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. 2014 Aug 3;3(8):e954893.
doi: 10.4161/21624011.2014.954893. eCollection 2014.

A catalog of HLA type, HLA expression, and neo-epitope candidates in human cancer cell lines

Sebastian Boegel  1 , Martin Löwer  2 , Thomas Bukur  1 , Ugur Sahin  3 , John C Castle  2
Affiliations

A catalog of HLA type, HLA expression, and neo-epitope candidates in human cancer cell lines

Sebastian Boegel et al. Oncoimmunology. .

Abstract

Cancer cell lines are a tremendous resource for cancer biology and therapy development. These multipurpose tools are commonly used to examine the genetic origin of cancers, to identify potential novel tumor targets, such as tumor antigens for vaccine devel-opment, and utilized to screen potential therapies in preclinical studies. Mutations, gene expression, and drug sensitivity have been determined for many cell lines using next-generation sequencing (NGS). However, the human leukocyte antigen (HLA) type and HLA expression of tumor cell lines, characterizations necessary for the development of cancer vaccines, have remained largely incomplete and, such information, when available, has been distributed in many publications. Here, we determine the 4-digit HLA type and HLA expression of 167 cancer and 10 non-cancer cell lines from publically available RNA-Seq data. We use standard NGS RNA-Seq short reads from "whole transcriptome" sequencing, map reads to known HLA types, and statistically determine HLA type, heterozygosity, and expression. First, we present previously unreported HLA Class I and II genotypes. Second, we determine HLA expression levels in each cancer cell line, providing insights into HLA downregulation and loss in cancer. Third, using these results, we provide a fundamental cell line "barcode" to track samples and prevent sample annotation swaps and contamination. Fourth, we integrate the cancer cell-line specific HLA types and HLA expression with available cell-line specific mutation information and existing HLA binding prediction algorithms to make a catalog of predicted antigenic mutations in each cell line. The compilation of our results are a fundamental resource for all researchers selecting specific cancer cell lines based on the HLA type and HLA expression, as well as for the development of immunotherapeutic tools for novel cancer treatment modalities VSports手机版. .

Keywords: BRENDA, BRaunschweig ENzyme Database; CCLE, Cancer Cell Line Encyclopedia; COSMIC, Catalog of Somatic Mutations in Cancer; DLBCL, diffuse large B-cell lymphoma; HLA expression; HLA type; HLA, Human Leukocyte Antigen; IEDB, Immune Epitope Database; NGS, Next Generation Sequencing; RNA-Seq; RNA-Seq, RNA Sequencing; RPKM, reads per kilobase of exon model per million mapped reads; SNV, single nucleotide variation; SRA, Sequence Read Archive; cancer cell lines; immunotherapy; neoepitopes; nsSNV, non synonymous SNV; somatic mutations. V体育安卓版.

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Figures

Figure 1.
Figure 1.
Data integration and computational workflow. Cancer cell line RNA-Seq samples were retrieved from NCBI Sequence Read Archive (SRA) (A), which are input into our bioinformatics software seq2HLA to determine the 4-digit HLA expression (B) and type (C). The cell-line specific HLA types (C) and cell-line specific non-synonymous somatic mutations (D) from mutation repositories, such as Broad-Novartis Cancer Cell Line Encyclopedia (CCLE), were processed with the Immune Epitope Database (IEDB) consensus HLA presentation algorithm to predict high-affinity HLA-presented (antigenic) mutations. The list of predicted HLA-binding mutation epitopes is output (E), containing the HLA allele to which the neo-epitope is predicted to bind and the predicted IC50 value in nanomolar (nM).
Figure 3.
Figure 3.
HLA expression profiles of 167 cancer cell lines grouped according to the tissue/disease of origin. The 167 cancer cell lines analyzed in the study are grouped according to their cancer type and each point represents the HLA expression level in one distinct cell line and in cases of replicate RNA-Seq datasets (for 45 cell lines), a point represents the mean expression value of the respective cell line. (red) and SEM (gray) is plotted for HLA Class I (A) and HLA Class II (B). HLA Class I expression is defined as the sum of individual reads for each HLA-A, HLA-B and HLA-C and HLA Class II expression is defined as the sum of individual reads for HLA-DQA1, HLA-DQB1, HLA-DRB1.RPKM, reads per kilobase of exon model per million mapped reads.
Figure 2.
Figure 2.
HLA expression levels of replicate cell line RNA-Seq samples. For 45 cancer cell lines, multiple RNA-Seq datasets were available, often from different laboratories. Each point represents HLA expression of one RNA-Seq sample, retrieved from public databases. There is an overall good agreement of HLA Class I (A) and HLA Class II (B) expression levels between those replicate samples. HLA Class I expression is defined as sum of individual reads for each HLA-A,HLA-B and HLA-C and HLA Class II expression is defined as the sum of individual reads for HLA-DQA1, HLA-DQB1, HLA-DRB1. The mean (red) and SEM (gray) are plotted for each cell line with replicate RNA-Seq reads for HLA Class I (A) and HLA Class II (B).
Figure 4.
Figure 4.
Comparison of HLA expression profiles of cancer cell lines versus primary samples. (A) Analyses from seq2HLA of 13 Burkitt lymphoma cell lines (green) and 28 primary samples (blue) showing comparable HLA Class I and Class II locus specific expression profiles (SRA: SRP009316). Shown are the means (red) and SEM (gray). (B) The glioblastoma cell lines U-251 MG (2 samples), U-87MG (5 replicates) and the neuroblastoma cell line SK-N-SH (2 replicates) - shown in red – HLA Class I expression levels (red; the sum of HLA-A,HLA-B and HLA-C expression) compared to wild-type primary brain samples (blue; SRA:SRR332171 and Illumina body map project with SRA ID ERR030882, one replicate each).
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