J. Lynn Fink

Whole genomes redefine the mutational landscape of pancreatic cancer

Pancreatic cancer remains one of the most lethal of malignancies and a major health burden. We performed whole-genome sequencing and copy number variation (CNV) analysis of 100 pancreatic ductal adenocarcinomas (PDACs). Chromosomal rearrangements leading to gene disruption were prevalent, affecting genes known to be important in pancreatic cancer (TP53, SMAD4, CDKN2A, ARID1A and ROBO2) and new candidate drivers of pancreatic carcinogenesis (KDM6A and PREX2). Patterns of structural variation (variation in chromosomal structure) classified PDACs into 4 subtypes with potential clinical utility: the subtypes were termed stable, locally rearranged, scattered and unstable. A significant proportion harboured focal amplifications, many of which contained druggable oncogenes (ERBB2, MET, FGFR1, CDK6, PIK3R3 and PIK3CA), but at low individual patient prevalence. Genomic instability co-segregated with inactivation of DNA maintenance genes (BRCA1, BRCA2 or PALB2) and a mutational signature of DNA damage repair deficiency. Of 8 patients who received platinum therapy, 4 of 5 individuals with these measures of defective DNA maintenance responded.

Whole–genome characterization of chemoresistant ovarian cancer

Patients with high-grade serous ovarian cancer (HGSC) have experienced little improvement in overall survival, and standard treatment has not advanced beyond platinum-based combination chemotherapy, during the past 30 years. To understand the drivers of clinical phenotypes better, here we use whole-genome sequencing of tumour and germline DNA samples from 92 patients with primary refractory, resistant, sensitive and matched acquired resistant disease. We show that gene breakage commonly inactivates the tumour suppressors RB1, NF1, RAD51B and PTEN in HGSC, and contributes to acquired chemotherapy resistance. CCNE1 amplification was common in primary resistant and refractory disease. We observed several molecular events associated with acquired resistance, including multiple independent reversions of germline BRCA1 or BRCA2 mutations in individual patients, loss of BRCA1 promoter methylation, an alteration in molecular subtype, and recurrent promoter fusion associated with overexpression of the drug efflux pump MDR1.

LOCATE: a mammalian protein subcellular localization database.

LOCATE is a curated, web-accessible database that houses data describing the membrane organization and subcellular localization of mouse and human proteins. Over the past 2 years, the data in LOCATE have grown substantially. The database now contains high-quality localization data for 20% of the mouse proteome and general localization annotation for nearly 36% of the mouse proteome. The proteome annotated in LOCATE is from the RIKEN FANTOM Consortium Isoform Protein Sequence sets which contains 58 128 mouse and 64 637 human protein isoforms. Other additions include computational subcellular localization predictions, automated computational classification of experimental localization image data, prediction of protein sorting signals and third party submission of literature data. Collectively, this database provides localization proteome for individual subcellular compartments that will underpin future systematic investigations of these regions. It is available at http://locate.imb.uq.edu.au/

Computational approaches to identify functional genetic variants in cancer genomes

The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype.

Genomic catastrophes frequently arise in esophageal adenocarcinoma and drive tumorigenesis

Oesophageal adenocarcinoma (EAC) incidence is rapidly increasing in Western countries. A better understanding of EAC underpins efforts to improve early detection and treatment outcomes. While large EAC exome sequencing efforts to date have found recurrent loss-of-function mutations, oncogenic driving events have been underrepresented. Here we use a combination of whole-genome sequencing (WGS) and single-nucleotide polymorphism-array profiling to show that genomic catastrophes are frequent in EAC, with almost a third (32%, n = 40/123) undergoing chromothriptic events. WGS of 22 EAC cases show that catastrophes may lead to oncogene amplification through chromothripsis-derived double-minute chromosome formation (MYC and MDM2) or breakage-fusion-bridge (KRAS, MDM2 and RFC3). Telomere shortening is more prominent in EACs bearing localized complex rearrangements. Mutational signature analysis also confirms that extreme genomic instability in EAC can be driven by somatic BRCA2 mutations. These findings suggest that genomic catastrophes have a significant role in the malignant transformation of EAC.

LOCATE: a mouse protein subcellular localization database

We present here LOCATE, a curated, web-accessible database that houses data describing the membrane organization and subcellular localization of proteins from the FANTOM3 Isoform Protein Sequence set. Membrane organization is predicted by the high-throughput, computational pipeline MemO. The subcellular locations of selected proteins from this set were determined by a high-throughput, immunofluorescence-based assay and by manually reviewing >1700 peer-reviewed publications. LOCATE represents the first effort to catalogue the experimentally verified subcellular location and membrane organization of mammalian proteins using a high-throughput approach and provides localization data for ∼40% of the mouse proteome. It is available at .

Evaluation and comparison of mammalian subcellular localization prediction methods

BackgroundDetermination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance.ResultsIn order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER), peroxisome, and lysosome). The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance.ConclusionNo individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE dataset and variable performance on individual subcellular localizations was observed. Proteins localized to the secretory pathway were the most difficult to predict, while nuclear and extracellular proteins were predicted with the highest sensitivity.

The PlantsP and PlantsT Functional Genomics Databases

PlantsP and PlantsT allow users to quickly gain a global understanding of plant phosphoproteins and plant membrane transporters, respectively, from evolutionary relationships to biochemical function as well as a deep understanding of the molecular biology of individual genes and their products. As one database with two functionally different web interfaces, PlantsP and PlantsT are curated plant-specific databases that combine sequence-derived information with experimental functional-genomics data. PlantsP focuses on proteins involved in the phosphorylation process (i.e., kinases and phosphatases), whereas PlantsT focuses on membrane transport proteins. Experimentally, PlantsP provides a resource for information on a collection of T-DNA insertion mutants (knockouts) in each kinase and phosphatase, primarily in Arabidopsis thaliana, and PlantsT uniquely combines experimental data regarding mineral composition (derived from inductively coupled plasma atomic emission spectroscopy) of mutant and wild-type strains. Both databases provide extensive information on motifs and domains, detailed information contributed by individual experts in their respective fields, and descriptive information drawn directly from the literature. The databases incorporate a unique user annotation and review feature aimed at acquiring expert annotation directly from the plant biology community. PlantsP is available at http://plantsp.sdsc.edu and PlantsT is available at http://plantst.sdsc.edu.

Hypermutation In Pancreatic Cancer

Pancreatic cancer is molecularly diverse, with few effective therapies. Increased mutation burden and defective DNA repair are associated with response to immune checkpoint inhibitors in several other cancer types. We interrogated 385 pancreatic cancer genomes to define hypermutation and its causes. Mutational signatures inferring defects in DNA repair were enriched in those with the highest mutation burdens. Mismatch repair deficiency was identified in 1% of tumors harboring different mechanisms of somatic inactivation of MLH1 and MSH2. Defining mutation load in individual pancreatic cancers and the optimal assay for patient selection may inform clinical trial design for immunotherapy in pancreatic cancer.

IL-2- and STAT5-regulated cytokine gene expression in cells expressing the Tax protein of HTLV-1

Interleukin-2 (IL-2) mediates cell cycle progression and antiapoptosis in human T cells via several signal transduction pathways. The Tax protein of the human T-cell leukemia virus type I (HTLV-1) deregulates cell growth and alters the role of IL-2 in infected cells. However, Tax-immortalized cells stay dependent on IL-2, suggesting that events besides HTLV-1 gene expression are required for leukemia to develop. Here, IL-2-dependent and -independent events were analysed in a human T cell line immortalized by Tax. These studies show that, of the signaling pathways evaluated, only STAT5 remains dependent. Microarray analyses revealed several genes, including il-5, il-9 and il-13, are uniquely upregulated by IL-2 in the presence of Tax. Bioinformatics and supporting molecular biology show that some of these genes are STAT5 targets, explaining their IL-2 upregulation. These results suggest that IL-2 and viral proteins work together to induce gene expression, promoting the hypothesis that deregulation via the constitutive activation of STAT5 may lead to the IL-2-independent phenotype of HTLV-1-transformed cells.