Colorectal cancer vaccines: in silico identification of tumor-specific antigens associated with frequent HLA-I alleles in the costa rican Central Valley population

Main Article Content

Abstract

Colorectal cancer is a complex disease in which uncontrolled growth of abnormal cells occurs in the large intestine (colon or rectum). The study of tumor-specific antigens (neoantigens), molecules that interact with the immune system, has been extensively explored as a possible therapy called in silico cancer vaccine. Cancer vaccine studies have been triggered by the current high-throughput DNA sequencing technologies. However, there is no universal bioinformatic protocol to study tumor-antigens with DNA sequencing data.


We propose a bioinformatic protocol to detect tumor-specific antigens associated with single nucleotide variants (SNVs) or “mutations” in colorectal cancer and their interaction with frequent HLA alleles (complex that present antigens to immune cells) in the Costa Rican Central Valley population. We used public data of human exome (DNA regions that produce functional products, including proteins). A variant calling analysis was implemented to detect tumorspecific SNVs, in comparison to healthy tissue. We then predicted and analyzed the peptides (protein fragments, the tumor specific antigens) derived from these variants, in the context of its affinity with frequent alleles of HLA type I of the Costa Rican population.


We found 28 non-silent SNVs, present in 26 genes. The protocol yielded 23 strong binders peptides derived from the SNVs for frequent alleles (greater than 8%) for the Costa Rican population at the HLA-A, B and C loci. It is concluded that the standardized protocol was able to identify neoantigens and this can be considered a first step for the eventual design of a colorectal cancer vaccine for Costa Rican patients. To our knowledge, this is the first study of an in silico cancer vaccine using DNA sequencing data in the context of the Costa Rican HLA alleles.

Article Details

How to Cite
Diego, & José Arturo. (2022). Colorectal cancer vaccines: in silico identification of tumor-specific antigens associated with frequent HLA-I alleles in the costa rican Central Valley population. Tecnología En Marcha Journal, 35(8), Pág. 83–92. https://doi.org/10.18845/tm.v35i8.6458
Section
Artículo científico

References

World Health Organization, Colorectal Cancer Statistics: 2020. Available from: https://gco.iarc.fr/today/data/factsheets/cancers/10_8_9-Colorectum-fact-sheet.pdf

Palucka AK, Coussens LM. The Basis of Oncoimmunology. Cell [Internet]. 2016;164(6):1233–47. Available from: http://dx.doi.org/10.1016/j.cell.2016.01.049

Hackl H, Charoentong P, Finotello F, Trajanoski Z. Computational genomics tools for dissecting tumour–immune cell interactions. Nat Rev Genet. 2016;17:441–58.

Spencer DH, Zhang B, Pfeifer J. Single Nucleotide Variant Detection Using Next Generation Sequencing. In: Kulkarni, S.; Pfeifer J, editor. Clinical Genomics [Internet]. Elsevier Inc.; 2014. p. 109–27. Available from: http:// dx.doi.org/10.1016/B978-0-12-404748-8.00008-3

Ding L, Wendl MC, McMichael JF, Raphael BJ. Expanding the computational toolbox for mining cancer genomes. Nat Rev Genet [Internet]. 2014;15(8):556–70. Available from: http://dx.doi.org/10.1038/nrg3767 [6] Liu XS, Mardis ER. Applications of Immunogenomics to Cancer. Cell. 2017;168(4):600–12.

Su Z, Ning B, Fang H, Hong H, Perkins R, Shi L. Next-generation sequencing and its application in molecular diagnostics. Expert Rev Mol Diagn. 2011;11(3):1–16.

Heather JM, Chain B. The sequence of sequencers: The history of sequencing DNA. Genomics [Internet]. 2016;107(1):1–8. Available from: http://dx.doi.org/10.1016/j.ygeno.2015.11.003

Rizzo JM, Buck MJ. Key principles and clinical applications of "next-generation"; DNA sequencing. Cancer Prev Res (Phila) [Internet]. 2012 Jul 1 [cited 2018 Apr 25];5(7):887–900. Available from: http://www. ncbi.nlm.nih.gov/pubmed/22617168

Blankenberg D, Gordon A, Von Kuster G, Coraor N, Taylor J, Nekrutenko A, et al. Manipulation of FASTQ data with galaxy. Bioinformatics. 2010;26(14):1783–5.

Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(15):1754–60.

Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.

Koboldt DC, Zhang Q, Larson DE, Shen D, Mclellan MD, Lin L, et al. VarScan 2 : Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 2012;22:568–76.

Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly (Austin). 2012;6(2):80–92.

Cingolani P, Patel VM, Coon M, Nguyen T, Land SJ, Ruden DM. Using Drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program , SnpSift. Front Genet. 2012;3(315):1–9.

Wang K, Li M, Hakonarson H. ANNOVAR : functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):1–7.

Chisanga D, Keerthikumar S, Pathan M, Ariyaratne D, Kalra H, Boukouris S, et al. Colorectal cancer atlas : An integrative resource for genomic and proteomic annotations from colorectal cancer cell lines and tissues. Nucleic Acids Res. 2016;44(D1):969–74.

Mette A, Morten B, Hadrup SR. MuPeXI : prediction of neo - epitopes from tumor sequencing data. Cancer Immunol Immunother. 2017;66(9):1123–30.

Arrieta-Bolaños E, Maldonado-Torres H, Dimitriu O, Hoddinott MA, Fowles F, Shah A, et al. HLA-A, -B, -C, -DQB1, and -DRB1,3,4,5 allele and haplotype frequencies in the Costa Rica Central Valley Population and its relationship to worldwide populations. Hum Immunol. 2011;72(1):80–6.

Nielsen M, Andreatta M. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med [Internet]. 2016;8(1):1–9. Available from: http://dx.doi.org/10.1186/s13073-016-0288-x

Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al. Protein Identification and Analysis Tools on the ExPASy Server. In: Wlaker JM, editor. The Proteomics Protocols Handbook. Human Press; 2005. p. 571–608.

Emens LA, Ascierto PA, Darcy PK, Demaria S, Eggermont AMM, Redmond WL, et al. Cancer immunotherapy: Opportunities and challenges in the rapidly evolving clinical landscape [Internet]. Vol. 81, European Journal of Cancer. 2017 [cited 2018 May 4]. p. 116–29. Available from: https://ac.els-cdn.com/S0959804917309188/1s2.0-S0959804917309188-main.pdf?_tid=852cff99-60b9-4e00-9a8c-eca592b4c0be&acdnat=1525459824_1 7fe72594087a4b1c451941055acd504

Andries V, Vandepoele K, Staes K, Berx G, Bogaert P, Isterdael G, et al. NBPF1, a tumor suppressor candidate in neuroblastoma, exerts growth inhibitory effects by inducing a G1 cell cycle arrest. BMC Cancer. 2015;15(1):1–25.

Shriver M, Stroka KM, Vitolo MI, Martin S, DL Huso DL, Konstantopoulos K, et al. Loss of giant obscurins from breast epithelium promotes epithelial-to-mesenchymal transition, tumorigenicity and metastasis. Oncogene. 2015;34(32):4248–59.

Liao W, Li G, You Y, Wan H, Wu Q, Wang C, et al. Antitumor activity of Notch-1 inhibition in human colorectal carcinoma cells. Oncol Rep. 2018;39(3):1063–71.

Spencer DH, Tyagi M, Vallania F, Bredemeyer AJ, Pfeifer JD, Mitra RD, et al. Performance of common analysis methods for detecting low-frequency single nucleotide variants in targeted next-generation sequence data. J Mol Diagnostics [Internet]. 2014;16(1):75–88. Available from: http://dx.doi.org/10.1016/j.jmoldx.2013.09.003

Xu C. A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data. Comput Struct Biotechnol J [Internet]. 2018;16:15–24. Available from: https://doi.org/10.1016/j.csbj.2018.01.003

Bae JM, Kim JH, Kang GH. Molecular subtypes of colorectal cancer and their clinicopathologic features, with an emphasis on the serrated neoplasia pathway. Arch Pathol Lab Med. 2016;140(5):406–12.

Wang Y, Liping GUO, Feng L, Zhang W, Xiao T, Xuebing DI, et al. Single nucleotide variant profiles of viable single circulating tumour cells reveal CTC behaviours in breast cancer. Oncol Rep. 2018;39(5):2147–59.

Liu ZK, Shang YK, Chen ZN, Bian H. A three-caller pipeline for variant analysis of cancer whole-exome sequencing data. Mol Med Rep. 2017;15(5):2489–94.

Luo H, Ye H, Ng HW, Shi L, Tong W, Mattes W, et al. Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis. BMC Bioinformatics [Internet]. 2015 [cited 2018

May 16];16(13). Available from: http://www.biomedcentral.com/1471-2105/16/S13/S9

Giannakis M, Mu XJ, Shukla SA, Qian ZR, Cohen O, Nishihara R, et al. Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma. Cell Rep. 2016;15(4):857–65.

Karasaki T, Nagayama K, Kuwano H, Nitadori J ichi, Sato M, Anraku M, et al. An Immunogram for the Cancer-Immunity Cycle: Towards Personalized Immunotherapy of Lung Cancer. J Thorac Oncol [Internet]. 2017;12(5):791–803. Available from: http://dx.doi.org/10.1016/j.jtho.2017.01.005

Fleri W, Paul S, Dhanda SK, Mahajan S, Xu X, Peters B, et al. The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design. Front Immunol. 2017;8(278):1–16.

Kato T, Matsuda T, Ikeda Y, Park J-H, Leisegang M, Yoshimura S, et al. Effective screening of T cells recognizing neoantigens and construction of T-cell receptor-engineered T cells. Oncotarget. 2018;9(13):11009–19.