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Uccella S. Molecular Classification of Gastrointestinal and Pancreatic Neuroendocrine Neoplasms: Are We Ready for That? Endocr Pathol 2024; 35:91-106. [PMID: 38470548 PMCID: PMC11176254 DOI: 10.1007/s12022-024-09807-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/29/2024] [Indexed: 03/14/2024]
Abstract
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.
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Affiliation(s)
- Silvia Uccella
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy.
- Pathology Service IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy.
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Ueberroth BE, Lieu CH, Lentz RW. Prolonged Response to Dabrafenib/Trametinib in Grade 3 Metastatic Pancreatic Neuroendocrine Tumor (NET G3) with BRAF V600E Mutation. J Gastrointest Cancer 2024:10.1007/s12029-024-01072-0. [PMID: 38814411 DOI: 10.1007/s12029-024-01072-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2024] [Indexed: 05/31/2024]
Abstract
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.
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Affiliation(s)
- Benjamin E Ueberroth
- Division of Medical Oncology, Department of Medicine, University of Colorado School of Medicine, 12801 E 17th Ave, MS 8117, Aurora, CO, 80045, USA.
| | - Christopher H Lieu
- Division of Medical Oncology, Department of Medicine, University of Colorado School of Medicine, 12801 E 17th Ave, MS 8117, Aurora, CO, 80045, USA
| | - Robert W Lentz
- Division of Medical Oncology, Department of Medicine, University of Colorado School of Medicine, 12801 E 17th Ave, MS 8117, Aurora, CO, 80045, USA
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Chen HY, Pan Y, Chen JY, Chen J, Liu LL, Yang YB, Li K, Ma Q, Shi L, Yu RS, Shao GL. Machine Learning Methods Based on CT Features Differentiate G1/G2 From G3 Pancreatic Neuroendocrine Tumors. Acad Radiol 2024; 31:1898-1905. [PMID: 38052672 DOI: 10.1016/j.acra.2023.10.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 12/07/2023]
Abstract
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.
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Affiliation(s)
- Hai-Yan Chen
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Yao Pan
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China (Y.P., R.-S.Y.)
| | - Jie-Yu Chen
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Jia Chen
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou 311121, Zhejiang Province, China (J.C.)
| | - Lu-Lu Liu
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Yong-Bo Yang
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Kai Li
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Qian Ma
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang, China (H.-Y.C., J.-Y.C., L.-L.L., Y.-B.Y., K.L., Q.M., L.S.)
| | - Ri-Sheng Yu
- Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China (Y.P., R.-S.Y.)
| | - Guo-Liang Shao
- Department of Interventional Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, Zhejiang Province, China (G.-L.S.); Clinical Research Center of Hepatobiliary and pancreatic diseases of Zhejiang Province, Hangzhou 310006, Zhejiang Province, China (G.-L.S.).
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Ma YT, Hua F, Zhong XM, Xue YJ, Li J, Nie YC, Zhang XD, Ma JW, Lin CH, Zhang HZ, He W, Sha D, Zhao MQ, Yao ZG. 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. Front Immunol 2023; 14:1269097. [PMID: 38022513 PMCID: PMC10657814 DOI: 10.3389/fimmu.2023.1269097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
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.
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Affiliation(s)
- Yu-Ting Ma
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Fang Hua
- Department of Microbiology and Immunology, Tulane University, New Orleans, LA, United States
| | - Xiu-Ming Zhong
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Ying-Jie Xue
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Jia Li
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yi-Cong Nie
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Xue-Dong Zhang
- Department of Pathology, Liaocheng People’s Hospital, Liaocheng, Shandong, China
| | - Ji-Wei Ma
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Cun-Hu Lin
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Hao-Zhuang Zhang
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Wei He
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Dan Sha
- Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Miao-Qing Zhao
- Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Zhi-Gang Yao
- Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Pathology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
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