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

Boegel S, Löwer M, Bukur T, Sahin U, Castle JC - Oncoimmunology (2014)

Bottom Line: First, we present previously unreported HLA Class I and II genotypes.Third, using these results, we provide a fundamental cell line "barcode" to track samples and prevent sample annotation swaps and contamination.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.

View Article: PubMed Central - PubMed

Affiliation: TRON gGmbH - Translational Oncology at Johannes Gutenberg-University Medical Center gGmbH ; Langenbeckstr; Mainz, Germany ; University Medical Center of the Johannes Gutenberg-University Mainz ; Mainz, Germany.

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.

No MeSH data available.


Related in: MedlinePlus

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).
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f0001: 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).

Mentions: Applying our pipeline (Fig. 1) to RNA-Seq data from 167 human cancer and 10 non-cancer cell lines, we generated the largest catalog of cell line HLA types (Table 1;Table S1) compiled to date. The 177 cell lines include 62 from breast tissue, including 5 non-cancerous breast cell lines and one matched lymphocyte cell line, 34 from B cells and 24 from lung-derived cancer and normal cell lines (Fig. S1). As an exemplar, we found 2 RNA-Seq datasets from the breast adenocarcinoma cell line CAMA-1 and the workflow determined the HLA type of CAMA-1 as A*02:01, A*32:01, B*15:01, B*40:02, C*02:02, and C*03:03 (Fig. 1).Table 1.


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

Boegel S, Löwer M, Bukur T, Sahin U, Castle JC - Oncoimmunology (2014)

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).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4355981&req=5

f0001: 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).
Mentions: Applying our pipeline (Fig. 1) to RNA-Seq data from 167 human cancer and 10 non-cancer cell lines, we generated the largest catalog of cell line HLA types (Table 1;Table S1) compiled to date. The 177 cell lines include 62 from breast tissue, including 5 non-cancerous breast cell lines and one matched lymphocyte cell line, 34 from B cells and 24 from lung-derived cancer and normal cell lines (Fig. S1). As an exemplar, we found 2 RNA-Seq datasets from the breast adenocarcinoma cell line CAMA-1 and the workflow determined the HLA type of CAMA-1 as A*02:01, A*32:01, B*15:01, B*40:02, C*02:02, and C*03:03 (Fig. 1).Table 1.

Bottom Line: First, we present previously unreported HLA Class I and II genotypes.Third, using these results, we provide a fundamental cell line "barcode" to track samples and prevent sample annotation swaps and contamination.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.

View Article: PubMed Central - PubMed

Affiliation: TRON gGmbH - Translational Oncology at Johannes Gutenberg-University Medical Center gGmbH ; Langenbeckstr; Mainz, Germany ; University Medical Center of the Johannes Gutenberg-University Mainz ; Mainz, Germany.

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.

No MeSH data available.


Related in: MedlinePlus