Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation

Abstract

Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.

Keywords: MHC class I; MHC class II; neoantigen prediction; neoantigen prioritization; neoantigens (neoAgs).

Figure 1

Overview of neoantigen prediction, prioritization, and validation. Neoantigen prediction relies on sample acquisition, high quality sequencing data, variant calling, and variant annotation. Neoantigen prioritization requires predicting some combination of the potential for the neoantigen to be expressed, processed, bound by MHC, and recognized by the T cell receptor (TCR). The development of neoantigen prioritization models relies on the availability of validated datasets of neoantigen immunogenicity. Figure created with BioRender.com.

Figure 2

Sample collection and sequencing considerations. Here we describe considerations for obtaining sequencing data for neoantigen prediction including tissues needed, tissue collection method, and sequencing types. Figure created with BioRender.com.

Figure 3

Types of mutations that can lead to neoantigens. Single nucleotide variants (SNVs) caused by a point mutation in a single nucleic acid. Insertions and deletions (indels) caused by addition of nucleic acids or loss of nucleic acids. Indels with a frameshift occur when the number of nucleic acids is not a multiple of three, changing the reading frame. Gene fusions can be caused by either translocations at the DNA level or RNA splicing of independent transcripts. Figure created with BioRender.com.

Figure 4

Steps of MHC class I-restricted neoantigen prioritization and summary of characteristics considered for each step. Mutations in the DNA of a tumor cell are transcribed into RNA and translated into a protein. At the end of the life cycle of the protein, the protein is broken down into peptides by the proteasome and transported into the endoplasmic reticulum by the transporter associated with antigen presentation (TAP). Once inside the endoplasmic reticulum, the peptide has the opportunity to be loaded on MHC class I. If the peptide is successfully bound to MHC class I, the peptide:MHC complex is transported to the cell surface where the peptide:MHC complex has the opportunity to be recognized by the T cell receptor (TCR). Characteristics of the neoantigen encompassing expression, processing, MHC class I binding, and TCR recognition potential have been assessed to enhance prioritization of MHC class I-restricted neoantigens and are summarized in each of the boxes in the figure.

Figure 5

Steps of MHC class II-restricted neoantigen prioritization and summary of characteristics considered for each step. Mutations in the DNA of a tumor cell are transcribed into RNA and translated into a protein. The protein can either be taken up into the endocytic compartment of an antigen presenting cell or processed and presented by the tumor cell if the tumor cell expresses MHC class II (not pictured). In the late endosomes, protein cleavage and MHC class II loading occurs. The protein is cleaved by cathepsins at the N- and C-termini before and after binding to the MHC class II molecule. If the peptide is successfully bound to MHC class II, the peptide:MHC complex is transported to the cell surface where the peptide: MHC complex has the opportunity to be recognized by the T cell receptor (TCR). Characteristics of the neoantigen encompassing expression, processing, MHC class II binding, and TCR recognition potential that may enhance prioritization of MHC class II-restricted neoantigens are summarized in each of the boxes in the figure. * indicates characteristics that, to our knowledge, have not been assessed for the prioritization of MHC class II-restricted neoantigens.

Figure 6

Summary of three commonly applied validation techniques for the immunogenicity of MHC class I or II-restricted neoantigens. Mass spectrometry is performed by eluting peptides directly from tumor cells and validates the in vivo presentation of the neoantigen on the cell surface. MHC multimers (most commonly a tetramer) bind T cell receptors (TCR) specific for the particular neoantigen: MHC, validating TCR recognition of the neoantigen and expansion of neoantigen-specific T cells. ELISA, ELISpot, and intracellular cytokine staining detect the production of cytokines, typically interferon-gamma (IFNγ), interleukin-2 (IL-2), or tumor necrosis factor alpha (TNFα), to validate T cell activation. Figure created with BioRender.com.