Clinical Management of Neuroendocrine Neoplasms in Clinical Practice: A Formal Consensus Exercise

Many treatment approaches are now available for neuroendocrine neoplasms (NENs). While several societies have issued guidelines for diagnosis and treatment of NENs, there are still areas of controversy for which there is limited guidance. Expert opinion can thus be of support where firm recommendations are lacking. A group of experts met to formulate 14 statements relative to diagnosis and treatment of NENs and presented herein. The nominal group and estimate-talk-estimate techniques were used. The statements covered a broad range of topics from tools for diagnosis to follow-up, evaluation of response, treatment efficacy, therapeutic sequence, and watchful waiting. Initial prognostic characterization should be based on clinical information as well as histopathological analysis and morphological and functional imaging. It is also crucial to optimize RLT for patients with a NEN starting from accurate characterization of the patient and disease. Follow-up should be patient/tumor tailored with a shared plan about timing and type of imaging procedures to use to avoid safety issues. It is also stressed that patient-reported outcomes should receive greater attention, and that a multidisciplinary approach should be mandatory. Due to the clinical heterogeneity and relative lack of definitive evidence for NENs, personalization of diagnostic–therapeutic work-up is crucial

Phase 3 Trial of 177Lu-Dotatate for Midgut Neuroendocrine Tumors

Background Patients with advanced midgut neuroendocrine tumors who have had disease progression during first-line somatostatin analogue therapy have limited therapeutic options. This randomized, controlled trial evaluated the efficacy and safety of lutetium-177 (177Lu)-Dotatate in patients with advanced, progressive, somatostatin-receptor-positive midgut neuroendocrine tumors. Methods We randomly assigned 229 patients who had well-differentiated, metastatic midgut neuroendocrine tumors to receive either 177Lu-Dotatate (116 patients) at a dose of 7.4 GBq every 8 weeks (four intravenous infusions, plus best supportive care including octreotide long-acting repeatable [LAR] administered intramuscularly at a dose of 30 mg) (177Lu-Dotatate group) or octreotide LAR alone (113 patients) administered intramuscularly at a dose of 60 mg every 4 weeks (control group). The primary end point was progression-free survival. Secondary end points included the objective response rate, overall survival, safety, and the side-effect profile. The final analysis of overall survival will be conducted in the future as specified in the protocol; a prespecified interim analysis of overall survival was conducted and is reported here. Results At the data-cutoff date for the primary analysis, the estimated rate of progression-free survival at month 20 was 65.2% (95% confidence interval [CI], 50.0 to 76.8) in the 177Lu-Dotatate group and 10.8% (95% CI, 3.5 to 23.0) in the control group. The response rate was 18% in the 177Lu-Dotatate group versus 3% in the control group (P<0.001). In the planned interim analysis of overall survival, 14 deaths occurred in the 177Lu-Dotatate group and 26 in the control group (P=0.004). Grade 3 or 4 neutropenia, thrombocytopenia, and lymphopenia occurred in 1%, 2%, and 9%, respectively, of patients in the 177Lu-Dotatate group as compared with no patients in the control group, with no evidence of renal toxic effects during the observed time frame. Conclusions Treatment with 177Lu-Dotatate resulted in markedly longer progression-free survival and a significantly higher response rate than high-dose octreotide LAR among patients with advanced midgut neuroendocrine tumors. Preliminary evidence of an overall survival benefit was seen in an interim analysis; confirmation will be required in the planned final analysis. Clinically significant myelosuppression occurred in less than 10% of patients in the 177Lu-Dotatate group. (Funded by Advanced Accelerator Applications; NETTER-1 ClinicalTrials.gov number, NCT01578239 ; EudraCT number 2011-005049-11

Pitfalls in neuroendocrine tumor diagnosis

Not available<br /

Methodological framework for radiomics applications in Hodgkin’s lymphoma

Background: According to published data, radiomics features differ between lesions of refractory/relapsing HL patients from those of long-term responders. However, several methodological aspects have not been elucidated yet. Purpose: The study aimed at setting up a methodological framework in radiomics applications in Hodgkin’s lymphoma (HL), especially at (a) developing a novel feature selection approach, (b) evaluating radiomic intra-patient lesions’ similarity, and (c) classifying relapsing refractory (R/R) vs non-(R/R) patients. Methods: We retrospectively included 85 patients (male:female = 52:33; median age 35 years, range 19–74). LIFEx (www.lifexsoft.org) was used for [18F]FDG-PET/CT segmentation and feature extraction. Features were a-priori selected if they were highly correlated or uncorrelated to the volume. Principal component analysis-transformed features were used to build the fingerprints that were tested to assess lesions’ similarity, using the silhouette. For intra-patient similarity analysis, we used patients having multiple lesions only. To classify patients as non-R/R and R/R, the fingerprint considering one single lesion (fingerprint_One) and all lesions (fingerprint_All) was tested using Random Undersampling Boosting of Tree Ensemble (RUBTE). Results: HL fingerprints included up to 15 features. Intra-patient lesion similarity analysis resulted in mean/median silhouette values below 0.5 (low similarity especially in the non-R/R group). In the test set, the fingerprint_One classification accuracy was 62% (78% sensitivity and 53% specificity); the classification by RUBTE using fingerprint_All resulted in 82% accuracy (70% sensitivity and 88% specificity). Conclusions: Lesion similarity analysis was developed, and it allowed to demonstrate that HL lesions were not homogeneous within patients in terms of radiomics signature. Therefore, a random target lesion selection should not be adopted for radiomics applications. Moreover, the classifier to predict R/R vs non-R/R performed the best when all the lesions were used

Methodological framework for radiomics applications in Hodgkin’s lymphoma

According to published data, radiomics features differ between lesions of refractory/relapsing HL patients from those of long-term responders. However, several methodological aspects have not been elucidated yet. The study aimed at setting up a methodological framework in radiomics applications in Hodgkin’s lymphoma (HL), especially at (a) developing a novel feature selection approach, (b) evaluating radiomic intra-patient lesions’ similarity, and (c) classifying relapsing refractory (R/R) vs non-(R/R) patients. We retrospectively included 85 patients (male:female = 52:33; median age 35 years, range 19–74). LIFEx (www.lifexsoft.org) was used for [18F]FDG-PET/CT segmentation and feature extraction. Features were a-priori selected if they were highly correlated or uncorrelated to the volume. Principal component analysis transformed features were used to build the fingerprints that were tested to assess lesions’ similarity, using the silhouette. For intra-patient similarity analysis, we used patients having multiple lesions only. To classify patients as non-R/R and R/R, the fingerprint considering one single lesion (fingerprint_One) and all lesions (fingerprint_All) was tested using Random Undersampling Boosting of Tree Ensemble (RUBTE). HL fingerprints included up to 15 features. Intra-patient lesion similarity analysis resulted in mean/median silhouette values below 0.5 (low similarity especially in the non-R/R group). In the test set, the fingerprint_One classification accuracy was 62% (78% sensitivity and 53% specificity); the classification by RUBTE using fingerprint_All resulted in 82% accuracy (70% sensitivity and 88% specificity). Lesion similarity analysis was developed, and it allowed to demonstrate that HL lesions were not homogeneous within patients in terms of radiomics signature. Therefore, a random target lesion selection should not be adopted for radiomics applications. Moreover, the classifier to predict R/R vs non-R/R performed the best when all the lesions were used

miR-451a is underexpressed and targets AKT/mTOR pathway in papillary thyroid carcinoma

Papillary Thyroid Carcinoma (PTC) is the most frequent thyroid cancer. Although several PTC-specific miRNA profiles have been reported, only few upregulated miRNAs are broadly recognized, while less consistent data are available about downregulated miRNAs. In this study we investigated miRNA deregulation in PTC by miRNA microarray, analysis of a public dataset from The Cancer Genome Atlas (TCGA), literature review and meta-analysis based on a univocal miRNA identifier derived from miRBase v21. A list of 18 miRNAs differentially expressed between PTC and normal thyroid was identified and validated in the TCGA dataset. Furthermore, we compared our signature with miRNA profiles derived from 15 studies selected from literature. Then, to select possibly functionally relevant miRNA, we integrated our miRNA signature with those from two in vitro cell models based on the PTC-driving oncogene RET/PTC1. Through this strategy, we identified commonly deregulated miRNAs, including miR-451a, which emerged also by our meta-analysis as the most frequently reported downregulated miRNA. We showed that lower expression of miR-451a correlates with aggressive clinical-pathological features of PTC as tall cell variant, advanced stage and extrathyroid extension. In addition, we demonstrated that ectopic expression of miR-451a impairs proliferation and migration of two PTC-derived cell lines, reduces the protein levels of its recognized targets MIF, c-MYC and AKT1 and attenuates AKT/mTOR pathway activation.Overall, our study provide both an updated overview of miRNA deregulation in PTC and the first functional evidence that miR-451a exerts tumor suppressor functions in this neoplasia

Multiple endocrine neoplasia type 2 syndromes (MEN 2): results from the ItaMEN network analysis on the prevalence of different genotypes and phenotypes.

OBJECTIVE: Multiple endocrine neoplasia type 2 (MEN 2) is a genetic disease characterized by medullary thyroid carcinoma (MTC) associated (MEN 2A and 2B) or not familial MTC (FMTC) with other endocrine neoplasia due to germline RET gene mutations. The prevalence of these rare genetic diseases and their corresponding RET mutations are unknown due to the small size of the study population. METHODS: We collected data on germline RET mutations of 250 families with hereditary MTC followed in 20 different Italian centres. RESULTS AND CONCLUSIONS: The most frequent RET amino acid substitution was Val804Met (19.6%) followed by Cys634Arg (13.6%). A total of 40 different germline RET mutations were present. Six families (2.4%) were negative for germline RET mutations. The comparison of the prevalence of RET germline mutations in the present study with those published by other European studies showed a higher prevalence of Val804Met and Ser891Ala mutations and a lower prevalence of Leu790Phe and Tyr791Phe (P<0.0001). A statistically significant higher prevalence of mutations affecting non-cysteine codons was also found (P<0.0001). Furthermore, the phenotype data collection showed an unexpected higher prevalence of FMTC (57.6%) with respect to other MEN 2 syndromes (34% MEN 2A and 6.8% of MEN 2B). In conclusion, we observed a statistically significant different pattern of RET mutations in Italian MEN 2 families with respect to other European studies and a higher prevalence of FMTC phenotype. The different ethnic origins of the patients and the particular attention given to analysing apparently sporadic MTC for RET germline mutations may explain these findings

Building pathway clusters from Random Forests classification using class votes

<p>Abstract</p> <p>Background</p> <p>Recent years have seen the development of various pathway-based methods for the analysis of microarray gene expression data. These approaches have the potential to bring biological insights into microarray studies. A variety of methods have been proposed to construct networks using gene expression data. Because individual pathways do not act in isolation, it is important to understand how different pathways coordinate to perform cellular functions. However, there are no published methods describing how to build pathway clusters that are closely related to traits of interest.</p> <p>Results</p> <p>We propose to build pathway clusters from pathway-based classification methods. The proposed methods allow researchers to identify clusters of pathways sharing similar functions. These pathways may or may not share genes. As an illustration, our approach is applied to three human breast cancer microarray data sets. We found that our methods yielded consistent and interpretable results for these three data sets. We further investigated one of the pathway clusters found using PubMatrix. We found that informative genes in the pathway clusters do have more publications with keywords, like estrogen receptor, compared with informative genes in other top pathways. In addition, using the shortest path analysis in GeneGo's MetaCore and Human Protein Reference Database, we were able to identify the links which connect the pathways without shared genes within the pathway cluster.</p> <p>Conclusion</p> <p>Our proposed pathway clustering methods allow bioinformaticians and biologists to investigate how informative genes within pathways are related to each other and understand possible crosstalk between pathways in a cluster. Therefore, building pathway clusters may lead to a better understanding of molecular mechanisms affecting a trait of interest, and help generate further biological hypotheses from gene expression data.</p