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Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the US. Despite multiple large-scale genetic sequencing studies, identification of predictors of patient survival remains challenging. We performed a comprehensive assessment and integrative analysis of large-scale gene expression datasets, across multiple platforms, to enable discovery of a prognostic gene signature for patient survival in pancreatic cancer. PDAC RNA-Sequencing data from The Cancer Genome Atlas was stratified into Survival+ (>2-year survival) and Survival-(<1-year survival) cohorts (n = 47). Comparisons of RNA expression profiles between survival groups and normal pancreatic tissue expression data from the Gene Expression Omnibus generated an initial PDAC specific prognostic differential expression gene list. The candidate prognostic gene list was then trained on the Australian pancreatic cancer dataset from the ICGC database (n = 103), using iterative sampling based algorithms, to derive a gene signature predictive of patient survival. The gene signature was validated in 2 independent patient cohorts and against existing PDAC subtype classifications. We identified 707 candidate prognostic genes exhibiting differential expression in tumor versus normal tissue. A substantial fraction of these genes was also found to be differentially methylated between survival groups. From the candidate gene list, a 5-gene signature (ADM, ASPM, DCBLD2, E2F7, and KRT6A) was identified. Our signature demonstrated significant power to predict patient survival in two distinct patient cohorts and was independent of AJCC TNM staging. Cross-validation of our gene signature reported a better ROC AUC (≥ 0.8) when compared to existing PDAC survival signatures. Furthermore, validation of our signature through immunohistochemical analysis of patient tumor tissue and existing gene expression subtyping data in PDAC, demonstrated a correlation to the presence of vascular invasion and the aggressive squamous tumor subtype. Assessment of these genes in patient biopsies could help further inform risk-stratification and treatment decisions in pancreatic cancer.

Purpose Neurodevelopmental disorders represent a frequent indication for clinical exome sequencing. Fifty percent of cases, however, remain undiagnosed even upon exome reanalysis. Here we show RNA sequencing (RNA-seq) on human B-lymphoblastoid cell lines (LCL) is highly suitable for neurodevelopmental Mendelian gene testing and demonstrate the utility of this approach in suspected cases of Cornelia de Lange syndrome (CdLS). Methods Genotype–Tissue Expression project transcriptome data for LCL, blood, and brain were assessed for neurodevelopmental Mendelian gene expression. Detection of abnormal splicing and pathogenic variants in these genes was performed with a novel RNA-seq diagnostic pipeline and using a validation CdLS-LCL cohort ( n  = 10) and test cohort of patients who carry a clinical diagnosis of CdLS but negative genetic testing ( n  = 5). Results LCLs share isoform diversity of brain tissue for a large subset of neurodevelopmental genes and express 1.8-fold more of these genes compared with blood (LCL, n  = 1706; whole blood, n  = 917). This enables testing of more than 1000 genetic syndromes. The RNA-seq pipeline had 90% sensitivity for detecting pathogenic events and revealed novel diagnoses such as abnormal splice products in NIPBL and pathogenic coding variants in BRD4 and ANKRD11 . Conclusion The LCL transcriptome enables robust frontline and/or reflexive diagnostic testing for neurodevelopmental disorders.

The calcium-activated chloride channel anoctamin 1 (ANO1) is located within the 11q13 amplicon, one of the most frequently amplified chromosomal regions in human cancer, but its functional role in tumorigenesis has remained unclear. The 11q13 region is amplified in ∼15% of breast cancers. Whether ANO1 is amplified in breast tumors, the extent to which gene amplification contributes to ANO1 overexpression, and whether overexpression of ANO1 is important for tumor maintenance have remained unknown. We have found that ANO1 is amplified and highly expressed in breast cancer cell lines and primary tumors. Amplification of ANO1 correlated with disease grade and poor prognosis. Knockdown of ANO1 in ANO1-amplified breast cancer cell lines and other cancers bearing 11q13 amplification inhibited proliferation, induced apoptosis, and reduced tumor growth in established cancer xenografts. Moreover, ANO1 chloride channel activity was important for cell viability. Mechanistically, ANO1 knockdown or pharmacological inhibition of its chloride-channel activity reduced EGF receptor (EGFR) and calmodulin-dependent protein kinase II (CAMKII) signaling, which subsequently attenuated AKT, v-src sarcoma viral oncogene homolog (SRC), and extracellular signal-regulated kinase (ERK) activation in vitro and in vivo. Our results highlight the involvement of the ANO1 chloride channel in tumor progression and provide insights into oncogenic signaling in human cancers with 11q13 amplification, thereby establishing ANO1 as a promising target for therapy in these highly prevalent tumor types.

Neuroblastoma is characterized by a relative paucity of recurrent somatic mutations at diagnosis. However, recent studies have shown that the mutational burden increases at relapse, likely as a result of clonal evolution of mutation-carrying cells during primary treatment. To inform the development of personalized therapies, we sought to further define the frequency of potentially actionable mutations in neuroblastoma, both at diagnosis and after chemotherapy. We performed a retrospective study to determine mutation frequency, the only inclusion criterion being availability of cancer gene panel sequencing data from Foundation Medicine. We analyzed 151 neuroblastoma tumor samples: 44 obtained at diagnosis, 42 at second look surgery or biopsy for stable disease after chemotherapy, and 59 at relapse (6 were obtained at unknown time points). Nine patients had multiple tumor biopsies. ALK was the most commonly mutated gene in this cohort, and we observed a higher frequency of suspected oncogenic ALK mutations in relapsed disease than at diagnosis. Patients with relapsed disease had, on average, a greater number of mutations reported to be recurrent in cancer, and a greater number of mutations in genes that are potentially targetable with available therapeutics. We also observed an enrichment of reported recurrent RAS/MAPK pathway mutations in tumors obtained after chemotherapy. Our data support recent evidence suggesting that neuroblastomas undergo substantial mutational evolution during therapy, and that relapsed disease is more likely to be driven by a targetable oncogenic pathway, highlighting that it is critical to base treatment decisions on the molecular profile of the tumor at the time of treatment. However, it will be necessary to conduct prospective clinical trials that match sequencing results to targeted therapeutic intervention to determine if cancer genomic profiling improves patient outcomes.

Background Somatic genetic testing is rapidly becoming the standard of care in many adult and pediatric cancers. Previously, the standard approach was single-gene or focused multigene testing, but many centers have moved towards broad-based next-generation sequencing (NGS) panels. Here, we report the laboratory validation and clinical utility of a large cohort of clinical NGS somatic sequencing results in diagnosis, prognosis, and treatment of a wide range of pediatric cancers. Methods Subjects were accrued retrospectively at a single pediatric quaternary-care hospital. Sequence analyses were performed on 367 pediatric cancer samples using custom-designed NGS panels over a 15-month period. Cases were profiled for mutations, copy number variations, and fusions identified through sequencing, and their clinical impact on diagnosis, prognosis, and therapy was assessed. Results NGS panel testing was incorporated meaningfully into clinical care in 88.7% of leukemia/lymphomas, 90.6% of central nervous system (CNS) tumors, and 62.6% of non-CNS solid tumors included in this cohort. A change in diagnosis as a result of testing occurred in 3.3% of cases. Additionally, 19.4% of all patients had variants requiring further evaluation for potential germline alteration. Conclusions Use of somatic NGS panel testing resulted in a significant impact on clinical care, including diagnosis, prognosis, and treatment planning in 78.7% of pediatric patients tested in our institution. Somatic NGS tumor testing should be implemented as part of the routine diagnostic workup of newly diagnosed and relapsed pediatric cancer patients.

Changes in gene expression during tumorigenesis are often considered the consequence of de novo mutations occurring in the tumour. An alternative possibility is that the transcriptional response of oncogenic transcription factors evolves during tumorigenesis. Here we show that aberrant E2f activity, following inactivation of the Rb gene family in a mouse model of liver cancer, initially activates a robust gene expression programme associated with the cell cycle. Slowly accumulating E2f1 progressively recruits a Pontin/Reptin complex to open the chromatin conformation at E2f target genes and amplifies the E2f transcriptional response. This mechanism enhances the E2f-mediated transactivation of cell cycle genes and initiates the activation of low binding affinity E2f target genes that regulate non-cell-cycle functions, such as the Warburg effect. These data indicate that both the physiological and the oncogenic activities of E2f result in distinct transcriptional responses, which could be exploited to target E2f oncogenic activity for therapy. E2F transcription factors are primarily known for the regulation of the cell cycle and are often dysregulated in cancer. Here, the authors show that during cancer progression E2F1 recruits a Pontin/Reptin complex to E2F target genes to open chromatin and increase E2F transcriptional response.

Englerin A is a structurally unique natural product reported to selectively inhibit growth of renal cell carcinoma cell lines. A large scale phenotypic cell profiling experiment (CLiP) of englerin A on ¬over 500 well characterized cancer cell lines showed that englerin A inhibits growth of a subset of tumor cell lines from many lineages, not just renal cell carcinomas. Expression of the TRPC4 cation channel was the cell line feature that best correlated with sensitivity to englerin A, suggesting the hypothesis that TRPC4 is the efficacy target for englerin A. Genetic experiments demonstrate that TRPC4 expression is both necessary and sufficient for englerin A induced growth inhibition. Englerin A induces calcium influx and membrane depolarization in cells expressing high levels of TRPC4 or its close ortholog TRPC5. Electrophysiology experiments confirmed that englerin A is a TRPC4 agonist. Both the englerin A induced current and the englerin A induced growth inhibition can be blocked by the TRPC4/C5 inhibitor ML204. These experiments confirm that activation of TRPC4/C5 channels inhibits tumor cell line proliferation and confirms the TRPC4 target hypothesis generated by the cell line profiling. In selectivity assays englerin A weakly inhibits TRPA1, TRPV3/V4, and TRPM8 which suggests that englerin A may bind a common feature of TRP ion channels. In vivo experiments show that englerin A is lethal in rodents near doses needed to activate the TRPC4 channel. This toxicity suggests that englerin A itself is probably unsuitable for further drug development. However, since englerin A can be synthesized in the laboratory, it may be a useful chemical starting point to identify novel modulators of other TRP family channels.

Despite recent advances in cancer immunotherapy, the process of immunoediting early in tumorigenesis remains obscure. Here, we employ a mathematical model that utilizes the Cancer Genome Atlas (TCGA) data to elucidate the contribution of individual mutations and HLA alleles to the immunoediting process. We find that common cancer mutations including BRAF-V600E and KRAS-G12D are predicted to bind none of the common HLA alleles, and are thus \"immunogenically silent\" in the human population. We identify regions of proteins that are not presented by HLA at a population scale, coinciding with frequently mutated hotspots in cancer, and other protein regions broadly presented across the population in which few mutations occur. We also find that 9/29 common HLA alleles contribute disproportionately to the immunoediting of early oncogenic mutations. These data provide insights into immune evasion of common driver mutations and a molecular basis for the association of particular HLA genotypes with cancer susceptibility.

Background Gene fusion events are significant sources of somatic variation across adult and pediatric cancers and are some of the most clinically-effective therapeutic targets, yet low consensus of RNA-Seq fusion prediction algorithms makes therapeutic prioritization difficult. In addition, events such as polymerase read-throughs, mis-mapping due to gene homology, and fusions occurring in healthy normal tissue require informed filtering, making it difficult for researchers and clinicians to rapidly discern gene fusions that might be true underlying oncogenic drivers of a tumor and in some cases, appropriate targets for therapy. Results We developed annoFuse , an R package, and shinyFuse , a companion web application, to annotate, prioritize, and explore biologically-relevant expressed gene fusions, downstream of fusion calling. We validated annoFuse using a random cohort of TCGA RNA-Seq samples (N = 160) and achieved a 96% sensitivity for retention of high-confidence fusions (N = 603). annoFuse uses FusionAnnotator annotations to filter non-oncogenic and/or artifactual fusions. Then, fusions are prioritized if previously reported in TCGA and/or fusions containing gene partners that are known oncogenes, tumor suppressor genes, COSMIC genes, and/or transcription factors. We applied annoFuse to fusion calls from pediatric brain tumor RNA-Seq samples (N = 1028) provided as part of the Open Pediatric Brain Tumor Atlas (OpenPBTA) Project to determine recurrent fusions and recurrently-fused genes within different brain tumor histologies. annoFuse annotates protein domains using the PFAM database, assesses reciprocality, and annotates gene partners for kinase domain retention. As a standard function, reportFuse enables generation of a reproducible R Markdown report to summarize filtered fusions, visualize breakpoints and protein domains by transcript, and plot recurrent fusions within cohorts. Finally, we created shinyFuse for algorithm-agnostic interactive exploration and plotting of gene fusions. Conclusions annoFuse provides standardized filtering and annotation for gene fusion calls from STAR-Fusion and Arriba by merging, filtering, and prioritizing putative oncogenic fusions across large cancer datasets, as demonstrated here with data from the OpenPBTA project. We are expanding the package to be widely-applicable to other fusion algorithms and expect annoFuse to provide researchers a method for rapidly evaluating, prioritizing, and translating fusion findings in patient tumors.

Medulloblastoma is a highly heterogeneous pediatric brain tumor with five molecular subtypes, Sonic Hedgehog TP53 -mutant, Sonic Hedgehog TP53 -wildtype, WNT, Group 3, and Group 4, defined by the World Health Organization. The current mechanism for classification into these molecular subtypes is through the use of immunostaining, methylation, and/or genetics. We surveyed the literature and identified a number of RNA-Seq and microarray datasets in order to develop, train, test, and validate a robust classifier to identify medulloblastoma molecular subtypes through the use of transcriptomic profiling data. We have developed a GPL-3 licensed R package and a Shiny Application to enable users to quickly and robustly classify medulloblastoma samples using transcriptomic data. The classifier utilizes a large composite microarray dataset (15 individual datasets), an individual microarray study, and an RNA-Seq dataset, using gene ratios instead of gene expression measures as features for the model. Discriminating features were identified using the limma R package and samples were classified using an unweighted mean of normalized scores. We utilized two training datasets and applied the classifier in 15 separate datasets. We observed a minimum accuracy of 85.71% in the smallest dataset and a maximum of 100% accuracy in four datasets with an overall median accuracy of 97.8% across the 15 datasets, with the majority of misclassification occurring between the heterogeneous Group 3 and Group 4 subtypes. We anticipate this medulloblastoma transcriptomic subtype classifier will be broadly applicable to the cancer research and clinical communities.