Dataset Information

Expansive Genomic Mapping of Mutant Peptides and Characterisation of Allelic Bias in Cancer Cell Lines

ABSTRACT: Investigation of non-synonymous mutation as a major driver for cancer is essential for discovery of biomarkers and therapeutics. Although, many cancer-associated mutations have been identified through genomics, there is a poor understanding of how they manifest in the proteome. In fact, detection of mutation derived peptides is confounded by technical and biological factors. In this study cancer associated mutations from the COSMIC database were integrated into proteins, to generate cell line specific sequence databases. We used a robust workflow for high-throughput processing of 375 cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE) to identify mutant peptides and generate PRIDE compatible mzTab results. This includes a peptide to genome mapping tool, PepGenome, allowing targeted mapping of mutant peptides. Examining the allelic paired non-mutant reference peptides, we were able to profile significantly more potential mutant peptides and determine allelic biases in the expression and observation of mutant peptides. We present quantification, genomic mapping, and evaluation of 1,336 cancer associated mutant peptides and infer protein allelic bias for 31,219 mutations in. We highlight the problems faced in detecting and characterising mutant peptides by mass spectrometry and show that allelic bias plays a significant role in suppressing the expression of mutant proteoforms.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Yasset Perez-Riverol

LAB HEAD: Jyoti S

PROVIDER: PXD044389 | Pride | 2025-12-17

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	CCLE.sdrf.tsv	Tabular
	CCLE_COSMIC-Mutants_Bridge_CellLines.fasta	Fasta
	CCLE_COSMIC-Mutants_Prot_01_CellLines.fasta	Fasta
	CCLE_COSMIC-Mutants_Prot_02_CellLines.fasta	Fasta
	CCLE_COSMIC-Mutants_Prot_03_CellLines.fasta	Fasta

Items per page:

1 - 5 of 1400

Similar Datasets

Project description:In-depth information regarding the DFCI OncoPanel sequencing panel has been described previously. Briefly, sequencing is performed using an Illumina HiSeq 2500 system (RRID:SCR_016383) with 2×100 paired-end reads. Samples must meet an average 50X coverage and minimum of 30X coverage for 80% of targets for analysis. For all samples, a board-certified molecular pathologist evaluates alterations and writes interpretations of the results. The three generations of OncoPanel use Mutect (RRID:SCR_000559) and GATK (RRID:SCR_001876) to interrogate possible alterations in the complete exonic DNA sequences of 275, 309, and 447 cancer-related genes, respectively, including substitutions, insertions, and deletions. Because tumor tissues are tested without a paired normal from individual patients, additional informatics steps are taken to identify common single nucleotide polymorphisms (SNPs). Any SNP present at >0.1% in Exome Variant Server, NHLBI GO Exome Sequencing Project (ESP) (RRID:SCR_012761), or present in dbSNP (RRID:SCR_002338) is filtered; however, variants also present in at least twice in COSMIC (RRID:SCR_002260) are rescued for manual review. Variants that appear two or more times in a panel of 150 normal samples sequenced in-house and are not present in COSMIC are also filtered. For copy number alteration (CNA) analysis, the workflow employs an in-house algorithm (RobustCNV) to cover exonic regions of targeted genes as well as select intronic regions. The depth of coverage of these regions is determined by counting the number of reads aligning within defined genomic intervals and then normalized against a panel of normals and corrected for GC bias. The copy number for a segmented genomic interval is calculated as a log2 ratio of the depth of coverage of this sample compared to a panel of normal (non-cancer) samples that are run on the same plate. In parallel (samples not included), additional cases were previously sequenced via the MSK-IMPACT platform. To ensure maximum overlap between the platforms, only single nucleotide variants (SNVs), dinucleotide/oligonucleotide variants (DNVs/ONVs), insertions/deletions (indels), high copy gains (amplifications), and homozygous deletions from 182 genes shared between all versions of both platforms were evaluated in our analyses involving samples from both platforms. To filter pathogenic and passenger mutations, missense, truncating (nonsense, frameshift, and splice site), and in-frame indel mutations from the 182 genes overlapping between the DFCI OncoPanel and MSK-IMPACT panels were subjected to analysis in the Cancer Genome Interpreter (CGI) suite (RRID:SCR_023752) with cancer type set to “Skin Cutaneous Melanoma (SKCM)”. Known pathogenic mutations, defined as those listed as pathogenic in ClinVar (RRID:SCR_006169), OncoKB (RRID:SCR_014782), and/or CGI databases, were included in downstream analyses. Mutations which had not been previously annotated but were predicted to be pathogenic mutations by CGI were also included. Missense SNV mutations called passengers in CGI were subjected to another round of analysis in the ChasmPlus suite with cancer type set to SKCM. Missense mutations called pathogenic via this approach (p < 0.05; FDR Q < 0.3) were salvaged and included in further analyses. All other mutations were excluded. TERT promoter mutations were evaluated via FATHMM-MKL. Mutations called pathogenic via this tool were included in downstream analyses. Clinical data for these cases is available in a supplementary file from the associated publication. OF NOTE, THE RAW SEQUENCING DATA FROM THESE SAMPLES CANNOT BE MADE PUBLICLY AVAILABLE BECAUSE THE RESEARCH PARTICIPANT CONSENT DOES NOT INCLUDE AUTHORIZATION TO SHARE IDENTIFIABLE DATA.

Dataset Information

Expansive Genomic Mapping of Mutant Peptides and Characterisation of Allelic Bias in Cancer Cell Lines

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets