New computational pipeline revolutionizes personalized cancer vaccine design

Ludwig Cancer Research scientists have developed a complete computational pipeline that integrates multiple molecular and genetic analyzes of tumors and the specific molecular targets of T cells and leverages artificial intelligence algorithms to use its results to design personalized cancer vaccines for patients.

The design, validation, and benchmarking of this computational suite, NeoDisc, are detailed in the current issue of Biotechnology of nature in a paper led by Florian Huber and Michal Bassani-Sternberg of the Lausanne Branch of the Ludwig Institute for Cancer Research.

“NeoDisc provides unique insights into the immunobiology of tumors and the mechanisms by which they evade targeting by cytotoxic T cells of the immune system,” said Bassani-Sternberg. “These insights are invaluable for designing personalized immunotherapies, and the analytical and computational pipeline at the heart of NeoDisc is already being used here in Lausanne for clinical trials of personalized cancer vaccines and cell therapies.

Many types of cancer harbor multiple random mutations that should make them more visible to the immune system. Such mutations create aberrant proteins that cells, even cancerous ones, are programmed to cut into small pieces – known as peptides – and “present” as antigens to invite an attack by patrolling T cells.

The wide variety of these “neoantigens” is one of the reasons why patients respond so variably to immunotherapies. On the other hand, neoantigens can be harnessed to develop vaccines and other types of immunotherapies tailored to uniquely target each patient’s tumors. Personalized treatments of this kind are now being developed by researchers around the world.

Such efforts are technically challenging because not all neoantigens are recognized by a given patient’s T cells, and even many that are recognized fail to elicit a sufficiently strong T cell attack. Therefore, one approach to designing personalized vaccines and cell therapies involves identifying neoantigens that are most likely to elicit a vigorous T cell attack.

This requires sophisticated, large-scale analyzes of mutations that create potential neoantigens, the molecular scaffold (known as HLA molecules) that presents them to T cells, and the molecular features that enable recognition by T cell receptors. Bassani-Sternberg is among the pioneers of this field, a high-tech marriage of biochemical and large-scale computational analysis known as “immunopeptidomics.”

The design of personalized immunotherapies is also aided by the genomic analysis of both tumor and blood cells that represent the patient’s healthy genome, the large-scale analysis of gene expression known as “transcriptomics” as well as the sensitive analysis of what is called immunopeptide mass spectrometry . Until now, however, these powerful technologies have never been integrated into a single computational pipeline to predict which neoantigens found in a patient’s tumors should be used as vaccines or otherwise used for personalized immunotherapies.

Furthermore, neoantigens are not the only type of antigens available for immunotherapeutic targeting. Cancer cells also misexpress pieces of normally noncoding DNA, genes normally expressed only during development, other aberrantly expressed gene products, and viral antigens, in cases of virus-induced tumors—all of which can trigger an immune attack.

“NeoDisc can detect all these different types of tumor-specific antigens along with neoantigens, apply machine learning and rule-based algorithms to prioritize those most likely to elicit a T-cell response, and then use this information to design a personalized cancer vaccine. relevant patient,” Huber said.

NeoDisc additionally ranks the potential antigens it detects and creates visualizations of tumor cell heterogeneity within tumors.

“Notably, NeoDisc can also detect potential defects in the antigen presentation machinery, alerting vaccine designers and clinicians to a key immune evasion mechanism in tumors that can compromise the efficacy of immunotherapy,” said Bassani-Sternberg. “This can help them select patients for clinical studies who are likely to benefit from personalized immunotherapy, a skill that is also of great importance for optimizing patient care.”

The researchers further show in their study that NeoDisc provides a more accurate selection of effective cancer antigens for vaccines and cell therapies than other computational tools currently used for this purpose.

To further improve NeoDisc’s accuracy, researchers will continue to feed data obtained from a variety of tumors and incorporate additional machine learning algorithms into the software suite to advance its training and improve its predictive accuracy.