- Cao, Shaolong;
- Wang, Jennifer R;
- Ji, Shuangxi;
- Yang, Peng;
- Dai, Yaoyi;
- Guo, Shuai;
- Montierth, Matthew D;
- Shen, John Paul;
- Zhao, Xiao;
- Chen, Jingxiao;
- Lee, Jaewon James;
- Guerrero, Paola A;
- Spetsieris, Nicholas;
- Engedal, Nikolai;
- Taavitsainen, Sinja;
- Yu, Kaixian;
- Livingstone, Julie;
- Bhandari, Vinayak;
- Hubert, Shawna M;
- Daw, Najat C;
- Futreal, P Andrew;
- Efstathiou, Eleni;
- Lim, Bora;
- Viale, Andrea;
- Zhang, Jianjun;
- Nykter, Matti;
- Czerniak, Bogdan A;
- Brown, Powel H;
- Swanton, Charles;
- Msaouel, Pavlos;
- Maitra, Anirban;
- Kopetz, Scott;
- Campbell, Peter;
- Speed, Terence P;
- Boutros, Paul C;
- Zhu, Hongtu;
- Urbanucci, Alfonso;
- Demeulemeester, Jonas;
- Van Loo, Peter;
- Wang, Wenyi
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.