ProteoMixture: A Cell Type Deconvolution Tool for Bulk Tissue Proteomics Data

Teng, P.-n.; Schaaf, J. P.; Abulez, T.; Hood, B. L.; Wilson, K. N.; Litzi, T. J.; Conrads, K. A.; Hunt, A. L.; Olowu, V.; Oliver, J.; Park, F. S.; Edwards, M.; Chiang, A.; Raj-Kumar, P.-K.; Tarney, C. M.; Darcy, K. M.; Phippen, N. T.; Maxwell, G. L.; Conrads, T. P.; Bateman, N. W.

doi:10.1101/2023.07.27.550810

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bioRxiv · 10.1101/2023.07.27.550810

ProteoMixture: A Cell Type Deconvolution Tool for Bulk Tissue Proteomics Data

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Abstract

Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms, ESTIMATE and ConsensusTME (r [≥] 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomics data are available at (https://lmdomics.org/ProteoMixture/.

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Teng, P.-n., Schaaf, J. P., Abulez, T., Hood, B. L., Wilson, K. N., Litzi, T. J., Conrads, K. A., Hunt, A. L., Olowu, V., Oliver, J., Park, F. S., Edwards, M., Chiang, A., Raj-Kumar, P.-K., Tarney, C. M., Darcy, K. M., Phippen, N. T., Maxwell, G. L., Conrads, T. P., Bateman, N. W.. 2023-07-29. ProteoMixture: A Cell Type Deconvolution Tool for Bulk Tissue Proteomics Data. https://doi.org/10.1101/2023.07.27.550810

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