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University of Utah researchers have developed an algorithm they believe could improve diagnosis and treatment of ovarian cancer.

By studying patterns of DNA anomalies, the team developed a mathematical technique that can better predict how long a woman will live and how her tumors will respond to chemotherapy.

"We believe this is a first step toward bringing ovarian cancer into the age of precision medicine," said team leader Orly Alter, an associate professor of bioengineering and member of the U.'s Scientific Computing and Imaging Institute.

The team started with DNA profiles from the national Cancer Genome Atlas, a database with records from hundreds of ovarian cancer patients.

After analyzing the DNA of individual patients' tumors and normal cells, the researchers were able to separate out anomalies in the cancer cells.

From those patterns, the team developed mathematical models that can be used to distinguish between short-term survivors — those who live on average for three more years after an ovarian cancer diagnosis — from long-term survivors, who live almost twice as long.

The models also can be used to identify patients whose tumors will respond to platinum-based chemotherapy drugs.

Right now, most ovarian cancers are diagnosed in advanced stages. And 30-year-old treatment options are determined mostly by tumor stage. Most patients die within five years.

"If we have a tool that can more accurately predict survival and distinguish who is who, we can revamp our entire approach to how we treat patients," Margit-Maria Janat-Amsbury, director of gynecologic oncology research at the U.'s School of Medicine, said in a statement.

Janat-Amsbury and Alter are collaborating. If the algorithm is validated in clinical observations, the patterns could become the basis of personalized diagnosis.

"For those with a poor prognosis, we can suggest other therapies," Janat-Amsbury said, "or we can focus on taking measures to improve quality of life."

The study was published in the journal PLOS ONE.