how well the patients will respond to the drugs we prescribe," said Dr. Cogle in a UF Health press release.1

This kind of close examination is an entirely new approach to cancer treatment based on the latest computer technology. It takes into account how well a therapy targets cancer and how that therapy impacts the health of a patient. Typically, to find the significance of a cancer mutation, clinicians use a manual approach through PubMed, which can be very time-consuming. That’s where Cellworks Group Inc. comes in.

Dr. Cogle and UF Health researcher Jatinder Lamba, PhD, are working with the California-based technology group in a clinical trial. The company created a simulation technology to generate a computer model of each person’s cancer. Cellworks then models how the cancer responds to standard chemotherapies.

"Think of the simulation as a map of a city. Hypothetically, if you have a major highway and intersections, you could predict what happened in a model of that traffic map by inputting different traffic situations. It’s the same with patients. The model is basically an internal map of all of these processes happening inside the body," said Dr. Cogle.1

If the computer method is validated, this prediction technology could help oncologists and patients select treatment regimens with the greatest likelihood of shrinking the cancer while avoiding toxic therapies with a low chance of success. For patients who aren’t responding to standard chemotherapy and for patients whose cancer has relapsed, each patient’s computer model could be used to search for other agents that may be more effective.