Multi-omic dataset of patient-derived tumor organoids of neuroendocrine neoplasms

Background: Organoids are 3-dimensional experimental models that summarize the anatomical and functional structure of an organ. Although a promising experimental model for precision medicine, patient-derived tumor organoids (PDTOs) have currently been developed only for a fraction of tumor types. Results: We have generated the first multi-omic dataset (whole-genome sequencing [WGS] and RNA-sequencing [RNA-seq]) of PDTOs from the rare and understudied pulmonary neuroendocrine tumors (n = 12; 6 grade 1, 6 grade 2) and provide data from other rare neuroendocrine neoplasms: small intestine (ileal) neuroendocrine tumors (n = 6; 2 grade 1 and 4 grade 2) and large-cell neuroendocrine carcinoma (n = 5; 1 pancreatic and 4 pulmonary). This dataset includes a matched sample from the parental sample (primary tumor or metastasis) for a majority of samples (21/23) and longitudinal sampling of the PDTOs (1 to 2 time points), for a total of n = 47 RNA-seq and n = 33 WGS. We here provide quality control for each technique and the raw and processed data as well as all scripts for genomic analyses to ensure an optimal reuse of the data. In addition, we report gene expression data and somatic small variant calls and describe how they were generated, in particular how we used WGS somatic calls to train a random forest classifier to detect variants in tumor-only RNA-seq. We also report all histopathological images used for medical diagnosis: hematoxylin and eosin–stained slides, brightfield images, and immunohistochemistry images of protein markers of clinical relevance. Conclusions: This dataset will be critical to future studies relying on this PDTO biobank, such as drug screens for novel therapies and experiments investigating the mechanisms of carcinogenesis in these understudied diseases

Shared Oncogenic Pathways Implicated in Both Virus-Positive and UV-Induced Merkel Cell Carcinomas

Merkel cell carcinoma (MCC) is a highly malignant neuroendocrine tumor of the skin whose molecular pathogenesis is not completely understood, despite the role that Merkel cell polyomavirus can play in 55e90% of cases. To study potential mechanisms driving this disease in clinically characterized cases, we searched for somatic mutations using whole-exome sequencing, and extrapolated our findings to study functional biomarkers reporting on the activity of the mutated pathways. Confirming previous results, Merkel cell polyomavirus-negative tumors had higher mutational loads with UV signatures and more frequent mutations in TP53 and RB compared with their Merkel cell polyomavirus-positive counterparts. Despite important genetic differences, the two Merkel cell carcinoma etiologies both exhibited nuclear accumulation of oncogenic transcription factors such as NFAT or nuclear factor of activated T cells (NFAT), P-CREB, and P-STAT3, indicating commonly deregulated pathogenic mechanisms with the potential to serve as targets for therapy. A multivariable analysis identified phosphorylated CRE-binding protein as an independent survival factor with respect to clinical variables and Merkel cell polyomavirus status in our cohort of Merkel cell carcinoma patients.This work was supported by grants from Instituto de Salud-Carlos III (ISCIII); cofinanced by the European Union; (FEDER) (PI12/00357), and a Ramón and Cajal research program (MINECO; RYC-2013-14097) to JPV, Asociación Española Contra el Cáncer and ISCIII grants (RD06/0020/0107, RD012/0036/0060) to MAP, and Coordinated Project of Excellence inter-Institutos de investigación acreditados institutes (ISCIII; PIE15/00081) to MAP. The Ramón and Cajal research program also supports IV. SD was supported by the Torres Quevedo subprogram (MICINN; PTQ-12-05391)

Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data

Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties. Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets. We developed TRUP (Tumor-specimen suited RNA-seq Unified Pipeline) (https://github.com/ruping/TRUP), a computational approach that combines split-read and read-pair analysis with de novo assembly for the identification of chimeric transcripts in cancer specimens. We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondary rearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8

Integrative and comparative genomic analyses identify clinically relevant pulmonary carcinoid groups and unveil the supra-carcinoids

International audienceThe worldwide incidence of pulmonary carcinoids is increasing, but little is known about their molecular characteristics. Through machine learning and multi-omics factor analysis, we compare and contrast the genomic profiles of 116 pulmonary carcinoids (including 35 atypical), 75 large-cell neuroendocrine carcinomas (LCNEC), and 66 small-cell lung cancers. Here we report that the integrative analyses on 257 lung neuroendocrine neoplasms stratify atypical carcinoids into two prognostic groups with a 10-year overall survival of 88% and 27%, respectively. We identify therapeutically relevant molecular groups of pulmonary car-cinoids, suggesting DLL3 and the immune system as candidate therapeutic targets; we confirm the value of OTP expression levels for the prognosis and diagnosis of these diseases, and we unveil the group of supra-carcinoids. This group comprises samples with carcinoid-like morphology yet the molecular and clinical features of the deadly LCNEC, further supporting the previously proposed molecular link between the low-and high-grade lung neuroendocrine neoplasms

Highlights of the 14th international mesothelioma interest group meeting: Pathologic separation of benign from malignant mesothelial proliferations and histologic/molecular analysis of malignant mesothelioma subtypes

Objectives: The separation of benign from malignant mesothelial proliferations and exact subclassification of mesothelioma subtypes is crucial to determining patient care and prognosis but morphologically can be very difficult.Methods: This session of the 2018 IMIG meeting addressed these problems.Results: A new immunohistochemical marker, methylthioadenosine phosphorylase, was shown to correlate well with CDKN2A FISH and is cheaper and faster to run. A 117 gene expression panel also provided good separation on both tissue biopsy and cytology samples. Review of a series of mesotheliomas thought to be biphasic produced only a moderate level of agreement among expert pathologists with some cases being classified as purely epithelioid or sarcomatoid; these classifications had prognostic significance. The entity called transitional mesothelioma was found to behave exactly like sarcomatoid mesothelioma. RNA-seq analysis of a large series of mesotheliomas from a public database showed that, genetically, the morphologic breakdown into epithelioid, sarcomatoid, or biphasic mesotheliomas is artificial because there is a continuous spectrum of genomic changes. There are now criteria for the diagnosis of mesothelioma in situ and this is potentially important, since such cases might be curable.Conclusions: This session documented new morphological and molecular approaches to separating benign from malignant mesothelial proliferations and to subclassifying malignant mesoteheliomas in clinical relevant ways