Publications by authors named "Matthew E Stokes"

Diffuse large B-cell lymphoma (DLBCL) patients that fail to achieve a complete metabolic response with frontline immunochemotherapy have a poor prognosis. Genomic profiling has led to a broader understanding of the molecular drivers in DLBCL, but it is unknown how well current classifiers identify patients that will experience primary treatment resistance (PTR). Using whole exome and RNA sequencing data from newly diagnosed DLBCL patients, we evaluated the genomic landscape of PTR and compared it to that of non-PTR DLBCL.

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A robust prognostic and biological classification for newly diagnosed follicular lymphoma (FL) using molecular profiling remains challenging. FL tumors from patients treated in the RELEVANCE trial with rituximab-chemotherapy (R-chemo) or rituximab-lenalidomide (R2) were analyzed using RNA sequencing, DNA sequencing, immunohistochemistry (IHC), and/or fluorescence in situ hybridization. Unsupervised gene clustering identified 2 gene expression signatures (GSs) enriched in normal memory (MEM) B cells and germinal center (GC) B-cell signals, respectively.

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Article Synopsis
  • Immunochemotherapy is currently the primary treatment for newly diagnosed diffuse large B-cell lymphoma (ndDLBCL), but it's ineffective for some patients, prompting research into better prognostic methods.
  • By analyzing transcriptomic data from a large group of patients, researchers identified seven distinct clusters of ndDLBCL, with one specific cluster (A7) linked to poorer outcomes due to characteristics like low immune cell presence and high MYC expression.
  • The study also explores how certain drugs, like lenalidomide, may improve treatment for the high-risk A7 cluster by enhancing T-cell movement into tumors and the expression of key tumor markers, while identifying TCF4 as a crucial factor in MYC biology for this group.
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Article Synopsis
  • * Researchers used a multiomic approach on tumor samples from 444 newly diagnosed DLBCL patients, combining gene analysis methods to identify a signature predictive of high early clinical failure risk.
  • * The study found that this signature, which includes ARID1A mutations, accurately predicted 45% of early clinical failures and could significantly influence future treatment strategies.
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Multiple myeloma (MM) is a plasma cell malignancy characterised by aberrant production of immunoglobulins requiring survival mechanisms to adapt to proteotoxic stress. We here show that glutamyl-prolyl-tRNA synthetase (GluProRS) inhibition constitutes a novel therapeutic target. Genomic data suggest that GluProRS promotes disease progression and is associated with poor prognosis, while downregulation in MM cells triggers apoptosis.

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Article Synopsis
  • - The study examines biomarkers that link disease severity and drug response in clinical trials, aimed at improving patient outcomes by enhancing therapeutic mechanisms and speeding up drug development.
  • - Researchers presented a large dataset from Phase III trials of apremilast, a medication for psoriasis and related conditions, analyzing plasma samples from 526 subjects to identify key biomarkers related to treatment effects.
  • - Important biomarkers such as IL-17A and KLK-7 were linked to disease severity and drug response in psoriasis, while IL-6 and LRG-1 were associated with ankylosing spondylitis, providing insights into the drug's mechanism and paving the way for future therapeutic advancements. !*
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Ozanimod (RPC1063) is a specific and potent small molecule modulator of the sphingosine 1-phosphate receptor 1 (S1PR1) and receptor 5 (S1PR5), which has shown therapeutic benefit in clinical trials of relapsing multiple sclerosis and ulcerative colitis. Ozanimod and its active metabolite, RP-101075, exhibit a similar specificity profile at the S1P receptor family in vitro and pharmacodynamic profile in vivo. The NZBWF1 mouse model was used in therapeutic dosing mode to assess the potential benefit of ozanimod and RP-101075 in an established animal model of systemic lupus erythematosus.

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Background: Ranking and identifying biomarkers that are associated with disease from genome-wide measurements holds significant promise for understanding the genetic basis of common diseases. The large number of single nucleotide polymorphisms (SNPs) in genome-wide studies (GWAS), however, makes this task computationally challenging when the ranking is to be done in a multivariate fashion. This paper evaluates the performance of a multivariate graph-based method called label propagation (LP) that efficiently ranks SNPs in genome-wide data.

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Background: Identification of genetic variants that are associated with disease is an important goal in elucidating the genetic causes of diseases. The genetic patterns that are associated with common diseases are complex and may involve multiple interacting genetic variants. The Relief family of algorithms is a powerful tool for efficiently identifying genetic variants that are associated with disease, even if the variants have nonlinear interactions without significant main effects.

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