Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
- Author(s)
- Cao, S; Wang, JR; Ji, S; Yang, P; Dai, Y; Guo, S; Montierth, MD; Shen, JP; Zhao, X; Chen, J; Lee, JJ; Guerrero, PA; Spetsieris, N; Engedal, N; Taavitsainen, S; Yu, K; Livingstone, J; Bhandari, V; Hubert, SM; Daw, NC; Futreal, PA; Efstathiou, E; Lim, B; Viale, A; Zhang, J; Nykter, M; Czerniak, BA; Brown, PH; Swanton, C; Msaouel, P; Maitra, A; Kopetz, S; Campbell, P; Speed, TP; Boutros, PC; Zhu, H; Urbanucci, A; Demeulemeester, J; Van Loo, P; Wang, W;
- Journal Title
- Nature Biotechnology
- Publication Type
- epub ahead of print
- Abstract
- Single-cell RNA sequencing studies have suggested that total mRNA content correlates with tumor phenotypes. Technical and analytical challenges, however, have so far impeded at-scale pan-cancer examination of total mRNA content. Here we present a method to quantify tumor-specific total mRNA expression (TmS) from bulk sequencing data, taking into account tumor transcript proportion, purity and ploidy, which are estimated through transcriptomic/genomic deconvolution. We estimate and validate TmS in 6,590 patient tumors across 15 cancer types, identifying significant inter-tumor variability. Across cancers, high TmS is associated with increased risk of disease progression and death. TmS is influenced by cancer-specific patterns of gene alteration and intra-tumor genetic heterogeneity as well as by pan-cancer trends in metabolic dysregulation. Taken together, our results indicate that measuring cell-type-specific total mRNA expression in tumor cells predicts tumor phenotypes and clinical outcomes.
- Publisher
- NPG
- Research Division(s)
- Bioinformatics
- PubMed ID
- 35697807
- Publisher's Version
- https://doi.org/10.1038/s41587-022-01342-x
- Open Access at Publisher's Site
https://doi.org/10.1038/s41587-022-01342-x- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2022-06-17 09:28:42
Last Modified: 2022-06-17 09:43:41