Translational challenges in pancreatic neuroendocrine tumor immunotherapy

RN0D, a galactoglucan from Panax notoginseng flower induces cancer cell death via PINK1/Parkin mitophagy

Metabolic alterations within mitochondria, encompassing processes such as autophagy and energy metabolism, play a pivotal role in facilitating the swift proliferation, invasion, and metastasis of cancer cells. Despite this, there is a scarcity of currently available medications with proven anticancer efficacy through the modulation of mitochondrial dysfunction in a clinical setting. Here, we introduce the structural characteristics of RN0D, a galactoglucan isolated and purified from Panax notoginseng flowers, mainly composed of β-1,4-galactan and β-1,3/1,6-glucan. RN0D demonstrates the capacity to induce mitochondrial impairment in cancer cells, leading to the accumulation of reactive oxygen species, initiation of mitophagy, and reduction in both mitochondrial number and size. This sequence of events ultimately results in the inhibition of mitochondrial and glycolytic bioenergetics, culminating in the demise of cancer cells due to adenosine triphosphate (ATP) deprivation. Notably, the observed bioactivity is attributed to RN0D's direct targeting of Galectin-3, as affirmed by surface plasmon resonance studies. Furthermore, RN0D is identified as an activator of the PTEN-induced kinase 1 (PINK1)/Parkin pathway, ultimately instigating cytotoxic mitophagy in tumor cells. This comprehensive study substantiates the rationale for advancing RN0D as a potentially efficacious anticancer therapeutic.

Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study

Background

Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to the surgical methods, prognosis, recurrence. Our aim is to develop and validate a combination model based on contrast-enhanced CT images to predict the lymph node metastasis (LNM) in NF-PanNETs.

Methods

Retrospective data were gathered for 320 patients with NF-PanNETs who underwent curative pancreatic resection and CT imaging at two institutions (Center 1, n = 236 and Center 2, n = 84) between January 2010 and March 2022. RDPs (Radiomics deep learning signature) were developed based on ten machine-learning techniques. These signatures were integrated with the clinicopathological factors into a nomogram for clinical applications. The evaluation of the model's performance was conducted through the metrics of the area under the curve (AUC).

Findings

The RDPs showed excellent performance in both centers with a high AUC for predicting LNM and disease-free survival (DFS) in Center 1 (AUC, 0.88; 95% CI: 0.84-0.92; DFS, p < 0.05) and Center 2 (AUC, 0.91; 95% CI: 0.85-0.97; DFS, p < 0.05). The clinical factors of vascular invasion, perineural invasion, and tumor grade were associated with LNM (p < 0.05). The combination nomogram showed better prediction capability for LNM (AUC, 0.93; 95% CI: 0.89-0.96). Notably, our model maintained a satisfactory predictive ability for tumors at the 2-cm threshold, demonstrating its effectiveness across different tumor sizes in Center 1 (≤2 cm: AUC, 0.90 and >2 cm: AUC, 0.86) and Center 2 (≤2 cm: AUC, 0.93 and >2 cm: AUC, 0.91).

Interpretation

Our RDPs may have the potential to preoperatively predict LNM in NF-PanNETs, address the insufficiency of clinical guidelines concerning the 2-cm threshold for tumor lymph node dissection, and provide precise therapeutic strategies.

Funding

This work was supported by JSPS KAKENHI Grant Number JP22K20814; the Rare Tumor Research Special Project of the National Natural Science Foundation of China (82141104) and Clinical Research Special Project of Shanghai Municipal Health Commission (202340123).

Models in Pancreatic Neuroendocrine Neoplasms: Current Perspectives and Future Directions

Pancreatic neuroendocrine neoplasms (pNENs) are a heterogeneous group of tumors derived from multiple neuroendocrine origin cell subtypes. Incidence rates for pNENs have steadily risen over the last decade, and outcomes continue to vary widely due to inability to properly screen. These tumors encompass a wide range of functional and non-functional subtypes, with their rarity and slow growth making therapeutic development difficult as most clinically used therapeutics are derived from retrospective analyses. Improved molecular understanding of these cancers has increased our knowledge of the tumor biology for pNENs. Despite these advances in our understanding of pNENs, there remains a dearth of models for further investigation. In this review, we will cover the current field of pNEN models, which include established cell lines, animal models such as mice and zebrafish, and three-dimensional (3D) cell models, and compare their uses in modeling various disease aspects. While no study model is a complete representation of pNEN biology, each has advantages which allow for new scientific understanding of these rare tumors. Future efforts and advancements in technology will continue to create new options in modeling these cancers.

MK2 Promotes the Development and Progression of Pancreatic Neuroendocrine Tumors Mediated by Macrophages and Metabolomic Factors

Cases of pancreatic neuroendocrine tumors (PNETs) are growing in number, and new treatment options are needed in order to improve patient outcomes. The mitogen-activated protein kinase-activated protein kinase 2 (MK2) is a crucial regulator of cytokine/chemokine production. The significance of MK2 expression and signaling pathway mediated by MK2 in PNETs has not been investigated. To characterize the impact of MK2 on PNET growth, we used the RipTag2 transgenic murine model of PNETs, and we developed a primary PNET cell line for both in vitro and in vivo studies. In the transgenic murine model of PNETs, we found that MK2 inhibition improves survival of mice and prevents PNET progression. MK2 blockade abolished cytokine/chemokine production, which was related to macrophage function. A role for MK2 in the regulation of metabolic factor secretion in PNETs was identified, making this the first study to identify a potential role for the MK2 pathway in regulation of tumor metabolism. Moreover, using an in vitro approach and allograft model of PNETs, we were able to show that macrophages with MK2 depletion exhibit increased cytotoxicity against PNET cells and substantially decreased production of pro-inflammatory cytokines and chemokines, as well as metabolic factors. Taken together, our work identifies MK2 as a potent driver of immune response and metabolic effectors in PNETs, suggesting it is a potential therapeutic target for patients with PNETs.

Maximum Value on Arterial Phase Computed Tomography Predicts Prognosis and Treatment Efficacy of Sunitinib for Pancreatic Neuroendocrine Tumours

PURPOSE

This study was designed to assess the computed tomography maximum (CTmax) value on pretherapeutic arterial phase computed tomography (APCT) images to predict pancreatic neuroendocrine tumours (pNETs) recurrence and clarify its role in predicting the outcome of tumour therapy.

METHODS

This retrospective study enrolled 250 surgical patients and 24 nonsurgical patients with sunitinib-based treatment in our hospital from 2008 to 2019. CT images were assessed, the maximum value was defined as "CTmax," and recurrence-free survival (RFS) or progression-free survival (PFS) was compared between a high-CTmax group and a low-CTmax group among patients who underwent surgical resection or nonsurgical, sunitinib-based treatment according to the CTmax cutoff value.

RESULTS

In ROC curve analysis, a CTmax of 108 Hounsfield units, as the cutoff value, achieved an AUC of 0.796 in predicting recurrence. Compared with the low-CTmax group, the high-CTmax group had a longer RFS (p < 0.001). Low CTmax was identified as an independent factor for RFS (p < 0.001) in multivariate analysis; these results were confirmed using the internal validation set. The CTmax value was significantly correlated with the microvascular density (MVD) value (p < 0.001) and the vascular endothelial growth factor receptor 2 (VEGFR2) score (p < 0.001). Furthermore, the high-CTmax group had a better PFS than the low-CTmax group among the sunitinib treatment group (p = 0.007).

CONCLUSIONS

The tumour CTmax on APCT might be a potential and independent indicator for predicting recurrence in patients who have undergone surgical resection and assessing the efficacy of sunitinib for patients with advanced metastatic pNETs.