← Back to Publications

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

Cao, S., Wang, J.R., Ji, S., Yang, P., Dai, Y., Guo, S., Montierth, M.D., Shen, J.P., Zhao, X., Chen, J., Lee, J.J., Guerrero, P.A., Spetsieris, N., Engedal, N., Taavitsainen, S., Yu, K., Livingstone, J., Bhandari, V., Hubert, S.M., Daw, N.C., Futreal, P.A., Efstathiou, E., Lim, B., Viale, A., Zhang, J., Nykter, M., Czerniak, B.A., Brown, P.H., Swanton, C., Msaouel, P., Maitra, A., Kopetz, S., Campbell, P., Speed, T.P., Boutros, P.C., Zhu, H., Urbanucci, A., Demeulemeester, J., Van Loo, P., Wang, W., “Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression.” Nature Biotechnology 2022. doi: 10.1038/s41587-022-01342-xEpub 2022 Jun 13. PMID: 35697807; PMCID: PMC9646498

Published

June 2022

Doi

10.1038/s41587-022-01342-x

Abstract

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.

Citation

@article{cao2022estimation,
  title={Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression},
  author={Cao, Shaolong and Wang, Jennifer R and Ji, Shuangxi and Yang, Peng and Dai, Yaoyi and Guo, Shuai and Montierth, Matthew D and Shen, John Paul and Zhao, Xiao and Chen, Jingxiao and others},
  journal={Nature biotechnology},
  volume={40},
  number={11},
  pages={1624--1633},
  year={2022},
  publisher={Nature Publishing Group US New York}
}