Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression.
journal contribution
posted on 2022-11-10, 11:14 authored by Shaolong Cao, Jennifer R Wang, Shuangxi Ji, Peng Yang, Yaoyi Dai, Shuai Guo, Matthew D Montierth, John Paul Shen, Xiao Zhao, Jingxiao Chen, Jaewon James Lee, Paola A Guerrero, Nicholas Spetsieris, Nikolai Engedal, Sinja Taavitsainen, Kaixian Yu, Julie Livingstone, Vinayak Bhandari, Shawna M Hubert, Najat C Daw, P Andrew Futreal, Eleni Efstathiou, Bora Lim, Andrea Viale, Jianjun Zhang, Matti Nykter, Bogdan A Czerniak, Powel H Brown, Charles Swanton, Pavlos Msaouel, Anirban Maitra, Scott Kopetz, Peter Campbell, Terence P Speed, Paul C Boutros, Hongtu Zhu, Alfonso Urbanucci, Jonas Demeulemeester, Peter Van Loo, Wenyi WangSingle-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.
