Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progressionMore about Open Access at the Crick
Authors listShaolong 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 Wang
Toggle all authors (40)
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.
Journal Nature Biotechnology