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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution
Song Feng1, Anna Calinawan2, Pietro Pugliese3
1Pacific Northwest National Laboratory, Seattle, WA, USA.
Abstract:
Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.
Insights
Decomprolute is a new computational framework for comparing tumor deconvolution algorithms using proteogenomic data. This open-source platform enhances the analysis of cell types within solid tumors, improving rare cell identification.
Area of Science:
- Computational Biology and Bioinformatics
- Proteogenomics
- Cancer Research
Background:
- Tumor deconvolution identifies cell types in solid tumors, but current methods primarily focus on gene expression (e.g., RNA sequencing) rather than protein levels.
- Protein levels offer a more accurate representation of rare cell types compared to gene expression data.
- A need exists for standardized tools to evaluate deconvolution algorithms across diverse, multiomic datasets.
Purpose of the Study:
- To introduce Decomprolute, a Common Workflow Language (CWL) framework for comparing tumor deconvolution algorithms.
- To facilitate the development, use, and reproducibility of multiomic deconvolution algorithms.
- To enable benchmarking of algorithms using large-scale proteogenomic datasets.
Main Methods:
- Developed Decomprolute as an open-source, containerized CWL framework.
- Integrated large-scale multiomic datasets from the Clinical Proteomic Tumor Analysis Consortium (CPTAC).
- Included matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types.
Main Results:
- Established a fully open-source, containerized platform for benchmarking proteogenomic tumor deconvolution algorithms.
- Enabled comparison of deconvolution algorithms across multiomic datasets, leveraging CPTAC data.
- Facilitated reproducible analysis of tumor cellularity using both gene expression and protein abundance.
Conclusions:
- Decomprolute provides a robust and reproducible platform for advancing multiomic tumor deconvolution.
- The framework supports the development and validation of algorithms that utilize protein-level data for improved cell type identification.
- This resource promotes standardized benchmarking in cancer proteogenomics research.
