Dataset Information

A high throughput method for identifying personalized tumor-associated antigens.

ABSTRACT: Circulating autoantibodies against tumor-associated antigens (TAAs) and their pattern of glycosylation can be used as diagnostic indicators of cancer. Using random peptide library screening, we identified patient-specific sets of peptides recognized by colon cancer patients' serum IgG and IgM antibodies. We demonstrate a strategy for analyzing BLAST search results for identifying tumor-associated antigens represented by peptides that mimic sequential epitopes. Statistical analysis of the frequency with which the proteins are retrieved by BLAST homology searching and an estimation of the probability of a match by chance can identify the proteins that are the real targets of the immune response against tumors. In addition, we observed an over-expression of the mRNA for the match-producing protein only in the corresponding tumor sample, out of fourteen tumor and normal samples analyzed. This observation confirms that personalized tumor-associated antigens can be identified by BLAST homology search following random peptide library screening on cancer patient's serum antibodies.

SUBMITTER: Ionov Y

PROVIDER: S-EPMC2920534 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A high throughput method for identifying personalized tumor-associated antigens.

Ionov Yurij Y

Oncotarget 20100601 2

Circulating autoantibodies against tumor-associated antigens (TAAs) and their pattern of glycosylation can be used as diagnostic indicators of cancer. Using random peptide library screening, we identified patient-specific sets of peptides recognized by colon cancer patients' serum IgG and IgM antibodies. We demonstrate a strategy for analyzing BLAST search results for identifying tumor-associated antigens represented by peptides that mimic sequential epitopes. Statistical analysis of the frequen ...[more]

PMID: 20711419

Similar Datasets

Project description:MotivationB cells derive their antigen-specificity through the expression of Immunoglobulin (Ig) receptors on their surface. These receptors are initially generated stochastically by somatic re-arrangement of the DNA and further diversified following antigen-activation by a process of somatic hypermutation, which introduces mainly point substitutions into the receptor DNA at a high rate. Recent advances in next-generation sequencing have enabled large-scale profiling of the B cell Ig repertoire from blood and tissue samples. A key computational challenge in the analysis of these data is partitioning the sequences to identify descendants of a common B cell (i.e. a clone). Current methods group sequences using a fixed distance threshold, or a likelihood calculation that is computationally-intensive. Here, we propose a new method based on spectral clustering with an adaptive threshold to determine the local sequence neighborhood. Validation using simulated and experimental datasets demonstrates that this method has high sensitivity and specificity compared to a fixed threshold that is optimized for these measures. In addition, this method works on datasets where choosing an optimal fixed threshold is difficult and is more computationally efficient in all cases. The ability to quickly and accurately identify members of a clone from repertoire sequencing data will greatly improve downstream analyses. Clonally-related sequences cannot be treated independently in statistical models, and clonal partitions are used as the basis for the calculation of diversity metrics, lineage reconstruction and selection analysis. Thus, the spectral clustering-based method here represents an important contribution to repertoire analysis.Availability and implementationSource code for this method is freely available in the SCOPe (Spectral Clustering for clOne Partitioning) R package in the Immcantation framework: www.immcantation.org under the CC BY-SA 4.0 license.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

A high throughput method for identifying personalized tumor-associated antigens.

Publications

A high throughput method for identifying personalized tumor-associated antigens.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets