ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

Pang-Ning Teng; Joshua P Schaaf; Tamara Abulez; Brian L Hood; Katlin N Wilson; Tracy J Litzi; David Mitchell; Kelly A Conrads; Allison L Hunt; Victoria Olowu; Julie Oliver; Fred S Park; Marshé Edwards; AiChun Chiang; Matthew D Wilkerson; Praveen-Kumar Raj-Kumar; Christopher M Tarney; Kathleen M Darcy; Neil T Phippen; G Larry Maxwell; Thomas P Conrads; Nicholas W Bateman

doi:10.1016/j.isci.2024.109198

ProteoMixture: A cell type deconvolution tool for bulk tissue proteomic data

iScience. 2024 Feb 12;27(3):109198. doi: 10.1016/j.isci.2024.109198. eCollection 2024 Mar 15.

Authors

Pang-Ning Teng^{1

2}, Joshua P Schaaf^{1

2}, Tamara Abulez^{1

2}, Brian L Hood^{1

2}, Katlin N Wilson^{1

2}, Tracy J Litzi^{1

2}, David Mitchell^{1

2}, Kelly A Conrads^{1

2}, Allison L Hunt^{1

3}, Victoria Olowu^{1

2}, Julie Oliver^{1

2}, Fred S Park^{1

2}, Marshé Edwards^{1

2}, AiChun Chiang^{1

2}, Matthew D Wilkerson⁴, Praveen-Kumar Raj-Kumar⁵, Christopher M Tarney^{1

6}, Kathleen M Darcy^{1

2

6}, Neil T Phippen^{1

6}, G Larry Maxwell^{1

3

6}, Thomas P Conrads^{1

3

6}, Nicholas W Bateman^{1

2

6}

Affiliations

¹ Gynecologic Cancer Center of Excellence and the Women's Health Integrated Research Center, Annandale, VA 22003, USA.
² The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD 20817, USA.
³ Women's Health Integrated Research Center, Women's Service Line, Inova Health System, Falls Church, VA 22042, USA.
⁴ Center for Military Precision Health, Department of Anatomy, Physiology and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA.
⁵ Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA 15963, USA.
⁶ The John P. Murtha Cancer Center, Department of Surgery, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD 20889, USA.

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 (r ≥ 0.63) in two commonly used deconvolution algorithms, ESTIMATE or Consensus^TME. We further developed and optimized protein-based signatures estimating cell admixture proportions and benchmarked these using bulk tumor proteomic data from over 150 patients with HGSOC. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

Keywords: Computational bioinformatics; Proteomics; Transcriptomics.