Genomic landscape of pancreatic neuroendocrine tumours: the International Cancer Genome Consortium

Neuroendocrine tumours (NETs) may arise throughout the body and are a highly heterogeneous, relatively rare class of neoplasms difficult to study also for the lack of disease models. Despite this, knowledge on their molecular alterations has expanded in the latest years, also building from genetic syndromes causing their onset. Pancreatic NETs (PanNETs) have been among the most studied, and research so far has outlined a series of recurring features, as inactivation of MEN1, VHL, TSC1/2 genes and hyperactivation of the PI3K/mTOR pathway. Next-generation sequencing has added new information by showing the key role of alternative lengthening of telomeres, driven in a fraction of PanNETs by inactivation of ATRX/DAXX. Despite this accumulation of knowledge, single studies often relied on few cases or were limited to the DNA, RNA, protein or epigenetic level with lack of integrative analysis. The International Cancer Genome Consortium aimed at removing these barriers through a strict process of data and samples collection, to produce whole-genome integrated analyses for many tumour types. The results of this effort on PanNETs have been recently published and, while confirming previous observations provide a first snapshot of how heterogeneous is the combination of genetic alterations that drive this tumour type, yet converging into four pathways whose alteration has been enriched by newly discovered mechanisms. While calling for further integration of genetic and epigenetic analyses, these data allow to reconcile previous findings in a defined frame and may provide clinical research with markers for patients stratification and to guide targeted therapy decisions.

IW-Scoring: an Integrative Weighted Scoring framework for annotating and prioritizing genetic variations in the noncoding genome

IW-Scoring represents a new Integrative Weighted Scoring model to annotate and prioritise functionally relevant noncoding variations. The pipeline integrates 11 popular algorithms and outperforms individual methods in three independent data sets, including variants in ClinVar database and GWAS studies, and cancer mutations. Using IW-Scoring, we located 11 recurrently mutated noncoding regions enriched for at least three functional mutations in 14 follicular lymphoma genomes, and validated 9 clusters (82%) in the International Cancer Genome Consortium cohort (n=36), including promoter and enhancer regions of PAX5. IW-Scoring offers greater versatility to identify trait and disease associated noncoding variants.

Framework for quality assessment of whole genome, cancer sequences

Working with cancer whole genomes sequenced over a period of many years in different sequencing centres requires a validated framework to compare the quality of these sequences. The Pan-Cancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC), a project a cohort of over 2800 donors provided us with the challenge of assessing the quality of the genome sequences. A non-redundant set of five quality control (QC) measurements were assembled and used to establish a star rating system. These QC measures reflect known differences in sequencing protocol and provide a guide to downstream analyses of these whole genome sequences. The resulting QC measures also allowed for exclusion samples of poor quality, providing researchers within PCAWG, and when the data is released for other researchers, a good idea of the sequencing quality. For a researcher wishing to apply the QC measures for their data we provide a Docker Container of the software used to calculate them. We believe that this is an effective framework of quality measures for whole genome, cancer sequences, which will be a useful addition to analytical pipelines, as it has to the PCAWG project.

TSNAD: an integrated software for cancer somatic mutation and tumour-specific neoantigen detection

Tumour antigens have attracted much attention because of their importance to cancer diagnosis, prognosis and targeted therapy. With the development of cancer genomics, the identification of tumour-specific neoantigens became possible, which is a crucial step for cancer immunotherapy. In this study, we developed software called the tumour-specific neoantigen detector for detecting cancer somatic mutations following the best practices of the genome analysis toolkit and predicting potential tumour-specific neoantigens, which could be either extracellular mutations of membrane proteins or mutated peptides presented by class I major histocompatibility complex molecules. This pipeline was beneficial to the biologist with little programmatic background. We also applied the software to the somatic mutations from the International Cancer Genome Consortium database to predict numerous potential tumour-specific neoantigens. This software is freely available from https://github.com/jiujiezz/tsnad.