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• INSPIRE - Investigator-initiated Phase 2 Study of Pembrolizumab Immunological Response in Metastatic Solid Tumors

  Whole exome and RNA sequencing source data for: Clouthier, D.L., Lien, S.C., Yang, S.Y.C. et al. An interim report on the investigator-initiated phase 2 study of pembrolizumab immunological response evaluation (INSPIRE). j. immunotherapy cancer 7, 72 (2019). https://doi.org/10.1186/s40425-019-0541-0 Bratman S*, Yang SYC*, Iafolla MAJ, Liu Z, Hansen AR, Bedard P, Lheureux S, Spreafico A, Razak AA, Shchegrova S, Louie M, Billings P, Zimmerman B, Sethi H, Aleshi A, Torti D, Marsh K, Eagles J, Cirlan I, Hanna Y, Clouthier D, Lien SC, Ohashi PS, Xu W, Siu LL, Pugh TJ. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nature Cancer. 2020:1:873-881. https://doi.org/10.1038/s43018- 020-0096-5. Yang SYC, Lien SC, Wang BX, Clouthier DL, Hanna Y, Cirlan I, Zhu K, Bruce JP, El Ghamrasni S, Iafallo MAJ, Oliva M, Hansen AR, Spreafico A, Bedard PL, Lheureux S, Razak A, Speers V, Berman HK, Aleshin A, Haibe-Kains B, Brooks DG, McGaha TL, Butler MO, Bratman SV, Ohashi PS, Siu LL, and Pugh TJ. Pan-cancer analysis of longitudinal metastatic tumors reveals genomic alterations and immune landscape dynamics associated with pembrolizumab sensitivity. Nature Comm 2021:12, 5137. https://doi.org/10.1038/s41467-021-25432-7

  Study EGAS00001003280

• RBMX functional retrocopy safeguards brain development

  Retrotransposition has generated thousands of intronless gene copies in mammalian genomes, yet their contribution to brain development and evolution remains largely unexplored. We investigated the role for RBMX retrocopy in shaping neurodevelopment and modulating disease. First, we reported 8 distinct pathogenic variants in RBMX, an X-linked splicing regulator, in 11 males presenting with intellectual disability, microcephaly, and cortical malformations. Then, we combined transcriptomic profiling, protein–protein and protein–RNA interaction studies both in human cellular models and mouse embryonic cortices to assess the functional redundancy between RBMX and its retrocopy RBMXL1. Finally, we use mouse genetics to dissect RBMX function and its compensation by RBMXL1 in corticogenesis. We show that RBMX pathogenic variants disrupt cortical development through both loss- and gain-of-function mechanisms. Surprisingly, despite severe phenotypes in humans, Rbmx-deficient mice display only mild cortical abnormalities, a discrepancy likely due to compensation by Rbmxl1, a retrocopy that arose independently in mice and humans. We demonstrate that RBMX and RBMXL1 share protein and RNA partners and act redundantly in brain development, with RBMXL1 buffering the impact of RBMX deficiency. Together, these findings establish that RBMXL1 functions as a functional paralog of RBMX safeguarding neurodevelopment by buffering deleterious variation. More broadly, these results identify retrocopies as active contributors to neurodevelopmental robustness and suggest that functional retrocopies may have facilitated the evolutionary diversification of the mammalian brain.

  Study EGAS50000001650

• What is metadata?

  What is metadata? Data represents units of information and metadata can be seen, simply put, as data about other data. For example, in a simple experiment in which a patient genome was sequenced, there are multiple and diverse fields of metadata: from the biological sex of the patient to the sequencing platform used. Another way to understand metadata, following our genomic experiment example, is going through the different layers of (meta)data: One of the basic units of information (data) would be the sequence of nucleotides (e.g. GCTGG and GCCGG). This DNA sequence can be contextualised within a chromosome by providing its locus (e.g. β-globin gene). This data, the locus, is about other data, the sequence of nucleotides, which would render it metadata. We can go one step further, and provide more context, by giving the associated phenotype of each of these sequences: GCTGG corresponds to a healthy individual, while GCCGG corresponds to a person with β-thalassaemia. Once again, this data is contextualising the data we have provided up to this point, which makes it metadata. With this simple example, what we are doing is adding layers of data that describe the ones below. Had we given the sequences alone, little would be possible from the researchers' point of view. But, now that the data has its accompanying metadata, a researcher could compare both sequences and their phenotypes to infer a possible cause of the disorder. Each layer of information broadens the possible applications of the data. Now that we know what metadata is, it is time to emphasise the importance of submitting good quality metadata. The quality of the metadata will determine whether the data is usable, discoverable, and interpretable by researchers and clinicians. One example that highlights the importance of good quality metadata is the description of biological sex of a patient: while the terms "female," "girl," or "woman" all represent the same concept, they are not harmonised. These different representations of a concept can create confusion and errors in data analysis. By applying standard definitions and terminologies to biological sex metadata, we can avoid misunderstandings and facilitate the integration of different datasets.  In order to comply with the FAIR principles and, more specifically, to allow for the (meta)data to be interoperable (i.e. used universally and unambiguously), we need to place some standards onto the (meta)data EGA  archives. These can be as simple as asserting that the given patient's biological sex is one of the correct terms (e.g. female or male); or as complex as multiple layers and combinations of validation steps with existing records. You can find further details on how EGA manages these standards on our metadata model page.

  Documentation submission/metadata/what-is-metadata

• Enrichment of oral-derived bacteria in inflamed colorectal tumors and distinct associations of Fusobacterium in the mesenchymal subtype

  The association between colorectal cancer (CRC) clinical variables and Fusobacterium, but not other intra-tumoral bacteria, has been extensively studied. Here we leveraged whole-transcriptome sequencing from 807 CRC tumor samples from the AVANT phase III trial to dually characterize tumor gene expression and intra-tumoral bacteria. After stringent filtering, 74 high-confidence taxa were identified. 17 of these species, including 4 Fusobacterium spp., were classified as orally-derived and had a robust signal within right-sided, MSI-H, and BRAF mutant tumors. Across consensus molecular subtypes (CMS), integration of Fusobacterium animalis presence and tumor gene expression revealed that F. animalis had the greatest number of associations in mesenchymal CMS4 tumors, despite an overall lower prevalence than in immune CMS1 tumors. Pathway analysis within CMS4 revealed that F. animalis, but not other highly prevalent species, was uniquely associated with pathways for collagen degradation and formation as well as IL-6 and IL-1 cytokine signaling. These associations could explain in part why Fusobacterium has been previously associated with reduced survival in mesenchymal populations. Overall, this study identified oral-derived bacteria, including Fusobacterium, as broadly more prevalent in inflamed CRC tumors compared to the other subtypes, and the association of intra-tumoral bacteria on tumor gene expression is context- and species-specific.

  Study EGAS00001006757

• Human Pancreatic Islet RNAseq - Lund

  Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

  Study EGAS00001004042

• Phenotyping data on human pancreatic islets from 191 donors - Lund

  Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

  Study EGAS00001004056

• GWAS data on human pancreatic islets from 191 donors - Lund

  Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

  Study EGAS00001004044

• Prebiotic inulin-type fructans induce specific changes in the human gut microbiota

  ObjectiveContrary to the long-standing prerequisite of inducing selective (i.e. bifidogenic) effects, recent findings suggest that prebiotic interventions lead to ecosystem-wide microbiota shifts. Yet, a comprehensive characterization of this process is still lacking. Here, we apply 16S rDNA microbiota profiling and matching (GC-MS) metabolomics to assess the consequences of inulin fermentation both on the composition of the colon bacterial ecosystem and fecal metabolites profiles.DesignFecal samples collected during a double blind, randomized, cross-over intervention study (NCT02548247) set up to assess the effect of inulin consumption on stool frequency in healthy adults with mild constipation were analyzed. Fecal microbiota composition and metabolite profiles were linked to the study’s clinical outcome as well as to quality-of-life measurements recorded.ResultsWhile fecal metabolite profiles were not significantly altered by inulin consumption, our analyses did detect a modest effect on global microbiota composition. At the same time, specific inulin-induced changes in relative abundances of Anaerostipes, Bilophila, and Bifidobacterium were identified. The observed decrease in Bilophila abundances following inulin consumption was associated with both softer stools and a favorable change in constipation-specific quality of life measures.ConclusionsEcosystem-wide analysis of the effect of a dietary intervention with prebiotic inulin-type fructans on the colon microbiota revealed that this effect is specifically associated to three genera, one of which (Bilophila) representing a promising novel target for mechanistic research.

  Study EGAS00001002173

• Transcriptional regulatory networks of tumor-associated macrophages that drive malignancy in mesenchymal glioblastoma

  Background: Glioblastoma (GBM) is a complex disease with extensive molecular and transcriptional heterogeneity. GBM can be subcategorized into four distinct subtypes; tumors that shift towards the mesenchymal phenotype upon recurrence are generally associated with treatment resistance, unfavorable prognosis, and the infiltration of pro-tumorigenic macrophages. Results: We explore the transcriptional regulatory networks of mesenchymal-associated tumor-associated macrophages (MA-TAMs), which drive the malignant phenotypic state of GBM, and identify macrophage receptor with collagenous structure (MARCO) as the most highly differentially expressed gene. MARCOhigh TAMs induce a phenotypic shift towards mesenchymal cellular state of glioma stem cells, promoting both invasive and proliferative activities, as well as therapeutic resistance to irradiation. MARCOhigh TAMs also significantly accelerate tumor engraftment and growth in vivo. Moreover, both MA-TAM master regulators and their target genes are significantly correlated with poor clinical outcomes, and are often associated with genomic aberrations in neurofibromin 1 (NF1) and phosphoinositide 3-kinases/mammalian target of rapamycin/Akt pathway (PI3K-mTOR-AKT)-related genes. We further demonstrate origination of MA-TAMs from peripheral blood, as well as their potential association with tumor-induced polarization states and immunosuppressive environments. Conclusions: Collectively, our study characterizes the global transcriptional profile of TAMs driving mesenchymal GBM pathogenesis, providing potential therapeutic targets for improving the effectiveness of GBM immunotherapy.

  Study EGAS00001002443

• MutWP1__CRUK_Grand_Challenge_Mutographs_of_Cancer__pancreas

  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, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute (Illumina HiSeqX, 40X and 20X depth respectively). Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors 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 EGAS00001002726