CanProVar: a human cancer proteome variation database

Li, Jing; Duncan, Dexter T.; Zhang, Bing

doi:10.1002/humu.21176

Cited by 70 publications

(76 citation statements)

References 74 publications

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“…The CanProVar database comprises 8570 cancer-related variants in 2921 proteins derived from six different sources and 41 541 noncancer specific variants in 30 322 proteins derived from the dbSNP database. 11 Subsequently, this group developed an integrated bioinformatics workflow to detect variant peptides, and 204 variant peptides, including 5 peptides known for cancer-related mutations, were identified from three colorectal tumor specimens. 12 A more comprehensive variant-associated database was constructed by Song et al, where the humsavar database and CanProVar database were integrated into the UniProtKB/Swiss-Prot canonical protein database and 282 unique SAAVs sites were quantified in human liver tissues.…”

Section: Introductionmentioning

confidence: 99%

Single Amino Acid Variant Profiles of Subpopulations in the MCF-7 Breast Cancer Cell Line

et al. 2017

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Cancers are initiated and developed from a small population of stem-like cells termed cancer stem cells (CSCs). There is heterogeneity among this CSC population that leads to multiple subpopulations with their own distinct biological features and protein expression. The protein expression and function may be impacted by amino acid variants that can occur largely due to single nucleotide changes. We have thus performed proteomic analysis of breast CSC subpopulations by mass spectrometry to study the presence of single amino acid variants (SAAVs) and their relation to breast cancer. We have used CSC markers to isolate pure breast CSC subpopulation fractions (ALDH+ and CD44+/CD24− cell populations) and the mature luminal cells (CD49f−EpCAM+) from the MCF-7 breast cancer cell line. By searching the Swiss-CanSAAVs database, 374 unique SAAVs were identified in total, where 27 are cancer-related SAAVs. 135 unique SAAVs were found in the CSC population compared with the mature luminal cells. The distribution of SAAVs detected in MCF-7 cells was compared with those predicted from the Swiss-CanSAAVs database, where we found distinct differences in the numbers of SAAVs detected relative to that expected from the Swiss-CanSAAVs database for several of the amino acids.

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Section: Introductionmentioning

confidence: 99%

Single Amino Acid Variant Profiles of Subpopulations in the MCF-7 Breast Cancer Cell Line

et al. 2017

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“…The current study is based on 60 vaccine candidates, 26 reported from the analysis of NGS data from CCLE database [16] and remaining 34 candidates from CanProVar [17] based on their association with cancer. The 26 genes (vaccine candidates) were selected from CCLE as they frequently mutate in different types of cell lines (see Methods section).…”

Section: Resultsmentioning

confidence: 99%

A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer

et al. 2016

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Due to advancement in sequencing technology, genomes of thousands of cancer tissues or cell-lines have been sequenced. Identification of cancer-specific epitopes or neoepitopes from cancer genomes is one of the major challenges in the field of immunotherapy or vaccine development. This paper describes a platform Cancertope, developed for designing genome-based immunotherapy or vaccine against a cancer cell. Broadly, the integrated resources on this platform are apportioned into three precise sections. First section explains a cancer-specific database of neoepitopes generated from genome of 905 cancer cell lines. This database harbors wide range of epitopes (e.g., B-cell, CD8+ T-cell, HLA class I, HLA class II) against 60 cancer-specific vaccine antigens. Second section describes a partially personalized module developed for predicting potential neoepitopes against a user-specific cancer genome. Finally, we describe a fully personalized module developed for identification of neoepitopes from genomes of cancerous and healthy cells of a cancer-patient. In order to assist the scientific community, wide range of tools are incorporated in this platform that includes screening of epitopes against human reference proteome (http://www.imtech.res.in/raghava/cancertope/).

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“…Variations and Protein Reference Database-Protein variation data were downloaded from the CanProVar database on 9/26/2009, which included 41,541 nonsynonymous SNPs in 30,322 proteins and 8570 cancer-related variations in 2921 proteins (23). A corresponding normal protein database was downloaded from Ensembl (human, v53) at ftp://ftp.ensembl.org/pub/current_fasta/homo_sapiens/pep/.…”

Section: Methodsmentioning

confidence: 99%

“…The variation-containing protein sequence database was created based on the Ensembl protein database (human, v53) and the CanProVar database (23). Missense variations, nonsense variations and single amino acid deletions and insertions were included in the database.…”

Section: Setup Of the Workflow-as Illustrated Inmentioning

confidence: 99%

A Bioinformatics Workflow for Variant Peptide Detection in Shotgun Proteomics

et al. 2011

Molecular & Cellular Proteomics

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133

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Shotgun proteomics data analysis usually relies on database search. However, commonly used protein sequence databases do not contain information on protein variants and thus prevent variant peptides and proteins from been identified. Including known coding variations into protein sequence databases could help alleviate this problem. Based on our recently published human Cancer Proteome Variation Database, we have created a protein sequence database that comprehensively annotates thousands of cancer-related coding variants collected in the Cancer Proteome Variation Database as well as noncancer-specific ones from the Single Nucleotide Polymorphism Database (dbSNP). Using this database, we then developed a data analysis workflow for variant peptide identification in shotgun proteomics. The high risk of false positive variant identifications was addressed by a modified false discovery rate estimation method. Analysis of colorectal cancer cell lines SW480, RKO, and HCT-116 revealed a total of 81 peptides that contain either noncancer-specific or cancer-related variations. Twenty-three out of 26 variants randomly selected from the 81 were confirmed by genomic sequencing. We further applied the workflow on data sets from three individual colorectal tumor specimens. A total of 204 distinct variant peptides were detected, and five carried known cancer-related mutations. Each individual showed a specific pattern of cancer-related mutations, suggesting potential use of this type of information for personalized medicine. Compatibility of the workflow has been tested with four popular database search engines including Sequest, Mascot, X!Tandem, and MyriMatch. In summary, we have developed a workflow that effectively uses existing genomic data to enable variant peptide detection in proteomics. DNA sequence variation is associated with diseases and differential drug response. As a paradigmatic example, cancers are diseases of clonal proliferations caused by mutations in oncogenes and tumor suppressor genes (1). After several decades of searching through traditional biology approaches, many mutant genes have been causally implicated in oncogenesis (2). Facilitated by the new genomic techniques such as SNP (single nucleotide polymorphism) arrays and deepsequencing, the identification of cancer genes has made enormous progress over the past several years (3-7). The genomic abnormalities of cancer are expressed through aberrant proteins and proteomes and their altered functions. Although proteins reflecting the genomic changes in cancer have the potential to become clinically meaningful biomarkers, their discovery and validation has proven to be challenging. As a result, few biomarker candidates have translated into clinical use.Over the past decade, mass spectrometry (MS)-based shotgun proteomics has emerged as a high-throughput, unbiased method for the identification of proteins in complex samples (8,9). Its application to tumor specimens holds great potential in identifying mutant proteins in human cancers. However, because sh...

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CanProVar: a human cancer proteome variation database

Cited by 70 publications

References 74 publications

Single Amino Acid Variant Profiles of Subpopulations in the MCF-7 Breast Cancer Cell Line

Single Amino Acid Variant Profiles of Subpopulations in the MCF-7 Breast Cancer Cell Line

A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer

A Bioinformatics Workflow for Variant Peptide Detection in Shotgun Proteomics

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