AI can accurately predict cancer patients’ survival chances: study

By Stephen Beech via SWNS

Artificial intelligence can accurately predict cancer patients' survival chances, according to a new study.

American scientists have developed an AI model that is able to predict if and when patients with various types of cancer will die.

The researchers found that by examining the gene expression patterns of epigenetic factors - those that influence how genes are turned on or off - in tumors, they could categorize them into distinct groups to predict patient outcomes better than traditional measures.

The UCLA research team say their findings, published in the journal Communications Biology, also lay the groundwork for developing targeted therapies aimed at regulating epigenetic factors in cancer therapy.

Co-senior author Professor Hilary Coller, of UCLA Health Jonsson Comprehensive Cancer Centre, said: "Traditionally, cancer has been viewed as primarily a result of genetic mutations within oncogenes or tumor suppressors.

“However, the emergence of advanced next-generation sequencing technologies has made more people realize that the state of the chromatin and the levels of epigenetic factors that maintain this state are important for cancer and cancer progression.

"There are different aspects of the state of the chromatin - like whether the histone proteins are modified, or whether the nucleic acid bases of the DNA contain extra methyl groups - that can affect cancer outcomes.

"Understanding these differences between tumors could help us learn more about why some patients respond differently to treatments and why their outcomes vary.”

While previous studies have shown that mutations in the genes that encode epigenetic factors can affect a person's susceptibility to cancer, little is known about how the levels of these factors impact cancer progression.

Prof. Coller says that knowledge gap is "crucial" in fully understanding how epigenetics affect the chances of a patient surviving.

To see if there was a relationship between epigenetic patterns and clinical outcomes, the research team analyzed the expression patterns of 720 epigenetic factors to classify tumors from 24 different cancer types into distinct clusters.

Of the 24 adult cancer types, the team found that for 10 of the cancers, the clusters were associated with significant differences in patient outcomes, including progression-free survival, disease-specific survival, and overall survival.

The clusters with poor outcomes tended to have higher cancer stage, larger tumor size, or more severe spread indicators.

Study co-senior author Dr. Mithun Mitra said: “We saw that the prognostic efficacy of an epigenetic factor was dependent on the tissue-of-origin of the cancer type.

“We even saw this link in the few pediatric cancer types we analyzed. This may be helpful in deciding the cancer-specific relevance of therapeutically targeting these factors.”

The research team then used epigenetic factor gene expression levels to train and test an AI model to predict patient survival.

The model was specifically designed to predict what might happen for the five cancer types that had significant differences in survival measurements.

The team found the model could successfully divide patients with those five cancer types into two groups: one with a significantly higher chance of better outcomes and another with a higher chance of poorer outcomes.

They also saw that the genes that were most crucial for the AI model had a "significant" overlap with the cluster-defining signature genes.

Dr. Mitra said: “The pan-cancer AI model is trained and tested on the adult patients from the TCGA cohort and it would be good to test this on other independent datasets to explore its broad applicability.

“Similar epigenetic factor-based models could be generated for pediatric cancers to see what factors influence the decision-making process compared to the models built on adult cancers.”

Study first author Michael Cheng said: “Our research helps provide a roadmap for similar AI models that can be generated through publicly-available lists of prognostic epigenetic factors."

Cheng, a graduate student in the Bioinformatics Interdepartmental Program at UCLA, added: “The roadmap demonstrates how to identify certain influential factors in different types of cancer and contains exciting potential for predicting specific targets for cancer treatment.”