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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Neoantigen prediction from genomic and transcriptomic data.

Sebastiano Battaglia1

  • 1Center For Immunotherapy, Department of Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States.

Methods in Enzymology
|March 4, 2020
PubMed
Summary

Identifying tumor-specific neoantigens is crucial for cancer immunotherapy. This study provides a technical overview of bioinformatics methods to accurately detect these crucial neoantigens from clinical tumor samples.

Keywords:
BioinformaticsCancerImmunotherapyMutationsNeoantigens

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Area of Science:

  • Oncology
  • Immunology
  • Bioinformatics

Background:

  • Tumor cells possess unique genetic and protein signatures absent in normal tissues.
  • These tumor-specific alterations, particularly neoantigens, offer targeted therapeutic opportunities.
  • Neoantigens are short peptides derived from mutated sequences, recognized by T cells to elicit anti-tumor responses.

Purpose of the Study:

  • To provide a technical overview of bioinformatics methods for neoantigen identification.
  • To guide researchers in profiling the neoantigenic load of clinical tumor samples.
  • To suggest reliable tools and methods for downstream in vitro and in vivo applications.

Main Methods:

  • Genomic data analysis of tumor samples.
  • Bioinformatic pipeline for neoantigen prediction.
  • Utilizing computational tools for identifying mutated sequences and their immunogenicity.

Main Results:

  • Detailed technical overview of neoantigen identification strategies.
  • Guidance on selecting appropriate bioinformatics tools for neoantigen discovery.
  • Emphasis on achieving high confidence in neoantigen identification for therapeutic development.

Conclusions:

  • Accurate neoantigen identification is key for developing effective cancer immunotherapies.
  • Bioinformatics approaches are essential for navigating the complexity of neoantigen discovery.
  • This work facilitates reliable neoantigen identification from clinical specimens for translational research.