A Machine Learning Model to Predict Immunotherapy Response in Cancer Patients

Immunotherapy is a new cancer treatment that activates the body's immune system to fight against cancer cells without using chemotherapy or radiotherapy. It has fewer side effects than conventional anticancer drugs because it attacks only cancer cells using the body's immune system. In addition, because it uses the memory and adaptability of the immune system, patients who have benefited from its therapeutic effects experience sustained anticancer effects.

The recently developed immune checkpoint inhibitor has considerably improved the survival rate of patients with cancer. However, the problem with cancer immunotherapy is that only approximately 30% of cancer patients receive benefits from its therapeutic effect, and the current diagnostic techniques do not accurately predict the patient's response to the treatment.

Under this circumstance, the research team led by Professor Sanguk Kim (Department of Life Sciences) at POSTECH is gaining attention as they have improved the accuracy of predicting patient response to immune checkpoint inhibitors (ICIs) by using network-based machine learning. The research team discovered new network-based biomarkers by analyzing the clinical results of more than 700 patients with three different cancers (melanoma, gastric cancer, and bladder cancer) and the transcriptome data of the patients' cancer tissues. By utilizing the network-based biomarkers, the team successfully developed artificial intelligence that could predict the response to anticancer treatment. The team further proved that the treatment response prediction based on the newly discovered biomarkers was superior to that based on conventional anticancer treatment biomarkers including immunotherapy targets and tumor microenvironment markers.

In their previous study, the research team had developed machine learning that could predict drug responses to chemotherapy in patients with gastric or bladder cancer. This study has shown that artificial intelligence using the interactions between genes in a biological network could successfully predict the patient response to not only chemotherapy, but also immunotherapy in multiple cancer types.

This study helps detect patients who will respond to immunotherapy in advance and establish treatment plans, resulting in customized precision medicine with more patients to benefit from cancer treatments. Supported by the POSTECH Medical Device Innovation Center, the Graduate School of Artificial Intelligence, and ImmunoBiome Inc, this study was recently published in Nature Communications.

Kong J, Ha D, Lee J, Kim I, Park M, Im SH, Shin K, Kim S.
Network-based machine learning approach to predict immunotherapy response in cancer patients.
Nat Commun. 2022 Jun 28;13(1):3703. doi: 10.1038/s41467-022-31535-6

Most Popular Now

Mahana Therapeutics Signs Agreement with…

Mahana Therapeutics, a leading provider of prescription digital therapeutics, announced today that the company has entered into a multi-million-dollar distribution and marketing partnership with the Consumer Health division of Bayer...

ChatGPT can Outperform University Studen…

ChatGPT may match or even exceed the average grade of university students when answering assessment questions across a range of subjects including computer science, political studies, engineering, and psychology, reports...

NHS AI Diagnostic Funding: Five Things t…

Opinion Article by Guilherme Carvalho, Sales & Contracts Manager, Sectra. A new £21 million fund for AI was announced by the UK government in June, with the intention of providing NHS...

ChatGPT Shows Limited Ability to Recomme…

For many patients, the internet serves as a powerful tool for self-education on medical topics. With ChatGPT now at patients’ fingertips, researchers from Brigham and Women’s Hospital, a founding member...

Combining AI Models Improves Breast Canc…

Combining artificial intelligence (AI) systems for short- and long-term breast cancer risk results in an improved cancer risk assessment, according to a study published in Radiology, a journal of the...

AI Predictions for Colorectal Cancer: On…

Colorectal cancer (CRC) ranks second in leading causes of cancer-related deaths globally, according to the WHO. For the first time, researchers from Helmholtz Munich and the University of Technology Dresden...

Healthcare Chatbot: Expand Support with …

The Danish eHealth platform, sundhed.dk, has faced a substantial surge in requests from Danish citizens and has swiftly expanded its support and effectively adapt to the ongoing changes in queries due...

ChatGPT Shows 'Impressive' Acc…

A new study led by investigators from Mass General Brigham has found that ChatGPT was about 72 percent accurate in overall clinical decision making, from coming up with possible diagnoses...

WiFi SPARK's Healthcare Business Re…

Leading WiFi provider WiFi SPARK is rebranding its healthcare arm as SPARK Technology Services Limited. The new identity marks the completion of the integration of the former Hospedia bedside unit...

AI Performs Comparably to Human Readers …

Using a standardized assessment, researchers in the UK compared the performance of a commercially available artificial intelligence (AI) algorithm with human readers of screening mammograms. Results of their findings were...

ChatGPT is Debunking Myths on Social Med…

ChatGPT could help to increase vaccine uptake by debunking myths around jab safety, say the authors of a study published in the peer-reviewed journal Human Vaccines and Immunotherapeutics. The researchers asked...

Online AI-Based Test for Parkinson'…

An artificial intelligence (AI) tool developed by researchers at the University of Rochester can help people with Parkinson's disease remotely assess the severity of their symptoms within minutes. A study...