The Mutational, Prognostic, and Therapeutic Landscape of Neuroendocrine Neoplasms

Molecular Classification of Gastrointestinal and Pancreatic Neuroendocrine Neoplasms: Are We Ready for That?

In the last two decades, the increasing availability of technologies for molecular analyses has allowed an insight in the genomic alterations of neuroendocrine neoplasms (NEN) of the gastrointestinal tract and pancreas. This knowledge has confirmed, supported, and informed the pathological classification of NEN, clarifying the differences between neuroendocrine carcinomas (NEC) and neuroendocrine tumors (NET) and helping to define the G3 NET category. At the same time, the identification genomic alterations, in terms of gene mutation, structural abnormalities, and epigenetic changes differentially involved in the pathogenesis of NEC and NET has identified potential molecular targets for precision therapy. This review critically recapitulates the available molecular features of digestive NEC and NET, highlighting their correlates with pathological aspects and clinical characteristics of these neoplasms and revising their role as predictive biomarkers for targeted therapy. In this context, the feasibility and applicability of a molecular classification of gastrointestinal and pancreatic NEN will be explored.

Prolonged Response to Dabrafenib/Trametinib in Grade 3 Metastatic Pancreatic Neuroendocrine Tumor (NET G3) with BRAF V600E Mutation

PURPOSE

Treatment of metastatic pancreatic neuroendocrine tumors (pancNETs), particularly grade 2 (G2) and grade 3 (G3), often presents a dilemma in choosing from multiple similarly efficacious therapies. Data on targeted therapies for these tumor types is limited, and this report presents BRAF-targeted therapy as a therapeutic option for metastatic pancNET G3.

METHODS

This is a case report of a patient with G3 pancNET metastatic to the liver, lung, lymph node, and scalp (soft tissue) treated with dabrafenib/trametinib (D/T) in the presence of a BRAF V600E mutation detected in tumor tissue.

RESULTS

This patient has demonstrated an ongoing partial response to therapy at all involved sites for nearly 15 months with minimal side effects attributable to D/T.

CONCLUSION

Dabrafenib/trametinib therapy for BRAF-mutated metastatic pancNETs provides a novel treatment option and, especially in the G3 setting, should be considered a first-line option. Tumor testing for actionable mutations should be undertaken at the time of diagnosis and/or progression to identify novel therapeutic avenues in these rare tumors.

Machine Learning Methods Based on CT Features Differentiate G1/G2 From G3 Pancreatic Neuroendocrine Tumors

RATIONALE AND OBJECTIVES

To identify CT features for distinguishing grade 1 (G1)/grade 2 (G2) from grade 3 (G3) pancreatic neuroendocrine tumors (PNETs) using different machine learning (ML) methods.

MATERIALS AND METHODS

A total of 147 patients with 155 lesions confirmed by pathology were retrospectively included. Clinical-demographic and radiological CT features was collected. The entire cohort was separated into training and validation groups at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) algorithm and principal component analysis (PCA) were used to select features. Three ML methods, namely logistic regression (LR), support vector machine (SVM), and K-nearest neighbor (KNN) were used to build a differential model. Receiver operating characteristic (ROC) curves and precision-recall curves for each ML method were generated. The area under the curve (AUC), accuracy rate, sensitivity, and specificity were calculated.

RESULTS

G3 PNETs were more likely to present with invasive behaviors and lower enhancement than G1/G2 PNETs. The LR classifier yielded the highest AUC of 0.964 (95% confidence interval [CI]: 0.930, 0.972), with 95.4% accuracy rate, 95.7% sensitivity, and 92.9% specificity, followed by SVM (AUC: 0.957) and KNN (AUC: 0.893) in the training group. In the validation group, the SVM classier reached the highest AUC of 0.952 (95% CI: 0.860, 0.981), with 91.5% accuracy rate, 97.3% sensitivity, and 70% specificity, followed by LR (AUC: 0.949) and KNN (AUC: 0.923).

CONCLUSIONS

The LR and SVM classifiers had the best performance in the training group and validation group, respectively. ML method could be helpful in differentiating between G1/G2 and G3 PNETs.

Clinicopathological characteristics, molecular landscape, and biomarker landscape for predicting the efficacy of PD-1/PD-L1 inhibitors in Chinese population with mismatch repair deficient urothelial carcinoma: a real-world study

Urothelial carcinoma (UC) with deficient mismatch repair (dMMR) is a specific subtype of UC characterized by the loss of mismatch repair (MMR) proteins and its association with Lynch syndrome (LS). However, comprehensive real-world data on the incidence, clinicopathological characteristics, molecular landscape, and biomarker landscape for predicting the efficacy of PD-1/PD-L1 inhibitors in the Chinese patients with dMMR UC remains unknown. We analyzed 374 patients with bladder urothelial carcinoma (BUC) and 232 patients with upper tract urothelial carcinoma (UTUC) using tissue microarrays, immunohistochemistry, and targeted next-generation sequencing. Results showed the incidence of dMMR UC was higher in the upper urinary tract than in the bladder. Genomic analysis identified frequent mutations in KMT2D and KMT2C genes and LS was confirmed in 53.8% of dMMR UC cases. dMMR UC cases displayed microsatellite instability-high (MSI-H) (PCR method) in 91.7% and tumor mutational burden-high (TMB-H) in 40% of cases. The density of intratumoral CD8+ T cells correlated with better overall survival in dMMR UC patients. Positive PD-L1 expression was found in 20% cases, but some patients positively responded to immunotherapy despite negative PD-L1 expression. Our findings provide valuable insights into the characteristics of dMMR UC in the Chinese population and highlights the relevance of genetic testing and immunotherapy biomarkers for treatment decisions.