nvestigators
from the UCLA Health Jonsson Comprehensive Cancer Center have developed an
artificial intelligence (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 -; factors that influence how genes are turned on or off -; in tumors,
they could categorize them into distinct groups to predict patient outcomes
across various cancer types better than traditional measures like cancer grade
and stage.
These
findings, described in Communications Biology, also lay the groundwork for
developing targeted therapies aimed at regulating epigenetic factors in cancer
therapy, such as histone acetyltransferases and SWI/SNF chromatin remodelers.
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."
Hilary
Coller, co-senior author, 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
While
previous studies have 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 in fully understanding how epigenetics affects patient outcomes,
noted Coller.
To see
if there was a relationship between epigenetic patterns and clinical outcomes,
the researchers analyzed the expression patterns of 720 epigenetic factors to
classify tumors 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 true 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 tumor size, or
more severe spread indicators.
"We
saw that the prognostic efficacy of an epigenetic factor was dependent on the
tissue-of-origin of the cancer type," said Mithun Mitra, co-senior author
of the study and an associate project scientist in the Coller laboratory.
"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 team
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 might happen for
the five
cancer types that had significant differences in survival measurements.
The
scientists 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 saw that the genes that were most crucial for the AI model had a
significant overlap with the cluster-defining signature genes.
"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," said Mitra. "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."
"Our
research helps provide a roadmap for similar AI models that can be generated
through publicly-available lists of prognostic epigenetic factors," said
the study's first author, Michael Cheng, a graduate student in the
Bioinformatics Interdepartmental Program at UCLA. "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."
The
study was funded in part by grants from the National Cancer Institute, Cancer
Research Institute, Melanoma Research Alliance, Melanoma Research Foundation,
National Institutes of Health and the UCLA Spore in Prostate Cancer.
