Machine Learning to Predict Efficacy of Anti-Cancer Drug

With the advent of pharmacogenomics, machine learning research is well underway to predict the patients' drug response that varies by individual from the algorithms derived from previously collected data on drug responses. Entering high-quality learning data that can reflect a person's drug response as much as possible is the starting point for improving the accuracy of the prediction. Previously, preclinical study of animal models were used which were relatively easier to obtain compared to human clinical data.

In light of this, a research team led by Professor Sanguk Kim in the Department of Life Sciences at POSTECH is drawing attention by successfully increasing the accuracy of anti-cancer drug response predictions by using data closest to a real person's response. The team developed this machine learning technique through algorithms that learn the transcriptome information from artificial organoids derived from actual patients instead of animal models. These research findings were published in the international journal Nature Communications on October 30.

Even patients with the same cancer have different reactions to anti-cancer drugs so customised treatment is considered paramount in treatment development. However, the current predictions were based on genetic information of cancer cells, limiting their accuracy. Due to unnecessary biomarker information, machine learning had an issue of learning based on false signals.