Scientists develop artificial intelligence method to predict anti-cancer immunity
Machine learning algorithms are shedding light on neoantigen T cell-
receptor pairs

Date:
September 23, 2021
Source:
UT Southwestern Medical Center
Summary:
Researchers and data scientists have developed an artificial
intelligence technique that can identify which cell surface peptides
produced by cancer cells called neoantigens are recognized by the
immune system.



FULL STORY ========================================================================== Researchers and data scientists at UT Southwestern Medical Center and
MD Anderson Cancer Center have developed an artificial intelligence
technique that can identify which cell surface peptides produced by
cancer cells called neoantigens are recognized by the immune system.


==========================================================================
The pMTnet technique, detailed online in Nature Machine Intelligence,
could lead to new ways to predict cancer prognosis and potential
responsiveness to immunotherapies.

"Determining which neoantigens bind to T cell receptors and which don't
has seemed like an impossible feat. But with machine learning, we're
making progress," said senior author Dr. Tao Wang, Ph.D., Assistant
Professor of Population and Data Sciences, and with the Harold C. Simmons Comprehensive Cancer Center and the Center for Genetics of Host Defense
at UT Southwestern.

Mutations in the genome of cancer cells cause them to display different neoantigens on their surfaces. Some of these neoantigens are recognized
by immune T cells that hunt for signs of cancer and foreign invaders,
allowing cancer cells to be destroyed by the immune system. However,
others seem invisible to T cells, allowing cancers to grow unchecked.

"For the immune system, the presence of neoantigens is one of the biggest differences between normal and tumor cells," said Tianshi Lu, first
co-author with Ze Zhang, doctoral students in the Tao Wang lab, which
uses state-of-the- art bioinformatics and biostatistics approaches to
study the implications of tumor immunology for tumorigenesis, metastasis, prognosis, and treatment response in a variety of cancers. "If we can
figure out which neoantigens stimulate an immune response, then we may
be able to use this knowledge in a variety of different ways to fight
cancer," Ms. Lu said.

Being able to predict which neoantigens are recognized by T cells could
help researchers develop personalized cancer vaccines, engineer better T cell-based therapies, or predict how well patients might respond to other
types of immunotherapies. But there are tens of thousands of different neoantigens, and methods to predict which ones trigger a T cell response
have proven to be time- consuming, technically challenging, and costly.

Searching for a better technique with support of grants from the National Institutes of Health (NIH) and Cancer Prevention and Research Institute of Texas (CPRIT), the research team looked to machine learning. They trained
a deep learning-based algorithm that they named pMTnet using data from
known binding or nonbinding combinations of three different components: neoantigens; proteins called major histocompatibility complexes (MHCs)
that present neoantigens on cancer cell surfaces; and the T cell receptors (TCRs) responsible for recognizing the neoantigen-MHC complexes. They then tested the algorithm against a dataset developed from 30 different studies
that had experimentally identified binding or nonbinding neoantigen T cell-receptor pairs. This experiment showed that the new algorithms had
a high level of accuracy.

The researchers used this new tool to gather insights on neoantigens
cataloged in The Cancer Genome Atlas, a public database that holds
information from more than 11,000 primary tumors. pMTnet showed that neoantigens generally trigger a stronger immune response compared with tumor-associated antigens. It also predicted which patients had better responses to immune checkpoint blockade therapies and had better overall survival rates.

"As an immunologist, the most significant hurdle currently facing
immunotherapy is the ability to determine which antigens are recognized
by which T cells in order to leverage these pairings for therapeutic
purposes," said corresponding author Alexandre Reuben, Ph.D., Assistant Professor of Thoracic-Head & Neck Medical Oncology at MD Anderson. "pMTnet outperforms its current alternatives and brings us significantly closer
to this objective." Other UTSW researchers who contributed to this
study include James Zhu, Yunguan Wang, Xue Xiao, and Lin Xu. Other MD
Anderson scientists who contributed to this work include Peixin Jiang,
Chantale Bernatchez, John V. Heymach, and Don L. Gibbons. Dr. Jun Wang
from NYU Langone Health also contributed to this work.

UT Southwestern's Simmons Cancer Center and MD Anderson Cancer Center
are among the exclusive 51 designated comprehensive centers with the
National Cancer Institute, which includes a joint effort with the
National Human Genome Research Institute to oversee The Cancer Genome
Atlas project. The study was supported by the NIH (grants 5P30CA142543/TW
and R01CA258584/TW), CPRIT (RP190208/TW), MD Anderson (Lung Cancer Moon
Shot), the University Cancer Foundation at MD Anderson, the Waun Ki Hong
Lung Cancer Research Fund, Exon 20 Group, and Rexanna's Foundation for
Fighting Lung Cancer.

========================================================================== Story Source: Materials provided by UT_Southwestern_Medical_Center. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Tianshi Lu, Ze Zhang, James Zhu, Yunguan Wang, Peixin Jiang,
Xue Xiao,
Chantale Bernatchez, John V. Heymach, Don L. Gibbons, Jun Wang,
Lin Xu, Alexandre Reuben, Tao Wang. Deep learning-based prediction
of the T cell receptor-antigen binding specificity. Nature Machine
Intelligence, 2021; DOI: 10.1038/s42256-021-00383-2 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210923164817.htm

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