In silico pipeline to identify tumor-specific antigens associated with frequent HLA-I alleles in the Costa Rican Central Valley Population

doi:10.21203/rs.3.rs-1609267/v1

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In silico pipeline to identify tumor-specific antigens associated with frequent HLA-I alleles in the Costa Rican Central Valley Population

https://doi.org/10.21203/rs.3.rs-1609267/v1

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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 tumor-specific 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 their affinity with frequent alleles of HLA type I in 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.

Bioinformatics

Computational Biology

Cancer Biology

Immunology

Neopeptide

Colorectal cancer

Cancer vaccine

Single nucleotide variants

HLA

Costa Rica

Colorectal cancer is a complex disease in which uncontrolled growth of abnormal cells occurs in the large intestine (colon or rectum). By year, colorectal cancer worldwide reached an incidence of > 1.8 million cases and > 8 thousand deaths, occupying the third and second cause of cancer incidence and deaths respectively (World Health Organization, 2020). Cancer vaccines and others immunotherapies have emerged as an alternative to treat this type of cancer. As summarized in Fig. 1, there is evidence that immune system cells, such as CD8 + cytotoxic lymphocytes, can recognize tumor-specific peptides or protein fragments derived from gene mutations or variants in malignant cells. In the immune response, these neopeptides are presented by cells (cancer or normal) to CD8 + cells. The antigen presentation occurs with the participation of the human major histocompatibility complex in tumor (and normal) cells defined by specific HLA alleles, and the TCR (T-cell receptor) in CD8 + cells. If the antigen is tumor-specific, the death of tumor cells is induced (Palucka & Coussens, 2016).

With the advance in high-throughput DNA sequencing technologies, cancer vaccines and tumor/ immune system interaction cells have been extensively studied (Ding, Wendl, McMichael, & Raphael, 2014; Hackl, Charoentong, Finotello, & Trajanoski, 2016; Spencer, Zhang, & Pfeifer, 2014). Cancer vaccine studies must consider HLA alleles (“gene versions”) that are frequent in the target population, the HLA type (type I or HLA-I for interaction with CD8 + cells), the peptide size (between 8–11 amino acids for HLA-I) and others (X. S. Liu & Mardis, 2017). However, the data complexity and the specific steps do not allow the application of universal protocols. Benchmark strategies are recommended for specific cases and populations (Heather & Chain, 2016; Rizzo & Buck, 2012; Su et al., 2011). Thus, this work aimed to implement a bioinformatic protocol of an in silico cancer vaccine to identify tumor-specific peptides in colorectal cancer considering the context of frequent HLA-I alleles from the Costa Rican Central Valley Population.

General workflow of the analysis is shown in Fig. 2.

Sequence data of tumor samples and pre-processing

Raw data from chromosome 1 of 7 exomes from human colorectal cancer (4 primary tumor and 3 from liver metastasis) and normal mucosa samples (all from the same patient) were downloaded from NCBI Bioproject PRJNA271316 CHET9 experiment (sequenced by Ilumina® technology, access: www.ncbi.nlm.nih.gov/bioproject/PRJNA271316). The exome reads were downloaded in Fastq format for subsequent analyzes. Quality control was done using FastQC, and trimming of reads was done with Trimmomatic, including filtering by quality and adapters removal (Blankenberg et al., 2010).

Mapping to human genome and Variant Calling analysis

To map reads to the human reference genome (version hg19), the BWA program (Li & Durbin, 2009) was used with default parameters. Samtools (Li et al., 2009) was used for extra data manipulation as usual. Picard (http://broadinstitute.github.io/picard/) was used to evaluate the quality of the mapping. VarScan2 program (Koboldt et al., 2012)was used to detect SNVs with quality Q higher than 20 in all samples; other parameters were used by default in the deduplication and re-calibration steps. To select mutations present in all the tumor samples, VCFlib (https://github.com/vcflib/vcflib) was used. Subsequently, the SNPeff program (Cingolani, Platts, et al., 2012) was used to evaluate and compare the variants between samples. SnpSift (Cingolani, Patel, et al., 2012) was used to filter all those SNVs that were in the normal mucosa sample to finally obtain the tumor exclusive variants. Finally, these mutations were annotated with ANNOVAR (K. Wang, Li, & Hakonarson, 2010), obtaining the description of the SNVs and their possible effects on genes. The Atlas repository of colorectal cancer was used to see the involvement of the genes with selected SNVs and the possible metabolic or signaling pathways affected (Chisanga et al., 2016).

Putative tumor-specific peptides (neoantigens) identification

Selected tumor-specific variants were analyzed with MuPeXi package (Mette, Morten, & Hadrup, 2017), to generate the peptides (8–11 amino acid residues) to the different alleles of HLA type I and the possible priorities of related mutant peptides. We chose those HLA type I alleles with frequencies greater than 8% for the Costa Rican Central Valley population (Arrieta-Bolaños et al., 2011), using Allele Frequency Net Database (http://www.allelefrequencies.net/default.asp). Due to HLA data had a resolution of two-digits, HLA supertypes (four-digits) were considered.

Mutant peptides were analyzed with the NetMHCpan program (Nielsen & Andreatta, 2016), to establish the affinity of these peptides to the respective alleles of HLA type I utilizing the rank percentile (% rank). The 0.5% threshold was used for strong binders. Finally, the characterization of peptides was done using the Protparam tool of the ExPasy server (Gasteiger et al., 2005), which provides physicochemical properties.

Table 1

**Strong binding peptides for most frequent HLA alleles of the Costa Rican population.** Candidates are derived from the selected SNVs in the primary and metastatic tumor samples (pI: Isoelectric point, GRAVY: Grand average of hydropathy).
Alleles	Peptides	pI	Acidic nature	GRAVY	Lenght (AA)
A*02:01	KLVGFSLDV	5.84	acidic	1.133	9
	LLIHCDAYL	5.80	acidic	1.356	9
	YLHSPMYFFI	6.74	neutral	0.76	10
	ILLIHCDAYL	5.08	acidic	1.67	10
A*03:01	AMNILEINEK	4.53	acidic	-0.14	10
A*03:01	GSSFYALEK	6.00	neutral	-0.256	9
A*24:02	AYLHSPMYFF	6.78	neutral	0.49	10
	AYLHSPMYFFI	6.78	neutral	0.855	11
	CDAYLHSPMYF	5.08	acidic	0.1	11
	DAYLHSPMYF	5.08	acidic	-0.14	10
	DAYLHSPMYFF	5.08	acidic	0.127	11
	RWYSTPSSYL	8.59	basic	-0.89	10
	YLHSPMYFF	6.74	neutral	0.344	9
B*07:02	GPLSSEKAAM	6.00	neutral	-0.17	10
B*35:01	DAYLHSPMY	5.08	acidic	-0.467	9
B*40:01	AEINILEI	3.80	acidic	1.075	8
	EEKLVGFSL	4.53	acidic	0.278	9
	LEEKLVGFSL	4.53	acidic	0.63	10
	RELTHLREKL	8.75	basic	-1.24	10
	SEKAAMNIL	5.52	acidic	0.233	9
B*44:02	AEINILEI	3.80	acidic	1.075	8
C*03:02	KAAMNILEI	6.00	neutral	0.822	9
C*04:01	AYLHSPMYF	6.78	neutral	0.233	9
	RWYSTPSSYL	8.59	basic	-0.89	10
	WYSTPSSYL	5.52	acidic	-0.489	9
	YLHSPMYFF	6.74	neutral	0.344	9
C*06:02	WYSTPSSYL	5.52	acidic	-0.489	9
C*07:01	WYSTPSSYL	5.52	acidic	-0.489	9

Identification of neoantigens or tumor-specific antigens was described at the end of the last century (Emens et al., 2017). DNA sequencing technologies and bioinformatic protocols have triggered the understanding of the interactions of tumor cells with the immune system, including the design of cancer vaccines. This strategy is not suitable for all cancer types due to a relatively high mutation rate is expected to generate new antigens. In colorectal cancer, melanoma, and other tumor types, a high number of neoantigens is associated with patient response to immune therapies, making them suitable for cancer vaccines.

In this context, to detect possible variants with the potential to induce tumor-specific peptides in samples of colorectal tumors, a bioinformatic protocol was implemented with samples of human colorectal cancer for chromosome 1. We identified 395 SNVs shared by all tumor samples (and absent in the normal mucosa sample). See the Supplementary file for the whole list of SNVs.

Despite this, only 28 non-silent SNPs in exonic regions were identified (see Supplementary file for details). They were selected to predict neoantigens. These variants were distributed in 26 genes, most of them related to cell cycle control. Interestingly, multiple of these genes, including NOTCH, OBSCN, HSPG2, and NBPF, have been demonstrated to be expressed in colorectal cancer (Andries et al., 2015; Liao et al., 2018; Shriver et al., 2015). Further analyses are required to assess the role of these specific genes in the development of the disease and the immune response, including studies of driving genes (important for the tumor survival), transcriptomic/proteomic profiling, epigenetic mechanisms, or other genomic variants.

Regarding the SNVs identification, we used VarScan2 to call variants (mutations). This software has been shown to have a high sensitivity (81–100%) in the variant calling analysis, in particular with cancer data (Spencer, Tyagi, et al., 2014; Xu, 2018). In addition, the use of a normal tissue sample to filter variants has been advised by several studies to reduce false positives (such as germline mutations) (Bae, Kim, & Kang, 2016; Y. Wang et al., 2018), as we did here. Confirmation strategies are required to validate identified variants (for example using Sanger technology to re-sequence genomic regions). Also, other approaches to call variants can be used to assess other DNA data, to deal with complexity, other sequencing technologies, and biological variability (Z. K. Liu, Shang, Chen, & Bian, 2017).

On the other hand, for HLA of the Costa Rican Central Valley population, information only had a two-digits resolution. Thus, we used the HLA supertypes (A*01, A*02, A*03, A*24 and B*07) or the representative allele of the group to predict interactions (four-digits resolution as required). The length of tumor-specific peptides was considered between 8–11 amino acids, which is the known peptide size that can be presented by HLA-I. It has been suggested that simultaneous testing of peptides between 8 and 11 amino acid residues is advisable in peptide prediction for putative cancer vaccines, due to the "core" or the common sequence (8 amino acids) may have greater versatility in different fragments (Luo et al., 2015). This allowed us to test several possible peptides of the same variant and to improve the search for possible neoantigens.

Considering the above, 23 candidate peptides were found to have affinity against the most prevalent Costa Rican Central Valley HLA I alleles (Table 1). Some peptides were identified to be presented by different alleles of the HLA, such as AYLHSPMYF (two different loci HLA A*2402 and HLA * C0401), however, most peptides are predicted to be presented in only one allele.

Regarding the number of peptides and haplotypes, the HLA-A locus was the most significant, contributing to most of the high-affinity peptides for the antigenic presentation (Fig. 3 and Table 1). For example, up to nine peptides can be presented in the case of haplotype A*2402/B*3501, in which the first allele is responsible for the presentation of nine peptides.

We also analyzed the physicochemical nature of the peptides (Table 1). Many of them are acidic peptides and, based on the hydrophobicity index, most of them have a slight hydrophobicity. Some “core” peptides are found in several peptides, such as AYLHSPMY which is present as part of seven candidate peptides of different HLA alleles (HLA-A*24:02, B*35:01, and C*04:01).

In the context of the selection of peptides, the algorithms use metrics such as IC50 value (concentration necessary to displace 50% of the classical peptide bound to an HLA allele) (Giannakis et al., 2016; Karasaki et al., 2017). However, it has been shown that it can yield a greater number of false positives in the detection of neopeptides, since some alleles can bind a significant percentage of random peptides (Nielsen & Andreatta, 2016). This motivated the present investigation to use only peptides with strong affinities (rank < 0.5%).

Other biologic factors should be considered to identify possible neoantigens and not included in our study. For example, protein degradation is possible at the endoplasmic reticulum (Fleri et al., 2017). In addition, peptides affinity must be validated using in vitro assays with antigen-presenting cells of the Costa Rican population, for example, the Enzyme-linked Immunospot Assay (ELISPOT) (Kato et al., 2018).

In summary, our approach was able to find 23 candidate neoantigens in all samples of colorectal cancer to eventually create a vaccine. The contribution of this study is the development of the first bioinformatic protocol to detect tumor-specific antigens in the context of the HLA alleles of the Costa Rican population. We hope that this study establishes a useful basis for future studies with therapeutic uses for cancer vaccines against colorectal tumors or other cancer types.

Author contributions

D.M.F. and J.A.M.M. participated in the conception and design of the study. J.A.M.M. implemented and standardized the bioinformatics pipelines. D.M.F. processed all data using the pipelines. D.M.F., J.A.M.M., and J.M. participated in the interpretation of the results. D.M.F. drafted the manuscript. All authors reviewed and approved the final manuscript.

Declaration of Competing Interest

The authors declare that there is no conflict of interest.

Material availability

Processed data is found in the Supplementary material.

Funding

This work was funded by Vicerrectoría de Investigación – Universidad de Costa Rica, with the Project “C1163 proNGS 2.0: Protocolos operativos estandarizados de análisis de datos moleculares obtenidos por NGS o afines y de algoritmos de inteligencia artificial en modelos biológicos (2021-2023)”.

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In silico pipeline to identify tumor-specific antigens associated with frequent HLA-I alleles in the Costa Rican Central Valley Population

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Abstract

Figures

Introduction

methods

Sequence data of tumor samples and pre-processing

Mapping to human genome and Variant Calling analysis

Putative tumor-specific peptides (neoantigens) identification

Results And Discussion

Conclusion

Declarations

References

Supplementary Files

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