PDGFD induces ibrutinib resistance of diffuse large B‑cell lymphoma through activation of EGFR

DEPTOR suppresses lymphomagenesis by promoting EGFR degradation via HUWE1 E3 ligase

DEPTOR, a naturally occurring inhibitor of mTOR, plays crucial roles in tumorigenesis and is frequently dysregulated in a variety of human cancers. Interestingly, DEPTOR could act either as a tumor suppressor or as an oncogene in a manner dependent of cellular context or tissue environment. Whether and how DEPTOR regulates lymphomagenesis remains elusive. In this study, we report that in a mouse lymphoma model induced by heterozygous Pten loss, Deptor knockout (KO) markedly accelerates lymphomagenesis, whereas degradation-resistant DeptorS275A knock-in (KI) variant significantly inhibits it. Furthermore, Deptor KO mice spontaneously developed lymphomas in the later stages of their lifespan, and Deptor KO further shortened overall lifespan in Ptenfl/fl;MMTV-Cre mice. Consistently, DEPTOR protein levels are significantly lower in human lymphoma tissues, as compared to normal lymph nodes. Mechanistically, DEPTOR, on one hand, enhances the interaction of EGFR to HUWE1 E3 ubiquitin ligase for targeted ubiquitination and proteasomal degradation, and subsequent inactivation of the MAPK signal. On the other hand, DEPTOR inactivates both mTORC1 and mTORC2 signals. Collectively, our study demonstrated that DEPTOR is a tumor suppressor that inhibits lymphomagenesis upon Pten-loss. The strategy that reactivates DEPTOR could be a promising approach for the treatment of lymphoma.

In vitro and in vivo anti-lymphoma effects of Ophiorrhiza pumila extract

Background: Current therapeutic strategies on patients with lymphomas remains limited. Previously we found the suppressive effect of Ophiorrhiza pumila (OPE) on hepatocarcinoma. In present study, the effect of OPE on lymphoma in vitro and in vivo were investigated.

Methods: CCK-8 assay was applied to detect the effect of OPE on cell proliferation. Flow cytometry was used to analyze the effect of OPE on cell cycle distribution and apoptosis. Xenograft mouse model was conducted to determine the anti-tumor activity of OPE. TNUEL assay was performed to detect the apoptosis in tumor tissues. Western blot and immuno-histochemistry were used to determine protein expression.

Results: In vitro tests indicate that OPE suppressed A20 cell proliferation in a dose- and time-dependent manner. OPE treatment induced cell cycle arrest at S phase and elevated apoptosis in A20 cells. OPE displayed a significant inhibition in tumor growth in a mouse xenograft model. OPE promoted apoptosis of tumor cell in the mouse model Cleaved caspase 3 expression and Bax/Bcl2 ratio were also enhanced. In addition, OPE suppressed A20 cell viability partially by reducing phosphorylation of EGFR.

Conclusions: Our data showed that OPE suppressed the proliferation of lymphoma cells and promoted apoptosis in vitro and in vivo, which might be partially mediated by inactivating EGFR signaling.

From multi-omics integration towards novel genomic interaction networks to identify key cancer cell line characteristics

Cancer is a complex disease where cancer cells express epigenetic and transcriptomic mechanisms to promote tumor initiation, progression, and survival. To extract relevant features from the 2019 Cancer Cell Line Encyclopedia (CCLE), a multi-layer nonnegative matrix factorization approach is used. We used relevant feature genes and DNA promoter regions to construct genomic interaction network to study gene-gene and gene-DNA promoter methylation relationships. Here, we identified a set of gene transcripts and methylated DNA promoter regions for different clusters, including one homogeneous lymphoid neoplasms cluster. In this cluster, we found different methylated transcription factors that affect transcriptional activation of EGFR and downstream interactions. Furthermore, the hippo-signaling pathway might not function properly because of DNA hypermethylation and low gene expression of both LATS2 and YAP1. Finally, we could identify a potential dysregulation of the CD28-CD86-CTLA4 axis. Characterizing the interaction of the epigenome and the transcriptome is vital for our understanding of cancer cell line behavior, not only for deepening insights into cancer-related processes but also for future disease treatment and drug development. Here we have identified potential candidates that characterize cancer cell lines, which give insight into the development and progression of cancers.

Targeting Bruton tyrosine kinase using non-covalent inhibitors in B cell malignancies

B cell receptor (BCR) signaling is involved in the pathogenesis of B cell malignancies. Activation of BCR signaling promotes the survival and proliferation of malignant B cells. Bruton tyrosine kinase (BTK) is a key component of BCR signaling, establishing BTK as an important therapeutic target. Several covalent BTK inhibitors have shown remarkable efficacy in the treatment of B cell malignancies, especially chronic lymphocytic leukemia. However, acquired resistance to covalent BTK inhibitors is not rare in B cell malignancies. A major mechanism for the acquired resistance is the emergence of BTK cysteine 481 (C481)  mutations, which disrupt the binding of covalent BTK inhibitors. Additionally, adverse events due to the off-target inhibition of kinases other than BTK by covalent inhibitors are common. Alternative therapeutic options are needed if acquired resistance or intolerable adverse events occur. Non-covalent BTK inhibitors do not bind to C481, therefore providing a potentially effective option to patients with B cell malignancies, including those who have developed resistance to covalent BTK inhibitors. Preliminary clinical studies have suggested that non-covalent BTK inhibitors are effective and well-tolerated. In this review, we discussed the rationale for the use of non-covalent BTK inhibitors and the preclinical and clinical studies of non-covalent BTK inhibitors in B cell malignancies.

B-cell clonogenic activity of HIV-1 p17 variants is driven by PAR1-mediated EGF transactivation

Combined antiretroviral therapy (cART) for HIV-1 dramatically slows disease progression among HIV⁺ individuals. Currently, lymphoma represents the main cause of death among HIV-1-infected patients. Detection of p17 variants (vp17s) endowed with B-cell clonogenic activity in HIV-1-seropositive patients with lymphoma suggests their possible role in lymphomagenesis. Here, we demonstrate that the clonogenic activity of vp17s is mediated by their binding to PAR1 and to PAR1-mediated EGFR transactivation through Gq protein. The entire vp17s-triggered clonogenic process is MMPs dependent. Moreover, phosphoproteomic and bioinformatic analysis highlighted the crucial role of EGFR/PI3K/Akt pathway in modulating several molecules promoting cancer progression, including RAC1, ABL1, p53, CDK1, NPM, Rb, PTP-1B, and STAT1. Finally, we show that a peptide (F1) corresponding to the vp17s functional epitope is sufficient to trigger the PAR1/EGFR/PI3K/Akt pathway and bind PAR1. Our findings suggest novel potential therapeutic targets to counteract vp17-driven lymphomagenesis in HIV⁺ patients.

Multivariate gene expression-based survival predictor model in esophageal adenocarcinoma

Background

Despite the recent development of molecular‐targeted treatment and immunotherapy, survival of patients with esophageal adenocarcinoma (EAC) with poor prognosis is still poor due to lack of an effective biomarker. In this study, we aimed to explore the ceRNA and construct a multivariate gene expression predictor model using data from The Cancer Genome Atlas (TCGA) to predict the prognosis of EAC patients.

Methods

We conducted differential expression analysis using mRNA, miRNA and lncRNA transciptome data from EAC and normal patients as well as corresponding clinical information from TCGA database, and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of those unique differentially expressed mRNAs using the Integrate Discovery Database (DAVID) database. We then constructed the lncRNA‐miRNA‐mRNA competing endogenous RNA (ceRNA) network of EAC and used Cox proportional hazard analysis to generate a multivariate gene expression predictor model. We finally performed survival analysis to determine the effect of differentially expressed mRNA on patients' overall survival and discover the hub gene.

Results

We identified a total of 488 lncRNAs, 33 miRNAs, and 1207 mRNAs with differentially expressed profiles. Cox proportional hazard analysis and survival analysis using the ceRNA network revealed four genes (IL‐11, PDGFD, NPTX1, ITPR1) as potential biomarkers of EAC prognosis in our predictor model, and IL‐11 was identified as an independent prognostic factor.

Conclusions

In conclusion, we identified differences in the ceRNA regulatory networks and constructed a four–gene expression‐based survival predictor model, which could be referential for future clinical research.