scholarly journals A molecular map of lung neuroendocrine neoplasms

GigaScience ◽  
2020 ◽  
Vol 9 (11) ◽  
Author(s):  
Aurélie A G Gabriel ◽  
Emilie Mathian ◽  
Lise Mangiante ◽  
Catherine Voegele ◽  
Vincent Cahais ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Large Cell ◽  
Molecular Data ◽  
Gene Expression Omnibus ◽  
Neuroendocrine Neoplasms ◽  
Published Data ◽  
Data Generation ◽  
Data Repositories ◽  
Molecular Map ◽  
Pulmonary Carcinoids

Abstract Background Lung neuroendocrine neoplasms (LNENs) are rare solid cancers, with most genomic studies including a limited number of samples. Recently, generating the first multi-omic dataset for atypical pulmonary carcinoids and the first methylation dataset for large-cell neuroendocrine carcinomas led us to the discovery of clinically relevant molecular groups, as well as a new entity of pulmonary carcinoids (supra-carcinoids). Results To promote the integration of LNENs molecular data, we provide here detailed information on data generation and quality control for whole-genome/exome sequencing, RNA sequencing, and EPIC 850K methylation arrays for a total of 84 patients with LNENs. We integrate the transcriptomic data with other previously published data and generate the first comprehensive molecular map of LNENs using the Uniform Manifold Approximation and Projection (UMAP) dimension reduction technique. We show that this map captures the main biological findings of previous studies and can be used as reference to integrate datasets for which RNA sequencing is available. The generated map can be interactively explored and interrogated on the UCSC TumorMap portal (https://tumormap.ucsc.edu/?p=RCG_lungNENomics/LNEN). The data, source code, and compute environments used to generate and evaluate the map as well as the raw data are available, respectively, in a Nextjournal interactive notebook (https://nextjournal.com/rarecancersgenomics/a-molecular-map-of-lung-neuroendocrine-neoplasms/) and at the EMBL-EBI European Genome-phenome Archive and Gene Expression Omnibus data repositories. Conclusions We provide data and all resources needed to integrate them with future LNENs transcriptomic studies, allowing meaningful conclusions to be drawn that will eventually lead to a better understanding of this rare understudied disease.

scholarly journals Mixed Neuroendocrine/Non-neuroendocrine Neoplasm (MiNEN) of the Ovary Arising from Endometriosis: Molecular Pathology Analysis in Support of a Pathogenetic Paradigm

2021 ◽  
Author(s):  
Roberta Maragliano ◽  
Laura Libera ◽  
Ileana Carnevali ◽  
Valeria Pensotti ◽  
Giovanna De Vecchi ◽  
...  
Keyword(s):  
Large Cell ◽  
Neuroendocrine Neoplasm ◽  
Molecular Profile ◽  
Neuroendocrine Neoplasms ◽  
Fish Analysis ◽  
Sequencing Analysis ◽  
Endometrioid Carcinoma ◽  
Preneoplastic Lesions ◽  
Ovarian Endometriosis ◽  
Neuroendocrine Carcinomas

AbstractPrimary ovarian neuroendocrine neoplasms (Ov-NENs) are infrequent and mainly represented by well-differentiated forms (neuroendocrine tumors — NETs — or carcinoids). Poorly differentiated neuroendocrine carcinomas (Ov-NECs) are exceedingly rare and only few cases have been reported in the literature. A subset of Ov-NECs are admixed with non-neuroendocrine carcinomas, as it occurs in other female genital organs, as well (mostly endometrium and uterine cervix), and may be assimilated to mixed neuroendocrine/non-neuroendocrine neoplasms (MiNENs) described in digestive and extra-digestive sites. Here, we present a case of large cell Ov-NEC admixed with an endometrioid carcinoma of the ovary, arising in the context of ovarian endometriosis, associated with a uterine endometrial atypical hyperplasia (EAH). We performed targeted next-generation sequencing analysis, along with a comprehensive immunohistochemical study and FISH analysis for TP53 locus, separately on the four morphologically distinct lesions (Ov-NEC, endometrioid carcinoma, endometriosis, and EAH). The results of our study identified molecular alterations of cancer-related genes (PIK3CA, CTNNB1, TP53, RB1, ARID1A, and p16), which were present with an increasing gradient from preneoplastic lesions to malignant proliferations, both neuroendocrine and non-neuroendocrine components. In conclusion, our findings underscored that the two neoplastic components of this Ov-MiNEN share a substantially identical molecular profile and they progress from a preexisting ovarian endometriotic lesion, in a patient with a coexisting preneoplastic proliferation of the endometrium, genotypically and phenotypically related to the ovarian neoplasm. Moreover, this study supports the inclusion of MiNEN in the spectrum ovarian and, possibly, of all gynecological NENs, among which they are currently not classified.

scholarly journals SpiderSeqR: an R package for crawling the web of high-throughput multi-omic data repositories for data-sets and annotation

2020 ◽  
Author(s):  
Anna M. Sozanska ◽  
Charles Fletcher ◽  
Dóra Bihary ◽  
Shamith A. Samarajiwa
Keyword(s):  
High Throughput ◽  
R Package ◽  
Data Reuse ◽  
Massively Parallel ◽  
Data Sets ◽  
Similar Data ◽  
Data Generation ◽  
Data Repositories ◽  
Public Data ◽  
Omic Data

AbstractMore than three decades ago, the microarray revolution brought about high-throughput data generation capability to biology and medicine. Subsequently, the emergence of massively parallel sequencing technologies led to many big-data initiatives such as the human genome project and the encyclopedia of DNA elements (ENCODE) project. These, in combination with cheaper, faster massively parallel DNA sequencing capabilities, have democratised multi-omic (genomic, transcriptomic, translatomic and epigenomic) data generation leading to a data deluge in bio-medicine. While some of these data-sets are trapped in inaccessible silos, the vast majority of these data-sets are stored in public data resources and controlled access data repositories, enabling their wider use (or misuse). Currently, most peer reviewed publications require the deposition of the data-set associated with a study under consideration in one of these public data repositories. However, clunky and difficult to use interfaces, subpar or incomplete annotation prevent discovering, searching and filtering of these multi-omic data and hinder their re-purposing in other use cases. In addition, the proliferation of multitude of different data repositories, with partially redundant storage of similar data are yet another obstacle to their continued usefulness. Similarly, interfaces where annotation is spread across multiple web pages, use of accession identifiers with ambiguous and multiple interpretations and lack of good curation make these data-sets difficult to use. We have produced SpiderSeqR, an R package, whose main features include the integration between NCBI GEO and SRA databases, enabling an integrated unified search of SRA and GEO data-sets and associated annotations, conversion between database accessions, as well as convenient filtering of results and saving past queries for future use. All of the above features aim to promote data reuse to facilitate making new discoveries and maximising the potential of existing data-sets.Availabilityhttps://github.com/ss-lab-cancerunit/SpiderSeqR

scholarly journals scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Combiz Khozoie ◽  
Nurun Fancy ◽  
Mahdi Moradi Marjaneh ◽  
Alan E. Murphy ◽  
Paul M. Matthews ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Sparse Matrix ◽  
Data Generation ◽  
Sequencing Data ◽  
Alternative Analysis ◽  
Analysis Pipeline ◽  
Matrix Quality ◽  
Single Cell Rna Sequencing ◽  
Public Datasets

Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput: datasets with over a million cells are becoming commonplace. The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods, led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data. The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities. Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users. We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.

scholarly journals Shaping our Taxonomic Legacy through Openly Sharing Primary Biodiversity Data in Taxonomic Revisions

2019 ◽  
Vol 3 ◽  
Author(s):  
Torsten Dikow
Keyword(s):  
Data Dissemination ◽  
Original Description ◽  
Published Data ◽  
Data Standards ◽  
Global Biodiversity Information Facility ◽  
High Quality ◽  
Data Repositories ◽  
The Past ◽  
Text Document ◽  
Digital Format

Taxonomy has a long tradition of describing earth’s biodiversity. For the past 20 years or so, taxonomic revisions have become available in PDF format, which is regarded by most practicing taxonomists to be a good means of digital dissemination. However, a PDF document is nothing more than a text document that can be transferred easily for viewing among researchers and computer platforms. In today’s world, traditional taxonomic techniques need to be met with novel tools to make data dissemination a reality, make species hypotheses more robust, and open the field up to rigorous scientific testing. Here, I argue that high-quality taxonomic output is not just the publication of detailed species descriptions and re-descriptions, precise taxon delimitations, easy-to-use identification keys, and comprehensively undertaken and illustrated revisions. Rather, in addition high-quality taxonomic output embraces digital workflows and data standards to disseminate captured and published data in structured, machine-readable formats to data repositories so as to make all data openly accessible. Imagine that a taxonomist today has every original description and every subsequent re-description of a species at her/his fingertips online, has every specimen photograph produced by a previous reviser digitally available in the original resolution, and can take advantage of existing, openly accessible data and resources produced by peers in digital format in the past. When we as taxonomists provide such findable, accessible, interoperable, and reusable (FAIR) data, the future of biodiversity discovery will accelerate and our own taxonomic legacy will be enhanced. Cybertaxonomic tools provide methods to accomplish this goal and their use and implementation is here summarized in the context of revisionary taxonomy from the standpoint of a publishing taxonomist. While many of the tools have been around for some time now, very few taxonomists embrace and utilize these tools in their publications. This presentation will provide information on what kind of data can and should be openly shared (e.g., specimen occurrence data, digital images, names, descriptions, authors) and outline best practices utilizing globally unique identifiers for specimens and data. Data standards and the best-suited data repositories such as the Global Biodiversity Information Facility (GBIF) and Zenodo, with its Biodiversity Literature Repository, and the Plazi TreatmentBank, an emerging species portal, are discussed to illustrate retrospective and prospective data capture of taxonomic revisions.

Patterns of lung cancer in people living with human immunodeficiency virus (HIV): A retrospective, single-center study in Washington, D.C.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21154-e21154
Author(s):  
Margaret Pruitt ◽  
Rajesh Naidu Janapala ◽  
Faysal Haroun
Keyword(s):  
Lung Cancer ◽  
Late Stage ◽  
Early Stage ◽  
Primary Lung Cancer ◽  
Large Cell ◽  
Molecular Data ◽  
Stage I ◽  
Stage Iii ◽  
Small Cell Lung ◽  
Neuroendocrine Cancer

e21154 Background: Lung cancer is the leading cause of cancer death and the most common non-acquired immune deficiency syndrome defining malignancy in people living with HIV (PLWH). Disparities in outcomes have been observed despite lung cancer mortality reportedly decreasing in the general population over the last decade due to lower rates of smoking and the advent of novel therapies. To better understand the current trend in lung cancer in PLWH, we explored demographic characteristics, comorbidities, and lung cancer pathology and molecular data in this population. Methods: A retrospective search of patient charts was conducted from 2004 to January 2021 using billing codes for HIV and primary lung cancer. Patients who had incorrect HIV or primary lung cancer diagnoses were excluded. Results: The search yielded 45 patients, of which 11 were excluded as described above: 66% were males, 82% African American, and 18% Caucasian. About two-thirds of patients were living in zip codes with predominantly low to medium household incomes. The median pack years of patients diagnosed with Stage I or II non-small cell lung cancer (NSCLC) was 40, Stage III or IV NSCLC was 20, early stage small cell lung cancer (SCLC) was 30, and late stage SCLC was 60. The median time between HIV and lung cancer diagnoses was 21.7 years for Stage I or II NSCLC, 17.1 years for Stage III or IV NSCLC, 15.2 for early stage SCLC, and 13.3 for late stage SCLC. Of 26 patients with viral load (VL) data, 21 (80.7%) had VL less than 500 when lung cancer was diagnosed. Of the 33 charts with available pathology data, there were 16 adenocarcinomas, 6 squamous carcinomas, 3 adenosquamous carcinomas, 1 large cell neuroendocrine cancer, 4 SCLCs, 1 mesothelioma, and 2 unspecified NSCLCs. Of 19 patients with a histologic grade, 11 had a high-grade tumor (57.9%). For the NSCLCs, 8 were Stage I (28.5%), 2 Stage II (7.1%), 8 Stage III (28.5%), 9 Stage IV (32.1%), and 1 with an unspecified stage. One SCLC was early stage and the remaining 3 were late stage. Five patients had brain metastasis. Molecular data or PDL-1 expression was available for 10 adenocarcinomas (62.5%), 1 adenosquamous (33%), 3 squamous carcinomas (50%), and the large cell neuroendocrine cancer. An EGFR mutation was detected in 2 cancers. ALK rearrangement was found in 1. Other mutations were detected. Two cancers were in each PDL1 expression category: < 1%, 1-50%, and > 50%. Conclusions: Our study suggests that PLWH with lung cancer continue to have high rates of smoking. Viral load was well controlled. A range in stages of lung cancer was observed including earlier stages. Although molecular data was limited, available EGFR and ALK gene alterations, and PD-L1 expression prevalence were on par with that of the general population. With advancements in lung cancer treatment, additional research is needed in the PLWH population to better understand and mitigate disparities.

scholarly journals Gynecologic large cell neuroendocrine carcinoma: A review

Rare Tumors ◽  
2020 ◽  
Vol 12 ◽  
pp. 203636132096840
Author(s):  
Grant Burkeen ◽  
Aman Chauhan ◽  
Rohitashva Agrawal ◽  
Riva Raiker ◽  
Jill Kolesar ◽  
...  
Keyword(s):  
Overall Survival ◽  
Median Overall Survival ◽  
Cell Lineage ◽  
Large Cell ◽  
Driver Mutations ◽  
Neuroendocrine Neoplasms ◽  
Disease Biology ◽  
Oncogenic Pathways ◽  
Pubmed Search ◽  
Disease Stages

Large cell neuroendocrine carcinomas (LCNEC) are rare, aggressive high-grade neuroendocrine neoplasms within the neuroendocrine cell lineage spectrum. This manuscript provides a detailed review of published literature on LCNEC of gynecological origin. We performed a PubMed search for material available on gynecologic LCNEC. We analyzed 104 unique cases of gynecologic LCNECs, of which 45 were cervical primary, 45 were ovarian, 13 were uterine, and 1 was vaginal. A total of 45 cases of cervical LCNEC were identified with a median age of 36 years. Median overall survival was 16 months. We identified 45 ovarian LCNEC cases in the published literature with a median age of 54 years. Median overall survival was 8 months. 13 LCNEC cases of uterine origin were identified; 12 out of 13 were of endometrial origin and the median age was 71 years. The majority of patients presented with Stage III/IV disease (stages I–IV were 31%, 8%, 38%, and 23%, respectively). Gynecologic LCNEC is an aggressive malignancy. Our current understanding of the disease biology is very limited. Efforts are required to better understand the genomic and molecular characterizations of gynecological LCNEC. These efforts will elucidate the underlying oncogenic pathways and driver mutations as potential targets.

Giant Primary Neuroendocrine Neoplasms of the Liver: Report of 2 Cases With Molecular Characterization

2019 ◽  
Vol 27 (8) ◽  
pp. 893-899
Author(s):  
Laura G. Pastrián ◽  
Ignacio Ruz-Caracuel ◽  
Raul S. Gonzalez
Keyword(s):  
World Health Organization ◽  
Neuroendocrine Tumor ◽  
Large Cell ◽  
The Other ◽  
World Health ◽  
Neuroendocrine Neoplasms ◽  
Molecular Testing ◽  
Well Differentiated ◽  
Grade 3 ◽  
Health Organization

Primary neuroendocrine neoplasms of the liver have occasionally been reported in the liver, though many reports do not convincingly exclude metastases. In this article, we report 2 “giant” hepatic neuroendocrine lesions without evidence of a primary elsewhere after clinical workup. One occurred in a 21-year-old male; the lesion was a large cell neuroendocrine carcinoma measuring 24 cm. The patient died of disease in 10 months. The other occurred in a 25-year-old patient, was 18 cm wide, and was diagnosed as a well-differentiated neuroendocrine tumor, World Health Organization grade 3. The patient died of disease after 30 months. Molecular testing demonstrated only the presence of TP53 mutations in common. These cases expand our knowledge of seemingly primary neuroendocrine neoplasms of the liver, in particular, giant cases measuring more than 8 cm. Guidelines for clinical workup and therapy for these lesions remain unclear, but future thorough workup of such cases is necessary for specific characterization.

scholarly journals Neuroendocrine Carcinoma of the Uterine Cervix: A Clinicopathologic and Immunohistochemical Study with Focus on Novel Markers (Sst2–Sst5)

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1211
Author(s):  
Frediano Inzani ◽  
Angela Santoro ◽  
Giuseppe Angelico ◽  
Angela Feraco ◽  
Saveria Spadola ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Target Therapy ◽  
Somatostatin Receptors ◽  
Neuroendocrine Differentiation ◽  
Large Cell ◽  
Cell Types ◽  
Somatostatin Analogues ◽  
Small Cell ◽  
Neuroendocrine Neoplasms ◽  
Neuroendocrine Carcinomas

Background. Gynecological neuroendocrine neoplasms (NENs) are extremely rare, accounting for 1.2–2.4% of the NENs. The aim of this study was to test cervical NENs for novel markers of potential utility for differential diagnosis and target therapy. Methods. All cases of our center (n = 16) were retrieved and tested by immunohistochemistry (IHC) for 12 markers including markers of neuroendocrine differentiation (chromogranin A, synaptophysin, CD56), transcription factors (CDX2 and TTF1), proteins p40, p63, p16INK4a, and p53, somatostatin receptors subtypes (SST2-SST5) and the proliferation marker Ki67 (MIB1). Results. All cases were poorly differentiated neuroendocrine carcinomas (NECs), 10 small cell types (small cell–neuroendocrine carcinomas, SCNECs) and 6 large cell types (large cell–neuroendocrine carcinomas, LCNECs); in 3 cases a predominant associated adenocarcinoma component was observed. Neuroendocrine cancer cells expressed at least 2 of the 3 tested neuroendocrine markers; p16 was intensely expressed in 14 (87.5%) cases; SST5 in 11 (56.25%, score 2–3, in 9 cases); SST2 in 8 (50%, score 2–3 in 8), CDX2 in 8 (50%), TTF1 in 5 (31.25%), and p53 in 1 case (0.06%). P63 and p40 expressions were negative, with the exception of one case that showed moderate expression for p63. Conclusions. P40 is a more useful marker for the differential diagnosis compared to squamous cell carcinoma. Neither CDX2 nor TTF1 expression may help the differential diagnosis versus potential cervical metastasis. P16 expression may suggest a cervical origin of NEC; however, it must be always integrated by clinical and instrumental data. The expression of SST2 and SST5 could support a role for SSAs (Somatostatin Analogues) in the diagnosis and therapy of patients with cervical NECs.

scholarly journals Diagnostic, Prognostic, and Therapeutic Value of Non-Coding RNA Expression Profiles in Renal Transplantation

Diagnostics ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 60 ◽  
Author(s):  
Adriana Franco-Acevedo ◽  
Zesergio Melo ◽  
Raquel Echavarria
Keyword(s):  
Large Scale ◽  
Expression Profiles ◽  
Graft Loss ◽  
Large Body ◽  
Public Health Problem ◽  
Molecular Data ◽  
Data Generation ◽  
Post Transplantation ◽  
Protein Coding ◽  
Stage Renal Disease

End-stage renal disease is a public health problem responsible for millions of deaths worldwide each year. Although transplantation is the preferred treatment for patients in need of renal replacement therapy, long-term allograft survival remains challenging. Advances in high-throughput methods for large-scale molecular data generation and computational analysis are promising to overcome the current limitations posed by conventional diagnostic and disease classifications post-transplantation. Non-coding RNAs (ncRNAs) are RNA molecules that, despite lacking protein-coding potential, are essential in the regulation of epigenetic, transcriptional, and post-translational mechanisms involved in both health and disease. A large body of evidence suggests that ncRNAs can act as biomarkers of renal injury and graft loss after transplantation. Hence, the focus of this review is to discuss the existing molecular signatures of non-coding transcripts and their value to improve diagnosis, predict the risk of rejection, and guide therapeutic choices post-transplantation.

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