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• Smoking-dependent expression alterations in nasal epithelium reveal immune impairment linked to germline variation and lung cancer risk.

  Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to lifestyle risk in the form of cigarette smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, often many years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk are unclear. CT screening of current and former smokers has been shown to reduce lung cancer mortality by up to 26%. To examine whether clinical risk stratification can be improved upon by the addition of genetic data, and to explore the mechanisms of the persisting risk in former smokers, we have analyzed transcriptomic data from accessible airway tissues of 487 subjects. We developed a model to assess smoking associated gene expression changes and their reversibility after smoking is stopped, in both healthy subjects and clinic patients. We find persistent smoking associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune and interferon related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier. Our results provide initial evidence for germline-mediated personalised smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

  Dac EGAC50000000169

• High-depth whole genome sequencing of 26 premalignant breast lesions

  Ductal carcinoma in situ (DCIS) is a non-obligate precursor lesion of breast cancer, representing 20-25% of newly diagnosed breast cancer lesions. It is currently not possible to predict which DCIS patients will progress to invasive breast cancer. The samples deposited here are part of a larger study using high-depth whole-genome sequencing (WGS) to investigate the somatic mutation genomic landscape of DCIS. Comprehensive evaluation of DCIS genomes aims to uncover biological insights into breast cancer progression and potential opportunities to stratify DCIS patients for personalised treatment options or active surveillance. Due to specific restrictions imposed by the ethical approval at sample collection, the use of the germline data is restricted to filtering of somatic mutation calls only and cannot be used outside this purpose.

  Study EGAS50000001559

• Influence of the Microbiome on Epigenetic Mechanisms in IBD

  Live CD4 T cells were sorted from inflamed and non-inflamed tissue samples of IBD patients or from healthy and IBD blood samples. ATAC-Seq libraries were generated from live CD4 T cells sorted from i) inflamed and non-inflamed tissue samples, ii) healthy and IBD blood samples, or from iii) CD4 T cell subsets polarised from healthy blood samples. After isolating crude nuclei, live CD4+ T cells were treated with Tagment DNA buffer and Tagment DNA Enzyme (Nextera DNA Library Prep Kit, Illumina), and then the DNA was purified by MinElute PCR Purification Kit (Qiagen). Transposed DNA fragments were amplified using specific adapters followed by purification with MinElute PCR Purification Kit (Qiagen). Fragments from 240-360pb were selected in the PippinHT system (Sage Science). The quality of the library and its DNA concentration were assessed by Bioanalyzer instruments (Agilent Technologies) and ultimately submitted for sequencing using Illumina HiSeq 2500 sequencer, V4 chemistry. On the other hand, single cell RNA-Seq libraries were generated exclusively from inflamed and non-inflamed tissue samples of Crohn’s disease patients. Briefly, live CD4 T cells were captured and encapsulated before cDNA amplification using the 10X Genomics Chromium Platform. Samples were prepared as outlined by 10x genomics Single Cell 3’ Reagent Kits v2 user guide. Samples were sequenced on a HiSeq 2500 with the following run parameters: Read 1 – 26 cycles, read 2 – 98 cycles, index 1 – 8 cycles.

  Dataset EGAD00001011066

• scRNA-seq of total bone marrow and T cells from multiple myeloma long-term survivors

  scRNA This dataset contains 50 scRNA-seq samples from bone marrow aspirates of 11 multiple myeloma patients experiencing long-term survival and 3 healthy donors. For each donor, total bone marrow and CD3+ T cells were sequenced. For multiple myeloma patients, paired samples were collected at initial diagnosis and between 7-17 years after first-line therapy. Bone marrow mononuclear cells were isolated by Ficoll density gradient centrifugation. For sorting of total bone marrow cells singlet, live cells were gated and sorted, for sorting of T cells CD45+, CD3+ cells were gated and sorted on either FACSAria Fusion or FACSAria II. Single-cell RNA sequencing were generated using 10x Genomics single-cell RNAseq technology (Chromium Single Cell 3’ Solution v2) according to the manufacturer’s protocol and sequenced on an Illumina HiSeq4000 (paired end, 26 and 74 bp). Bulk RNA Singlet, live CD3+CD4- CXCR3+CD8+ and CD3+CD4- CXCR3-CD8+ cells were sorted from 7 bone marrow and 3 peripheral blood samples of 7 multiple myeloma patients using a FACSAria Fusion machine. Bulk-RNA sequencing libraries were generated using the SMART Seq Stranded Total RNA-Seq kit (Takara) and sequenced using the Illumina NovaSeq 6000 platform (2 x 100 bp).

  Dataset EGAD00001010025

• Whole Exome Sequencing of Congenital Diaphragmatic Hernia Patients and Trios

  Congenital diaphragmatic hernia (CDH) is a common and severe birth defect characterized by structural defects of the diaphragm and by pulmonary hypoplasia. Congenital diaphragmatic hernia patient may present either as an isolated phenotype or together with other congenital anomalies in a complex phenotype. Despite the clinical significance of CDH, the underlying genetic and developmental pathways are incompletely understood. In order to establish a catalog of human genetic variation for this condition, we performed whole exome sequencing (WES) on 275 carefully phenotyped individuals with CDH in the Pediatric Surgical Research Laboratories at the Massachusetts General Hospital (Boston, MA, USA) and Boston Children's Hospital (Boston, MA, USA). The exome data generated are valuable for comparison of candidate genes derived from WES of other CDH cohorts or affected kindreds, and to provide ideal candidates for further functional studies with the ultimate goal of enhancing our understanding of the heterogeneous and, possibly, oligogenic molecular etiology of CDH. While familial clustering has been reported in rare kindreds, the majority of probands with CDH have no family history of CDH leading to the hypothesis that de novo variants are an important and relatively frequent etiological mechanism. In the second version of the study, we performed WES analysis on 87 trios, to assess the contribution of de novo mutations in the etiology of diaphragmatic and pulmonary defects and to identify previously unknown candidate genes. This dbGaP submission includes WES data on: (a) 20 new probands, (b) 9 probands also reported in the previous version (dbGaP accession no. phs000783.v1.p1), (c) 174 unaffected parents, including the parents of 67 previously reported probands. Combined analysis with other available cohorts of congenital diaphragmatic hernia revealed a genome-wide enrichment of likely gene-disrupting de novo variants (i.e., nonsense, frameshift or splice site), and missense de novo variants predicted in silico to be damaging.

  Study phs000783

• National Institute on Aging (NIA) Late-Onset Alzheimer's Disease Genetics Initiative: The Multiplex Family Study

  Multiplex Family StudyThe purpose of the NIA Genetics Initiative: Multiplex Family Study is to identify families with multiple members diagnosed with late-onset Alzheimer's Disease. Families will be characterized clinically and blood samples will be collected to establish cell lines. If a blood sample is not available, autopsy samples will be collected for DNA extraction and storage. Our goal is to recruit 1,000 families over the course of the study. Clinical and demographic data from these families will be collected at the local site and coded data, without identifiers, will be sent and included in a national database of families with Alzheimer's Disease. This database, along with the biological samples, will be housed at the National Cell Repository for Alzheimer's Disease (NCRAD) at Indiana University. The Center or provider and the Cell Repository must sign a Material Transfer Agreement for shipment of biological samples and phenotypic data to NCRAD. The biological samples and data from these families will be available to qualified researchers, who must sign a Material Transfer Agreement before receiving DNA and data. An oversight committee known as the Cell Bank Advisory Committee (CBAC) will review and monitor the process of family identification and enrollment, data collection, the establishment of cell lines and access to samples. LOAD CIDR ProjectThe first 362 families of the Multiplex Family Study (including 2,105 family members) were included in a 6K SNP genome-wide scan at the NIH-supported, Center for Inherited Disease Research (CIDR). An additional 297 unrelated, healthy controls were also genotyped at CIDR. The average age of onset of AD in the genotyped sample is 74 years and 62% of the sample is women. While primarily Caucasian, 3% are African-American, and 3% are of Hispanic ancestry. 72% of the families had at least 2 affected siblings, whereas 21% had 3 or more sampled affected individuals and 7% had 4 or more genotyped, affected family members.

  Study phs000160

• Autism Sequencing Consortium (ASC)

  The ARRA Autism Sequencing Collaboration was created in 2010 bringing together expert large-scale sequencing center (at the Baylor College of Medicine, PI Richard Gibbs and the Board Institute of MIT and Harvard, PI Mark J. Daly) and a collaborative network of research labs focused on the genetics of autism (brought together by the Autism Genome Project and the Autism Consortium). These groups worked together to utilize dramatic new advances in DNA sequencing technology to reveal the genetic architecture of autism through comprehensive examination of the exotic sequence of all genes. The Autism Sequencing Consortium (ASC) was founded by Joseph D. Buxbaum and colleagues as an international group of scientists who share autism spectrum disorder (ASD) samples and genetic data. The PIs are Drs. Joseph D. Buxbaum (Icahn School of Medicine at Mount Sinai), Mark J. Daly (Broad Institute of MIT and Harvard), Bernie Devlin (University of Pittsburgh School of Medicine), Kathryn Roeder (Carnegie Mellon University, Matthew State and Stephan Sanders (University of California, San Francisco). The rationale for the ASC is described in Buxbaum et al. 2012, and this paper should be cited when referencing the data set. All shared data and analysis is hosted at a single site, which enables joint analysis of large-scale data from many groups. The ASC was first supported by a cooperative agreement grant to four lead sites funded by the National Institute of Mental Health (U01MH100233, U01MH100209, U01MH100229, U01MH100239), with additional support from the National Human Genome Research Institute. The NIMH recently renewed their support with a second grant (U01MH111661, U01MH111660, U01MH111658 and U01MH111662) to expand the project from 29,000 genomes to more than 50,000 exomes over the next 5 years. NHGRI provides ongoing sequencing support for the ASD through the Broad Center for Common Disease Genomics (UM1HG008895, Mark Daly, PI).

  Study phs000298

• Human Dorsal Root Ganglion RNA Landscape Profiling for Neuropathic and Chronic Pain

  Bulk and spatial RNA sequencing were performed on human Dorsal Root Ganglia (DRGs), peripheral sensory nerves and nodose ganglia and relative gene abundances were calculated. Various analyses were performed: Human DRG gene expression profiles were contrasted with a panel of gene expression profiles of relevant tissues in human and mouse (integrating, among other sources, datasets from ENCODE and GTex) in order to identify. DRG-enriched gene expression, co-expression modules of DRG-expressed genes, and key transcriptional regulators in humans. Contrasting the human and mouse DRG transcriptomes to identify DRG-enriched gene expression patterns that were conserved between human and mouse, identifying putative cell types of expression of these genes, and potential known drugs that might target the corresponding gene products. Characterization of non-coding RNA profile of human and mouse DRGs. Characterization of DRG-enriched alternative splicing and alternative transcription start site usage based transcript variants in humans and mouse, and the overlap between these two species. Contrasting of human DRG and GTex human tibial nerve samples to identify putative axonally transported mRNAs in sensory neurons. Human DRG and peripheral nerves transcriptomes from donors suffering from neuropathic and/or chronic pain were contrasted with controls to identify. Differentially expressed genes, pathways and regulators path play a potential role in neuronal plasticity, electrophysiological activity, immune signaling and response. Predictive models (Random Forests) were built to jointly predict the sex and pain state of samples based on information contained solely in autosomal gene expression profile. Gene co-expression modules were identified and gene set enrichment analysis performed.to identify sample - pathway associations, and to broadly characterize plasticity in human DRG cell types. Spatial transcriptomics was performed on human DRGs and nodose ganglia to obtain near single-neuron resolution. We identified distinct clusters of human DRG sensory neurons. We characterized the expression of different gene families (e.g., GPCRs, sodium channels, potassium channels, etc.). We investigated differences between male and female neurons.

  Study phs001158

• Genetic analysis of HLA and immune escape genes in Diffuse Large B-cell Lymphoma

  Fifty percent of diffuse large B-cell lymphoma (DLBCL) cases lack cell-surface expression of the class-I major histocompatibility complex (MHC-I), thereby escaping immune recognition by cytotoxic T cells. In order to comprehensively identify the mechanisms involved in MHC-I loss, we first performed immunophenotypic analysis of both MHC class-I and -II in 657 cases across the spectrum of B-NHL, revealing that loss of MHC-I, but not MHC-II, is preferentially restricted to DLBCL. We then used whole exome and targeted deep-sequencing to examine genes involved in MHC-I expression in 74 DLBCL samples representative of MHC-I positive and negative cases. We show here that somatic biallelic or monoallelic inactivation of B2M and/or HLA-I is present in 80.9% (34/42) of MHC-I negative tumors. Furthermore, 68.8% (22/32) of MHC-I positive DLBCLs also harbored monoallelic HLA-I genetic alterations (MHC-I positivemono) that lead to allelic imbalance, suggesting allele-specific inactivation. Both MHC-I negative and MHC-I positivemono cases showed a significantly higher mutational burden as well as a higher number of inferred neo-antigens, suggesting co-selection of HLA-I loss and sustained neo-antigen production. Interestingly, the analysis of > 500.000 individuals in two databases revealed homozygosity of germline HLA-I genes in 26-38% of DLBCL patients, a frequency significantly higher than that observed in any other cancer type. In mice, germinal-center B cells lacking HLA-I expression did not progress to lymphoma and were counterselected in the context of oncogene-driven lymphomagenesis, suggesting that additional events are needed to license immune evasion. These results suggest a multi-step process of HLA-I loss including both germ-line and somatic events in DLBCL development, and have direct implications for the pathogenesis and immunotherapeutic targeting of this disease.

  Study EGAS00001005054

• Upcycling and merging data to challenge the dogma and identify new therapeutic targets for Glioblastoma

  Glioblastoma is the most common brain tumour. Characterised by a poor prognosis and its recurrence after multimodal treatments, the search for preventable risk factors has been mainly inconclusive up to date. Recently, the data merge from datasetsdeposited at the EGA allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. Challenges in glioblastoma research As for virtually all cancer types, many efforts have been made to unveil the molecular features responsible of the disease, and several cellular pathways have indeed been identified as being frequently mutated in glioblastoma. Nonetheless, targeted therapies based on identified genes have so far failed to improve outcome, thus survival mostly relies on a standard treatment unchanged since 2005. This is a frustrating situation for both the scientific and medical communities, and above all for the patients, still facing a dreadful path. Some researchers hypothesised that this may be due to the inability to efficiently target cancer stem cells, the originators of the other cell types, thus inducing cancer relapse. In 2019 Charles P. Couturier and colleagues sequenced RNA from single cells of freshly excised glioblastomas of 16 patients, and demonstrated that glioblastoma cells replicate normal brain cell development with a conserved neural cancer cell hierarchy centered around glial progenitor-like cells. In this way, they helped identify the possible target cells to improve efficacy and durability of treatment. Data upcycling at the EGA: the glioblastoma case study Single cell RNA sequencing is generated with a laborious and expensive protocol. The quality of the starting material is crucial (cells sample freshly extracted from the patients) and often several attempts are needed before producing reliable quality sequencing results. Collecting big numbers of patients is also challenging. The sequencing data produced by Kevin Petrecca’s group in Montreal, Canada and deposited at the EGA (EGAS00001004422) was recently upcycled by Lin Wang in San Francisco, California and pooled with their freshly produced ones, and then deposited at the EGA as Dataset EGAS00001004909 The data merge allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. They profiled 86 primary-recurrent patient-matched paired glioblastoma samples with single-nucleus RNA, among other techniques. These very comprehensive results lead the team to challenge the findings from several other cancer fields where chemotherapy standard chemo-radiation therapy selection pressure at the level of genomic alterations; this is indeed not the case for glioblastoma, where the pressure results in phenotypic transition between cellular states. Several technical controls were made to ensure that the merge of the data was not introducing a bias in the results, and an Inter-table analysis demonstrated nearly equal contribution to overall variance from each of the studies included, indicating that the findings were not due to inter-laboratory technical effect. A novel principal-component analysis showed that the largest contribution to variation in primary glioblastoma neoplastic cells was an axis between MES (mesenchymal) and proneural expression programs. In summary, treating glioblastoma often makes a MES, as commented by Lucy Stead about this remarkable work. Check Aaron Diaz’s team Nature Cancer paper, with strong technical tools, state of the art analysis and data from different sources converging to the same outcomes that made possible a significant step toward a better handling of a frightful cancer. References Couturier, C.P., Ayyadhury, S., Le, P.U. et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11, 3406 (2020). Stead, L.F. Treating glioblastoma often makes a MES. Nat Cancer 3, 1446–1448 (2022). Wang, L., Jung, J., Babikir, H. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat Cancer 3, 1534–1552 (2022). Related links: Kevin Petrecca’s group dataset deposited at the EGA: EGAS00001004422 Lin Wang dataset deposited at the EGA: EGAS00001004909

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