The Cancer Vaccine Study of Uğur Şahin and Özlem Türeci

Neoantigens in Personalized Cancer Vaccines

The scientists who became widely discussed around the world during the Covid-19 pandemic through the mRNA vaccine they developed, Uğur Şahin and Özlem Türeci led a team that achieved an important advance in therapeutic cancer vaccines.

This study addresses scientific gaps between the mechanisms by which T cells recognize neoantigens to activate the immune system against cancer cells, the identification of somatic mutations and computational approaches to predicting neoantigens. It was published in Nature and presents a new classification that evaluates neoantigens according to how adequately they provide antitumor immunity in clinical practice.

What Is a Neoantigen?

Neo- derives from the Greek neos, meaning "new", and is a prefix that adds the meaning of "new" to a word, or emphasizes newness. An antigen is a protein-based substance, such as a virus, bacterium or parasite, that causes antibody formation when it enters the body. A neoantigen refers to molecules produced by cancer cells that are new to the body. These neoantigens, present in cancer cells but absent from normal cells, are among the main targets of new cancer treatments. Antibodies are protein molecules produced by immune system cells that bind to antigens and suppress their harmful effects. 

Neoepitopes are the antibody-binding regions of antigens that the human immune system has not previously encountered. In cancer cells, somatic mutations produce these neoantigens, which our T cells can recognize. Because they are not expressed in healthy tissues, these neoantigens are potential therapeutic targets for cancer vaccines.

Furthermore, because most mutations are patient-specific, these potential targets offer personalized treatment approaches.

Using the increasingly familiar methods of machine learning it is possible to identify cancer mutations and prioritize those that will produce neoepitopes highly likely to be recognized by T cells. This means that individually designed vaccines can be produced, providing personalized cancer treatment.

Classification of Neoantigens

The fundamental somatic mutations that distinguish cancer cells from normal cells produce different protein sequences depending on their type. Single Nucleotide Variants (SNVs) are changes in a single nucleotide within the genome and are the most abundant mutation type in most cancers. Most SNVs produce neoantigens by replacing one amino acid with another. SNV-derived neoantigens have been successfully targeted in personalized neoantigen vaccine studies in cancers such as melanoma and glioblastoma.

However, insertions or deletions of single nucleotides (indels), gene fusions and alternative splicing are also mutations that produce epitopes. The immunotherapeutic benefit of neoantigens derived from fusion-type genetic mutations is not yet fully understood.

Table: Neoantigens are classified according to their potential functional effects.

Neoantigen Presentation and Recognition

Dying tumor cells release neoantigens, which can reach lymph nodes either in soluble form in extracellular fluid or after transport from the tumor site by antigen-presenting cells (ASH). Cells in lymph nodes present neoantigens to T cells, priming and activating naive new-generation T cells. Activated neoantigen-specific CD4+ and CD8+ T cells leave the lymph nodes and enter tumor cells, where they exert antitumor activity.

Figure 1: Mechanism of neoantigen-mediated tumor control

Neoantigen Prediction and Personalized Cancer Vaccines

Using machine learning techniques, selection of neoantigens with the greatest clinical benefit and the first step toward a personalized cancer vaccine begin by comparing samples taken from the patient's own tumor and healthy tissue, using next-generation sequencing (NGS) and, in protein-coding genes, identifying tumor-specific mutations (Figure 2.1 – 2.2).

Not all these mutations elicit an immune response, and not all neoepitopes have equal antitumor effects in T-cell-mediated tumor killing. The characteristics of mutated regions must therefore be evaluated to select clinically relevant candidates capable of triggering antitumor T cells (Figure 2.3).

Important parameters at this step;

• Transcription level
• MHC binding, stability and presentation on the cell surface
• Similarity to pathogen-associated epitopes
• T-cell receptor recognition
• Mutation clonality
• Loss of heterozygosity of important gene products

The process continues with the production of a personalized, polyspecific neoantigen vaccine using a platform such as mRNA or long peptides (Figure 2.4). By inducing a T-cell response, the administered vaccine is expected to enable immune cells to recognize the cancer and restore the cancer-immunity cycle to its intended form (Figure 2.6 – 2.7).

Figure 2: Engineering a personalized neoantigen vaccine

In conclusion, this study led by Uğur Şahin and Özlem Türeci represents an important step toward much more effective and precise therapeutic cancer vaccines. Its proposed neoantigen classification will help connect neoantigen biology with the medical and clinical characteristics of cancer. It will also enable future neoantigen-based treatments to provide greater clinical benefit.