Quantifying Cellular Admixture in Proteomic Analyses of Ovarian Tumor, Stroma and Immune Cell Populations

Purpose Funded in part by Defense Health Program to the Uniformed Services University for the Gynecologic Cancer Center of Excellence HU0001-16-2-0006 and HU0001-16-2-00014 (N.W.B., T.P.C. and G.L.M.). Disclaimer: The view(s) expressed herein also do not reflect the official views of the Department of Defense, the Uniformed Services University, the Henry M Jackson Foundation for the Advancement of Military Medicine, Inc. or the Inova Health System. T.P.C. is a ThermoFisher Scientific Inc. SAB member and receives funding from Abbvie Inc. References: 1xCell (PMID: 29141660), 2ESTIMATE (PMID: 24113773), 3PMID: 34278265, 4LMD images adapted from leica-microsystems.com. • To assess performance of current transcript-based cell type prediction tools using both protein and transcript data generated from in vitro models of tumor, stroma and immune-related cells, as well as high grade serous ovarian cancer (HGSOC) tissues. • To identify and optimize protein and transcript-based signatures supporting cell admixture prediction from bulk tissue proteomic data. Quantifying Cellular Admixture in Proteomic Analyses of Ovarian Tumor, Stroma and Immune Cell Populations Pang-ning Teng1,2, Sasha Makohon-Moore1,2, Kelly A. Conrads1,2, Brian L. Hood1,2, Praveen-Kumar Raj-Kumar3, Yovanni Casablanca1,4, Neil T. Phippen1,4, Kathleen M. Darcy1,2,4, G. Larry Maxwell1,4,5, Thomas P. Conrads1,4,5, Nicholas W. Bateman1,2,4 Results 1 Gynecologic Cancer Center of Excellence and Women’s Health Integrated Research Center, Annandale, VA. 2 The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 3Chan Soon-Shiong Institute of Molecular Medicine at Windber, Windber, PA. 4The John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD. 5Women’s Health Integrated Research Center, Women’s Service Line, Inova Health System, Falls Church, VA. Figu
RITM0023829
To assess performance of current transcriptbased cell type prediction tools using both protein and transcript data generated from in vitro models of tumor, stroma and immunerelated cells, as well as high grade serous ovarian cancer (HGSOC) tissues. To identify and optimize protein and transcript based signatures supporting cell admixture prediction from bulk tissue proteomic data.