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• Immune landscape of oncohistone-mutant gliomas reveals diverse myeloid populations and tumor-promoting behavior

  Histone H3-mutant gliomas are deadly brain tumors characterized by a dysregulated epigenome and stalled differentiation. In contrast to the extensive datasets available on tumor cells, limited information exists on their tumor microenvironment (TME), including the immune infiltrate. Here, we characterize the TME of H3.3K27M and G34R/V-mutant gliomas, and multiple H3.3K27M mouse models, using transcriptomic and proteomic, including spatial single-cell approaches. Resolution of immune lineages indicates uniform high-infiltration of H3-mutant gliomas with diverse myeloid populations, high-level expression of immune checkpoints, and scarce lymphoid cells, findings uniformly reproduced in all H3.3K27M models tested. We show these myeloid populations communicate with H3-mutant cells mediating immunosuppression and sustaining tumor formation and maintenance. Dual inhibition of myeloid cells and immune checkpoints showed significant therapeutic benefit in pre-clinical syngeneic models. Our findings provide valuable characterization of the TME of oncohistone-mutant gliomas, and insights into means to modulate the myeloid infiltrate for the benefit of patients.

  Study EGAS00001007510

• Cross-species Comparison Reveals Therapeutic Vulnerabilities Halting Glioblastoma Progression

  The growth of a tumor is tightly linked to the distribution of its cells along a continuum of activation states. Here, we systematically decode the activation state architecture (ASA) in a glioblastoma (GBM) patient cohort through comparison to adult murine neural stem cells. Modelling of these data forecast how tumor cells organize to sustain growth and identifies rate of activation as the main predictor of growth. Accordingly, patients with a higher quiescence fraction exhibit improved outcomes. Further, DNA methylation arrays enable ASA- related patient stratification. Comparison of healthy and malignant gene expression dynamics reveals dysregulation of the Wnt- antagonist SFRP1 at the quiescence to activation transition. SFRP1 overexpression renders GBM quiescent and increases overall survival of tumor-bearing mice. Surprisingly, it does so through reprogramming the tumor’s stem-like methylome into an astrocyte- like one. Our findings offer a framework for patient stratification with prognostic value, biomarker identification, and therapeutic avenues to halt GBM progression.

  Study EGAS00001008155

• Exome sequencing of Congenital Heart Disease families Leuven

  This project aims to study at least 90 exomes from families with congenital heart disease. The samples have been selected in Leuven in collaboration with Koen Devriendt. Ethic approval has been sought for in Leuven, Belgium and a HDMMC agreement for submitting these samples is in place at the WTSI. The phenotype we wil primarily focus our analysis is severe Left Ventricular Outflow Tract Obstructions (LVOTO) and Atrioventricular Septal Defect (AVSD). The indexed Agilent whole exome pulldown libraries will be sequenced on 75bp PE HiSeq (Illumina).

  Dataset EGAD00001000796

• Exome sequencing of Congenital Heart Disease families Royal Brompton

  This project aims to study at least 90 exomes from families with congenital heart disease. The samples have been selected at the Royal, Brompton Hospital in collaboration with Stuart Cook and Piers Daubeney. Ethic approval has been sought for in the UK and a HDMMC agreement for submitting these samples is in place at the WTSI. The phenotype we wil primarily focus our analysis is severe Left Ventricular Outflow Tract Obstructions (LVOTO) and Atrioventricular Septal Defect (AVSD). The indexed Agilent whole exome pulldown libraries will be sequenced on 75bp PE HiSeq (Illumina).

  Dataset EGAD00001000797

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: pancreas (2019-04-03)

  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. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004893

• Immune variation leads to diverse outcomes in human malaria (2020-01-15)

  Falciparum malaria is clinically heterogeneous and yet in most cases the risk of life-threatening disease dramatically declines after the first few infections of life because children rapidly acquire disease tolerance (resistance to severe malaria without improved control of parasite burden). Identifying the factors that determine clinical outcome in a malaria-naive host is therefore paramount to reduce malaria mortality. However, the relative contribution of disease-causing variants of the Plasmodium var gene family versus pathogenic inflammatory cytokine cascades remains fiercely debated - we sought to reconcile these conflicting arguments by studying their interaction in vivo. To this end, two human challenge models were used to reveal the parasite-host interactions that underpin variation in falciparum malaria. To capture the diversity of human immune responses, each individual was analysed independently by tracking dynamic changes in their whole blood transcriptome through time. And to uncover evidence of preferential expansion of disease-causing variants, var gene expression was tracked in vivo from the start to end of infection. In this way, we could show that group A var genes are always expressed upon liver egress but in a minority population that does not increase over 10-days of blood cycling; there is no selection of disease-causing variants in the naive host. In fact, parasites do not respond in any way to differences or changes in host environment. On the other hand, host-intrinsic variation determines the intensity of inflammation and progression to clinical malaria. And furthermore, regulation of the interferon signaling network controls host fate. These data emphasise the role of human immune decision-making in shaping course & outcome of infection. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005790

• pancreatic ductal adenocarcinoma exomes in study: Pro-immunogenic Impact of MEK inhibition combined with an anti-CD40 immunostimulatory antibody

  This dataset contains 3 pairs of exomes, germline (from whole blood) and patient-derived xenograft (PDX), from human pancreatic durctal adenocarcinoma patients. The data is referred to in the publication: "Pro-immunogenic impact of MEK inhibition synergizes with agonist anti-CD40 immunostimulatory antibodies in tumor therapy" (Nature Communications, 2020) Abstract: Cancer types with lower mutational load and a non-permissive tumor microenvironment are intrinsically resistant to immune checkpoint blockade. While the combination of cytostatic drugs and immunostimulatory antibodies constitutes an attractive concept for overcoming this refractoriness, suppression of immune cell function by cytostatic drugs may limit therapeutic efficacy. Here we show that targeted inhibition of mitogen-activated protein kinase (MAPK) kinase (MEK) does not impair dendritic cell-mediated T-cell priming and activation. Accordingly, combining MEK inhibitors (MEKi) with agonist antibodies (Abs) targeting the immunostimulatory CD40 receptor resulted in potent synergistic anti-tumor efficacy. Detailed analysis of the mechanism of action of MEKi GDC-0623 by means of flow cytometric analysis of the tumor immune infiltrate and whole tumor transcriptomics showed that, in addition to its cytostatic impact on tumor cells, this drug exerts multiple pro-immunogenic effects, including the suppression of M2-type macrophages, myeloid derived suppressor cells and CD4+ T-regulatory cells. In addition, MEKi was found to induce tumor-cell intrinsic interferon signaling, which contributed to antigen presentation by tumor cells. Finally, the tumoridical impact of MEKi involves the activation of multiple pro-inflammatory pathways involved in immune cell effector function in the tumor microenvironment. Our data therefore indicate that the combination of MEK inhibition with agonist anti-CD40 Ab is a promising therapeutic concept, especially for the treatment of mutant Kras-driven tumors such as pancreatic ductal adenocarcinoma.

  Dataset EGAD00001005938

• Phylogenetic reconstruction of adult blood cancer reveals early origins and lifelong evolution - TGS

  Mutations in cancer-associated genes drive tumour outgrowth. However, the timing of driver mutations and dynamics of clonal expansion that lead to human cancers are largely unknown. We used 580,133 somatic mutations from whole-genome sequencing of 1013 clonal haematopoietic colonies to reconstruct the phylogeny of haematopoiesis, from embryogenesis to clinical disease, in 12 patients with myeloproliferative neoplasms which are blood cancers more common in older age. JAK2V617F, the pathognomonic mutation driving the majority of these cancers, was acquired in utero or childhood, with upper estimates of age of acquisition from 33 weeks gestation to 10.8 years, in all 5 patients in whom JAK2V617F was either the only or the first driver event.  Driver mutations associated with age-related clonal haematopoiesis occurred prior to or following JAK2V617F,  as independent clonal expansions in JAK2V617F-mutated patients, and as large clonal expansions in JAK2V617F-unmutated patients . These mutations were also acquired in utero or childhood, with DNMT3A mutations occurring by 8 weeks of gestation to 7.6 years across 4 patients, and PPM1D mutation occurring by age 5.8yrs in a patient with MPN lacking phenotypic driver mutations. Sequential driver mutation acquisition was common, separated by decades across life, and often outcompeted ancestral clones. The mean latency between JAK2V617F acquisition and clinical presentation was 30 years (range 11-54 years). Rates of clonal expansion were inferred from phylogenetic trees and varied substantially (3% to 190% expansion/year), were affected by additional driver mutations, and were predictive of latency to clinical presentation. Driver mutations and rates of expansion would have been detectable in blood one to four decades before clinical presentation. This study reveals how driver mutation acquisition early in life with life-long growth and evolution underlie adult myeloproliferative neoplasms, providing opportunities for early detection and intervention and a new paradigm for cancer development.

  Dataset EGAD00001007715

• Comparison of sequencing assays for sensitive detection of circulating tumour DNA in stage IA-IV breast cancer

  This study compared different assays for the detection of circulating tumour DNA (ctDNA) in serial plasma from stage IA-IV breast cancer patients, targeting structural variants (SVs), single nucleotide variants (SNVs) and/or somatic copy-number aberrations (SCNAs). SV-multiplex PCR, SNV-/SV-hybrid capture, and different depths of whole-genome sequencing (WGS) were used to evaluate ctDNA levels, demonstrating concordant results. SNV-hybrid capture targeting 1,347-7,491 mutations was the most sensitive assay, detecting 67% (36/54) of samples down to an allele fraction (AF) of 0.00024%. SV-multiplex PCR, targeting 21-47 mutations, detected 63% (34/54) of samples down to 0.00047% AF and has potential as a clinical assay.

  Dataset EGAD00001008589

• RNA sequencing data for primary and recurrent ovarian granulosa cell tumors

  RNA sequencing dataset for primary and recurrent ovarian granulosa cell tumors consists of 24 .bam files with aligned reads, including 8 primary and 16 recurrent tumors. Total RNA was extracted from cryopreserved tissue of adult-type granulosa cell tumor samples. Libraries were prepared from cDNA using the NuGEN Ovation Ultralow Library System V2 (San Carlos, CA). Paired-end bulk RNA sequencing was performed on the Illumina HiSeq 2000 platform. RNA sequencing reads were aligned to the hg19/GRCh37 reference human genome using the STAR software (version 2.6.0b) with default parameters. Samples description: GCT001 Adult-type Granulosa Cell Tumor: recurrent tumor GCT002 Adult-type Granulosa Cell Tumor: primary tumor GCT003 Adult-type Granulosa Cell Tumor: recurrent tumor GCT004 Adult-type Granulosa Cell Tumor: primary tumor GCT005 Adult-type Granulosa Cell Tumor: recurrent tumor GCT006 Adult-type Granulosa Cell Tumor: recurrent tumor GCT007 Adult-type Granulosa Cell Tumor: primary tumor GCT008 Adult-type Granulosa Cell Tumor: recurrent tumor GCT009 Adult-type Granulosa Cell Tumor: recurrent tumor GCT010 Adult-type Granulosa Cell Tumor: recurrent tumor GCT011 Adult-type Granulosa Cell Tumor: primary tumor GCT012 Adult-type Granulosa Cell Tumor: recurrent tumor GCT013 Adult-type Granulosa Cell Tumor: recurrent tumor GCT014 Adult-type Granulosa Cell Tumor: recurrent tumor GCT015 Adult-type Granulosa Cell Tumor: recurrent tumor GCT016 Adult-type Granulosa Cell Tumor: recurrent tumor GCT017 Adult-type Granulosa Cell Tumor: recurrent tumor GCT018 Adult-type Granulosa Cell Tumor: primary tumor GCT019 Adult-type Granulosa Cell Tumor: recurrent tumor GCT020 Adult-type Granulosa Cell Tumor: primary tumor GCT021 Adult-type Granulosa Cell Tumor: primary tumor GCT022 Adult-type Granulosa Cell Tumor: recurrent tumor GCT023 Adult-type Granulosa Cell Tumor: recurrent tumor GCT024 Adult-type Granulosa Cell Tumor: primary tumor

  Dataset EGAD00001009108