Abstract Background Many complementary solutions are available for the identifier mapping problem. This creates an opportunity for bioinformatics tool developers. Tools can be made to flexibly support multiple mapping services or mapping services could be combined to get broader coverage. This approach requires an interface layer between tools and mapping services. Results Here we present BridgeDb, a software framework for gene, protein and metabolite identifier mapping. This framework provides a standardized interface layer through which bioinformatics tools can be connected to different identifier mapping services. This approach makes it easier for tool developers to support identifier mapping. Mapping services can be combined or merged to support multi-omics experiments or to integrate custom microarray annotations. BridgeDb provides its own ready-to-go mapping services, both in webservice and local database forms. However, the framework is intended for customization and adaptation to any identifier mapping service. BridgeDb has already been integrated into several bioinformatics applications. Conclusion By uncoupling bioinformatics tools from mapping services, BridgeDb improves capability and flexibility of those tools. All described software is open source and available at http://www.bridgedb.org.

Summary

We developed a new software tool, BioJava-ModFinder, for identifying protein modifications observed in 3D structures archived in the Protein Data Bank (PDB). Information on more than 400 types of protein modifications were collected and curated from annotations in PDB, RESID, and PSI-MOD. We divided these modifications into three categories: modified residues, attachment modifications, and cross-links. We have developed a systematic method to identify these modifications in 3D protein structures. We have integrated this package with the RCSB PDB web application and added protein modification annotations to the sequence diagram and structure display. By scanning all 3D structures in the PDB using BioJava-ModFinder, we identified more than 30 000 structures with protein modifications, which can be searched, browsed, and visualized on the RCSB PDB website.

Availability and implementation

BioJava-ModFinder is available as open source (LGPL license) at ( https://github.com/biojava/biojava/tree/master/biojava-modfinder ). The RCSB PDB can be accessed at http://www.rcsb.org .

Contact

[email protected].

Unlabelled

BioJava is an open-source project for processing of biological data in the Java programming language. We have recently released a new version (3.0.5), which is a major update to the code base that greatly extends its functionality.

Results

BioJava now consists of several independent modules that provide state-of-the-art tools for protein structure comparison, pairwise and multiple sequence alignments, working with DNA and protein sequences, analysis of amino acid properties, detection of protein modifications and prediction of disordered regions in proteins as well as parsers for common file formats using a biologically meaningful data model.

Availability

BioJava is an open-source project distributed under the Lesser GPL (LGPL). BioJava can be downloaded from the BioJava website (http://www.biojava.org). BioJava requires Java 1.6 or higher. All inquiries should be directed to the BioJava mailing lists. Details are available at http://biojava.org/wiki/BioJava:MailingLists.

Objective

Paclitaxel and carboplatin (PC) is a standard initial therapy for advanced endometrial cancer. We evaluated the efficacy and tolerability of incorporating three novel agents into initial therapy.

Methods

In this randomized phase II trial, patients with chemotherapy-naïve stage III/IVA (with measurable disease) and stage IVB or recurrent (with or without measurable disease) endometrial cancer were randomly assigned to treatment with PC plus bevacizumab (Arm 1), PC plus temsirolimus (Arm 2) or ixabepilone and carboplatin (IC) plus bevacizumab (Arm 3). The primary endpoint was progression-free survival (PFS). Comparable patients on the PC Arm of trial GOG209 were used as historical controls. Secondary endpoints were response rate, overall survival (OS), and safety.

Results

Overall, 349 patients were randomized. PFS duration was not significantly increased in any experimental arm compared with historical controls (p > 0.039). Treatment HRs (92% CI) for Arms 1, 2, and 3 relative to controls were 0.81 (0.63-1.02), 1.22 (0.96-1.55) and 0.87 (0.68-1.11), respectively. Response rates were similar across arms (60%, 55% and 53%, respectively). Relative to controls, OS duration (with censoring at 36 months), was significantly increased in Arm 1 (p < 0.039) but not in Arms 2 and 3; the HRs (92% CIs) were 0.71 (0.55-0.91), 0.99 (0.78-1.26), and 0.97 (0.77-1.23), respectively. No new safety signals were identified. Common mutations and rates of mismatch repair protein loss are described by histotype. Potential predictive biomarkers for temsirolimus and bevacizumab were identified.

Conclusion

PFS was not significantly increased in any experimental arm compared to historical controls. NRG Oncology/Gynecologic Oncology Group Study GOG-86P.

OBJECTIVE: Paclitaxel and carboplatin (PC) is a standard initial therapy for advanced endometrial cancer. We evaluated the efficacy and tolerability of incorporating three novel agents into initial therapy. METHODS: In this randomized phase II trial, patients with chemotherapy-naïve stage III/IVA (with measurable disease) and stage IVB or recurrent (with or without measurable disease) endometrial cancer were randomly assigned to treatment with PC plus bevacizumab (Arm 1), PC plus temsirolimus (Arm 2) or ixabepilone and carboplatin (IC) plus bevacizumab (Arm 3). The primary endpoint was progression-free survival (PFS). Comparable patients on the PC Arm of trial GOG209 were used as historical controls. Secondary endpoints were response rate, overall survival (OS), and safety. RESULTS: Overall, 349 patients were randomized. PFS duration was not significantly increased in any experimental arm compared with historical controls (p > 0.039). Treatment HRs (92% CI) for Arms 1, 2, and 3 relative to controls were 0.81 (0.63-1.02), 1.22 (0.96-1.55) and 0.87 (0.68-1.11), respectively. Response rates were similar across arms (60%, 55% and 53%, respectively). Relative to controls, OS duration (with censoring at 36 months), was significantly increased in Arm 1 (p < 0.039) but not in Arms 2 and 3; the HRs (92% CIs) were 0.71 (0.55-0.91), 0.99 (0.78-1.26), and 0.97 (0.77-1.23), respectively. No new safety signals were identified. Common mutations and rates of mismatch repair protein loss are described by histotype. Potential predictive biomarkers for temsirolimus and bevacizumab were identified. CONCLUSION: PFS was not significantly increased in any experimental arm compared to historical controls. NRG Oncology/Gynecologic Oncology Group Study GOG-86P.

Human cancers arise from environmental, heritable and somatic factors, but how these mechanisms interact in tumorigenesis is poorly understood. Studying 17,152 prospectively sequenced patients with cancer, we identified pathogenic germline variants in cancer predisposition genes, and assessed their zygosity and co-occurring somatic alterations in the concomitant tumors. Two major routes to tumorigenesis were apparent. In carriers of pathogenic germline variants in high-penetrance genes (5.1% overall), lineage-dependent patterns of biallelic inactivation led to tumors exhibiting mechanism-specific somatic phenotypes and fewer additional somatic oncogenic drivers. Nevertheless, 27% of cancers in these patients, and most tumors in patients with pathogenic germline variants in lower-penetrance genes, lacked particular hallmarks of tumorigenesis associated with the germline allele. The dependence of tumors on pathogenic germline variants is variable and often dictated by both penetrance and lineage, a finding with implications for clinical management.

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods.

Invasive lobular carcinoma (ILC) is the second most prevalent histologic subtype of invasive breast cancer. Here, we comprehensively profiled 817 breast tumors, including 127 ILC, 490 ductal (IDC), and 88 mixed IDC/ILC. Besides E-cadherin loss, the best known ILC genetic hallmark, we identified mutations targeting PTEN, TBX3, and FOXA1 as ILC enriched features. PTEN loss associated with increased AKT phosphorylation, which was highest in ILC among all breast cancer subtypes. Spatially clustered FOXA1 mutations correlated with increased FOXA1 expression and activity. Conversely, GATA3 mutations and high expression characterized luminal A IDC, suggesting differential modulation of ER activity in ILC and IDC. Proliferation and immune-related signatures determined three ILC transcriptional subtypes associated with survival differences. Mixed IDC/ILC cases were molecularly classified as ILC-like and IDC-like revealing no true hybrid features. This multidimensional molecular atlas sheds new light on the genetic bases of ILC and provides potential clinical options.

Malignant pleural mesothelioma (MPM) is a highly lethal cancer of the lining of the chest cavity. To expand our understanding of MPM, we conducted a comprehensive integrated genomic study, including the most detailed analysis of BAP1 alterations to date. We identified histology-independent molecular prognostic subsets, and defined a novel genomic subtype with TP53 and SETDB1 mutations and extensive loss of heterozygosity. We also report strong expression of the immune-checkpoint gene VISTA in epithelioid MPM, strikingly higher than in other solid cancers, with implications for the immune response to MPM and for its immunotherapy. Our findings highlight new avenues for further investigation of MPM biology and novel therapeutic options. SIGNIFICANCE: Through a comprehensive integrated genomic study of 74 MPMs, we provide a deeper understanding of histology-independent determinants of aggressive behavior, define a novel genomic subtype with TP53 and SETDB1 mutations and extensive loss of heterozygosity, and discovered strong expression of the immune-checkpoint gene VISTA in epithelioid MPM.See related commentary by Aggarwal and Albelda, p. 1508.This article is highlighted in the In This Issue feature, p. 1494.

We describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy-number analysis revealed frequent occurrence of massive DNA loss followed by whole-genome doubling (WGD), which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell-cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68-CpG probe DNA-methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers.