Cancer transcriptomes vary greatly. Single-cell RNA sequencing shows that total mRNA content correlates with tumor phenotypes. Technical and analytical challenges, however, impede at-scale pan-cancer examination of total mRNA content. We propose to quantify tumor-specific total mRNA expression (TmS) from bulk sequencing data, taking into account tumor transcript proportion, purity and ploidy, which are estimable through transcriptomic/genomic deconvolution. We estimate and validate TmS in 6,590 patients across 15 cancer types identifying significant inter-tumor variability. Across cancers, high TmS is associated with increased risk of disease progression and death. Cancer-specific patterns of gene alterations, intra-tumor genetic heterogeneity, as well as pan-cancer trends in metabolic dysregulation contribute to TmS. Taken together, our results suggest that measuring cell type-specific total mRNA expression offers an unique perspective on cancer transcriptomes, with biological and clinical implications [1].
We used DeMixT v1.2.2 to calculate the TmS values provided in our current study [1]. The DeMixT source files are compatible with Windows, Linux and MacOS. For further information about DeMixT, please visit https://github.com/wwylab/DeMixT.
DeMixT
git clone https://github.com/wwylab/DeMixT.git
cd DeMixT
R CMD INSTALL DeMixT_1.2.2.tar.gzCheck if DeMixT is installed successfully:
# load package
library(DeMixT)OpenMP
DeMixT requires OpenMP to enable the parallel computing. We provide a brief instruction for installing OpenMP. Please check the file https://github.com/wwylab/DeMixT/raw/master/HowtoinstallOpenMP.docx.
Please visit the TmS resource page for tutorial and data.
For questions or support related to the inquiries of TmS, please contact Dr. Wenyi Wang (wwang7@mdanderson.org).