MassIVE MSV000095036

Partial Public PXD053122

Analytical insights from FFPE-based proteome profiling of 1,200 pan cancer cases

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Description

Proteome profiling of formalin-fixed paraffin-embedded (FFPE) specimens has gained traction in recent years for the analysis of human clinical material, notably cancer tissue for the discovery of molecular biomarkers. However, published reports so far focused on certain cancer entities and comprised a limited number of cases. In addition, no in-depth investigation into the long-term performance of experimental workflows for FFPE proteomics has been reported yet. In study, we assessed a previously published workflow in terms of robustness and scalability by analyzing the proteomes of 1,220 tumor specimens from six cancer entities processed over the course of three years. Important analytical insights include the necessity of introducing a new normalization method that ensures equal and reproducible sample loading for LC-MS/MS analysis across cohorts, the benefit of spiking retention time standards to be able to compare samples across extended periods of time, that tumors can, on average, be profiled to a depth of >5,000 proteins within an acceptable time frame and, surprisingly, that current software is unable to process such large data sets simultaneously. Beyond these analytical insights, this study provides protein expression information on 11,000 proteins in >1,200 tumor samples from six cancer entities representing the first comprehensive pan-cancer proteome resource for FFPE material to date. The authors believe that the current study provides useful guidance for planning large-scale FFPE proteome projects and is also of immediate utility to the scientific community as the data is publicly accessible via a custom-built ShinyApp enabling a wide range of analysis. This includes e. g. the quantitative comparison of proteins of interest between patients or cohorts or the discovery of protein fingerprints that represent the tissue of origin and proteins that are enriched in certain cancer entities. [doi:10.25345/C5JQ0T61T] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: FFPE ; pan-cancer ; DLBCL ; OSCC ; PDAC ; CRC ; Glioma ; Melanoma ; FAIMS ; proteomics ; large-scale ; clinical proteomics ; cancer ; expression profiles

Contact

Principal Investigators:
(in alphabetical order) 		Bernhard Kuster, Technical University of Munich (TUM), Germany
Submitting User: stephan_eckert
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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
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