Therapeutic effect of a MUC1-specific monoclonal antibody-drug conjugates against pancreatic cancer model

Wu, Guang; Li, Lan; Liu, Mengnan; Chen, Chunyan; Wang, Guangze; Jiang, Zewei; Qin, Yaqian; He, Licai; Li, Hongzhi; Cao, Jianhua; Gu, Haihua

doi:10.1186/s12935-022-02839-w

Cited by 9 publications

(3 citation statements)

References 41 publications

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“…Subsequently, they developed a humanized MUC1 antibody (HzMUC1) targeting the interaction region between MUC1-N and MUC1-C. They coupled HzMUC1 with monomethyloristatin (MMAE) to generate an antibody-drug coupling (ADC), which can inhibit the growth of PAAD cells by inducing PAAD cell cycle arrest and apoptosis [177].…”

Section: Antibodiesmentioning

confidence: 99%

Mucin Glycans: A Target for Cancer Therapy

Sun,

Zhang,

et al. 2023

Molecules

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Mucin glycans are an important component of the mucus barrier and a vital defence against physical and chemical damage as well as pathogens. There are 20 mucins in the human body, which can be classified into secreted mucins and transmembrane mucins according to their distributions. The major difference between them is that secreted mucins do not have transmembrane structural domains, and the expression of each mucin is organ and cell-specific. Under physiological conditions, mucin glycans are involved in the composition of the mucus barrier and thus protect the body from infection and injury. However, abnormal expression of mucin glycans can lead to the occurrence of diseases, especially cancer, through various mechanisms. Therefore, targeting mucin glycans for the diagnosis and treatment of cancer has always been a promising research direction. Here, we first summarize the main types of glycosylation (O-GalNAc glycosylation and N-glycosylation) on mucins and the mechanisms by which abnormal mucin glycans occur. Next, how abnormal mucin glycans contribute to cancer development is described. Finally, we summarize MUC1-based antibodies, vaccines, radio-pharmaceuticals, and CAR-T therapies using the best characterized MUC1 as an example. In this section, we specifically elaborate on the recent new cancer therapy CAR-M, which may bring new hope to cancer patients.

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Section: Antibodiesmentioning

confidence: 99%

Mucin Glycans: A Target for Cancer Therapy

Sun,

Zhang,

et al. 2023

Molecules

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“…Additionally, MUC4 influences HER2/ErbB2 signaling and significantly impacts the therapeutic outcomes of Herceptin-based therapy, underscoring its potential utility in cancer treatment and planning. [25] [26] [27], [28]…”

Section: Introductionmentioning

confidence: 99%

Graph Attention Networks for Drug Combination Discovery: Targeting Pancreatic Cancer Genes with RAIN Protocol

Parichehreh,

Kiaei,

Boush

et al. 2024

Preprint

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BackgroundMalignant neoplasm of the pancreas (MNP), a highly lethal illness with bleak outlook and few therapeutic avenues, entails numerous cellular transformations. These include irregular proliferation of ductal cells, activation of stellate cells, initiation of epithelial-to-mesenchymal transition, and changes in cell shape, movement, and attachment. Discovering potent drug cocktails capable of addressing the genetic and protein factors underlying pancreatic cancer’s development is formidable due to the disease’s intricate and varied nature.MethodIn this study, we introduce a fresh model utilizing Graph Attention Networks (GATs) to pinpoint potential drug pairings with synergistic effects for MNP, following the RAIN protocol. This protocol comprises three primary stages: Initially, employing Graph Neural Network (GNN) to suggest drug combinations for disease management by acquiring embedding vectors of drugs and proteins from a diverse knowledge graph encompassing various biomedical data types, such as drug-protein interactions, gene expression, and drug-target interactions. Subsequently, leveraging natural language processing to gather pertinent articles from clinical trials incorporating the previously recommended drugs. Finally, conducting network meta-analysis to assess the relative effectiveness of these drug combinations.ResultWe implemented our approach on a network dataset featuring drugs and genes as nodes, connected by edges representing their respective p-values. Our GAT model identified Gemcitabine, Pancrelipase Amylase, and Octreotide as the optimal drug combination for targeting the human genes/proteins associated with this cancer. Subsequent scrutiny of clinical trials and literature confirmed the validity of our findings. Additionally, network meta-analysis confirmed the efficacy of these medications concerning the pertinent genes.ConclusionBy employing GAT within the RAIN protocol, our approach represents a novel and efficient method for recommending prominent drug combinations to target proteins/genes associated with pancreatic cancer. This technique has the potential to aid healthcare professionals and researchers in identifying optimal treatments for patients while also unveiling underlying disease mechanisms.HighlightsGraph Attention Networks (GATs) used to recommend drug combinations for pancreatic cancerRAIN protocol applied to extract relevant information from clinical trials and literatureGemcitabine, Pancrelipase Amylase, and Octreotide identified as optimal drug combinationNetwork meta-analysis confirmed the effectiveness of the drug combination on gene targetsNovel and efficient method for drug discovery and disease mechanism elucidationAbstract Figure

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“…This ADC was shown to induce G2/M arrest and cause apoptosis in pancreatic cells in vitro. This was mirrored in vivo, with three doses of the ADC significantly decreasing the growth of pancreatic cancer xenografts by reducing proliferation and inducing apoptosis within the tumor [ 52 ].…”

Section: Introductionmentioning

confidence: 99%

Antibody drug conjugates: hitting the mark in pancreatic cancer?

Wittwer,

Brown,

Liapis

et al. 2023

J Exp Clin Cancer Res

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Pancreatic cancer is one of the most common causes of cancer-related death, and the 5-year survival rate has only improved marginally over the last decade. Late detection of the disease means that in most cases the disease has advanced locally and/or metastasized, and curative surgery is not possible. Chemotherapy is still the first-line treatment however, this has only had a modest impact in improving survival, with associated toxicities. Therefore, there is an urgent need for targeted approaches to better treat pancreatic cancer, while minimizing treatment-induced side-effects. Antibody drug conjugates (ADCs) are one treatment option that could fill this gap. Here, a monoclonal antibody is used to deliver extremely potent drugs directly to the tumor site to improve on-target killing while reducing off-target toxicity. In this paper, we review the current literature for ADC targets that have been examined in vivo for treating pancreatic cancer, summarize current and on-going clinical trials using ADCs to treat pancreatic cancer and discuss potential strategies to improve their therapeutic window.

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Therapeutic effect of a MUC1-specific monoclonal antibody-drug conjugates against pancreatic cancer model

Cited by 9 publications

References 41 publications

Mucin Glycans: A Target for Cancer Therapy

Mucin Glycans: A Target for Cancer Therapy

Graph Attention Networks for Drug Combination Discovery: Targeting Pancreatic Cancer Genes with RAIN Protocol

Antibody drug conjugates: hitting the mark in pancreatic cancer?

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