Artificial Intelligence Used to Predict Chemotherapy Resistance in Breast Cancer Patients


Technology may help improve patient outcomes through enhanced personalized medicine.

Technology may help improve patient outcomes through enhanced personalized medicine.

Researchers at Western University are trying to develop a way to use artificial intelligence to predict a patient’s response to 2 common chemotherapy medications used to treat breast cancer: paclitaxel and gemcitabine.

Peter Rogan, PhD, Stephanie Dorman, PhD and Katherina Baranova, BMSc and other researchers at Western’s Schulich School of Medicine & Dentistry hope to remove the guesswork from breast cancer treatment with this new technique.

Patients with the same type of cancer can have completely different responses to the same treatment based on genetic analysis of their tumors. This means that some patients will respond well and go into remission, while others will become resistant to treatment and worsen.

Identifying which factors go into remission or resistance can go a long way in developing more personalized, targeted treatment regimens with better patient outcomes.

“Treating patients with therapies that are the most likely to be successful can help reduce unnecessary toxicity and improve overall outcomes,” Dorman said.

Rogan and Joan Knoll, PhD, began by defining a stable set of genes in 90% of breast cancer tumors in 2012.

The team then used a combination of artificial intelligence with data from cell lines and tumor tissue from cancer patients who had treatment with at least 1 of the medications to narrow down and identify the genetic signatures most important for determining resistance and remission for each medication.

With this data, researchers were able to determine that 84% of breast cancer patients in the study would go into remission in response to the drug paclitaxel. The genetic signature of the drug gemcitabine was able to predict remission using preserved tumor tissue with 62 to 71% accuracy.

Researchers plan to advance this study by further refining the genetic signatures and improving predictions.

“Artificial intelligence is a powerful tool for predicting drug outcomes because it looks at the sum of all the interacting genes,” Rogan said. “If we can use this technology to improve our knowledge of which medications to use, it could improve patient outcomes. The earlier we treat a patient with the most effective medication, the more likely we can effectively treat or possible even cure that patient.”

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