Dataset Information

Proteogenomics of malignant melanoma cell lines: searching for genetically encoded point mutations in LC-MS/MS data

ABSTRACT: In this study we performed proteogenomic analysis for 9 cell lines of malignant melanoma. The main objectives of the study were identifying the variants originating from point mutations and analyzing the effect of exome data filtering on the outcome of variant identification.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Permanent Cell Line Cell

DISEASE(S): Melanoma

SUBMITTER: Irina Ilina

LAB HEAD: Sergei A. Moshkovskii

PROVIDER: PXD007662 | Pride | 2018-12-05

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	1_tsv_raw_sprot_decoy.fasta	Fasta
	1lev0_sprot_decoy.fasta	Fasta
	1lev1_sprot_decoy.fasta	Fasta
	1lev2_sprot_decoy.fasta	Fasta
	1lev3_sprot_decoy.fasta	Fasta

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Publications

Proteogenomics of Malignant Melanoma Cell Lines: The Effect of Stringency of Exome Data Filtering on Variant Peptide Identification in Shotgun Proteomics.

Lobas Anna A AA Pyatnitskiy Mikhail A MA Chernobrovkin Alexey L AL Ilina Irina Y IY Karpov Dmitry S DS Solovyeva Elizaveta M EM Kuznetsova Ksenia G KG Ivanov Mark V MV Lyssuk Elena Y EY Kliuchnikova Anna A AA Voronko Olga E OE Larin Sergey S SS Zubarev Roman A RA Gorshkov Mikhail V MV Moshkovskii Sergei A SA

Journal of proteome research 20180416 5

The identification of genetically encoded variants at the proteome level is an important problem in cancer proteogenomics. The generation of customized protein databases from DNA or RNA sequencing data is a crucial stage of the identification workflow. Genomic data filtering applied at this stage may significantly modify variant search results, yet its effect is generally left out of the scope of proteogenomic studies. In this work, we focused on this impact using data of exome sequencing and LC ...[more]

PMID: 29619825

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Project description:Background: Renal cell carcinoma (RCC) is characterized by a number of diverse molecular aberrations that differ among individuals. Recent approaches to molecularly classify RCC were based on clinical, pathological as well as on single molecular parameters. As a consequence, gene expression patterns reflecting the sum of genetic aberrations in individual tumors may not have been recognized. In an attempt to uncover such molecular features in RCC, we used a novel, unbiased and integrative approach. Methods: We integrated gene expression data from 97 primary RCCs of different pathologic parameters, 15 RCC metastases as well as 34 cancer cell lines for two-way nonsupervised hierarchical clustering using gene groups suggested by the PANTHER Classification System. We depicted the genomic landscape of the resulted tumor groups by means of Single Nuclear Polymorphism (SNP) technology. Finally, the achieved results were immunohistochemically analyzed using a tissue microarray (TMA) composed of 254 RCC. Results: We found robust, genome wide expression signatures, which split RCC into three distinct molecular subgroups. These groups remained stable even if randomly selected gene sets were clustered. Notably, the pattern obtained from RCC cell lines was clearly distinguishable from that of primary tumors. SNP array analysis demonstrated differing frequencies of chromosomal copy number alterations among RCC subgroups. TMA analysis with group-specific markers showed a prognostic significance of the different groups. Conclusion: We propose the existence of characteristic and histologically independent genome-wide expression outputs in RCC with potential biological and clinical relevance. Expression profiling by array, combined data analysis with genomic profiling data. Genomic DNA from renal cell was hybridized to renal cell carcinoma samples and matched normal kidney tissue biopsies, using the Affymetrix GenomewideSNP_6 platform. CEL files were processed using R, Bioconductor and software from the aroma.affymetrix project. Visualized Copy number profiles are accessible through the Progenetix site (www.progenetix.net). CN,raw.csv and segments.csv: Probes are mapped by their position in genome build 36 / HG18. Probes are ordered according to their linear position on the Golden Path.