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. 2019 Jan 29;9(1):895.
doi: 10.1038/s41598-018-37273-4.

Multi-omics dataset to decipher the complexity of drug resistance in diffuse large B-cell lymphoma

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Multi-omics dataset to decipher the complexity of drug resistance in diffuse large B-cell lymphoma

Luc-Matthieu Fornecker et al. Sci Rep. .

Abstract

The prognosis of patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) remains unsatisfactory and, despite major advances in genomic studies, the biological mechanisms underlying chemoresistance are still poorly understood. We conducted for the first time a large-scale differential multi-omics investigation on DLBCL patient's samples in order to identify new biomarkers that could early identify patients at risk of R/R disease and to identify new targets that could determine chemorefractoriness. We compared a well-characterized cohort of R/R versus chemosensitive DLBCL patients by combining label-free quantitative proteomics and targeted RNA sequencing performed on the same tissues samples. The cross-section of both data levels allowed extracting a sub-list of 22 transcripts/proteins pairs whose expression levels significantly differed between the two groups of patients. In particular, we identified significant targets related to tumor metabolism (Hexokinase 3), microenvironment (IDO1, CXCL13), cancer cells proliferation, migration and invasion (S100 proteins) or BCR signaling pathway (CD79B). Overall, this study revealed several extremely promising biomarker candidates related to DLBCL chemorefractoriness and highlighted some new potential therapeutic drug targets. The complete datasets have been made publically available and should constitute a valuable resource for the future research.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Top six differentially abundant proteins and genes between chemorefractory and chemosensitive patients. For each protein, dots represent the mean of the log2 intensities of all the peptides quantified for each patient in each group. For each gene, dots represent the log2 normalized read count distribution of the considered gene for each patient in each group.

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