A New Tumor Classification to Potentially Predict Patient Survival


 
 
 
 
 
 
 
 
 
 
 
  
 


Researchers of Stanford University published this week on Nature Communications a new subtyping of cancer types based on a multi-omic approach. As stated by the authors in the manuscript, "Efforts to distinguish (cancer) subtypes are complicated by the many kinds of genomic changes that contribute to cancer. (...) For example, a copy number change may be relevant only if it causes a gene expression change; gene expression data ignores point mutations that alter the function of the gene product; and point mutations in two different genes may have the same downstream effect, which may become apparent only when also considering methylation or gene expression.". The systematic subtype analysis was carried out across 32 cancer types (TCGA database) using a new algorithm able of incorporating complete genomes and scaling to many data types, termed Cancer Integration via Multikernel LeaRning (CIMLR). Four data types were considered for the analysis: point mutations, copy number alterations, promoter CpG methylation, and gene expression. The subtypes were validated on lower-gliomas, a well-studied example of genomic subtyping, and showed significant differences in patient survival in 23 cancers types. Here, we report some examples of subtypes associated to lower patient survival:

• In hepatocellular carcinomas, lower patient survival was associated to TP53 point mutations, losses on 13q (RB1) and 17p (MAR2K4, TP53), high expression MYC and E2F targets, as well as high expression of genes involved in mTORC1 signaling, mitotic spindle and PI3K activity.
• In lung adenocarcinomas, lower patient survival was associated to TP53 point mutations, loss of 19p (MAP2K7, STK11), high expression of RNA methyltrasnferase NSUNC2, high expression MYC and E2F targets, as well as high expression of genes involved in mTORC1 signaling and mitotic spindle.
• In head and neck squamous cell carcinomas, lower patient survival was associated to high genomic instability and TP53 point mutations.
• In clear cell renal carcinoma, lower patient survival was associated to loss on chromosome 14, including WDR20, or to VHL point mutations, high hypoxia pathway activity, low expression of SETD2, and high expression of DDX11.
• In cutaneous melanoma, lower patient survival was associated to low point mutation burden, high expression of BTB9, CDYL and TFAP2A, and high methylation at 100 promoters.

We help our customers in choosing the proper in vitro models for thei projects by providing the molecular features of our cell lines. A proper selection of cell lines in drug discovery is a fundamental step to correlate in vitro to in vivo efficacy and to focus on most aggressive diseases.


Reference
Ramazzotti et al., Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival. Nat Commun. 2018; 9: 4453. Published online 2018 Oct 26. doi:  [10.1038/s41467-018-06921-8].

Link to the open access paper
​