Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
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 (2022)
2022
Nature Biotechnology
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202208206571
https://urn.fi/URN:NBN:fi:tuni-202208206571
Kuvaus
Peer reviewed
Tiivistelmä
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.
Kokoelmat
- TUNICRIS-julkaisut [24199]