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

Shaolong CaoJennifer R WangShuangxi JiPeng YangYaoyi DaiShuai GuoMatthew D MontierthJohn Paul ShenXiao ZhaoJingxiao ChenJaewon James LeePaola A GuerreroNicholas SpetsierisNikolai EngedalSinja TaavitsainenKaixian YuJulie LivingstoneVinayak BhandariShawna Marie HubertNajat C DawP Andrew FutrealEleni EfstathiouBora LimAndrea VialeJianjun ZhangMatti NykterBogdan A CzerniakPowel H BrownCharles SwantonPavlos MsaouelAnirban MaitraEdmund S KopetzPeter J CampbellTerence P SpeedPaul C BoutrosHongtu ZhuAlfonso UrbanucciJonas DemeulemeesterPeter Van LooWenyi Wang
Published in: Nature biotechnology (2022)
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.
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