Pancreatic neuroendocrine tumors (pNETs) are uncommon cancers arising from pancreatic islet cells. Here we report the analysis of gene mutation, copy number, and RNA expression of 57 sporadic well-differentiated pNETs. pNET genomes are dominated by aneuploidy, leading to concordant changes in RNA expression at the level of whole chromosomes and chromosome segments. We observed two distinct patterns of somatic pNET aneuploidy that are associated with tumor pathology and patient prognosis. Approximately 26% of the patients in this series had pNETs with genomes characterized by recurrent loss of heterozygosity (LoH) of 10 specific chromosomes, accompanied by bi-allelic MEN1 inactivation and generally poor clinical outcome. Another ~40% of patients had pNETs that lacked this recurrent LoH pattern but had chromosome 11 LoH, bi-allelic MEN1 inactivation, and universally good clinical outcome. The somatic aneuploidy allowed pathogenic germline variants (e.g., ATM) to be expressed unopposed, with RNA expression patterns showing inactivation of downstream tumor suppressor pathways. No prognostic associations were found with tumor morphology, single gene mutation, or expression of RNAs reflecting the activity of immune, differentiation, proliferative or tumor suppressor pathways. In pNETs, single gene mutations appear to be less important than aneuploidy, with MEN1 the only statistically significant recurrently mutated driver gene. In addition, only one pNET in the series had clearly actionable single nucleotide variants (SNVs) (in PTEN and FLCN) confirmed by corroborating RNA expression changes. The two clinically relevant patterns of LoH described here define a novel oncogenic mechanism and a plausible route to genomic precision oncology for this tumor type.

miRNAs are a well-studied class of non-coding RNAs, predominantly functioning to down-regulate gene expression from messenger RNA (mRNA) in a targeted manner by binding to complementary sequence on the target mRNA. Many miRNAs have been linked to the development of hallmarks of cancer. miRNAs represent valuable therapeutic targets to exploit in the search for novel cancer treatments, due to their ubiquitous expression and their ability to tightly regulate the gene expression of a whole host of genes and pathways in a single hit. The miRNA system may be harnessed for therapeutic use either through replacement of tumour suppressive miRNAs lost in cancer, or through inhibition of oncogenic miRNAs overexpressed in cancer. There is a large body of work investigating optimal systemic and localised delivery strategies, and while miRNA therapeutics show promise, it is clear that further developments to delivery strategies may be required to allow safe translation of miRNAs to the clinic. The information gleaned from miRNA signatures as biomarkers is already proving invaluable in the fight to better understand and treat individual tumours, and there is great promise to the applications of these small, but mighty molecules in the future of cancer therapeutics.

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Pancreatic neuroendocrine tumors (pNETs) are uncommon cancers arising from pancreatic islet cells. Analysis of gene mutation, copy number and RNA expression of 57 sporadic pNETs showed that pNET genomes are dominated by aneuploidy. Remarkably, ~25% of pNETs had genomes characterized by recurrent loss of heterozygosity (LoH) of the same 10 chromosomes, accompanied by bi-allelic MEN1 inactivation, and these cases had generally poor clinical outcome. Another ~25% of all pNETs had chromosome 11 LoH and bi-allelic MEN1 inactivation, lacking the recurrent LoH pattern -these had universally good clinical outcome. Some level of aneuploidy was common, and overall ~80% of pNETs had LoH of ≥1 chromosome. This aneuploidy led to changes in RNA expression at the level of whole chromosomes and allowed pathogenic germline variants (e.g. ATM) to be expressed unopposed, inactivating downstream tumor suppressor pathways. Some pNETs appear to utilize VHL gene methylation or mutation to activate pseudohypoxia. Contrary to expectation neither tumor morphology within well-differentiated pNETs nor single gene mutation had significant associations with clinical outcome, nor did expression of RNAs reflecting the activity of immune, differentiation, proliferative or tumor suppressor pathways. MEN1 was the only statistically significant recurrently mutated driver gene in pNETs. Only one pNET had clearly oncogenic and actionable SNVs (in PTEN and FLCN) confirmed by corroborating RNA expression changes. The two distinct patterns of aneuploidy described here, associated with markedly poor and good clinical outcome respectively, define a novel oncogenic mechanism and the first route to genomic precision oncology for this tumor type. IntroductionPancreatic neuroendocrine tumors (pNETs) are clinically heterogeneous tumors derived from neuroendocrine cells of pancreatic islets, which differ from one other by their primary organ of origin and degree of cellular differentiation. Currently, therapeutic decisions must be made with little knowledge of the biological drivers of individual NETs, underlining the importance of improved genomic understanding of these tumors. Although driver mutations in tumor suppressor genes have been found in pNETs (e.g., MEN1, DAXX, ATRX, VHL, YY1, and mechanistic target of rapamycin (mTOR) pathway genes (1-3)) they are infrequent and are not generally able to indicate specific systemic therapies. In addition to these driver mutations, other genomic changes have been observed in pNETs, including: telomeric dysregulation (4, 5), copy number (CN) changes (6), changes in RNA expression that indicate mTOR pathway activation (7), germline MEN1 and MUTYH inactivation (2, 8). Epigenetic changes in methylation (9) and microRNA expression (10) have been described in pNETs, with insulinomas especially enriched for changes to the sequence, methylation and expression of genes encoding epigenetic modifiers (11).

An approach for generating high-resolution a priori maximum parsimony Y-chromosome ("chrY") phylogenies based on SNP and small INDEL variant data from massively-parallel short-read ("next-generation") sequencing data is described; the tree-generation methodology produces annotations localizing mutations to individual branches of the tree, along with indications of mutation placement uncertainty in cases for which "no-calls" (through lack of mapped reads or otherwise) at particular sites precludes precise phylogenetic placement of mutations. The approach leverages careful variant site filtering and a novel iterative reweighting procedure to generate high-accuracy trees while considering variants in regions of chrY that had previously been excluded from analyses based on short-read sequencing data. It is argued that the proposed approach is also superior to previous region-based filtering approaches in that it adapts to the quality of the underlying data and will automatically allow the scope of sites considered to expand as the underlying data quality improves (e.g. through longer read lengths). Key related issues, including calling of genotypes for the hemizygous chrY, reliability of variant results, read mismappings and "heterozygous" genotype calls, and the mutational stability of different variants are discussed and taken into account. The methodology is demonstrated through application to a dataset consisting of 1292 male samples from diverse populations and haplogroups, with the majority coming from low-coverage sequencing by the 1000 Genomes Project. Application of the tree-generation approach to these data produces a tree involving over 120,000 chrY variant sites (about 45,000 sites if "singletons" are excluded). The utility of this approach in refining the Ychromosome phylogenetic tree is demonstrated by examining results for several haplogroups. The results indicate a number of new branches on the Y-chromosome phylogenetic tree, many of them subdividing known branches, but also including some that inform the presence of additional levels along the "trunk" of the tree. Finally, opportunities for extensions of this phylogenetic analysis approach to other types of genetic data are noted.