Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression

Author:

Cao Shaolong,Wang Jennifer R.,Ji Shuangxi,Yang PengORCID,Dai Yaoyi,Guo Shuai,Montierth Matthew D.ORCID,Shen John Paul,Zhao Xiao,Chen Jingxiao,Lee Jaewon James,Guerrero Paola A.,Spetsieris Nicholas,Engedal NikolaiORCID,Taavitsainen SinjaORCID,Yu Kaixian,Livingstone JulieORCID,Bhandari VinayakORCID,Hubert Shawna M.ORCID,Daw Najat C.ORCID,Futreal P. AndrewORCID,Efstathiou Eleni,Lim BoraORCID,Viale Andrea,Zhang JianjunORCID,Nykter MattiORCID,Czerniak Bogdan A.,Brown Powel H.ORCID,Swanton CharlesORCID,Msaouel Pavlos,Maitra AnirbanORCID,Kopetz ScottORCID,Campbell PeterORCID,Speed Terence P.ORCID,Boutros Paul C.ORCID,Zhu HongtuORCID,Urbanucci Alfonso,Demeulemeester Jonas,Van Loo PeterORCID,Wang WenyiORCID

Abstract

AbstractSingle-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

Springer Science and Business Media LLC

Subject

Biomedical Engineering,Molecular Medicine,Applied Microbiology and Biotechnology,Bioengineering,Biotechnology

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