RT Journal Article
SR Electronic
T1 Community assessment of methods to deconvolve cellular composition from bulk gene expression
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.06.03.494221
DO 10.1101/2022.06.03.494221
A1 Brian S. White
A1 Aurélien de Reyniès
A1 Aaron M. Newman
A1 Joshua J. Waterfall
A1 Andrew Lamb
A1 Florent Petitprez
A1 Alberto Valdeolivas
A1 Yating Lin
A1 Haojun Li
A1 Xu Xiao
A1 Shun Wang
A1 Frank Zheng
A1 Wenxian Yang
A1 Rongshan Yu
A1 Martin E Guerrero-Gimenez
A1 Carlos A Catania
A1 Benjamin J Lang
A1 Sergii Domanskyi
A1 Thomas J Bertus
A1 Carlo Piermarocchi
A1 Gianni Monaco
A1 Francesca P Caruso
A1 Michele Ceccarelli
A1 Thomas Yu
A1 Xindi Guo
A1 John Coller
A1 Holden Maecker
A1 Caroline Duault
A1 Vida Shokoohi
A1 Shailja Patel
A1 Joanna E Liliental
A1 Stockard Simon
A1 Tumor Deconvolution DREAM Challenge consortium
A1 Julio Saez-Rodriguez
A1 Laura M. Heiser
A1 Justin Guinney
A1 Andrew J. Gentles
YR 2022
UL http://biorxiv.org/content/early/2022/06/07/2022.06.03.494221.abstract
AB Deconvolution methods infer levels of immune and stromal infiltration from bulk expression of tumor samples. These methods allow projection of characteristics of the tumor microenvironment, known to affect patient outcome and therapeutic response, onto the millions of bulk transcriptional profiles in public databases, many focused on uniquely valuable and clinically-annotated cohorts. Despite the wide development of such methods, a standardized dataset with ground truth to evaluate their performance has been lacking. We generated and sequenced in vitro and in silico admixtures of tumor, immune, and stromal cells and used them as ground truth in a community-wide DREAM Challenge that provided an objective, unbiased assessment of six widely-used published deconvolution methods and of 22 new analytical approaches developed by international teams. Our results demonstrate that existing methods predict many cell types well, while team-contributed methods highlight the potential to resolve functional states of T cells that were either not covered by published reference signatures or estimated poorly by some published methods. Our assessment and the open-source implementations of top-performing methods will allow researchers to apply the deconvolution approach most appropriate to querying their cell type of interest. Further, our publicly-available admixed and purified expression profiles will be a valuable resource to those developing deconvolution methods, including in non-malignant settings involving immune cells.Competing Interest StatementThe authors have declared no competing interest.