Part of precision medicine is being able to target diseases with specific treatments. Another part is ensuring those treatments are working.
That’s one way of summing up recent research by scientists at the Georgia Institute of Technology and the Atlanta-based Ovarian Cancer Institute. In a recent report, the team describe how they have tapped “ensemble-based” machine learning algorithms to predict how patients will respond to cancer-fighting drugs with high accuracy rates.
For their study, the team, led by John F. McDonald, director of the Integrated Cancer Research Center in the Petit Institute for Bioengineering and Bioscience at the Georgia Institute of Technology, decided to combine several algorithm approaches that use multiple ways to analyze complex data; one even uses a three-dimensional approach. They found using this ensemble-based approach significantly boosted predictive accuracy.
“In analyzing complex datasets in cancer biology, we can use machine learning, which is simply a sophisticated way to look for correlations. The advantage is that computers can look for these correlations in extremely large and complex data sets,” McDonald explained in a statement.
In the study, McDonald’s team developed predictive machine learning-based models for 15 distinct cancer types, using data from 499 independent cell lines provided by the National Cancer Institute. Those models were then validated against a clinical dataset containing seven chemotherapeutic drugs, administered either singularly or in combination, to 23 ovarian cancer patients. The researchers found an overall predictive accuracy of 91%.
“While additional validation will need to be carried out using larger numbers of patients with multiple types of cancer,” McDonald noted, “our preliminary finding of 90% accuracy in the prediction of drug responses in ovarian cancer patients is extremely promising and gives me hope that the days of being able to accurately predict optimal cancer drug therapies for individual patients is in sight.”
According to McDonald, cancer’s complexities make it difficult to predict drug responses. For example, even patients with the same types of cancer can respond differently to the same treatment.
“Part of the problem is that the cancer cell is a highly integrated network of pathways and patient tumors that display the same characteristics clinically may be quite different on the molecular level,” McDonald explained.
The goal of personalized cancer medicine is to accurately predict likely responses based upon genomic profiles of individual patient tumors.
“In our approach, we utilize an ensemble of machine learning methods to build predictive algorithms — based on correlations between gene expression profiles of cancer cell lines or patient tumors with previously observed responses — to a variety of cancer drugs,” McDonald explained. “The future goal is that gene expression profiles of tumor biopsies can be fed into the algorithms, and likely patient responses to different drug therapies can be predicted with high accuracy.”
He added that more patient datasets that combine genomic profiles with responses to cancer drugs are needed to advance the research.
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