Abstract
PurposeWe designed a gene profiling experiment to identify genes involved in secondary drug resistance in mantle cell lymphomas (MCL).Experimental DesignWe obtained paired tissue samples collected from the same patients before treatment and after relapse or progression. Variations in gene expression between the 2 samples were estimated for 5 patients. For each gene, the mean variation was estimated for patients with a refractory primary tumor and for responders who developed secondary drug resistance. Nine genes of interest were selected on the basis of the magnitude and statistical significance of the variation of expression in responders and non-responders.ResultsBMP7 was the only one with significantly increased expression at relapse in patients who developed secondary resistance. Validation of BMP7 as a key gene involved in secondary resistance was performed using cultures of cell line. Incubation of BMP7 with MCL cell lines increased their resistance to bortezomib and cytarabine, while inhibition of BMP7 expression by siRNA correlated with increased cell death linked to drug application.ConclusionVariations in gene expression after treatment point out BMP7 as a key gene involved in secondary resistance in mantle cell lymphoma.
Highlights
Mantle cell lymphoma is a rare lymphoma entity which is usually incurable with available therapies
The main analysis focused on the ratio between gene expression fold change in initially sensitive and primary refractory tumors or fold change ratio (FCR)
Because BMP7 had an increased expression at relapse in secondary resistant tumors, we investigated the putative role of BMP7 in secondary resistance
Summary
Mantle cell lymphoma is a rare lymphoma entity which is usually incurable with available therapies. In patients who achieve a primary response to therapy, secondary drug resistance almost invariably develops [2,3]. The current strategy of gene profiling is based on the use of tumor samples obtained exclusively before treatment. The objective of this strategy is to find genes or combinations of genes likely to predict the effect of treatment. This strategy leads to unstable results and to poor quality predictions [4] To overcome these problems, one solution is to increase the size of the studies; the price for credibility being the inclusion of tens or hundreds of patients. At the individual patient level small differences in gene expression have slim chances of being
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