News from Kitos
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:
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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