This thesis focuses on gene expression profiling (GEP) to identify multiple myeloma (MM) patients with high-risk disease.

Today primarily patient specific factors such as age, the presence of comorbidities, frailty and renal failure are used for treatment decisions. As a result, almost all newly diagnosed MM patients receive similar treatment. This treatment has been shown to be effective in the MM patient group as a whole. However, some patients respond only minimally or do not respond at all requiring treatment adjustments. This approach therefore fails to produce the best response in each patient.

In the future molecular biomarkers are likely to guide treatment decisions by identifying treatment specific markers for both toxicities and response. In the absence of reliable predictions, treatments can be adapted based on risk stratification. In this way, a most optimal treatment for each patient can be selected in order to achieve a better quality of life, deeper responses and possibly even a cure.

As a primary result we have shown that the EMC92-gene classifier is a valid prognostic marker. It effectively identifies a high-risk group of 18% of patients with unfavorable median survival of 24 months, independent of other prognostic markers. In combination with ISS, the EMC92 marker was able to identify 38% of patients with a favorable median survival which was not reached after 96 months. This thesis also highlights the power of routinely applied markers such as cytogenetics and ISS. Risk adapted strategies, hopefully coupled to predictive markers, must determine the best way to improve survival of this as yet incurable disease.

Additional Metadata
Keywords Multiple myeloma, Gene expression, Risk classification, Prognosis, Bioinformatics
Promotor P. Sonneveld (Pieter) , M. van Duin (Mark)
Publisher Erasmus University Rotterdam
Persistent URL hdl.handle.net/1765/113910
Note For copyright reasons there is a partial embargo for this dissertation
Citation
Kuiper, R. (2018, December 20). Gene expression based risk classification in multiple myeloma. Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/113910