Gene Array Analysis


MIRT is the only facility in the world that routinely offers gene array analysis for newly referred patients and utilizes this information for patient management and planning of therapy.

The Myeloma Institute performs multiple gene arrays on every patient enrolled on a clinical protocol

Based on a study of  more than 500 newly diagnosed patients treated at the Myeloma Institute for multiple myeloma, MIRT researchers found that the expression of just 17 genes (out of the 25,000 genes in the human body) revealed which form of myeloma a patient had.  The expression level of those 17 genes serves as a powerful predictor of response to therapy.

This enables doctors to more accurately predict which patients  will not respond to standard therapy and thereby spare patients from undergoing treatments that will not be effective.  The discovery is also important to the development of new treatments that specifically target the 17 genes.

Complete findings are reported in plenary paper published in Blood.

Blood, 15 March 2007, Vol. 109, No. 6, pp. 2276-2284

A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1

Summary of Abstract

To molecularly define high-risk disease, Dr. Shaughnessy’s group performed microarray analysis on tumor cells from 532 newly diagnosed patients with multiple myeloma.

Seventy genes were found to be linked to early disease-related death. Importantly, most up-regulated genes mapped to chromosome 1q, and down-regulated genes mapped to chromosome 1p. The ratio of mean expression levels of up-regulated to down-regulated genes defined a high-risk score present in 13% of patients with shorter durations of complete remission, event-free survival, and overall survival. Shaughnessy’s data suggest that altered transcriptional regulation of genes mapping to chromosome 1 may contribute to disease progression, and that expression profiling can be used to identify high-risk disease and guide therapeutic interventions.

A central hypothesis of the work presented in this paper was that expression extremes of a subset of genes correlating with survival might be representative of the effects of DNA copy changes in myeloma disease progression. Shaughnessy and colleagues were thus able to identify a set of 70 genes, the expression levels of which permitted the identification of a small cohort of 13% to 14% of patients at high risk for early disease-related death.

The marked increase in the frequency of high-risk designation from 13% at diagnosis to 76% at relapse provides molecular evidence of disease evolution that influences postrelapse outcome. With further refinement of the model, Shaughnessy expects to develop tools for quantitative risk assessment during the entire course of therapeutic management.

The findings may also shed important light on the underlying molecular mechanisms that drive disease progression.  This has the potential to translate into clinical applications that slow down or prevent disease progression. 

Through multivariate discriminant analyses, Shaughnessy found that of the original 70 genes, 17 probe sets could be used to detect high-risk myeloma. Assessment of the expression levels of these genes may provide a simple and powerful molecular-based prognostic test that would eliminate the need for testing many of the standard variables currently in use with limited prognostic implications. If these gene signatures are unique to myeloma tumor cells, such a test may be useful after treatment to assess minimal residual disease, possibly using peripheral blood as a sample source.