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Frontiers in Biology

Front. Biol.    2016, Vol. 11 Issue (5) : 366-375     DOI: 10.1007/s11515-016-1422-2
Finding neoepitopes in mouse models of personalized cancer immunotherapy
Sahar Al Seesi1,2,Alok Das Mohapatra1,Arpita Pawashe1,Ion I. Mandoiu2,Fei Duan1()
1. Department of Immunology and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut Cancer Center, Farmington, CT 06030, USA
2. Department of Computer Science & Engineering, University of Connecticut, Storrs, CT 06269, USA
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BACKGROUND: Cancer immunotherapy uses one’s own immune system to fight cancerous cells. As immune system is hard-wired to distinguish self and non-self, cancer immunotherapy is predicted to target cancerous cells specifically, therefore is less toxic than chemotherapy and radiation therapy, two major treatments for cancer. Cancer immunologists have spent decades to search for the specific targets in cancerous cells.

METHODS: Due to the recent advances in high throughput sequencing and bioinformatics, evidence has merged that the neoantigens in cancerous cells are probably the cancer-specific targets that lead to the destruction of cancer. We will review the transplantable murine tumor models for cancer immunotherapy and the bioinformatics tools used to navigate mouse genome to identify tumor-rejecting neoantigens.

RESULTS: Several groups have independently identified point mutations that can be recognized by T cells of host immune system. It is consistent with the note that the formation of peptide-MHC I-TCR complex is critical to activate T cells. Both anchor residue and TCR-facing residue mutations have been reported. While TCR-facing residue mutations may directly activate specific T cells, anchor residue mutations improve the binding of peptides to MHC I molecules, which increases the presentation of peptides and the T cell activation indirectly.

CONCLUSIONS: Our work indicates that the affinity of neoepitopes for MHC I is not a predictor for anti-tumor immune responses in mice. Instead differential agretopic index (DAI), the numerical difference of epitope-MHC I affinities between the mutated and un-mutated sequences is a significant predictor. A similar bioinformatics pipeline has been developed to generate personalized vaccines to treat human ovarian cancer in a Phase I clinical trial.

Keywords cancer immunotherapy      tumor antigens      neoantigens      neoepitopes      differential agretopic index (DAI)      RNA-Seq      single nucleotide variant (SNV)     
Corresponding Authors: Fei Duan   
Online First Date: 17 October 2016    Issue Date: 04 November 2016
 Cite this article:   
Sahar Al Seesi,Alok Das Mohapatra,Arpita Pawashe, et al. Finding neoepitopes in mouse models of personalized cancer immunotherapy[J]. Front. Biol., 2016, 11(5): 366-375.
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Sahar Al Seesi
Alok Das Mohapatra
Arpita Pawashe
Ion I. Mandoiu
Fei Duan
Advantages of transplantable murine tumor model Disadvantages of transplantable murine tumor models
Transplantable mouse tumor models allow rapid and reliable tumor growth. Rapid growth of tumor might prevent normal interaction between tumor cells and immune system.
They can be efficiently used to evaluate in both prophylactic and therapeutic models (in terms of altered tumor growth). Tissue damage associated with injection and death of tumor cells might induce inflammation and alter immune response to tumors.
Induced expression of model antigens (such as ovalbumin) or viral gene products in tumor cell lines permit better understanding of anti-tumor immune response. The intrinsic immunogenicity and tumor microenvironment of transplantable tumor cell lines shows wide variation from poorly immunogenic (e.g. B16 melanomas), moderately immunogenic (EL4 T lymphomas) and immunogenic (methylcholanthrene induced fibrosarcomas).
Models for various cancer types (e.g. melanoma, lymphoma, sarcoma, colon carcinoma etc.) are available. They serve as a weaker model of natural tumor microenvironment. (This limitation can be overcome to some extent by orthotropic injections).
Tab.1  The advantages and disadvantages of transplantable murine tumor models
Fig.1  Read quality control. Raw sequencing reads are examined for low quality bases and low quality reads. Low quality bases at the reads’ ends are trimmed. Reads containing a large percentage of low quality bases are discarded. Duplicate reads are also discarded. Duplicate reads are the results of sequencing protocol bias and may lead to false positive calls.
Fig.2  Mapping decision tree. This figure illustrates the effect of different factors on choosing a read aligner and setting the mapping parameters. Factors include the sequencing technology and platform, as well as the question being studied.
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