Gene Expression Profiling of Pancreas Neuroendocrine Tumors with Different Ki67-Based Grades

RIPK3 activation promotes DAXX-dependent neuronal necroptosis after intracerebral hemorrhage in mice

BACKGROUND

Necroptosis induced by receptor-interacting protein kinase 3 (RIPK3) is engaged in intracerebral hemorrhage (ICH) pathology. In this study, we explored the impact of RIPK3 activation on neuronal necroptosis and the mechanism of the death domain-associated protein (DAXX)-mediated nuclear necroptosis pathway after ICH.

METHODS

Potential molecules linked to the progression of ICH were discovered using RNA sequencing. The level of DAXX was assessed by quantitative real-time PCR, ELISA, and western blotting. DAXX localization was determined by immunofluorescence and immunoprecipitation assays. The RIPK3 inhibitor GSK872 and DAXX knockdown with shRNA-DAXX were used to examine the nuclear necroptosis pathway associated with ICH. Neurobehavioral deficit assessments were performed.

RESULTS

DAXX was increased in patients and mice after ICH. In an ICH mouse model, shRNA-DAXX reduced brain water content and alleviated neurologic impairments. GSK872 administration reduced the expression of DAXX. shRNA-DAXX inhibited the expression of p-MLKL. Immunofluorescence and immunoprecipitation assays showed that RIPK3 and AIF translocated into the nucleus and then bound with nuclear DAXX.

CONCLUSIONS

RIPK3 revitalization promoted neuronal necroptosis in ICH mice, partially through the DAXX signaling pathway. RIPK3 and AIF interacted with nuclear DAXX to aggravate ICH injury.

Blood-based Proteomic Signatures Associated With MEN1-related Duodenopancreatic Neuroendocrine Tumor Progression

PURPOSE

Patients with multiple endocrine neoplasia type 1 (MEN1) are predisposed to develop duodenopancreatic neuroendocrine tumors (dpNETs), and metastatic dpNET is the primary cause of disease-related mortality. Presently, there is a paucity of prognostic factors that can reliably identify patients with MEN1-related dpNETS who are at high risk of distant metastasis. In the current study, we aimed to establish novel circulating molecular protein signatures associated with disease progression.

EXPERIMENTAL DESIGN

Mass spectrometry-based proteomic profiling was conducted on plasmas procured through an international collaboration between MD Anderson Cancer Center, the National Institutes of Health, and the University Medical Center Utrecht from a cohort of 56 patients with MEN1 [14 with distant metastasis dpNETs (cases) and 42 with either indolent dpNETs or no dpNETs (controls)]. Findings were compared to proteomic profiles generated from serially collected plasmas from a mouse model of Men1-pancreatic neuroendocrine tumors (Men1fl/flPdx1-CreTg) and control mice (Men1fl/fl).

RESULTS

A total of 187 proteins were found to be elevated in MEN1 patients with distant metastasis compared to controls, including 9 proteins previously associated with pancreatic cancer and other neuronal proteins. Analyses of mouse plasmas revealed 196 proteins enriched for transcriptional targets of oncogenic MYCN, YAP1, POU5F1, and SMAD that were associated with disease progression in Men1fl/flPdx1-CreTg mice. Cross-species intersection revealed 19 proteins positively associated with disease progression in both human patients and in Men1fl/flPdx1-CreTg mice.

CONCLUSIONS

Our integrated analyses identified novel circulating protein markers associated with disease progression in MEN1-related dpNET.

Transcriptome analysis of primary sporadic neuroendocrine tumours of the intestine identified three different molecular subgroups

BACKGROUND

Intestinal neuroendocrine tumours (I-NETs) represent a non-negligible entity among intestinal neoplasms, with metastatic spreading usually present at the time of diagnosis. In this context, effective molecular actionable targets are still lacking. Through transcriptome analysis, we aim at refining the molecular taxonomy of I-NETs, also providing insights towards the identification of new therapeutic vulnerabilities.

MATERIALS AND METHODS

A retrospective series of 38 primary sporadic, surgically-resected I-NETs were assessed for transcriptome profiling of 20,815 genes.

RESULTS

Transcriptome analysis detected 643 highly expressed genes. Unsupervised hierarchical clustering, differential expression analysis and gene set enriched analysis identified three different tumour clusters (CL): CL-A, CL-B, CL-C. CL-A showed the overexpression of ARGFX, BIRC8, NANOS2, and SSTR4 genes. Its most characterizing signatures were those related to cell-junctions, and activation of mTOR and WNT pathway. CL-A was also enriched in T CD8 + lymphocytes. CL-B showed the overexpression of PCSK1, QPCT, ST18, and TPH1 genes. Its most characterizing signatures were those related to adipogenesis, neuroendocrine metabolism, and splice site machinery-related processes. CL-B was also enriched in T CD4 + lymphocytes. CL-C showed the overexpression of ALB, ANG, ARG1, and HP genes. Its most characterizing signatures were complement/coagulation and xenobiotic metabolism. CL-C was also enriched in M1/2 macrophages. These CL-based differences may have therapeutic implications in refining the management of I-NET patients. At last, we described a specific gene-set for differentiating I-NET from pancreatic NET.

DISCUSSION

Our data represent an additional step for refining the molecular taxonomy of I-NET, identifying novel transcriptome subgroups with different biology and therapeutic opportunities.

Diagnostic, Prognostic, and Predictive Role of Ki67 Proliferative Index in Neuroendocrine and Endocrine Neoplasms: Past, Present, and Future

The introduction of Ki67 immunohistochemistry in the work-up of neuroendocrine neoplasms (NENs) has opened a new approach for their diagnosis and prognostic evaluation. Since the first demonstration of the prognostic role of Ki67 proliferative index in pancreatic NENs in 1996, several studies have been performed to explore its prognostic, diagnostic, and predictive role in other neuroendocrine and endocrine neoplasms. A large amount of information is now available and published results globally indicate that Ki67 proliferative index is useful to this scope, although some differences exist in relation to tumor site and type. In gut and pancreatic NENs, the Ki67 proliferative index has a well-documented and accepted diagnostic and prognostic role and its evaluation is mandatory in their diagnostic work-up. In the lung, the Ki67 index is recommended for the diagnosis of NENs on biopsy specimens, but its diagnostic role in surgical specimens still remains to be officially accepted, although its prognostic role is now well documented. In other organs, such as the pituitary, parathyroid, thyroid (follicular cell-derived neoplasms), and adrenal medulla, the Ki67 index does not play a diagnostic role and its prognostic value still remains a controversial issue. In medullary thyroid carcinoma, the Ki67 labelling index is used to define the tumor grade together with other morphological parameters, while in the adrenal cortical carcinoma, it is useful to select patients to treated with mitotane therapy. In the present review, the most important information on the diagnostic, prognostic, and predictive role of Ki67 proliferative index is presented discussing the current knowledge. In addition, technical issues related to the evaluation of Ki67 proliferative index and the future perspectives of the application of Ki67 immunostaining in endocrine and neuroendocrine neoplasms is discussed.

Transcriptomic Deconvolution of Neuroendocrine Neoplasms Predicts Clinically Relevant Characteristics

Pancreatic neuroendocrine neoplasms (panNENs) are a rare yet diverse type of neoplasia whose precise clinical-pathological classification is frequently challenging. Since incorrect classifications can affect treatment decisions, additional tools which support the diagnosis, such as machine learning (ML) techniques, are critically needed but generally unavailable due to the scarcity of suitable ML training data for rare panNENs. Here, we demonstrate that a multi-step ML framework predicts clinically relevant panNEN characteristics while being exclusively trained on widely available data of a healthy origin. The approach classifies panNENs by deconvolving their transcriptomes into cell type proportions based on shared gene expression profiles with healthy pancreatic cell types. The deconvolution results were found to provide a prognostic value with respect to the prediction of the overall patient survival time, neoplastic grading, and carcinoma versus tumor subclassification. The performance with which a proliferation rate agnostic deconvolution ML model could predict the clinical characteristics was found to be comparable to that of a comparative baseline model trained on the proliferation rate-informed MKI67 levels. The approach is novel in that it complements established proliferation rate-oriented classification schemes whose results can be reproduced and further refined by differentiating between identically graded subgroups. By including non-endocrine cell types, the deconvolution approach furthermore provides an in silico quantification of panNEN dedifferentiation, optimizing it for challenging clinical classification tasks in more aggressive panNEN subtypes.

An Insight on Functioning Pancreatic Neuroendocrine Neoplasms

Pancreatic neuroendocrine neoplasms (PanNENs) are rare neoplasms arising from islets of the Langerhans in the pancreas. They can be divided into two groups, based on peptide hormone secretion, functioning and nonfunctioning PanNENs. The first group is characterized by different secreted peptides causing specific syndromes and is further classified into subgroups: insulinoma, gastrinoma, glucagonoma, somatostatinoma, VIPoma and tumors producing serotonin and adrenocorticotrophic hormone. Conversely, the second group does not release peptides and is usually associated with a worse prognosis. Today, although the efforts to improve the therapeutic approaches, surgery remains the only curative treatment for patients with PanNENs. The development of high-throughput techniques has increased the molecular knowledge of PanNENs, thereby allowing us to understand better the molecular biology and potential therapeutic vulnerabilities of PanNENs. Although enormous advancements in therapeutic and molecular aspects of PanNENs have been achieved, there is poor knowledge about each subgroup of functioning PanNENs.Therefore, we believe that combining high-throughput platforms with new diagnostic tools will allow for the efficient characterization of the main differences among the subgroups of functioning PanNENs. In this narrative review, we summarize the current landscape regarding diagnosis, molecular profiling and treatment, and we discuss the future perspectives of functioning PanNENs.

[Correlation Analysis of Ki67 Expression and EGFR Mutation on the Risk of Recurrence and Metastasis in Postoperative Patients with Stage I Lung Adenocarcinoma]

BACKGROUND

The prognosis of stage I non-small cell lung cancer (NSCLC) is generally good. However, some of the stage I NSCLC patients still may have early recurrence and metastasis, and there is no standard method to screen this part of the population. The aim of this study is to investigate the relationship between Ki67 expression as well as epidermal growth factor receptor (EGFR) mutation and the risk of recurrence in postoperative patients with stage I lung adenocarcinoma.

METHODS

We retrospectively enrolled 118 postoperative patients with stage I lung adenocarcinoma. EGFR mutation was tested using amplification refractory mutation system polymerase chain reaction (ARMS-PCR) , and Ki67 level was detected by immunohistochemistry (IHC), followed by the collection of the patients' clinical characteristics. Kaplan-Meier method, Log-rank test, and Cox proportional hazards regression model were used for the prognostic statistical analysis.

RESULTS

Among the 118 patients, the rate of high Ki67 expression was 43.22% (51/118), which is related to gender, smoking status, surgical method, differentiation degree, and postoperative stage (P<0.05). Meanwhile, EGFR mutation rate was 61.02% (72/118), of which EGFR exon 19 deletion mutation rate was 19.49% (23/118), and the EGFR exon 21 L858R mutation rate was 41.53% (49/118). However, Ki67 expression was not associated with EGFR mutation status (χ2=1.412, P=0.235). Survival analysis showed that high Ki67 expression was inversely associated with disease-free survival (DFS) and overall survival (OS) in stage I lung adenocarcinoma (P<0.05), but EGFR mutation status was not significantly associated with DFS and OS (P>0.05). In the subgroup analysis, the DFS of the EGFR exon 19 deletion group was significantly decreased compared with the EGFR exon 21 L858R mutation group (P=0.031), but there was no significant difference in OS (P=0.308). Multivariate analysis showed that there was statistical significance between Ki67 expression (P=0.001) and DFS in stage I lung adenocarcinoma; Ki67 expression (P=0.03) and gender (P=0.015) were associated with OS in stage I lung adenocarcinoma.

CONCLUSIONS

Ki67 expression is an independent influencing factor for postoperative recurrence and OS of stage I lung adenocarcinoma and it is not significantly associated with EGFR mutation. There is no significant difference between EGFR mutation status and the prognostis of stage I lung adenocarcinoma. However, the prognosis differed in EGFR mutation types; the patients with EGFR exon 19 deletion are at higher risk of recurrence than EGFR exon 21 L858R mutation.

Clinical and genomic analyses of neuroendocrine neoplasms of the breast

Breast neuroendocrine neoplasms (NENs) constitute a rare histologic subtype that includes both neuroendocrine tumors (NETs) and neuroendocrine carcinomas (NECs). In this study, we aimed to gain insight into the clinical and molecular characteristics of NENs of the breast. NEN and paired distant normal fresh tissues and clinicopathological data were obtained from 17 patients with NENs, and clinicopathological data were collected from 755 patients with invasive breast carcinomas of no special type (IBCs-NST). We compared the clinicopathological characteristics of NENs and IBCs-NST and performed whole-exome sequencing (WES) of both NEN and paired normal tissues. Compared with the IBC-NST patients, the NEN patients had a higher mean age, lower clinical stage, and lower pathological nodal (pN) stage (P < 0.001, P < 0.001, and P = 0.017, respectively). The most frequently mutated gene in NENs was KMT2C (3/17, 17.6%). NENs had copy number variations (CNVs) of 8q, 11q, and 17q amplification and 17q and 11q deletion and harbored the following specific genes related to tumorigenesis: (i) suppressor genes with loss of heterozygosity (LOH) such as ACE (2/17, 11.8%); (ii) tumor driver genes such as GATA3 (2/17, 11.8%); and (iii) susceptibility genes such as MAP3K4 (17/17, 100%) and PDE4DIP (17/17, 100%). The oncogenic/likely oncogenic mutations of NETs in PI3K pathway genes (50.0%, 18.2%; P < 0.001) and MAPK signaling pathway genes (83.3%, 18.2%; P = 0.035) affected higher proportions than those of NECs. In conclusion, this study provides certain clinical and molecular evidence supporting NENs as a distinct subtype of breast cancer and provides some potential molecular features for distinguishing NETs from NECs.

The Utility of Endoscopic-Ultrasonography-Guided Tissue Acquisition for Solid Pancreatic Lesions

Endoscopic-ultrasonography-guided tissue acquisition (EUS-TA) has been widely performed for the definitive diagnosis of solid pancreatic lesions (SPLs). As the puncture needles, puncture techniques, and sample processing methods have improved, EUS-TA has shown higher diagnostic yields and safety. Recently, several therapeutic target genomic biomarkers have been clarified in pancreatic ductal carcinoma (PDAC). Although only a small proportion of patients with PDAC can benefit from precision medicine based on gene mutations at present, precision medicine will also be further developed for SPLs as more therapeutic target genomic biomarkers are identified. Advances in next-generation sequencing (NGS) techniques enable the examination of multiple genetic mutations in limited tissue samples. EUS-TA is also useful for NGS and will play a more important role in determining treatment strategies. In this review, we describe the utility of EUS-TA for SPLs.