Machine-Learning Tool Improves Cancer Drug Response Prediction

Machine-Learning Tool Improves Cancer Drug Response Prediction
Healthcare Technology and Medical Scan of a Body Diagnosis

Applying a machine learning technique using algorithms that learn transcriptome information from artificial organoids derived from actual patients instead of animal models, researchers from the Pohang University of Science and Technology (POSTECH) in South Korea say they have successfully increased the accuracy of anti-cancer drug response predictions.

The team, led by Sanguk Kim, Ph.D., in the life sciences department, published its findings “Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients”  in Nature Communications

“Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models,” write the investigators.

“The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method.”

“This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.”

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

To increase the predictive accuracy, the research team introduced machine learning algorithms that use protein interaction networks that can interact with target proteins as well as the transcriptome of individual proteins that are directly related with drug targets. It induces learning the transcriptome production of a protein that is functionally close to the target protein. Thus, only selected biomarkers can be learned instead of false biomarkers that the conventional machine learning had to learn, which increases the accuracy, notes Kim.

In addition, data from patient-derived organoids, not animal models, were 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.

The researchers anticipate that these research findings will not only help identify the mechanism of new anti-cancer drugs but also implement precise personalized medical care to patients.