New Research: Using Mathematics to Treat Ovarian Cancer

There’s good news on the cancer front for those currently battling ovarian cancer or who will be in the future. Math researchers Mark Alber and Oleg Kim at the University of California, Riverside, have pinpointed, through the application of mathematics, a process to halt the action of a protein needed for ovarian cancer cells to spread to other parts of the body. The hope is that the results will lead to the creation of a new drug for use in the treatment of ovarian cancer.

Alber and Notre Dame biochemist M. Sharon Stack led the study. As a mathematician, Alber said he’s been doing similar biology-oriented work since the year 2000.

Math researchers Mark Alber and Oleg Kim at the University of California have shown that the action of certain proteins necessary to spread ovarian cancer cells can be halted through the use of mathematics. There is hope that this will lead to the creation of a new drug in the fight against ovarian cancer. The study was led by Alber and Notre Dame biochemist M. Sharon Stack.

The challenge in diagnosing ovarian cancer is that the definitive symptoms are often absent. The most common symptoms are vague stomach pains, back pain, fatigue, and an increased frequency of urination. Diagnosis is difficult because the symptoms are general in nature. During an internal gynecological exam, if a pelvic mass is discovered, ovarian cancer is the suspected culprit. However, when diagnosed, 70 percent of women are already in stage IV. Alber and Stack have developed an encouraging hypothesis for the treatment of ovarian cancer. The survival rates could greatly improve if their research continues to show favorable outcomes. A combination of surgery and chemotherapy is the current mainstay for treatment.

The research brings greater insight, purpose, and clarity when passing the perspective of two or more disciplines. The facts are streamlined into user-friendly images by using mathematical models such as graphs, diagrams, equations, and computer simulations. To present their interpretation of the outcome in a concise and usable form, the study combines the core knowledge of mathematics with that of biology.

Ovarian cancer is displayed with a medical-mathematical model that is already in use. To specifically address women carrying the genetic markers BRCA1/2, a probability model was created. If either of these markers is present, women are said to be at a high risk for ovarian cancer. This model helps to predict the survival rates of a woman who has the markers after her ovaries and fallopian tubes have been removed.

The Alber-Stack study showed how a protein involved in the process of metastasis in ovarian cancer is disabled by a certain antibody. Though both biology and mathematics may not seem like similar fields, they are both used to determine relationships between certain related subjects.

To complement their interpretations, many disciplines are collaborating with mathematicians and utilizing math techniques and terminologies to map out predictions and explain collected scientific data.

The behavior of two types of cadherin, which is a protein, has been modeled by researchers at the University of California, Riverside. They also modeled how interactions take place between the cells with the protein and the abdominal tissue. The walls of the healthy cells cannot be broken by cancer cells with E-cadherin. The cells carrying N-cadherin were made to behave as though they had E-cadherin protein by the GC-4 antibody. This caused metastasis to fail. This antibody was isolated by Alber and Stack. They also learned of the ability of this antibody to render the abdominal tissue unable to accept cancerous cells. Thus, the spread of ovarian cancer was hindered. With these study findings, the survival rates of ovarian cancer could improve markedly.

A soon-to-be-opened center for quantitative modeling in biology is being developed by Alber and Kim. The future for women with ovarian cancer is looking a bit brighter with ongoing studies such as these as well as new treatment drug categories.