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Last update 08 May 2025

DPEP3

Last update 08 May 2025

Basic Info

Synonyms

dipeptidase 3, DPEP3

Introduction

Lacks dipeptidase activity and is unable to hydrolyze cystinyl-bis-glycine, leukotriene D4 and the beta-lactam antibiotic imipenem (PubMed:32325220). The absence of activity may be due to the inability of asparagine (instead of aspartate found in DPEP1/2) at position 359 to function as the acid/base catalyst and activate the nucleophilic water/hydroxide (PubMed:32325220). A tyrosine (instead of histidine) at position 269 reduces affinity for the beta zinc and may cause substrate steric hindrance (PubMed:32325220).

Drugs associated with DPEP3

Tamrintamab pamozirine

Target

DPEP3 x TAA

Mechanism

DPEP3 modulators [+2]

Active Org.-

Originator Org.

AbbVie, Inc.

Active Indication-

Inactive Indication

Platinum-Resistant Ovarian Carcinoma

Drug Highest PhaseDiscontinued

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

Clinical Trials associated with DPEP3

NCT02539719

/ TerminatedPhase 1

A Phase 1a/1b Dose Escalation and Expansion Study of SC-003 as a Single-Agent and in Combination With ABBV-181 in Subjects With Platinum-Resistant/ Refractory Ovarian Cancer

This is a Phase 1a/1b study of SC-003 as a single agent and in combination with ABBV-181 in patients with platinum-resistant/refractory ovarian cancer. SC-003 is an antibody-drug conjugate (ADC) comprised of a monoclonal antibody linked to a potent chemotherapy. ABBV-181 is a humanized, recombinant, mAb that binds to cell surface expressed programmed cell death 1 (PD-1).

Start Date01 Aug 2015

Sponsor / Collaborator

Stemcentrx, Inc.

100 Clinical Results associated with DPEP3

100 Translational Medicine associated with DPEP3

0 Patents (Medical) associated with DPEP3

Literatures (Medical) associated with DPEP3

01 Mar 2024·Journal of Affective Disorders

Comprehensive bioinformatics analysis of co-expressed genes of post-traumatic stress disorder and major depressive disorder

Article

Author: Luo, Peng ; Jiang, Xiaofan ; Zhang, Haofuzi

BACKGROUNDPost-traumatic stress disorder (PTSD) is one of the most serious sequelae of trauma with serious impact worldwide. Studies have suggested an association between PTSD and major depressive disorder (MDD), but the underlying common mechanisms remain unclear. This study aimed to further explore the molecular mechanism between PTSD and MDD via comprehensive bioinformatics analysis.METHODSThe microarray data of PTSD and MDD were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify the co-expressed genes associated with PTSD and MDD. Gene Set Enrichment Analysis (GSEA), enrichment analyses based on Disease Ontology (DO), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed using R software. Then, R software was used for single-sample gene set enrichment analysis (ssGSEA) and immune infiltration analysis on the co-expressed genes in the two datasets., Therefore, a logistic regression model was constructed to predict PTSD and MDD using the R language. Ultimately, this study employed PTSD and MDD models to assess alterations in the expression of target genes within the mouse hippocampus.RESULTSFour core genes (GNAQ, DPEP3, ICAM2, PACSIN2) were obtained through different analyses, and these genes had predictive validity for PTSD and MDD, playing an important role in the common mechanism of PTSD and MDD. The study findings reveal decreased expression levels of DPEP3, GNAQ, and PACDIN2 in PTSD samples, accompanied by an increased expression of ICAM2. In MDD samples, the expression of DPEP3 and ICAM2 is reduced, whereas GNAQ and PACDIN2 show an increase in expression.CONCLUSIONSThis study provides a new perspective on the common molecular mechanisms of PTSD and MDD. These common pathways and core genes may provide promising clues for further experimental studies.

01 Feb 2024·Translational Cancer Research

A novel gene signature related to fatty acid metabolism predicts prognosis, immune landscape, and drug sensitivity in early-stage lung squamous cell carcinoma

Article

Author: Fan, Weifei ; Yu, Maoran ; Xu, Juqing ; Feng, Jifeng

BackgroundDysregulation of fatty acid metabolism (FAM) represents a significant metabolic alteration in tumorigenesis. However, the role of FAM-related genes (FAMRGs) in early-stage lung squamous cell carcinoma (LUSC) remains incompletely understood.MethodsA series of bioinformatic analyses and machine learning strategies were performed to construct a FAMRGs-based signature to predict prognosis and guide personalized treatment for early-stage LUSC patients. FAMRGs were screened through the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Molecular Signature Database (MSigDB). Prognosis FAMRGs were identified using univariate Cox regression, and unsupervised clustering analysis facilitated the division of the cohort into different clusters. The least absolute shrinkage and selection operator (LASSO)-Cox regression and multivariate regression analysis were employed to develop a FAMRGs-based signature for predicting overall survival (OS). A nomogram was subsequently constructed to facilitate risk assessment for individual patients. Comprehensive analyses of metabolic pathways, immune infiltration, immunomodulators, and potentially applicable drugs were conducted across different FAMRGs-related risk groups.ResultsThe FAMRGs-based signature, comprising nine genes (ACOT11, APOH, BMX, CYP2R1, DPEP3, FABP6, FADS2, GLYATL2, and THRSP), demonstrated robust predictive capabilities for prognosis in The Cancer Genome Atlas (TCGA)-LUSC dataset and validated across six independent Gene Expression Omnibus (GEO)-LUSC datasets. Notably, the FAMRGs-base signature exhibited superior prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. Furthermore, the signature was closely associated with immune cell infiltration, human leukocyte antigen (HLA) genes, and immune checkpoint genes expression. Additionally, the signature demonstrated potential sensitivity to chemo-/target-therapy.ConclusionsThe FAMRGs-based signature demonstrated superior sensitivity in predicting the prognosis of early-stage LUSC. Detecting FAMRGs may provide predictive targets for the development of clinical treatment strategies.

01 Dec 2023·Molecular Oncology

Expression of cancer–testis antigens in the immune microenvironment of non‐small cell lung cancer

Article

Author: Rassy, Marc ; Méar, Loren ; Brunnström, Hans ; Mattsson, Johanna Sofia Margareta ; Hikmet, Feria ; Micke, Patrick ; Botling, Johan ; Backman, Max ; Djureinovic, Dijana ; Lindskog, Cecilia

The antigenic repertoire of tumors is critical for successful anti‐cancer immune response and the efficacy of immunotherapy. Cancer–testis antigens (CTAs) are targets of humoral and cellular immune reactions. We aimed to characterize CTA expression in non‐small cell lung cancer (NSCLC) in the context of the immune microenvironment. Of 90 CTAs validated by RNA sequencing, eight CTAs (DPEP3, EZHIP, MAGEA4, MAGEB2, MAGEC2, PAGE1, PRAME, and TKTL1) were selected for immunohistochemical profiling in cancer tissues from 328 NSCLC patients. CTA expression was compared with immune cell densities in the tumor environment and with genomic, transcriptomic, and clinical data. Most NSCLC cases (79%) expressed at least one of the analyzed CTAs, and CTA protein expression correlated generally with RNA expression. CTA profiles were associated with immune profiles: high MAGEA4 expression was related to M2 macrophages (CD163) and regulatory T cells (FOXP3), low MAGEA4 was associated with T cells (CD3), and high EZHIP was associated with plasma cell infiltration (adj. P‐value < 0.05). None of the CTAs correlated with clinical outcomes. The current study provides a comprehensive evaluation of CTAs and suggests that their association with immune cells may indicate in situ immunogenic effects. The findings support the rationale to harness CTAs as targets for immunotherapy.

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DPEP3

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