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• MutWP4__CRUK_Grand_Challenge_Mutographs_of_Cancer__Mutagen_treated_organoids

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001004873

• MutWP4__CRUK_Grand_Challenge_Mutographs_of_Cancer__Gastric_Organoids

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001003151

• MutWP4__CRUK_Grand_Challenge_Mutographs_of_Cancer__Pancreatic_Organoids

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001003520

• NHLBI GO-ESP: Lung Cohorts Exome Sequencing Project (Lung Health Study of Chronic Obstructive Pulmonary Disease)

  The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. The Lung Health Study I was a randomized multicenter clinical trial with 5887 participants carried out from October 1986 to April 1994, designed to test the effectiveness of smoking cessation and bronchodilator administration in smokers aged 35 to 60 with mild lung function impairment. Participants were randomly assigned to one of three groups: usual care, who received no intervention smoking intervention with the inhaled bronchodilator ipratroprium bromide smoking intervention with an inhaled placebo The effect of intervention was evaluated by the rate of decline of forced expiratory volume in one second (FEV1).

  Study phs000291

• A Phase I/II Study of Revlimid (lenalidomide) in Combination with Vidaza (azacitidine) in Patients with Advanced Myelodysplastic Syndrome (MDS)

  This single-arm study design has been selected to investigate the safety of combination therapy with Vidaza® (azacitidine) and Revlimid® (lenalidomide) in a population of 18 participants with advanced MDS for the Phase I portion, and 18 participants for the Phase II portion. The multi-center nature of the study should enhance the general applicability of the results. Participants with advanced MDS were chosen because it is expected that the combination of the two drugs will by myelosuppressive, which can be considered a necessary toxicity to effect improvements in PR and CR rates in this advanced population, akin to participants with AML. Finally, the combination was chosen to be studied because currently available therapies in this population, and their effectiveness, are limited, and single-agent Vidaza® (azacitidine), while effective at delaying transformation to AML or death, is not curative. The doses of Revlimid® (lenalidomide) in this study are similar to those that were demonstrated to have an effect and to be well-tolerated in participants with MDS. Doses for Vidaza® (azacitidine) are similar to those used in the pivotal trial, though for a 5-day treatment period instead of 7 to minimize toxicity for the first 3 blocks of participants, and then at a lower dose over a protracted period of time to explore the safety of a 10-day treatment course.

  Study phs001318

• Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness (ESCAPE-BioLINCC)

  Accessing Data Please refer to “Authorized Access” below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP. Objectives To test whether pulmonary artery catheter use was safe and could improve clinical outcomes in participants hospitalized with recurrent heart failure. Background Pulmonary artery catheters have been used to guide adjustment of therapy in multiple settings, but recent studies have raised concern that pulmonary artery catheters may lead to increased mortality in hospitalized participants. Participants A total of 433 participants at 26 sites were enrolled, and randomly assigned to receive therapy guided by clinical assessment and the pulmonary artery catheter or clinical assessment alone. Patients with acute decompensation in which the attending heart failure physician considered pulmonary artery catheterization (PAC) was required or likely to be required within the next 24 hours were entered into a PAC registry. A total of 439 patients were added to the registry. Conclusions Therapy to reduce volume overload during hospitalization for heart failure led to marked improvement in signs and symptoms of elevated filling pressures, with or without the pulmonary artery catheter. Addition of the pulmonary artery catheter to careful clinical assessment did not impact overall mortality and hospitalization. Future trials should test noninvasive assessments with specific treatment strategies that could be used to better tailor therapy for both survival time and survival quality as valued by participants. (Binanay, C. et al., JAMA, 2005)

  Study phs003782

• Combining transcription factor binding affinities with open chromatin data for accurate gene expression prediction

  The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  Study EGAS00001002073

• Human Microbiome Project Demonstration Study of Cutaneous Microbiome in Psoriasis

  Psoriasis, a highly prevalent disease of humans of unknown cause, is a chronic inflammatory disorder primarily involving skin, with distinctive clinical characteristics. With the newly developed tools that facilitate microbiome research, it now is possible to assess whether the cutaneous microbiome plays a role in the pathogenesis of this disorder. Preliminary data from our studies suggest that the cutaneous microbiome in psoriasis is complex and possibly different from normal. To deal with this complexity, we propose to examine the cutaneous microbiome in relation to psoriasis with explorations at several taxonomic and informatic levels. Our overall objective is to examine how changes in the normal cutaneous microbiome contribute to the pathogenesis of psoriasis. Since causality is complex and often difficult to prove, and beyond the scope of this RFP, our overall hypothesis is that there are alterations in the cutaneous microbiome in areas of skin affected by psoriasis in comparison with the range observed in clinically unaffected areas, or in healthy persons. We also hypothesize that the characteristics of the microbiome may affect clinical responses to the immunomodulatory agents used to treat psoriasis. An alternative hypothesis is that effective treatment of psoriasis with systemic immunomodulatory agents will not substantially affect the disordered microbial ecosystem. Such observations would provide evidence for the roles of the microbiota in this disorder. Since an important consideration in microbiome research is the optimal level (e.g. phylum, genus, species, strain, gene) at which to examine a scientific question, and we are not yet certain what are the optimal levels for psoriasis, this also will be examined. Our studies of psoriasis should allow development of both approaches and tools that will have general utility for Microbiome research. To test our hypothesis, we propose the following specific aims: 1) To understand the cutaneous microbiome species composition overlaying psoriatic lesions; 2)To investigate differences in metagenome content for psoriatic lesions compared to normal skin; 3) To identify differences in the transcriptional profiles of the microbiome and the host between normal skin and psoriatic lesions using high-throughput sequencing; and 4) To estimate the effects of systemic immunomodulatory therapy for psoriasis on microbiome composition. In total, these studies should help us understand the role of the microbiome in psoriasis pathogenesis. We sought to characterize and compare the cutaneous microbiota of psoriatic lesions (lesion), unaffected contralateral skin from psoriatic patients (normal), and similar skin loci in matched healthy controls (control) in order to discern patterns that govern skin colonization and their relationship to clinical diagnosis. Using high-throughput 16S rRNA sequencing, we assayed the cutaneous bacterial communities of 51 matched triplets and characterized these samples using community data analysis techniques.

  Study phs000251

• A Clone's Genomic Stability as a Biomarker of Its DNA-Damage Resilience

  A main problem in the treatment of advanced cancers, including gastric cancers and glioblastoma, is the incertitude at which we predict how individual patients will respond to DNA-damaging agents, especially on the long run. Knowing the mechanism behind a patient's response, or the lack thereof, will help us depart from the oversimplified “more-is-better” and “one-size-fits-all” principles according to which DNA-damaging agents are administered. This will improve clinical outcome by allowing us to pinpoint those who would respond better and longer to lower doses than to higher doses of DNA-damaging agents. Under the assumption that the success of DNA-damaging therapy increases with the proliferation rate of a relatively homogeneous tumor population, there was little reason to assume anything other than monotonic dose-response relations. With the recent paradigm shift that most cancers are in fact DNA mosaic products of ongoing evolution, comes the urgency to reconsider these fundamental principles behind DNA-damaging therapy administration. As the developers of one of the first DNA deconvolution methods and with access to technologies to profile the transcriptomes of up to 10,000 cells simultaneously, we are equipped to embark on first personalized dose-finding strategies for DNA-damaging therapies. We will test the potential of the very long-term legacy that DNA-damage entails on a cell “genomic instability” as new biomarker of DNA-damage response. Our preliminary studies showed that, for most cancer types, DNA-damaging agents change a clone's genomic instability and that clones succumb to a limit in the amount of genomic instability they can tolerate. In particular, our results showed that patients with intermediate genomic instability have a very poor outcome and that this relation is only evident among treatment-naive patients, but not among patients treated with DNA-damaging agents. Further they show that we can measure genomic instability per clone and that clones with extreme genomic instability typically don't grow large. Our hypothesis that genomic instability, rather than proliferation rate, determines how sensitive a tumor is to DNA-damaging agents on the long-term, is founded on two unexpected findings. Patients with extremely high genomic instability per tumor clone have an exceptionally good outcome. Aim 1 will integrate exome- and single cell RNA-Seq (scRNA-Seq) data to characterize clones and to measure how much genomic instability they can tolerate. Low genomic instability is associated with reduced benefit from DNA-damaging agents. Aim 2 will use comet assays and treatment history to quantify DNA damage per clone, relating it to the clones' ability to tolerate DNA damage and to changes in the genomic instability of therapy-surviving clones.

  Study phs003762

• RNAseq data to study PRPF6 regulated splice forms in colon cancer cell lines

  Alternative splicing plays critical roles in differentiation, development, and cancer (Pettigrew et al., 2008; Chen and Manley, 2009). The recent identification of specific spliceosome inhibitors has generated interest in the therapeutic potential of targeting this cellular process (van Alphen et al., 2009). Using an integrated genomic approach, we have identified PRPF6, an RNA binding component of the pre-mRNA spliceosome, as an essential driver of oncogenesis in colon cancer. Importantly, PRPF6 is both amplified and overexpressed in colon cancer, and only colon cancer cells with high PRPF6 levels are sensitive to its loss. Our data clearly point to an important role for PRPF6 in colon cancer growth and suggest that a better understanding of its role in alternative splicing in colon cancer is warranted. To determine the specific alternative splice forms that PRPF6 regulates in colon cancer, we plan three experiments: 1. The first involves knocking down expression of PRPF6 in two different cancer cell lines with 3 different siRNAs, and then completing RNA-seq to determine the gene expression changes that occur relative to a non-targeting control siRNA. Because of the role for PRPF6 in pre-mRNA splicing, we especially want to quantify the changes in splice-specific forms of all genes genome-wide to identify genes whose splicing is altered upon PRPF6 knockdown. 2. The second involves immunoprecipitating PRPF6 from two different cancer cell lines and isolating any RNA that is bound to PRPF6, since PRPF6 is an RNA-binding protein. We then want to carry out RNA-seq to identify which RNA molecules co-immunoprecipitated with PRPF6. This will help us determine possible functions for PRPF6 in regulating colon cancer growth. 3. The third involves overexpressing PRPF6 in cell lines and then carrying out RNA-seq to identify any changes in splice-specific gene expression. This will allow us to determine whether increased PRPF6 expression is sufficient to drive alternative splicing changes.

  Study EGAS00001000761