Communications of the ACM
Tool Could Help Researchers Design Better Cancer Vaccines
Researchers at Harvard University, the Broad Institute of the Massachusetts Institute of Technology, the Dana-Farber Cancer Institute, and Massachusetts General Hospital have developed a computational tool that could help predict which tumor-specific antigens will be displayed on tumor cells, and hence would be the best to put in a cancer vaccine.
The researchers used machine learning to analyze a diverse set of more than 185,000 human antigens they had discovered. Then the team generated a new set of rules that predict which antigens are presented on the surface of a person's cells. The machine learning algorithm analyzed the data and determined new rules dictating which antigens are presented by each human leukocyte antigen (HLA) type. The team tested the new rules by inputting a second set of data, from 11 human tumor samples—three chronic lymphocytic leukemia, one ovarian, three glioblastoma and four melanomas—into the model.
"It identified nearly twice as many antigens than previous approaches, and correctly predicted more than 75% of the HLA-bound peptides that were detected using mass spectrometry," said Broad Institute researcher Susan Klaeger.
The researchers describe their work in "A Large Peptidome Dataset Improves HLA Class I Epitope Prediction Across Most of the Human Population," published in Nature Biotechnology.
From Broad Institute
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Abstracts Copyright © 2019 SmithBucklin, Washington, DC, USA
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