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AI can help predict survival outcomes for cancer patients



Researchers in the US have developed an AI model based on epigenetic factors that is able to predict patient outcomes successfully across multiple cancer types.

The researchers found that by examining the gene expression patterns of epigenetic factors in tumours, they could categorise them into distinct groups to predict patient outcomes across various cancer types better than traditional measures like cancer grade and stage.

These findings, published in Communications Biology, also lay the groundwork for developing targeted therapies aimed at regulating epigenetic factors in cancer therapy.

Co-senior author Hilary Coller is professor of molecular, cell, and developmental biology and a member of the UCLA Health Jonsson Comprehensive Cancer Center and the Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at UCLA.

The researcher said: “Traditionally, cancer has been viewed as primarily a result of genetic mutations within oncogenes or tumour suppressors.

“However, the emergence of advanced next-generation sequencing technologies has made more people realise 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 tumours could help us learn more about why some patients respond differently to treatments and why their outcomes vary.”

While previous research has shown that mutations in the genes that encode epigenetic factors can affect an individual’s cancer susceptibility, little is known about how the levels of these factors impact cancer progression.

This knowledge gap is crucial to fully understanding how epigenetics affects patient outcomes, Coller said.

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

Out 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.

This was especially apparent for adrenocortical carcinoma, kidney renal clear cell carcinoma, brain lower grade glioma, liver hepatocellular carcinoma and lung adenocarcinoma, where the differences were significant for all the survival measurements.

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

Mithun Mitra, co-senior author of the study and an associate project scientist in the Coller laboratory, 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 paediatric cancer types we analysed.

“This may be helpful in deciding the cancer-specific relevance of therapeutically targeting these factors.”

The researchers then used epigenetic factor gene expression levels to train and test an AI model to predict patient outcomes.

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

The researchers found the model could successfully divide patients with these 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 discovered that the genes that were most crucial for the AI model had a significant overlap with the cluster-defining signature genes.

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 paediatric cancers to see what factors influence the decision-making process compared to the models built on adult cancers.”

First author, Michael Cheng, a graduate student in the Bioinformatics Interdepartmental Program at UCLA, said: “Our research helps provide a roadmap for similar AI models that can be generated through publicly-available lists of prognostic epigenetic factors.

“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.”

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