Open Peer Review reports
Expression patterns of small numbers of transcripts from functionally-related pathways predict survival in multiple cancers
© The Author(s). 2019
- Published: 12 July 2019
Genetic profiling of cancers for variations in copy number, structure or expression of certain genes has improved diagnosis, risk-stratification and therapeutic decision-making. However the tumor-restricted nature of these changes limits their application to certain cancer types or sub-types. Tests with broader prognostic capabilities are lacking.
Using RNAseq data from 10,227 tumors in The Cancer Genome Atlas (TCGA), we evaluated 212 protein-coding transcripts from 12 cancer-related pathways. We employed t-distributed stochastic neighbor embedding (t-SNE) to identify expression pattern difference among each pathway’s transcripts. We have previously used t-SNE to show that survival in some cancers correlates with expression patterns of transcripts encoding ribosomal proteins and enzymes for cholesterol biosynthesis and fatty acid oxidation.
Using the above 212 transcripts, t-SNE-assisted transcript pattern profiling identified patient cohorts with significant survival differences in 30 of 34 different cancer types comprising 9350 tumors (91.4% of all TCGA cases). Small subsets of each pathway’s transcripts, comprising no more than 50–60 from the original group, played particularly prominent roles in determining overall t-SNE patterns. In several cases, further refinements in long-term survival could be achieved by sequential t-SNE profiling with two pathways’ transcripts, by a combination of t-SNE plus whole transcriptome profiling or by employing t-SNE on immuno-histochemically defined breast cancer subtypes. In two cancer types, individuals with Stage IV disease at presentation could be readily subdivided into groups with highly significant survival differences based on t-SNE-based tumor sub-classification.
t-SNE-assisted profiling of a small number of transcripts allows the prediction of long-term survival across multiple cancer types.
- Cancer metabolism
- PI3 kinase
- Transcriptional profiling