Challenges targeting cancer neoantigens in 2021: a systematic literature review

Area of Science:

• Oncology
• Immunology
• Bioinformatics

Background:

• Cancer neoantigens are crucial targets for cancer immunotherapy.
• Developing effective cancer neoantigen vaccines requires precise identification and prioritization of these targets.
• Early clinical trials reveal significant hurdles in translating neoantigen vaccine strategies into successful therapies.

Purpose of the Study:

• To review current strategies for identifying and prioritizing cancer neoantigens.
• To emphasize common neoantigen identification methods: peptide-MHC elution and next-generation sequencing with prediction algorithms.
• To discuss limitations in neoantigen prediction and broader challenges in vaccine development.

Main Methods:

• Review of existing literature on cancer neoantigen identification and prioritization.
• Analysis of two primary neoantigen identification strategies: peptide-MHC complex elution and next-generation sequencing (NGS) with computational prediction.
• Discussion of challenges including tumor heterogeneity and the immunosuppressive tumor microenvironment.

Main Results:

• Two main strategies for neoantigen identification are peptide-MHC elution and NGS-based prediction.
• Current neoantigen prediction pipelines have notable limitations.
• Tumor purity, heterogeneity, and the immunosuppressive tumor microenvironment pose significant challenges for cancer neoantigen vaccines.

Conclusions:

• Neoantigen prediction is expected to improve significantly with advancements in sequencing, machine learning, and data sharing.
• Overcoming current limitations is key to successful clinical translation of neoantigen cancer vaccines.
• Enhanced neoantigen prediction strategies hold substantial promise for advancing cancer vaccine design and efficacy.