In 2007, there was a crucial study from the Arkansas group showing that expression profile of 70 genes can be used to identify high-risk myeloma patients, about 10, 15%. And this recent genome challenge confirmed recently that this signature was able to discriminate patients with more recent treatment. It is possible to improve a gene expression profile by incorporating other scoring systems such as the ISS but also such as age...
In 2007, there was a crucial study from the Arkansas group showing that expression profile of 70 genes can be used to identify high-risk myeloma patients, about 10, 15%. And this recent genome challenge confirmed recently that this signature was able to discriminate patients with more recent treatment. It is possible to improve a gene expression profile by incorporating other scoring systems such as the ISS but also such as age. But the problem is that between all the signatures that have been described, there is almost no genes overlap between this signature suggesting maybe a lack of reproducibility and that each signature misses some high-risk patients. So despite efforts to try to combine this signature, the role of gene expression profile remains questionable in assessing the risk. In fact, by comparison with microarray, new tools such as RNA sequencing, next-generation sequencing allow to identify transcriptome modifiers such as splicing mechanisms, long intergenic non-coding RNA. And these elements are also a marker of aggressive disease. So it can be useful to look at this. In fact, for me, the transcriptome is an imperfect tool for assessing the risk, but it’s a key tool to the link between the DNA and the protein, so to understand the biology of plasma cell disease.