Chimeric RNAs reveal putative neoantigen peptides for developing tumor vaccines for breast cancer.

Introduction: We present here a strategy to identify immunogenic neoantigen candidates from unique amino acid sequences at the junctions of fusion proteins which can serve as targets in the development of tumor vaccines for the treatment of breastcancer. Method: We mined the sequence reads of breast tumor tissue that are usually discarded as discordant paired-end reads and discovered cancer specific fusion transcripts using tissue from cancer free controls as reference. Binding affinity predictions of novel peptide sequences crossing the fusion junction were analyzed by the MHC Class I binding predictor, MHCnuggets. CD8+ T cell responses against the 15 peptides were assessed through in vitro Enzyme Linked Immunospot (ELISpot). Results: We uncovered 20 novel fusion transcripts from 75 breast tumors of 3 subtypes: TNBC, HER2+, and HR+. Of these, the NSFP1-LRRC37A2 fusion transcript was selected for further study. The 3833 bp chimeric RNA predicted by the consensus fusion junction sequence is consistent with a read-through transcription of the 5'-gene NSFP1-Pseudo gene NSFP1 (NSFtruncation at exon 12/13) followed by trans-splicing to connect withLRRC37A2 located immediately 3' through exon 1/2. A total of 15 different 8-mer neoantigen peptides discovered from the NSFP1 and LRRC37A2 truncations were predicted to bind to a total of 35 unique MHC class I alleles with a binding affinity of IC50<500nM.); 1 of which elicited a robust immune response. Conclusion: Our data provides a framework to identify immunogenic neoantigen candidates from fusion transcripts and suggests a potential vaccine strategy to target the immunogenic neopeptides in patients with tumors carrying the NSFP1-LRRC37A2 fusion.

Copy number variation as a genetic basis for heterotaxy and heterotaxy-spectrum congenital heart defects.

Genomic disorders and rare copy number abnormalities are identified in 15-25% of patients with syndromic conditions, but their prevalence in individuals with isolated birth defects is less clear. A spectrum of congenital heart defects (CHDs) is seen in heterotaxy, a highly heritable and genetically heterogeneous multiple congenital anomaly syndrome resulting from failure to properly establish left-right (L-R) organ asymmetry during early embryonic development. To identify novel genetic causes of heterotaxy, we analysed copy number variants (CNVs) in 225 patients with heterotaxy and heterotaxy-spectrum CHDs using array-based genotyping methods. Clinically relevant CNVs were identified in approximately 20% of patients and encompassed both known and putative heterotaxy genes. Patients were carefully phenotyped, revealing a significant association of abdominal situs inversus with pathogenic or likely pathogenic CNVs, while d-transposition of the great arteries was more frequently associated with common CNVs. Identified cytogenetic abnormalities ranged from large unbalanced translocations to smaller, kilobase-scale CNVs, including a rare, single exon deletion in ZIC3, a gene known to cause X-linked heterotaxy. Morpholino loss-of-function experiments in Xenopus support a role for one of these novel candidates, the platelet isoform of phosphofructokinase-1 (PFKP) in heterotaxy. Collectively, our results confirm a high CNV yield for array-based testing in patients with heterotaxy, and support use of CNV analysis for identification of novel biological processes relevant to human laterality.This article is part of the themed issue 'Provocative questions in left-right asymmetry'.

A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics.

BACKGROUND: Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. METHODS: Our tool, "OMIM Explorer" ( http://www.omimexplorer.com ), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries--in essence, generating a computationally assisted differential diagnosis informed by the individual's personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. RESULTS: We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. CONCLUSIONS: Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge.

Fusion of large-scale genomic knowledge and frequency data computationally prioritizes variants in epilepsy.

Curation and interpretation of copy number variants identified by genome-wide testing is challenged by the large number of events harbored in each personal genome. Conventional determination of phenotypic relevance relies on patterns of higher frequency in affected individuals versus controls; however, an increasing amount of ascertained variation is rare or private to clans. Consequently, frequency data have less utility to resolve pathogenic from benign. One solution is disease-specific algorithms that leverage gene knowledge together with variant frequency to aid prioritization. We used large-scale resources including Gene Ontology, protein-protein interactions and other annotation systems together with a broad set of 83 genes with known associations to epilepsy to construct a pathogenicity score for the phenotype. We evaluated the score for all annotated human genes and applied Bayesian methods to combine the derived pathogenicity score with frequency information from our diagnostic laboratory. Analysis determined Bayes factors and posterior distributions for each gene. We applied our method to subjects with abnormal chromosomal microarray results and confirmed epilepsy diagnoses gathered by electronic medical record review. Genes deleted in our subjects with epilepsy had significantly higher pathogenicity scores and Bayes factors compared to subjects referred for non-neurologic indications. We also applied our scores to identify a recently validated epilepsy gene in a complex genomic region and to reveal candidate genes for epilepsy. We propose a potential use in clinical decision support for our results in the context of genome-wide screening. Our approach demonstrates the utility of integrative data in medical genomics.

Deletions of recessive disease genes: CNV contribution to carrier states and disease-causing alleles.

Over 1200 recessive disease genes have been described in humans. The prevalence, allelic architecture, and per-genome load of pathogenic alleles in these genes remain to be fully elucidated, as does the contribution of DNA copy-number variants (CNVs) to carrier status and recessive disease. We mined CNV data from 21,470 individuals obtained by array-comparative genomic hybridization in a clinical diagnostic setting to identify deletions encompassing or disrupting recessive disease genes. We identified 3212 heterozygous potential carrier deletions affecting 419 unique recessive disease genes. Deletion frequency of these genes ranged from one occurrence to 1.5%. When compared with recessive disease genes never deleted in our cohort, the 419 recessive disease genes affected by at least one carrier deletion were longer and located farther from known dominant disease genes, suggesting that the formation and/or prevalence of carrier CNVs may be affected by both local and adjacent genomic features and by selection. Some subjects had multiple carrier CNVs (307 subjects) and/or carrier deletions encompassing more than one recessive disease gene (206 deletions). Heterozygous deletions spanning multiple recessive disease genes may confer carrier status for multiple single-gene disorders, for complex syndromes resulting from the combination of two or more recessive conditions, or may potentially cause clinical phenotypes due to a multiply heterozygous state. In addition to carrier mutations, we identified homozygous and hemizygous deletions potentially causative for recessive disease. We provide further evidence that CNVs contribute to the allelic architecture of both carrier and recessive disease-causing mutations. Thus, a complete recessive carrier screening method or diagnostic test should detect CNV alleles.

The Hematopoietic Expression Viewer: expanding mobile apps as a scientific tool.

UNLABELLED: Many important data in current biological science comprise hundreds, thousands or more individual results. These massive data require computational tools to navigate results and effectively interact with the content. Mobile device apps are an increasingly important tool in the everyday lives of scientists and non-scientists alike. These software present individuals with compact and efficient tools to interact with complex data at meetings or other locations remote from their main computing environment. We believe that apps will be important tools for biologists, geneticists and physicians to review content while participating in biomedical research or practicing medicine. We have developed a prototype app for displaying gene expression data using the iOS platform. To present the software engineering requirements, we review the model-view-controller schema for Apple's iOS. We apply this schema to a simple app for querying locally developed microarray gene expression data. The challenge of this application is to balance between storing content locally within the app versus obtaining it dynamically via a network connection. AVAILABILITY: The Hematopoietic Expression Viewer is available at http://www.shawlab.org/he_viewer. The source code for this project and any future information on how to obtain the app can be accessed at http://www.shawlab.org/he_viewer.

A SUMOylation-dependent transcriptional subprogram is required for Myc-driven tumorigenesis.

Myc is an oncogenic transcription factor frequently dysregulated in human cancer. To identify pathways supporting the Myc oncogenic program, we used a genome-wide RNA interference screen to search for Myc-synthetic lethal genes and uncovered a role for the SUMO-activating enzyme (SAE1/2). Loss of SAE1/2 enzymatic activity drives synthetic lethality with Myc. Inactivation of SAE2 leads to mitotic catastrophe and cell death upon Myc hyperactivation. Mechanistically, SAE2 inhibition switches a transcriptional subprogram of Myc from activated to repressed. A subset of these SUMOylation-dependent Myc switchers (SMS genes) is required for mitotic spindle function and to support the Myc oncogenic program. SAE2 is required for growth of Myc-dependent tumors in mice, and gene expression analyses of Myc-high human breast cancers suggest that low SAE1 and SAE2 abundance in the tumors correlates with longer metastasis-free survival of the patients. Thus, inhibition of SUMOylation may merit investigation as a possible therapy for Myc-driven human cancers.

Activation of multiple proto-oncogenic tyrosine kinases in breast cancer via loss of the PTPN12 phosphatase.

Among breast cancers, triple-negative breast cancer (TNBC) is the most poorly understood and is refractory to current targeted therapies. Using a genetic screen, we identify the PTPN12 tyrosine phosphatase as a tumor suppressor in TNBC. PTPN12 potently suppresses mammary epithelial cell proliferation and transformation. PTPN12 is frequently compromised in human TNBCs, and we identify an upstream tumor-suppressor network that posttranscriptionally controls PTPN12. PTPN12 suppresses transformation by interacting with and inhibiting multiple oncogenic tyrosine kinases, including HER2 and EGFR. The tumorigenic and metastatic potential of PTPN12-deficient TNBC cells is severely impaired upon restoration of PTPN12 function or combined inhibition of PTPN12-regulated tyrosine kinases, suggesting that TNBCs are dependent on the proto-oncogenic tyrosine kinases constrained by PTPN12. Collectively, these data identify PTPN12 as a commonly inactivated tumor suppressor and provide a rationale for combinatorially targeting proto-oncogenic tyrosine kinases in TNBC and other cancers based on their profile of tyrosine-phosphatase activity.

Observation and prediction of recurrent human translocations mediated by NAHR between nonhomologous chromosomes.

Four unrelated families with the same unbalanced translocation der(4)t(4;11)(p16.2;p15.4) were analyzed. Both of the breakpoint regions in 4p16.2 and 11p15.4 were narrowed to large ∼359-kb and ∼215-kb low-copy repeat (LCR) clusters, respectively, by aCGH and SNP array analyses. DNA sequencing enabled mapping the breakpoints of one translocation to 24 bp within interchromosomal paralogous LCRs of ∼130 kb in length and 94.7% DNA sequence identity located in olfactory receptor gene clusters, indicating nonallelic homologous recombination (NAHR) as the mechanism for translocation formation. To investigate the potential involvement of interchromosomal LCRs in recurrent chromosomal translocation formation, we performed computational genome-wide analyses and identified 1143 interchromosomal LCR substrate pairs, >5 kb in size and sharing >94% sequence identity that can potentially mediate chromosomal translocations. Additional evidence for interchromosomal NAHR mediated translocation formation was provided by sequencing the breakpoints of another recurrent translocation, der(8)t(8;12)(p23.1;p13.31). The NAHR sites were mapped within 55 bp in ∼7.8-kb paralogous subunits of 95.3% sequence identity located in the ∼579-kb (chr 8) and ∼287-kb (chr 12) LCR clusters. We demonstrate that NAHR mediates recurrent constitutional translocations t(4;11) and t(8;12) and potentially many other interchromosomal translocations throughout the human genome. Furthermore, we provide a computationally determined genome-wide "recurrent translocation map."