Comparative analyses define differences between BHD-associated renal tumour and sporadic chromophobe renal cell carcinoma.

Background: Birt-Hogg-Dubé (BHD) syndrome, caused by germline alteration of folliculin (FLCN) gene, develops hybrid oncocytic/chromophobe tumour (HOCT) and chromophobe renal cell carcinoma (ChRCC), whereas sporadic ChRCC does not harbor FLCN alteration. To date, molecular characteristics of these similar histological types of tumours have been incompletely elucidated.

Methods: To elucidate renal tumourigenesis of BHD-associated renal tumours and sporadic renal tumours, we conducted whole genome sequencing (WGS) and RNA-sequencing (RNA-seq) of sixteen BHD-associated renal tumours from nine unrelated BHD patients, twenty-one sporadic ChRCCs and seven sporadic oncocytomas.

COOBoostR: An Extreme Gradient Boosting-Based Tool for Robust Tissue or Cell-of-Origin Prediction of Tumors.

We present here COOBoostR, a computational method designed for the putative prediction of the tissue- or cell-of-origin of various cancer types. COOBoostR leverages regional somatic mutation density information and chromatin mark features to be applied to an extreme gradient boosting-based machine-learning algorithm. COOBoostR ranks chromatin marks from various tissue and cell types, which best explain the somatic mutation density landscape of any sample of interest.

Hybrid Stomach-Intestinal Chromatin States Underlie Human Barrett's Metaplasia.

Background & Aims: Tissue metaplasia is uncommon in adults because established cis-element programs resist rewiring. In Barrett's esophagus, the distal esophageal mucosa acquires a predominantly intestinal character, with notable gastric features, and is predisposed to developing invasive cancers. We sought to understand the chromatin underpinnings of Barrett's metaplasia and why it commonly displays simultaneous gastric and intestinal properties.

Biomedical Entity Explorer: A Web Server for Biomedical Entity Exploration.

Biomedical Entity Explorer (BEE) is a web server that can search for biomedical entities from a database of six biomedical entity types (gene, miRNA, drug, disease, single nucleotide polymorphism [SNP], pathway) and their gene associations. The search results can be explored using intersections, unions, and negations. BEE has integrated biomedical entities from 16 databases (Ensemble, PharmGKB, Genetic Home Reference, Tarbase, Mirbase, NCI Thesaurus, DisGeNET, Linked life data, UMLS, GSEA MsigDB, Reactome, KEGG, Gene Ontology, HGVD, SNPedia, and dbSNP) based on their gene associations and built a database with their synonyms, descriptions, and links containing individual details.

An Organoid Biobank of Neuroendocrine Neoplasms Enables Genotype-Phenotype Mapping.

Gastroenteropancreatic (GEP) neuroendocrine neoplasm (NEN) that consists of neuroendocrine tumor and neuroendocrine carcinoma (NEC) is a lethal but under-investigated disease owing to its rarity. To fill the scarcity of clinically relevant models of GEP-NEN, we here established 25 lines of NEN organoids and performed their comprehensive molecular characterization. GEP-NEN organoids recapitulated pathohistological and functional phenotypes of the original tumors.

Somatic mutation landscape reveals differential variability of cell-of-origin for primary liver cancer.

Primary liver tissue cancer types are renowned to display a consistent increase in global disease burden and mortality, thus needing more effective diagnostics and treatments. Yet, integrative research efforts to identify cell-of-origin for these cancers by utilizing human specimen data were poorly established. To this end, we analyzed previously published whole-genome sequencing data for 384 tumor and progenitor tissues along with 423 publicly available normal tissue epigenomic features and single cell RNA-seq data from human livers to assess correlation patterns and extended this information to conduct prediction of the cell-of-origin for primary liver cancer subtypes.

Chromatin marks shape mutation landscape at early stage of cancer progression.

Somatic mutation rates in cancer differ across the genome in a cancer cell-type specific manner. Although key factors that contribute to the differences were identified, the major cancer progression stage when these factors associate with the mutation variance remained poorly investigated. Here, we analyzed whole-genome sequencing data of pre-cancerous and matching cancer tissues from 173 individuals and 423 normal tissue chromatin features to determine the critical stage of these features contributing to shaping the somatic mutation landscape.