Machine Learning Method Predicts Anti-Cancer Drug Efficacy

A team of researchers has successfully increased the accuracy of anti-cancer drug response predictions. The team from Pohang University of Science and Technology developed a machine learning model through algorithms that learn the transcriptome information from artificial organoids derived from actual patients instead of animal models. Their findings were published in Nature Communications. 

According to the researchers, current predictions are normally based on genetic information of cancer cells, limiting their accuracy. To increase the predictive accuracy, the research team introduced machine learning algorithms that use a protein interaction network that can interact with target proteins as well as the transcriptome of individual proteins that are directly related with drug targets. 

Their method learns the transcriptome production of a protein that is functionally close to the target protein. Through this, only selected biomarkers can be learned instead of false biomarkers that the conventional machine learning had to learn, which increases the accuracy.

In addition, data from patient-derived organoids was used to narrow the discrepancy of responses in actual patients. With this method, colorectal cancer patients treated with 5-fluorouracil and bladder cancer patients treated with cisplatin were predicted to be comparable to actual clinical results.