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CYCLON and NPM1 Cooperate within an Oncogenic Network Predictive of R-CHOP Response in DLBCL.

ABSTRACT: R-CHOP immuno-chemotherapy significantly improved clinical management of diffuse large B-cell lymphoma (DLBCL). However, 30-40% of DLBCL patients still present a refractory disease or relapse. Most of the prognostic markers identified to date fail to accurately stratify high-risk DLBCL patients. We have previously shown that the nuclear protein CYCLON is associated with DLBCL disease progression and resistance to anti-CD20 immunotherapy in preclinical models. We also recently reported that it also represents a potent predictor of refractory disease and relapse in a retrospective DLBCL cohort. However, only sparse data are available to predict the potential biological role of CYCLON and how it might exert its adverse effects on lymphoma cells. Here, we characterized the protein interaction network of CYCLON, connecting this protein to the nucleolus, RNA processing, MYC signaling and cell cycle progression. Among this network, NPM1, a nucleolar multi-functional protein frequently deregulated in cancer, emerged as another potential target related to treatment resistance in DLBCL. Immunohistochemistry evaluation of CYCLON and NPM1 revealed that their co-expression is strongly related to inferior prognosis in DLBCL. More specifically, alternative sub-cellular localizations of the proteins (extra-nucleolar CYCLON and pan-cellular NPM1) represent independent predictive factors specifically associated to R-CHOP refractory DLBCL patients, which could allow them to be orientated towards risk-adapted or novel targeted therapies.

SUBMITTER: Bouroumeau A

PROVIDER: S-EPMC8656558 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeDiffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin's lymphoma and of which the prognosis of activated B-cell-like (ABC) subtype is poor. Although R-CHOP significantly improves the survival of patients with DLBCL, 20% to 40% of patients were resistant to R-CHOP therapy. Thus, screening for candidate therapeutic targets for R-CHOP resistant patients is urgent. The previous researches have shown that CD24 is related to the development, invasion, and metastasis of cancer. Our project aims to clarify the relationship between CD24 and ABC-DLBCL.Patients and methodsThe expression of CD24 mRNA in 118 ABC-DLBCL cases treated with R-CHOP was detected by RNAscope, and the relationship between CD24 expression and R-CHOP treatment response was analyzed. The correlation between CD24 expression and treatment efficiency was further analyzed by data downloaded from the Gene Expression Omnibus (GEO) database. The association between CD24 expression and immune response was conducted using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) methodology and Gene Ontology (GO) biological process (BP) analysis.ResultsThe positive expression rate of CD24 mRNA in ABC-DLBCL patients was 38.1% (45/118). Complete Response (CR) rate was significantly higher in patients with CD24 high expression than those with CD24 low expression (P=0.039; 44.4% vs 26.0%). CR rate was significantly different between CD24 high and low expression groups in the analysis of GEO datasets (P=0.003; 83.2% vs 58.0%). The CD24 high expression patients had significantly lower proportions of T cells and nonspecific immune cells in the CIBERSORT analysis. In addition, T-helper 2 cell differentiation and monocyte chemotaxis were repressed in CD24 high expression group in the GO BP analysis.ConclusionCD24 was correlated with better R-CHOP treatment response and tumor immunosuppression in ABC-DLBCL. CD24 may be a promising signal in treatment and prognosis evaluation in ABC-DLBCL patients.

Project description:BackgroundGene regulatory networks have garnered a large amount of attention to understand disease mechanisms caused by complex molecular network interactions. These networks have been applied to predict specific clinical characteristics, e.g., cancer, pathogenicity, and anti-cancer drug sensitivity. However, in most previous studies using network-based prediction, the gene networks were estimated first, and predicted clinical characteristics based on pre-estimated networks. Thus, the estimated networks cannot describe clinical characteristic-specific gene regulatory systems. Furthermore, existing computational methods were developed from algorithmic and mathematics viewpoints, without considering network biology.ResultsTo effectively predict clinical characteristics and estimate gene networks that provide critical insights into understanding the biological mechanisms involved in a clinical characteristic, we propose a novel strategy for predictive gene network estimation. The proposed strategy simultaneously performs gene network estimation and prediction of the clinical characteristic. In this strategy, the gene network is estimated with minimal network estimation and prediction errors. We incorporate network biology by assuming that neighboring genes in a network have similar biological functions, while hub genes play key roles in biological processes. Thus, the proposed method provides interpretable prediction results and enables us to uncover biologically reliable marker identification. Monte Carlo simulations shows the effectiveness of our method for feature selection in gene estimation and prediction with excellent prediction accuracy. We applied the proposed strategy to construct gastric cancer drug-responsive networks.ConclusionWe identified gastric drug response predictive markers and drug sensitivity/resistance-specific markers, AKR1B10, AKR1C3, ANXA10, and ZNF165, based on GDSC data analysis. Our results for identifying drug sensitive and resistant specific molecular interplay are strongly supported by previous studies. We expect that the proposed strategy will be a useful tool for uncovering crucial molecular interactions involved a specific biological mechanism, such as cancer progression or acquired drug resistance.

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CYCLON and NPM1 Cooperate within an Oncogenic Network Predictive of R-CHOP Response in DLBCL.

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