Search Results - EGA European Genome-Phenome Archive

Center for Craniofacial and Dental Genetics (CCDG): Genetics of Orofacial Clefts and Related Phenotypes

The purpose of this study is to investigate the genetics of orofacial clefts (OFCs) in a large study population, and importantly, to incorporate subclinical phenotypic features into these studies. Orofacial clefts (OFCs) comprise a significant fraction of human birth defects (about 1/700 live births (Rahimov et al. 2012) and represent a major public health challenge, as individuals with these anomalies require surgical, nutritional, dental, speech, medical and behavioral interventions, thus imposing a substantial economic and personal burden (Berk and Marazita 2002*). The most common forms include OFCs of the lip alone (CL, Figure 1A), CL plus cleft palate (CL+CP, Figure 1B) or of the palate only (CP, Figure 1C). Individuals born with OFC may have their first surgical repair at age 3 months, but this initial surgery is just the beginning of a lifetime of health burdens. An individual born with an OFC has a hospital use rate increased for most ages (up to 233% increase for children ages 0-10 years and 16% for middle aged adults (Wehby et al. 2012). Healthcare costs for children with OFCs are estimated to be 800% greater compared with their unaffected peers (Boulet et al. 2009). Data from Denmark show that people born with CL with or without CP (CL/P) have an increased mortality up to age 55, which may be attributed to an increased risk of suicide and/or certain cancers (Christensen et al. 2004). The focus of most OFC genetic research has been CL and/or CP. Furthermore, the majority of OFC, i.e. about 70% of CL/P and 50% of CP is considered "nonsyndromic" (Jones 1988), i.e. isolated anomalies with no other apparent cognitive or structural abnormalities. Figure 1 Sample OFC Types A: Bilateral Cleft Lip; B:Cleft Lip plus Cleft Palate; C:Cleft Palate Alone The factors leading to the majority of nonsyndromic OFCs are still unclear, particularly at an individual family level. As is true for many complex traits, substantial progress in gene identification has occurred in the OFC field in the last two years (Dixon et al. 2011; Marazita 2012). Genome Wide Association Studies (GWAS) and sequencing studies to date by our research team and others have focused on genetic risk factors for overt CL/P and CPO-and have been very successful. A major finding from this work is that OFCs exhibit significant genetic heterogeneity, i.e., multiple genetic regions have been implicated (Beaty et al. 2010; Ludwig et al. 2012). Thus, approaches are needed to understand this genetic heterogeneity. Are there GxG interactions at work? Are there subsets of families, each due to a different gene? Our research group has shown that a promising approach to dissect the etiology of OFC is to focus on subclinical phenotypic features within entire cleft families (not just in affected cases, but also in their non-cleft relatives). These subtle features are believed to represent mild manifestations of the same underlying genetic susceptibility responsible for OFCs; as such, their inclusion in case-control and family-based genetic studies can help to clarify and refine the relationship between genotype and phenotype. The study population comprises a large number of families and individuals (~12,000 individuals) from multiple populations worldwide (Caucasians from the US and Europe, Asians from China and the Philippines, Mixed Native American/Caucasians from South America, and Africans from Nigeria and Ethiopia). There are cases, case families (nuclear families and extended kindreds), as well as controls with no history of OFC nor other developmental defects. *Berk NW, Marazita ML (2002) Costs of Cleft lip and Palate: Personal and Societal Implications. In: Wyszynski DF (ed) Cleft Lip and Palate: From Origin to Treatment. Oxford University Press, Inc., New York, pp 458-467.

Study phs000774

Papua New Guinean Genome Diversity Project (PGDP)

The PGDP dataset includes 58 whole genome sequences for Papua New Guinean individuals from different locations. DNA was extrated from saliva samples (Oragen kit). Sequencing libraries were prepared using the TruSeq DNA PCR-Free HT kit. 150 bp paired-end sequencing was performed on the Illumina HiSeq X5 sequencer. The PGDP dataset provides Fastq and BAM files.

Dataset EGAD00001007783

Plasma whole genome sequencing from patients with stage IV colorectal cancer and microsatellite instability

This dataset contains plasma WGS data from patients with stage IV colorectal cancer (CRC, n = 16) and healthy individuals (n = 21) used in the Pointy manuscript. Patients with CRC provided written consent and samples were collected as performed as described previously (Clinical-Trials.gov number NCT01876511; Georgiadis et al., 2019, Le et al., 2017). Plasma samples from 21 healthy control individuals were procured through BioIVT. Cell-free DNA (cfDNA) was extracted from plasma using the QIAamp Circulating Nucleic Acid Kit. Libraries were prepared with 5 to 250 ng of cfDNA using the NEBNext DNA Library Prep Kit. Libraries were sequenced on HiSeq2000/2500.

Dataset EGAD00001008999

WGBS Discovery Samples

The dataset contains valuable genomic data from a discovery cohort consisting of 29 individuals. This cohort is comprised of 8 healthy individuals (control), 8 patients with ST-segment elevation myocardial infarction (STEMI), 7 patients with non-ST-segment elevation myocardial infarction (NSTEMI), and 6 patients with unstable angina (UA). The genomic data was obtained by isolating cell-free circulating DNA (ccfDNA) and subjecting it to bisulfite conversion using a low-input BS-seq (PBAT) protocol. Sequencing was done on the Novaseq 6000 platform.

Dataset EGAD00001010935

Longitudinal Studies of Patients with Familial Platelet Disorder with Associated Myeloid Malignancy (FPDMM)

FPDMM (OMIM #601399) is a rare autosomal dominant disease characterized by defective megakaryocytic development, thrombocytopenia, platelet functional defects, and a life-long risk of developing hematological malignancies. FPDMM is caused by germline mutations in the RUNX1 gene, which encodes a master regulator of hematopoiesis. The overall lifetime risk of hematological malignancies in FPDMM patients is 35 - 45%. It is hypothesized that mutations in other genes cooperate with the germline RUNX1 mutations for leukemia development. However, it is not clear which genes need to be mutated for leukemia to develop and which biomarkers or clinical tests can be used to predict which patients will progress to leukemia. Likewise, there are no treatments and/or preventative measures that can reduce or stop disease progression except for stem cell transplantation, which is not available for all patients and may be associated with significant complications. To address these questions, we launched a longitudinal natural history study of patients associated with RUNX1 germline mutations at the NIH Clinical Center in early 2019 (Trial Registration ID: NCT03854318). The goals of this study are to: (1) perform comprehensive longitudinal phenotyping of FPDMM patients to document the full spectrum of both hematopoietic and non-hematopoietic manifestations of the disease; and (2) perform extensive genomic analyses of FPDMM patient samples to identify cooperating genomic variants, which may confer growth advantage that leads to clonal expansion of affected hematopoietic cells that may serve as the precursor to malignancies. The data available through dbGaP include exome sequencing and RNA-seq data from these patients. Diverse germline mutation types were identified in the RUNX1 gene, including splice site mutations and large deletions, in addition to nucleotide substitutions and small indels. Somatic mutations, especially in genes associated with clonal hematopoiesis of indeterminate potential (CHIP), were found in a substantial proportion of FPDMM patients who were leukemia-free. The findings suggest that the genomes in these patients are prone to somatic mutations and monitoring the dynamic changes of these somatic mutations prospectively may provide a way to predict progression to malignancies.

Study phs003075

Neoadjuvant atezolizumab plus chemotherapy in gastric and gastroesophageal junction adenocarcinoma: the phase 2 PANDA trial

Gastric and gastroesophageal junction (G/GEJ) cancers carry a poor prognosis, and despite recent advancements most patients die of their disease. While immune checkpoint blockade has become part of the standard of care for patients with metastatic G/GEJ cancers, its efficacy and impact on the tumor microenvironment in the early disease setting remain largely unknown. We hypothesized a higher efficacy of neoadjuvant immunotherapy plus chemotherapy in patients with non-metastatic G/GEJ cancer. In the phase 2 PANDA trial, patients with previously untreated resectable G/GEJ tumors (n=21) received neoadjuvant treatment with one cycle of atezolizumab monotherapy followed by 4 cycles of atezolizumab plus docetaxel, oxaliplatin and capecitabine. Treatment was well-tolerated, and all patients underwent resection without treatment-related delays, meeting the primary endpoint of safety and feasibility. Tissue was obtained at multiple time points, allowing analysis of the effects of single-agent PD-L1 blockade and the subsequent combination with chemotherapy on the tumor microenvironment. A total of 21 patients were included, of whom 20 patients underwent surgery and were evaluable for the secondary pathologic response and survival endpoints, while 19 were evaluable for exploratory translational analyses. A major pathologic response (MPR, ≤10% residual viable tumor) was observed in 14/20 (70%; 95% CI 46-88%) patients, including 9 (45%; 95% CI 23-68%) pathologic complete responses. At a median follow-up of 47 months, 13/14 responders were alive and disease-free, while 5/6 nonresponders had died due to disease recurrence. Notably, baseline PD-1+CD8+ T cell infiltration was significantly higher in responders than in nonresponders, and comparison of the tumor microenvironment alterations following anti-PD-L1 monotherapy versus the subsequent combination with chemotherapy revealed that increased immune activity was achieved upon single-agent PD-1/L1 axis blockade. Based on these data, anti-PD-L1 plus chemotherapy warrants further exploration in patients with non-metastatic G/GEJ cancer, and these results call for validation in a larger cohort.

Study EGAS50000000168

Gabriella Miller Kids First Pediatric Research Program in Pediatric T-Cell Acute Lymphoblastic Leukemia

The outcome for patients with relapsed T-ALL is dismal with 3-year event free survival of AALL0434 was a Children's Oncology Group-initiated phase 3 randomized clinical trial comparing Capizzi-style escalating methotrexate plus pegaspargase (CMTX) vs. high dose methotrexate (HDMTX), with/without six 5-day courses of nelarabine. Survival on this study was superior to any prior trial for de novo T-ALL, changing the standard of care. Yet, a substantial minority (~15%) of patients had relapsed or refractory (r/r) disease. Through the TARGET initiative, RNA sequencing (RNA-Seq), DNA copy number analysis, and whole-exome sequencing (WES) were performed on 264 T-ALL patients treated on AALL0434, demonstrating recurrent alterations could be grouped into 10 different potentially targetable functional pathways. This analysis was not powered to examine associations between genetic lesions with outcome, because too few patients with r/r disease were included. This project is dedicated to testing the hypothesis that comprehensive genomic profiling of the entire AALL0434 cohort will identify recurrent genetic alterations that can be segregated into biologically relevant deregulated pathways that can be combined with MRD to identify patients at risk for poor outcomes before they relapse and provide rationale for treatment with alternative therapies. In addition, a number of small recent studies demonstrated that many of the biologically relevant alterations in T-ALL occur in non-coding regions of the genome, but no large studies have performed whole genome sequencing (WGS) in T-ALL. This project also tests the hypothesis that WGS of a large cohort of patients with T-ALL will identify novel lesions in coding and non-coding regions that will be highly impactful in the understanding of T-ALL pathogenesis.These hypotheses are tested by performing comprehensive genomic profiling (WGS, WES, RNA-Seq, and copy number analysis) of the entire AALL0434 cohort with available samples with the following specific aims: (1) identify recurrent genetic alterations that predict poor outcome in T-ALL; (2) identify novel alterations, including non-coding alterations in T-ALL; and (3) identify germline genetic variants that predispose to T-ALL and to increased toxicity to chemotherapy.

Study phs002276

Single Cell Analysis Program - Transcriptome (SCAP-T)

This initiative is part of the Single Cell Analysis Program (SCAP) and is funded through the NIH Common Fund (See http://nihroadmap.nih.gov/), which supports cross-cutting programs that are expected to have exceptionally high impact. Common Fund initiatives address key roadblocks in biomedical research that impede basic scientific discovery and its translation into improved human health. In addition, these programs capitalize on emerging opportunities to catalyze progress across multiple biomedical fields. Single cell analysis has recently emerged as an important field of research because technologies have improved in sensitivity and throughput sufficiently to begin measuring and understanding heterogeneity in complex biological systems and correlating it with changes in biological function and disease processes. By profiling individual cells it is possible to resolve rare cells, transient cell states, and the influence of organization and environment on such cells and states, which cannot be described by ensemble measurements. The long-term goal of the SCAP is to accelerate this move towards personalizing health to the cellular level by understanding the link between cell heterogeneity, tissue function and emergence of disease through the discovery, development and translation of innovative approaches which will dramatically change the way cells are characterized. The SCAP will focus on research, which will systematically measure, analyze and model cell-to-cell variation, and identify crucial differences and rare biological states, which may have important functional consequences. Under SCAP, there are three studies to evaluate the cellular heterogeneity using transcriptional profiling of single cells (U01): University of Pennsylvania: Role of single cell mRNA variation in systems associated electrically excitable cells University of Southern California: Evaluation of cellular heterogeneity using patchclamp and RNA-seq of single cells University of California at San Diego: Single cell sequencing and in situ mapping of RNA transcripts in human brains The SCAP has been designed as a five-year program with several components: (1) the collection, analysis and sharing of comprehensive expression datasets to understand the role of heterogeneity in tissues and systemically and identify critical parameters and states; (2) the discovery of new, innovative tools for spatiotemporal imaging, manipulation, analysis and modeling of a biologically relevant population of cells with minimal perturbation; (3) milestone-driven validation and translation of technologies for characterizing single cells in situ meeting the needs of end-users; and (4) development and coordination of a multidisciplinary research community through workshops and other collective endeavors. Further details about the NIH SCAP-T program can be found here http://commonfund.nih.gov/singlecell/. The SCAP-T data sets include the detailed phenotype information, experimental protocols, QC information, RNA-sequencing data and NGS results from human heart and human brain cells. The first data release includes 697 single cells from human brain and heart. The second data release includes an additional 978 single cells from human brain and heart. The third data release includes an additional 1631 single cells from human brain and heart. The fourth data release is a correction to this study, but adds no new cells. The fifth data release includes an additional 2984 single cells from human brain and heart. The sixth data release includes an additional 944 single cells from human brain and heart. The seventh data release includes an additional 1977 single cells from human brain and heart. The SCAP-T data portal provides a customized interface for users to quickly identify and retrieve files by phenotypes, and data properties such as sequencing facility or coverage for all of these 9231 single cells. For more information about the SCAP-T study and the data portal, please visit http://www.scap-t.org.

Study phs000833

The EGA at the International Congress of Human Genetics

Today is the start of the International Congress of Human Genetics (ICHG). This event, hosted by the African Society of Human Genetics (AfSHG) and the Southern African Society for Human Genetics (SASHG), will reunite international experts to highlight how genomic technologies are being managed to address challenges generated by the current status of Human Health and Genomics. The European Genome-phenome Archive (EGA) will be at the Congress participating in workshops, meetings and talks where topics such as data access and discovery as well as the Federated EGA will be present. Data access and discovery In the context of the “Federated data analysis workshop“ that will be held the 26ᵗʰ, managed in the context of the CINECA project, a hands-on-session on Beacon v2 Discovery will be led by Mauricio Moldes. Another session on authorisation and data access will be presented by Mallory Freeberg and Coline Thomas, with and introduction by Thomas Keane. During the workshop, an end-to-end federated data analysis use case will be presented. Data access and discovery In the context of the “ Federated data analysis workshop“ that will be held the 26ᵗʰ, managed in the context of the CINECA project, a hands-on-session on Beacon v2 Discovery will be led by Mauricio Moldes. Another session on authorisation and data access will be presented by Mallory Freeberg and Coline Thomas, with and introduction by Thomas Keane. During the workshop, an end-to-end federated data analysis use case will be presented. During the last four years, CINECA has been identifying gaps in data sharing between Africa, Canada and Europe in order to build a federated solution for data access. Thomas Keane will present the project within the sessions happening the 24ᵗʰ. The Federated EGA at the ICHG A Federated EGA poster will be shown during the Poster Session 1 that will take place the 23ʳᵈ. The objective is to bring awareness of the European Genome-phenome Archive as a resource for permanent archive and secure data sharing. In this context, the network generated by the Federated EGA is key for cases where research data cannot be shared outside local jurisdictions. The Federated EGA was officially launched on 2022 with research institutes from five countries becoming the first nodes of this network. Nowadays, with more than 20 nodes preparing to join, it aims to become the largest human omics data sharing initiative towards understanding human health and disease. The H3Africa Initiative and the African research represented in the EGA 1.1% of the EGA studies include samples from African populations. Countries such as South Africa, Kenya, Uganda or Egypt, among others, are present in the 72.577 samples from African individuals archived at the EGA. 23 EGA datasets are managed by the Human Heredity and Health in Africa Initiative (H3Africa). This Initiative will celebrate its Consortium Meeting the 27ᵗʰ and 28ᵗʰ, back-to-back with the ICHG. H3ABioNet, the Pan African Bioinformatics Network for the H3Africa consortium, will also take this context to celebrate its 10-Year Symposium during the Congress. Members of the EGA team will attend both of them, as the H3ABioNet works in conjunction with H3Africa projects, to submit data to the European Genome-Phenome Archive.

Blog the-ega-at-the-international-congress-of-human-genetics

scRNAseq data of CAP

Fastq files from scRNAseq data of cancer associated pericytes-like from 3 patients. CAP were isolated from a total of 3 primary BC (surgical residues prior to any treatment) by using BDFACS ARIA III sorter (BD Biosciences). BC were collected directly from the operating room after surgical specimen macroscopic examination and selection of areas of interest by a pathologist. Samples were cut into small pieces (around 1 mm3) and digested in CO2-independent medium (Gibco #18045-054) supplemented with 150 μg/mL liberase (Roche #05401020001) and Dnase I (Roche #11284932001) for 40 min at 37°C with shaking (180 rpm). After digestion, cells were processed and stained as described above (#Flow Cytometry analysis of BC samples). CAP fibroblasts were then gated on the Live/Dead negative fraction and defined as EPCAM- CD45- CD31- CD235a- FAPMed CD29High. CAP scRNA-seq: Upon isolation, CAP cells were directly collected into RNase-free tubes (Thermo Fisher Scientific, #AM12450) precoated with DMEM (GE Life Sciences, #SH30243.01) supplemented with 10% FBS (Biosera, #1003/500). Single-cell capture, lysis, and cDNA library construction were performed using Chromium system from 10X Genomics, with the following kits: Chromium Single Cell 3′ Library & Gel Bead Kit v2 kit (10X Genomics, #120237) and Chromium Single Cell A Chip Kits (10X Genomics, #1000009). Generation of gel beads in Emulsion (GEM), barcoding, post GEM-reverse transcription cleanup and cDNA amplification were performed according to the manufacturer’s instructions. Cells were loaded accordingly on the Chromium Single cell A chips, and 12 cycles were performed for cDNA amplification. cDNA quality and quantity were checked on Agilent 2100 Bioanalyzer using Agilent High Sensitivity DNA Kit (Agilent, #5067-4626) and library construction followed according to 10X Genomics protocol. Libraries were next run on the Illumina HiSeq (for patients P1) and NovaSeq (for patients P2–3) with a depth of sequencing of 50,000 reads per cell.

Dataset EGAD50000000321

Plasma cell‐Free transcriptome profiling in chronic liver disease

Chronic liver disease (CLD) encompasses a wide range of hepatic disorders that progressively compromise liver function. In order to support the development of non-invasive diagnostic tools and improve patient stratification, we generated a high-throughput extracellular RNA sequencing dataset from blood plasma samples collected from patients at various stages of liver disease. Samples were obtained from two independent cohorts of 112 (cohort 1) and 489 (cohort 2) patients using standardized collection and processing protocols, and profiled using total RNA sequencing (SMARTer Stranded Total RNA-Seq Kit v3 - Pico Input Mammalian), resulting in 112 and 820 extracellular transcriptomic profiles for cohort 1 and 2, respectively. This dataset is provided with full documentation of the preprocessing analysis pipeline and accompanying metadata, enabling its use as an independent validation cohort for biomarker discovery and as a complementary resource for integrative multi-omics studies, even in cases with incomplete data modalities. The publicly available resource is intended to facilitate reproducibility and foster further research in the field of liver disease diagnostics

Study EGAS50000000545

Oncoarray Consortium - Lung Cancer Studies

The study was conducted under the auspices of the Transdisciplinary Research In Cancer of the Lung (TRICL) Research Team, which is a part of the Genetic Associations and MEchanisms in ONcology (GAME-ON) consortium, and associated with the International Lung Cancer Consortium (ILCCO). Ethics: All participants provided written informed consent. All studies were reviewed and approved by institutional ethics review committees at the involved institutions. Sequencing data are derived from four substudies. The substudies that contributed include Harvard, Liverpool, Toronto, and IARC. The Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study is a randomized primary prevention trial including 29,133 male smokers enrolled in Finland between 1985 and 1993. Participants ranged between ages of 50 to 69 at enrollment and were randomized in a factorial design to take either 50 milligrams of d-alpha tocopheryl acetate (Vitamin E), 20 mg of all-trans-beta-carotene, both or placebo. The study continued to monitor cancer incidence through 2012 and total mortality through December 2013. The CAncer de PUlmon en Asturias Study (CAPUA) is a hospital-based case-control study conducted in Asturias, Spain by the University of Oviedo. Lung cancer cases were recruited in three main hospitals of Asturias, following an identical protocol from 2002 to 2012. Eligible cases were incident cases of histologically confirmed lung cancer between 30 and 85 years of age and residents in the geographical area of each participating hospital. Controls were selected from patients admitted to those hospitals with diagnoses unrelated to the exposures of interest and individually matched by ethnicity, gender, age (± 5 years) and hospital. Epidemiologic data were collected personally through computer-assisted questionnaires by trained interviewers during the first hospital admission. Structured questionnaires collected information on sociodemographic characteristics, recent and prior tobacco use, environmental exposure (air pollution and passive smoking), diet, personal and family history of cancer, and occupational history from each participant. Peripheral blood samples (or mouthwash samples when they refused to donate blood) were collected from all participants. Coding of histology was based on 2001 WHO/IASLC. Genomic DNA was extracted based on standard protocol. The Canadian Screening Study includes the nested case-control samples from 3 screening programs: IELCAP-Toronto: Ever smokers of more than 10 pack-years age 50 and above were eligible for the I-ELCAP screening program since 2003, and a total of 4782 individuals have been enrolled in the Greater Toronto Area. Participants were administered a LDCT scan along with a standard study questionnaire at baseline. Blood samples were systematically collected at baseline since 2006. Participants who had an abnormality in a CT scan were followed up every 1 to 2 years. The screening program was organized by the Princess Margaret Hospital. PanCan: Ever smokers between the ages of 50-75 with no previous history of invasive cancer are eligible to participate in the study. The study was carried out across Canada in Vancouver, Calgary, Hamilton, Toronto, Ottawa, Quebec, Halifax, and St. John's. A total of 2537 smokers have been screened from 2008 to 2011. All study participants completed a detailed questionnaire, spirometry, collection of blood specimens for biomarker measurement and LDCT at baseline. All participants are followed for a minimum of 3 years. On yearly follow up, an updated shorter questionnaire is administered, blood is collected and CT scans are performed. Blood samples are available from all 2537 individuals. BCCA Screening Program: From 1990 to 2007, 4274 smokers above 40 years old who had smoked 20 pack-years or more were enrolled at BCCA. Upon enrollment, subjects completed a questionnaire for their lifestyle and medical history. Baseline spirometry was conducted using a flow-sensitive spirometer in accordance with the American Thoracic Society recommendations. Since 2000, a LDCT was obtained in 2440 individuals. The participants were followed prospectively to determine whether they developed lung cancer. A total of 9759 individuals participated in the CT screening program in Canada from these 3 programs. The samples included in this project is based on a subset of nested lung cancer case-control pairs based on 1:2 ratio. The Carotene and Retinol Efficacy Trial (CARET) was a randomized, double-blind, placebo-controlled trial of the cancer prevention efficacy and safety of a daily combination of 30 mg of beta-carotene and 25,000 IU of retinyl palmitate in 18,314 persons at high risk for lung cancer. CARET began in 1985, and the intervention was halted in January 1996, 21 months ahead of schedule, with the twin conclusions for definitive evidence of no benefit and substantial evidence of a harmful effect of the intervention on both lung cancer incidence and total mortality. CARET continued to follow and collect endpoints on their participants through 2005. Pathology reports and medical records were reviewed to confirm cancer endpoints, and death certificates obtained to capture cause of death. During the active intervention phase of CARET, serum, plasma, whole blood, and lung tissue specimens were collected on participants. These biospecimens make up the CARET Biorepository. For the OncoArray Project, CARET provided DNA extracted from whole blood of lung cancer cases and controls matched on age at baseline (± 4 years), sex, race, baseline smoking status, history of occupational asbestos exposure (asbestos vs heavy smoker), and year of enrollment (2-year intervals). The European Prospective Investigation into Cancer and Nutrition (EPIC) study is a multi-center cohort study involving 521,000 study participants from 10 European countries. The current study involved EPIC participants from 7 countries (Greece, Netherlands, UK, France, Germany, Spain, and Italy), including 1223 incident lung cancer cases and 1249 smoking matched controls. The Kentucky Lung Cancer Research Initiative is a study conducted by the Markey Cancer Center Cancer Center and the University of Kentucky using a population-based, case-control framework to study the extraordinarily high rates of lung cancer in Southeastern, Appalachian Kentucky. Cancer cases were recruited from the Kentucky Cancer Registry at the time of diagnosis and controls were recruited from a random digit dialing process from the same region. Study accrual began in January 5, 2012 and completed on September 5, 2014 and 520 subjects were recruited in a 4:1 ratio of controls: cases from Appalachian Kentucky. Of the 520 subjects recruited, 231 are included in the OncoArray analysis, including all 93 cancer cases, and 123 controls. Newly diagnosed lung cancer cases and controls underwent blood, toenail (for trace element analysis), urine, buffy coat, water, soil, and radon collection, residence GPS mapping, as well as an extensive epidemiologic, occupational, and health history questionnaire (Clinical Trials.gov Identifier: NCT01648166). The Harvard Lung Cancer Study (HLCS) is a case-control study based at Mass General Hospital (MGH) in Boston, Massachusetts from 1992 to 2004. Details of the study were described previously. Briefly, eligible cases included any person over the age of 18 years with a diagnosis of primary lung cancer that was further confirmed by an MGH lung pathologist. Controls were recruited from the friends or spouses of cancer patients or the friends or spouses of other surgery patients in the same hospital. Potential controls were excluded from participation if they had a diagnosis of any cancer (other than non-melanoma skin cancer). Interviewer-administered questionnaires, a modified version of the standardized American Thoracic Society respiratory questionnaire, collected information on demographics, medical history, family history of cancer, smoking history, and a detailed work history, including job titles and tasks. Genome-wide genotype data were first generated using Illumina Human 610-Quad BeadChips and then imputed by MACH against the 1000 Genome Project dataset (http://browser.1000genomes.org/index.html). The Institutional Review Board of MGH and the Human Subjects Committee of the Harvard School of Public Health approved the study. The Israel study (NICCC-LCA) is an ongoing case-control study of newly diagnosed lung cancer cases of any histology and population age/sex/ethnicity-matched "healthy" controls. All participants undergo face-to-face interviews, provide a venous blood sample (separated into DNA, Sera, lymphocytes) after signing an IRB-approved form. Histology reports, FFPE blocks and clinical follow-up are available for most cancer cases. The MD Anderson Cancer Center (MDACC) Study. Lung cancer cases and frequency-matched controls were ascertained from a large ongoing case-control study at the University of Texas MD Anderson Cancer Center (UTMDACC) since 1991. Detailed study description was provided previously (Spitz et al 2007). In brief, cases were newly-diagnosed and histologically confirmed lung cancer patients recruited from UTMDACC. Controls were healthy individuals without a history of cancer (except for nonmelanoma skin cancer) and recruited from the Kelsey-Seybold Clinics, the largest private multispecialty physician group in the Houston metropolitan area. Controls were frequency-matched to cases on age (±5 years), sex, and race/ethnicity. After providing written informed consent, each study participants completed an in-person interview by staff interviewers to collect information on demographics, smoking status, etc. Blood samples were also drawn from all the study participants. This study was approved by institutional review boards of UTMDACC and Kelsey-Seybold Clinics. The Malmö Diet and Cancer Study (MDCS) is a population-based prospective cohort study that recruited men and women aged at 44 to 74 years old of living in Malmö, Sweden between 1991 and 1996. The main goal of the MDCS is to study the impact of diet on cancer incidence and mortality. It consists of a baseline examination including dietary assessment, a self-administered questionnaire, anthropometric measurements and collection of blood samples. A total of 165 incident lung cancer cases and 174 individually smoking-matched controls were available for this analysis. The Multiethnic Cohort (MEC) Study includes 215,251 men and women aged 45-74 years at recruitment, primarily from five ethnic/racial groups - African Americans and Latinos mostly recruited from CA (mainly from Los Angeles County) and Japanese Americans, Native Hawaiians and whites (mostly recruited from HI). The cohort was assembled in 1993-1996 by mailing a self-administered questionnaire to persons identified primarily through driver's license files. The baseline questionnaire obtained information on demographics, anthropometry, smoking history, medical and reproductive histories, family history of cancer, diet and physical activity. Incident cancer cases are identified by regular linkage with the State of California Cancer Registry and the Hawaii Tumor Registry, both members of the SEER Program of the NCI. In 2001-2006, a prospective biorepository was assembled by collecting a pre-diagnostic blood specimen from 67,594 surviving MEC members. At the time of blood collection a short questionnaire was administered that included information on smoking during the previous 15 days. For this study, cases were all lung cancer cases incident to blood draw and diagnosed before December 2012. For each case, a control was selected among unaffected MEC participants who were alive at time of the case's diagnosis and matched on study site, sex, race/ethnicity, age (age at diagnosis for cases; age at blood collection for controls), and date of blood collection. The Mount-Sinai Hospital-Princess Margaret Study (MSH-PMH) was conducted in the greater Toronto area from 2008 to 2013. Lung cancer cases were recruited at the hospitals in the network of the University of Toronto. Controls were selected randomly from individuals registered in the family medicine clinics databases and were frequency matched with cases on age and sex. All subjects were interviewed, and information on lifestyle risk factors, occupational history and medical and family history was collected using a standard questionnaire. Tumors were centrally reviewed by the reference pathologist, a member of the International Association for the Study of Lung Cancer (IASLC) committee, and a second pathologist in the University Health Network. If the reviews conflicted, a consensus was arrived at after discussion. Coding of histology was based on 2001 WHO/IASLC. Genomic DNA was extracted based on standard protocol. The New England Lung Cancer Study (NELCS) is a population-based case-control study of lung cancer among residents of Northern and Central New Hampshire counties and the bordering region of Vermont. Cases with histologically confirmed primary incident lung cancer were identified from 2005 to 2007 using the New Hampshire State Cancer Registry and the Dartmouth-Hitchcock Medical Center (DHMC) Tumor Registry. Control participants were identified using a commercial database and matched to lung cancer cases within 5-year age groups, sex and county. Genomic DNA was isolated from blood or buccal specimens provided by consenting participants. The study complied with requirements of the Dartmouth College's Committee for Protection of Human Subjects. The Nijmegen Lung Cancer Study. The Netherlands patients with lung cancer were identified through the population-based cancer registry of the Netherlands Comprehensive Cancer Organisation in Nijmegen, the Netherlands. Patients who were diagnosed in one of three hospitals (Radboud University Medical Center, Canisius Wilhelmina Hospital in Nijmegen, and Rijnstate Hospital in Arnhem) since 1989 and who were still alive at April 15th, 2008 were recruited for a study on gene-environment interactions in lung cancer. 458 patients gave informed consent and donated a blood sample. This case series was expanded with 94 patients to a total of 552 by linking three other studies to the population-based cancer registry in order to identify new occurrences of lung cancer among the participants of these other studies. All three other studies (i.e., POLYGENE, the Nijmegen Biomedical Study, and the Radboudumc Urology Outpatient Clinic Epidemiology Study) were initiated to study genetic risk factors for disease and participants to these studies gave general informed consent for DNA-related research and linkage with disease registries. Information on histology, stage of disease, and age at diagnoses was obtained through the cancer registry. Lifestyle information was collected through a structured questionnaire and whole blood for DNA isolation was collected by the regional thrombosis services. The cancer-free controls (46% males) were selected from participants of the "Nijmegen Biomedical Study" (NBS), an age- and sex-stratified random sample of the general population of the municipality of Nijmegen, The Netherlands. All participants provided extensive lifestyle information by structured questionnaires and blood samples for DNA isolation, serum and plasma. All controls are of self-reported European descent. The study protocols of the NBS were approved by the Institutional Review Board of the Radboudumc and all study subjects signed a written informed consent form. The Northern Sweden Health and Disease Study (NSHDS) encompasses several prospective cohorts. The current study involves participants from the Västerbotten Intervention Project (VIP), a sub-cohort within NSHDS. VIP is an ongoing prospective cohort and intervention study intended for health promotion of the general population of the Västerbotten County in northern Sweden. VIP was initiated in 1985 and all residents in the Västerbotten County were invited to participate by attending a health check-up at 40, 50 and 60 years of age. Participants were asked to complete a self-administered questionnaire including various demographic factors such as education, smoking habits, physical activity and diet. In addition, height and weight were measured and participants were asked to donate a fasting blood sample for future research. A total of 243 incident lung cancer cases and 266 individually smoking-matched controls were available for this analysis. Norway National Institute of Occupational Health Study. Early-stage NSCLC cases and healthy controls at the time of enrollment were Caucasians of Norwegian origin and were recruited from the same geographical region (Western Norway). The patients were enrolled in the study, whenever practically feasible among patients admitted for lung cancer at the Haukeland University Hospital in Bergen, Norway. The informed written consents covering analysis of molecular and genetic markers was signed by the patients prior to surgery. Only patients with histologically confirmed early-stage NSCLC were included in our study. The subjects included in this project are a subgroup recruited into the project "lung cancer genetics" at NIOH. The controls were recruited from the same geographical region of Western Norway and frequency-matched with cases on cumulative smoking dose (pack-years). Pack-years smoked [( 20 cigarettes per day) x years smoked] were calculated to indicate the cumulative smoking dose. The Cases and controls were interviewed using similar questionnaires and were categorized as never smokers, ex-smokers or current smokers. Never smokers are subjects indicating having smoked less than 100 cigarettes in their life time. Ex-smokers were defined as those having quitted at least 1 year before sampling, and current smokers were those indicating that they were smokers at the time of sampling. The project has been approved by the Regional Committee for Medical and Health Research Ethics in Southern Norway in accordance with the WMA Declaration of Helsinki. The ethical approval covered access to the NSCLC databank. The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) Study, a randomized trial aimed at evaluating the efficacy of screening in reducing cancer mortality, recruited approximately 155,000 men and women age 55 to 74 years from 1992 to 20014. Screening for lung cancer among participants in the intervention arm included a chest x-ray at baseline followed by either three annual x-rays (for current or former smokers at enrollment) or two annual x-rays (for never smokers); participants in the control arm received routine health care. Screening-arm participants provided data on sociodemographic factors, smoking behavior, anthropometric characteristics, medical history, and family history of cancer, as well as blood samples annually for the first 6 years of the study (baseline T0 and T1 through T5). Lung cancers were ascertained through annual questionnaires mailed to the participants, and positive reports were followed up by abstracting medical records or death certificates. Follow-up in the trial as of July 2009 was 96.7%. Patients were excluded because of missing baseline questionnaire, previous history of any cancer, diagnosis of multiple cancers during follow-up, missing smoking information at baseline, missing consent for utilization of biologic specimens for etiologic studies, or unavailability/insufficient quantity of serum or DNA specimens. The Resource for the Study of Lung Cancer Epidemiology in North Trent (ReSoLuCENT) is an ongoing study conducted in Sheffield from 2006 and due to complete recruitment in 2016. The study recruited pathologically confirmed lung cancer cases diagnosed at age 60 years or younger and family matched controls. Lung cancer cases diagnosed at ages older than 60 years were recruited if they reported a family history of lung cancer. The cases and matched controls were recruited through several major cancer treatment centers, however, the majority were recruited in North Trent. All participants completed a detailed lifestyle questionnaire which included questions about occupational exposures, education, medical history and family history of cancer and lung disease. Participants also donated blood samples for DNA extraction. The ReSoLuCENT study has been funded by the Sheffield Hospitals Charity, Sheffield ECMC and Weston Park Hospital Cancer Charity. First degree relatives were removed from the sample deposited to dbGaP. The Roy Castle Lung Study of Liverpool Lung Project (LLP) is a case-control and cohort study which has recruited over 11,500 individuals since 1996 from the Liverpool region in the UK. Detailed epidemiological and clinical data is collected with associated specimens (i.e. tumor tissue, blood, plasma, sputum, bronchial lavage and oral brushings). The participants have completed a detailed lifestyle questionnaire at recruitment, with repeat questionnaires at intervals; updated data on clinical outcome and hospital events are collected through the Health and Social Care Information Center (including Office of National Statistics mortality data, Cancer Registry and Health Episode Statistics). The project is registered on the UK National Institute for Health Research (NIHR) lung cancer portfolio and has all the required ethical approvals and sponsorship arrangements in place. The lung tumors were reviewed by the reference pathologist. The Seoul Bundang Lung Cancer Study was conducted between 2005 and 2010 to discover genetic and environmental factors related with lung cancer development. Lung cancer cases were recruited at the Seoul National University Hospital in Bundang. Controls were selected randomly from individuals participated in health check-up program and were frequency matched with cases on age and sex. All subjects were interviewed, and information on lifestyle risk factors, occupational history and medical and family history was collected using a standard questionnaire. Tumors were reviewed by the pathologists in the hospital. If the reviews conflicted, a consensus was arrived at after discussion. Coding of histology was based on 2001 WHO/IASLC. Genomic DNA was extracted based on standard protocol. The Shanghai Cohort Study (SCS) consisted of 18,244 men in Shanghai, China, who were 45-64 years old at the time of enrollment during 1986-1989. Approximately 80% of eligible men participated in the study. At the time of recruitment, each cohort subject was interviewed in-person by a trained nurse interviewer using a structured questionnaire that included background information, history of tobacco and alcohol use, current diet, and medical history. At the completion of the interview, the nurse collected a 10 ml blood and a single void urine specimen from the study participant. The buccal cell samples were collected from all surviving cohort members (~15,000) in the 2001-2002 follow-up interviews. The cohort has been followed for the occurrence of cancer and death through routine ascertainment of new cases from the population-based Shanghai Cancer Registry and Shanghai Vital Statistics Units. To maximize the cancer findings and minimize the loss of follow-up, we contacted each surviving cohort member annually. Retired nurses visit the last known address of each living cohort member and record details of the interim health history of the cohort member. As of December 31, 2014, cumulatively 612 (3.4%) original subjects were lost to follow-up, and 574 (3.1%) refused to our continued follow-up interview. A nested case-control study of incident lung cancer cases within the Shanghai Cohort Study was used to examine the association between serum levels of vitamin B6 and other compounds in the one-carbon metabolism pathway and risk of lung cancer. Briefly, 516 lung cancer cases were identified among cohort participants with available serum samples as of 12/31/2006. For each case, we randomly selected one control subject from all cohort members who were free of cancer and alive at the time of cancer diagnosis of the index case. Controls were matched to the index case by age at enrollment (±2 years), date of biospecimen collection (±1 month) and neighborhood of residence at recruitment, and smoking status (current, former and never smokers) as established previously for other studies. For former smokers, cases and controls were further matched by years since quitting smoking (<10 vs ≥10 years). One serum vial per subject was retrieved from biorepository and all serum samples were sent to the laboratory (B-vital) for measurements. DNA samples of 250 lung cancer cases and 250 matched controls were available for the present study. The Singapore Chinese Health Study (SCHS) cohort consisted of 63,257 Chinese men and women in Singapore when they were 45-74 years old at the time of enrollment between April 1993 and December 1998. At recruitment, each study subject was interviewed in person by a trained interviewer using a structured questionnaire that emphasized current diet assessed via a validated, 165-item food frequency questionnaire. The questionnaire also requested information on demographics, lifetime use of tobacco, incense use, current physical activity, usual sleep duration, reproductive history (women only), occupational exposure, medical history, and family history of cancer. Blood or buccal cell, and spot urine samples were collected first from a random 3% sample of cohort participants in April 1994, and extended to all surviving cohort participants starting in January 2000. Overall approximately 60% of eligible cohort participants donated biospecimens. The cohort has been passively followed for death and cancer occurrence through regular record linkage with the population-based Singapore Cancer Registry and the Singapore Registry of Births and Deaths. Migration out of Singapore, especially among housing estate residents, was negligible. As of latest update, only 55 individuals from this cohort were known to be lost to follow-up due to migration and other reason. A nested case-control study of incident lung cancer cases within the Singapore Chinese Health Study was used to examine the association between serum levels of vitamin B6 and other compounds in the one-carbon metabolism pathway and risk of lung cancer. As of 12/31/2011, 422 lung cancer cases were identified among cohort participants with available prediagnostic plasma samples. For each case, one control subject was randomly selected from all eligible cohort members who were alive and free of cancer on the date of cancer diagnosis of the index case. The control subject was individually matched to the index case by gender, dialect group (Hokkien, Cantonese), age at enrollment (±3 years), date of baseline interview (±2 year), date of biospecimen collection (±6 months), and smoking status (current, former, and never smokers). For current smokers, cases and controls were further matched by number of cigarettes per day (<15, ≥15 cigarettes/day). For former smokers, cases and controls were further matched by years since quitting smoking (<10, ≥10 years). One plasma aliquot per subject was retrieved from the biorepository and all plasma samples were sent to the laboratory (B-vital) for measurements, and one aliquot of DNA per subject for the present study. The International Agency for Research on Cancer (IARC) L2 Study. Lung cancer cases and controls were recruited through a multicentric case-control study coordinated by the IARC in Russia, Poland, Serbia, Czech Republic, and Romania from 2005 to 2013. Cases were incident cancer patients collected from general hospitals. Controls were recruited from individuals visiting general hospitals and out-patient clinics for disorders unrelated to lung cancer and/or its associated risk factors, or from the general population. Information on lifestyle risk factors, medical and family history was collected from subjects by interview using a standard questionnaire. All study participants provided written informed consent. The current study included 1,133 lung cancer cases and 1,117 controls genotyped on the Oncoarray. The Washington State University Lung Cancer Study is a hospital case-control study of 511 subjects with newly-diagnosed (within 1 year of diagnosis) lung cancer and 820 race-, sex- and age-matched controls. Lung cancer cases were recruited from lung cancer clinics within the H. Lee Moffitt Cancer Center while controls were recruited from the Lifetime Cancer Screening Center, a H. Lee Moffitt Cancer Center affiliate. None of the controls were diagnosed with any form of cancer at the time of screening. Detailed questionnaire data and oral buccal cells were collected for all subjects. The Total Lung Cancer (TLC) Study is a hospital-based study that included 458 lung cancer patients recruited for Moffitt Cancer Center's Total Cancer Care™ protocol between April 2006 and August 2010. Total Cancer Care™ is a multi-institutional observational study of cancer patients that prospectively collects self-reported demographic and clinical data, medical record information and blood samples for research purposes. All patients used in this cohort were recruited from the Thoracic Oncology Clinic at the Moffitt Cancer Center. The Vanderbilt Lung Cancer Study (BioVU) is a case-control study nested within the Vanderbilt University Medical Center biobank, BioVU. BioVU is a biorepository of DNA extracted from blood drawn from patients seeking routine clinical care at Vanderbilt University Medical Center and linked to de-identified electronic health records for research purposes. Lung cancer cases and controls were identified from BioVU participants in February 2014. Lung cancer cases were identified from the Vanderbilt tumor registry. All specimens undergo pathologic review for determination of morphology. Coding of histology was based on SEER Program Coding Guidelines. Controls were randomly selected from BioVU participants, excluding cancer patients, and were matched to cases on age (± 5 years), sex, and race. Relevant covariates were identified from electronic health records using natural language processing. Genomic DNA was extracted based on a standard protocol.

Study phs001273

TRACERx 100: whole exome data of the first 100 TRACERx tumours

BACKGROUNDTRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC). METHODSMultiregion high-depth whole-exome sequencing (M-seq) was performed on 100 early stage NSCLC tumors resected prior to systemic therapy. A total of 327 tumor regions were sequenced and analyzed to define evolutionary histories, obtain a census of clonal and subclonal events, and assess the relationship between ITH and recurrence-free survival (RFS). RESULTSWidespread ITH was observed for both somatic copy number alterations (median 48% [0.03-88%]) and mutations (median 30% [0.5-93%]). Driver mutations in EGFR, MET, BRAF and TP53 were almost always clonal. However, heterogeneous driver alterations occurring later in evolution were found in over 75% of tumors and were common in PIK3CA, NF1 and genes involved in chromatin modification and DNA response and repair. Genome doubling and ongoing dynamic chromosomal instability (CIN), illustrated by mirrored subclonal allelic imbalance, were identified as causes of ITH resulting in parallel evolution of driver copy number events, including amplifications of CDK4, FOXA1, and BCL11A. Elevated copy number heterogeneity was associated with shorter RFS (HR=4.9, P=0.00044), which remained significant in a multivariate analysis.CONCLUSIONSITH mediated through CIN, rather than point mutational heterogeneity, was associated with increased risk of relapse, supporting its value as a prognostic predictor, and the need to target this high-risk phenotype.

Study EGAS00001002247

Clinical relevance of somatic copy-number alterations in plasma circulating tumor DNA from advanced EGFR-mutated NSCLC patients treated with osimertinib

Background: Somatic copy-number alterations (SCNAs) are associated with drug resistance in patients with advanced epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) treated with EGFR-tyrosine kinase inhibitors (TKIs). The purpose of our study was to assess the clinical relevance and development of genome-wide SCNAs in plasma circulating tumor DNA (ctDNA) under osimertinib therapy.Methods: We included 43 patients with advanced EGFR T790M-positive lung adenocarcinoma who were treated with osimertinib after progression under previous EGFR-TKI therapy. We performed genomic profiling of ctDNA in plasma samples from each patient obtained pre-osimertinib and after patients developed resistance to osimertinib. SCNAs were detected by shallow whole-genome plasma sequencing and EGFR mutations were assessed by droplet digital PCR.Results: SCNAs in resistance-related genes (rrSCNAs) were detected in 10 out of 39 (25.6%) evaluable patients before start of osimertinib. The presence of rrSCNAs in plasma before initiation of osimertinib therapy was associated with a lower response rate to osimertinib (50% versus 86%, p=0.02) and was an independent predictor for shorter progression-free survival (adjusted HR 3.26, 95% CI 1.37-7.75, p=0.008) and overall survival (adjusted HR 2.41, 95% CI 1.09-5.34, p=0.03).Conclusions: Genomic profiling of plasma ctDNA is clinically relevant and affects the efficacy and clinical outcome of osimertinib. Our approach allows comprehensive assessment of SCNAs in plasma samples of lung adenocarcinoma patients and may help to guide genotype-specific therapeutic strategies in the future.

Study EGAS00001004539

CNS Embryonal tumors

The dataset represents a total of 58 DNA samples from 16 male and 12 female pediatric patients affected with embryonal central nervous system tumors. The samples were subject to whole genome sequencing, WGS, [48 samples, (representing 12 male and 11 female individuals)] and whole exome sequencing, WES, [10 samples, (representing 4 male and 1 female individuals)]. One tumor tissue sample and one peripheral blood sample were analyzed from each of 26 patients, whereas two tumor tissue samples and one peripheral blood sample were analyzed from two patients. The WGS samples were sequenced 2x150 bp paired-end on an Illumina HiSeqX v2.5 instrument, and the WES samples were sequenced 2x100 bp paired-end on an Illumina HiSeq 2500 instrument. The FASTQ files generated were aligned to the human reference genome sequence GRCh38/hg38 using bwa-mem, with the ALT-aware option turned on. Sorting of reads and marking of PCR duplicates was performed with GATK. Base quality score recalibration and joint realignment of reads around insertions and deletions (indels) were conducted using GATK tools. The dataset consists of 58 files in the CRAM format (lossless compression) with a total file size of ~8,8 TB. All CRAM files but one, are derived from one sequence run and one sample. P4551_227N_P4552_112N is a CRAM file where 2 sequence runs (P4551_227N and P4552_112N) from peripheral blood samples from the same individual, P019, were aligned into one single CRAM file. Additional genomic and molecular data (FASTQ, BAM, IDAT, and VCF files) and limited clinical data can be requested by ethically approved projects conducting research in the field of pediatric cancer.

Dataset EGAD50000000298

Overcoming Clinical Resistance to EZH2 Inhibition Using Rational Epigenetic Combination Therapy

Essential epigenetic dependencies have become evident in many cancers. Based on functional antagonism between BAF/SWI/SNF and PRC2 in SMARCB1-deficient sarcomas, we and colleagues recently completed the clinical trial of the EZH2 inhibitor tazemetostat, leading to its FDA approval. However, the principles of tumor response to epigenetic therapy in general and tazemetostat in particular remain unknown. Using functional genomics of patient tumors and diverse experimental models, we defined molecular mechanisms of tazemetostat resistance. We found distinct classes of acquired mutations that converge on the RB1/E2F axis, decoupling EZH2-dependent differentiation and cell cycle control. This allows tumor cells to escape tazemetostat-induced G1 arrest despite EZH2 inhibition and suggests a general mechanism for effective EZH2 therapy. Using RNA-sequencing of patient tumor specimens, we identified specific genes as markers of a deficient RB1/E2F axis, which we propose as prospective biomarkers for therapy stratification. This offers a paradigm for rational epigenetic combination therapy suitable for immediate translation to clinical trials.This study was performed on archived tumor tissue collected as part of the above clinical trial at Memorial Sloan Kettering Cancer Center. Patient tumors were classified into "Response" or "Progression" groups based on RECIST 1.1 criteria. "Response" included tumors exhibiting a complete response, partial response, or stable disease. All other tumors were classified under the "Progression" group. Archived frozen tumor samples were weighed and up to 20-30mg were homogenized in RLT buffer, followed by extraction using the AllPrep DNA/RNA Mini Kit (QIAGEN catalog 80204) according to the manufacturer's instructions. RNA was eluted in 13 μL nuclease-free water. After RiboGreen quantification and quality control by Agilent BioAnalyzer, 1 μg of total RNA with DV200 percentages varying from 78% to 100% underwent ribosomal depletion and library preparation using the TruSeq Stranded Total RNA LT Kit (Illumina catalog RS-122-1202) according to instructions provided by the manufacturer with 8 cycles of PCR. Samples were barcoded and sequenced using NovaSeq 6000 in a PE150 mode, with the NovaSeq 6000 S4 Reagent Kit (Illumina). On average, 84 million paired reads were generated per sample and 70% of the data mapped to mRNA. For analysis, read adaptors were trimmed and quality filtered using 'trim_galore' and mapped to GRCh38/hg19 reference genome using STAR v2.7.9 with default parameters.

Study phs003188

Clinical outcomes in ctDNA-positive urothelial carcinoma patients treated with adjuvant immunotherapy

Minimally invasive approaches to detect residual disease after surgery are urgently needed to select patients at highest risk for metastatic relapse for additional therapies. Circulating tumour DNA (ctDNA) holds promise as a biomarker for molecular residual disease (MRD) and relapse,1-3 but its clinical value has yet to be demonstrated in a randomised clinical trial. We evaluated outcomes in post-surgical ctDNA-positive (+) patients in a randomised phase III trial of adjuvant atezolizumab versus observation. IMvigor010 enrolled 809 patients with muscle-invasive urothelial carcinoma and did not meet its primary endpoint of disease-free survival (DFS) in the intent-to-treat population. Within the study, an exploratory planned analysis of prospectively collected plasma was performed, which tested the utility of ctDNA to identify patients who may benefit from adjuvant atezolizumab treatment. ctDNA was measured at the start of therapy (cycle 1 day 1; C1D1) and at week 6 (cycle 3 day 1; C3D1), and 581 patients were evaluable for ctDNA. The prevalence of ctDNA positivity at C1D1 was 37% (n=214), and ctDNA positivity identified patients with poor prognosis (observation arm DFS HR= 6.19 (4.29, 8.91), p&lt;0.0001). Here we show that ctDNA(+) patients had improved DFS and overall survival (OS) with atezolizumab versus observation (DFS HR= 0.56 (0.41-0.77); p=0.0003 and OS HR= 0.58 (0.4-0.86); p=0.0063). No difference in DFS or OS between arms was noted for ctDNA-negative patients. The rate of ctDNA clearance was higher with atezolizumab (18%) versus observation (4%) (p=0.0041). Transcriptomic analysis revealed that tumours from ctDNA(+) patients had higher expression of cell cycle and keratin genes. Within the ctDNA(+) patient population in the atezolizumab arm, non-relapsing patients were further enriched in prominent immune response signatures including PD-L1, IFNG, CXCL9, and high tumour mutational burden, whereas relapse was associated with angiogenesis and fibroblast-transforming growth factor-beta signatures (F-TBRS). TCGA molecular subset analysis revealed increased efficacy of atezolizumab in patients with basal-squamous tumours, consistent with underlying tumour-immune contexture. Together these findings suggest that adjuvant atezolizumab may be associated with improved outcomes compared with observation in this high-risk ctDNA(+) population. These findings, if validated in other settings, would shift approaches to post-operative cancer care.

Study EGAS00001004997

Mutational Landscape and Tumor Burden Assessed by Cell-Free DNA in Diffuse Large B-Cell Lymphoma: a Population-based Study

Purpose: We analyzed the utility of cfDNA in a prospective population-based cohort to determine the mutational profile, assess tumor burden, and estimate its impact in response rate and outcome in patients with DLBCL. Experimental Design: One-hundred patients were diagnosed with DLBCL during the study period. Mutational status of 112 genes was studied in cfDNA by targeted next-generation sequencing. Paired formalin-fixed paraffin-embedded samples and volumetric PET/CT were assessed when available. Results: Appropriate cfDNA to perform the analyses was obtained in 79/100 cases. At least one mutation could be detected in 69/79 cases (87%). The sensitivity of cfDNA to detect the mutations was 68% (95% CI: 56.2-78.7). The mutational landscape found in cfDNA samples was highly consistent with that shown in the tissue and allowed genetic classification in 43% of the cases. A higher amount of ctDNA significantly correlated with clinical parameters related to tumor burden (elevated LDH and β2-microglobulin serum levels, advanced stage, and high-risk IPI) and total metabolic tumor volume assessed by PET/CT. In patients treated with curative intent, high ctDNA levels (&gt;2.5 log hGE/mL) were associated with lower complete response (65% vs. 96%, P&lt;0.004), shorter progression-free survival (65% vs. 85%, P=0.038) and overall survival (73% vs. 100%, P=0.007) at 2 years, although it did not maintain prognostic value in multivariate analyses. Conclusions: In a population-based prospective DLBCL series, cfDNA resulted an alternative source to estimate tumor burden and to determine the tumor mutational profile and genetic classification, which have prognostic implications and may contribute to a future tailored

Study EGAS00001004733

International Standards for Cytogenomic Arrays

The International Standards for Cytogenomic Arrays (ISCA) Consortium is a rapidly growing group of clinical cytogenetics and molecular genetics laboratories committed to improving quality of patient care related to clinical genetic testing using new molecular cytogenetic technologies including array comparative genomic hybridization (aCGH) and quantitative SNP analysis by microarrays or bead chip technology. The ISCA Consortium is now working with the sequencing community to extend the goals of standardization, collaboration, and data sharing. To reflect this combined effort, we are becoming ICCG, or the International Collaboration for Clinical Genomics. Efforts of the Consortium include: Clinical Utility: The ISCA Consortium has made recommendations regarding the appropriate clinical indications for cytogenetic array testing (Miller et al. AJHG 2010, PMID: 20466091). Currently, discussions are focused on pediatric applications for children with unexplained developmental delay, intellectual disability, autism and other developmental disabilities. A separate committee has been developed to address appropriate cancer genetic applications (http://www.urmc.rochester.edu/ccmc/). Evidence-based standards for cytogenomic array design: The Consortium will develop recommendations for standards for the design, resolution and content of cytogenomic arrays using an evidence-based process and an international panel of experts in clinical genetics, clinical laboratory genetics (cytogenetics and molecular genetics), genomics and bioinformatics. This design is intended to be platform and vendor-neutral (common denominator is genome sequence coordinates), and is a dynamic process with input from the broader genetics community and evidence-based review by the expert panel (a Steering Committee with international representation). Public Database for clinical and research community: It is essential that a publicly available database be created and maintained for cytogenetic array data generated in clinical testing laboratories. This will be integrated into the current dbGaP database at NCBI/NIH and released through dbVar, and curated by a committee of clinical genetics laboratory experts. The very high quality of copy number data (i.e., deletions and duplications) coming from clinical laboratories combined with expert curation will produce an invaluable resource to the clinical and research communities. Standards for interpretation of cytogenetic array results: Using the ISCA Database, along with other genomic and genetics databases, the Consortium will develop recommendations for the interpretation and reporting of pathogenic vs. benign copy number changes as well as imbalances of uncertain clinical significance. Membership in the ISCA Consortium is open to all individuals and laboratories involved in cytogenetic array testing who are committed to free data sharing and to participation in a process to develop evidence-based standards and guidelines to improve patient care. ISCA is available through dbVar: http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd37/ http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd75/ http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd45/ http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd101/

Study phs000205

Whole genome sequencing of 198 epileptic individuals.

This dataset includes whole genome sequencing of 198 epileptic individuals. Libraries preparation and whole-genome sequencing: gDNA was cleaned up using ZR-96 DNA Clean & ConcentratorTM-5 Kit (Zymo) prior to being quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit (Life Technologies) and its integrity assessed on agarose gels. Libraries were generated using the TruSeq DNA PCR-Free Library Preparation Kit (Illumina) according to the manufacturer‚Äôs recommendations. Libraries were quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit (Life Technologies) and the Kapa Illumina GA with Revised Primers-SYBR Fast Universal kit (Kapa Biosystems). Average size fragment was determined using a LabChip GX (PerkinElmer) instrument. The libraries were denatured in 0.05N NaOH and diluted to 8pM using HT1 buffer. The clustering was done on a Illumina cBot and the flowcell was ran on a HiSeq 2500 for 2x125 cycles (paired-end mode) using v4 chemistry and following the manufacturer's instructions. A phiX library was used as a control and mixed with libraries at 0.01 level. Bioinformatics: The Illumina control software was HCS 2.2.58, the real-time analysis program was RTA v. 1.18.64. Program bcl2fastq v1.8.4 was used to demultiplex samples and generate fastq reads. The filtered reads were aligned to reference Homo_sapiens assembly b37. Each readset was aligned to creates a Binary Alignment Map file (.bam).

Dataset EGAD00001004062

Multiple Myeloma Total Therapy trial patient sequencing

The study of multiple myeloma (MM) genomics has identified many abnormalities that are associated with poor progression free survival (PFS) and overall survival (OS). Copy number abnormalities have been extensively studied in many datasets with long follow-up, however, the prognostic impact of mutations have not been extensively studied and available datasets have generally had a relatively short follow-up. These analyses have identified a range of mutations that are associated with prognosis, making it important to extend these observations in larger studies with robust diagnostic technologies. Samples from newly diagnosed MM patients enrolled in Total Therapy trials (n=225) were sequenced on a targeted panel consisting of 140 genes and additional regions of interest for copy number, as well as tiling of the Ig and MYC loci for detection of translocations. Samples were sequenced to a median depth of 452x using 2x75 bp paired end reads. Reads were aligned to hg19 and mutations called using Strelka and filtered with fpfilter. Translocations were called by Manta, and copy number determined by read depth ratio and loss of heterozygosity comparison with a patient matched non-tumor sample. DNA was obtained from either CD138+ cells from the bone marrow of multiple myeloma patients (tumor) or from stem cell harvests or peripheral blood cells from the same patient (control). 100 ng of DNA was fragmented, end-repaired, and adapters ligated using the HyperPlus kit (KAPA Biosystems). After PCR amplification the libraries were hybridized with probes against either a targeted panel consisting of 140 genes and chromosomal regions (Nimblegen) using SeqCap reagents (Nimblegen). Hybridized libraries underwent further amplification before being sequenced on a NextSeq500 (Illumina) using 75 bp paired end reads There are 450 (225 tumor and 225 germline) samples in this study. 263 are available as part of this dataset. The remaining 187 are available with dataset accession id EGAD00001004117.

Study EGAS00001003223

ctDNA to predict risk of progression and death after trifluridin/tipiracil therapy

Background. Late-line treatment in metastatic colorectal cancer (mCRC) can improve the prognosis. However, not every patient has a benefit and may experience severe side effects. Thus, predictive/prognostic biomarkers are urgently needed. Despite many promising results, the role of circulating tumor DNA (ctDNA) analysis in trifluridin/tipiracil (FTD/TPI) treated mCRC patients is so far still elusive. Study design. This prospective non-interventional translational biomarker phase II study enrolled 30 metastatic CRC patients treated with FTD/TPI. The primary study aim was to identify a molecular biomarker to predict overall survival (OS) benefit upon FTD/TPI treatment. Methods. mCRC patients (60% male, 40% female) with FTD/TPI therapy were included. Molecular profiling in cell-free DNA from plasma was performed prior to treatment and on average 4, 8 and 12 weeks after FTD/TPI initiation. Tumor levels were assessed as the highest variant allele frequency (hVAF) and correlated with OS. Uni- and multivariate analyses for survival were performed with clinical variables. Multivariate analyses for survival and treatment efficacy were adjusted to age, gender and ECOG performance status. Results. Compared to previous data, our disease control rate of 30% was lower with a median OS of 8.1 months and a median PFS of 3.2 months. At baseline (BL) and the first two follow-ups (FU), a hVAF cut-off of 7.5% was most discriminative (BL HR 0.39 95% CI 0.16 - 0.98 P=0.0212, FU1 HR 0.30 95% CI 0.12 -0.77 P=0.0021, FU2 HR 0.23 95% CI 0.08-0.70 P=0.0004) whereas at the last follow-up the stratification was most pronounced at a 15% cut-off. An elevated hVAF trajectory over time was associated with a higher risk of death. Conclusion. ctDNA is detectable in a high proportion of mCRC patients. Changes in ctDNA levels are associated with survival among FTD/TPI therapy and may identify patients who benefit most from treatment in these late-stage disease.

Study EGAS00001006883

DAC Portal documentation

DAC Portal Welcome to the DAC Portal documentation! If you are involved in governance or legal aspects, technical or operational aspects, or serving as a data steward, this page will be helpful for you. By exploring these materials, you can define your own Data Access Committee (DAC) and policies, understand the minimal requirements for a DAC and a policy object, and comprehend how the EGA data access requests are managed. DAC Portal Index Setting up your account DACs and Policies Pending Request table Manage Data Requests History page Audit your DACs, policies, and datasets Deprecation User Preferences DAC API - A programmatic approach Setting up your account Register yourself as an EGA user. The Helpdesk team will validate your account (this could take up to 48 hours). After validation, you will receive an email with a link to verify your email - Make sure you click on the link to verify your account! If you don’t receive the email, please check your spam folder. Once your account is active you can login to the DAC Portal. We recommend you to check out the Take The Tour! DACs and Policies Create a DAC Click on Create a DAC Add the title and description to your DAC. Once ready, click on the ‘Create’ button. You will have to wait until the Helpdesk Team validates the creation of your DAC. Once approved by Helpdesk, your DAC will be assigned with a persistent identifier (EGAC). Edit a DAC Once your DAC is registered and approved, you can edit it and add contacts. Remember, you can add as many contacts as needed. And, you can also remove/add contacts anytime. To add a contact, you must write the username or email of a registered EGA user in the Members field. Once you have typed the whole username or email, a drop-down menu will appear where you can select the contact. Select a contact from the drop-down menu. Make sure you write the full username or email of the contact so it appears as an option. Make sure that you select one main contact. Set up the role you want to grant to the new contact of the DAC. We recommend you to check out the FAQ to learn more about the different roles! After adding all the necessary contacts and assigning roles, click on Update. This will send an email notification to the new DAC member, letting them know about the invitation to managing the DAC. Once they log into the DAC Portal, they will be able to either accept or turn down the invitation. Create a policy First, please note that all information registered in a policy metadata object will be publicly available on the EGA website. For each released dataset, all users will be able to read the policy under the “Data Access” tab. Select Create a Policy from the Policies tab. Select the DAC that you want your policy to depend on. Add the title and terms and conditions for accessing and using your data. If you already have an external website for data access requests, you can add the URL directly here (see example here). You can find a template of a Data Access Agreement (DAA) document in our Policy documentation page. The DAA template is provided for guidance only and should be adapted to suit your own purpose depending on the security policies, terms for publication or embargoes, and restrictions on data use or sharing. You can also add Data Use Ontologies (DUO) to your policy. These DUO codes are used to semantically tag the terms and conditions of using the data linked to the policy (example). For more information, you can refer to the Data Uso Ontology documentation. Once completed, make sure to click the Create button in order to register your policy. A persistent identifier (EGAP) will be assigned automatically. Edit a policy Please, note that you will not be able to edit the policy if it shows in orange. Meaning that you are a member of the DAC linked to that policy. Members don’t have edit rights. Only admins can edit objects. You can check your registered policies on My policies, available at the menu in the top-right corner. Select your policy. It will then display the information of that policy, allowing you to edit the information. Once you are done with the modifications, click Update. Pending Requests Table Table Settings Upon selecting a specific DAC, you will be presented with a customisable table showcasing all pending data access requests. This table allows you to tailor the displayed information according to your preferences by selecting desired columns. Available Columns (Displayed with the Eye Icon): Date: The date when the data access request was submitted. Full Name: Name and surname of the user submitting the request. Email: Email address associated with the user's request. Username: The username linked to the EGA account of the user submitting the request. Organisation: Affiliated organisation of the user submitting the request. Dataset: EGA accession ID for the requested EGA dataset. Dataset Title: Title of the requested dataset. DAC Comment: Space provided for internal comments related to the request. Expiration: Option to specify an expiration date for granted permissions. Check more information on the pending request table here! Table Filters By default, all data access requests are displayed. However, you have the option to apply filters to refine your view. Follow these steps to apply filters: Click on the More button. This action will reveal the different columns available for applying filters. As you select columns, the checked fields will be displayed at the top of the request table. Choose the specific values within the selected columns that you want to filter for. Click the Search button to apply the selected filters. Do you find yourself frequently applying the same filters? You can save multiple combinations of filters for easy access at any time! Here's how: Click the Save button. Provide a descriptive name for the filter combination. Click Save to confirm. To load saved filters, simply select the saved filter from the dropdown menu to apply it automatically. Check more information on filters here! Advanced Search For users requiring more granular control over their data access requests, the DAC Portal offers an Advanced Search feature using JIRA Query Language (JQL). With JQL, you can craft precise and complex queries to filter and retrieve specific data access requests based on various criteria such as date, user details, dataset attributes, and more. Using JQL's flexibility, you can create custom queries to meet your specific needs, allowing for advanced filtering options beyond the standard filters provided. You can find the Advanced Search feature following these steps: Click the … button Select Advanced To view the allowed fields for filtering and explore all available options, simply click on the info icon. Once you have written the filtering values, click the Search button to apply. Check more information on filters here! Manage Data Requests Upon logging into the DAC Portal, you'll notice a sand clock icon next to the DAC ID, indicating the number of pending requests. Click on the DAC to review these requests. Accept requests Click on the right side of the toggle button to grant access to the user. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with granting permissions. Deny requests Click on the left side of the toggle button to deny access to the user. Provide a reason for the denial. Note that the user will receive an email with this reason. Click on the Done button. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with denying permissions. Tips! Apply a filter to view all requests to be managed at once. Use the toggle in the row with column names to grant or deny permissions for multiple requests simultaneously. You can grant and deny permissions in the same action, simplifying the process. The confirmation box will provide a summary of all actions, including grants, denials, DAC comments, and expiration dates. History Page The History page serves as a dedicated space to view information regarding all requests managed by all DAC members. Here, you can review active permissions and revoke them as needed. Go to the History page by clicking on the "HISTORY" button from a DAC page in the DAC Portal. Here are the different row types you may find: Current permissions: row in green with a toggle button to revoke permissions on the right. Approved requests: row in green, with no toggle button. Request denied: row in red with “request denied” on the right. Permission revoked: row in red with “permission revoked” on the right. Distinctions to Note! Between Request Denied and Permission Revoked: Request Denied: Refers to requests that were rejected from the outset, indicating that access to the dataset was never granted. Permission Revoked: Indicates that permissions were previously granted but have since been revoked. Users with permissions revoked have previously accessed the dataset. Between Approved Request and Current Permissions: Approved Request: Represents an entry when a data access request has been approved in the past. Current Permissions: Denotes ongoing permissions where a user has present access to the dataset. An approved request may now appear as Permission Revoked in the present. By observing these different rows, users can gain insight into the complete history of a user's interactions and permissions regarding a dataset over time. To revoke access to a specific user for a dataset, follow these steps: Go to History page Look for the row with the specific permissions. You can use the filters! Click on the toggle button to revoke access. Add a denial reason. Bear in mind that the requester will receive the denial reason! Click on the Apply button. Check more information on the Hisotry page here! Audit your metadata objects In the DAC Portal, you can efficiently manage and audit various metadata objects pertinent to your role as a Data Controller. Upon accessing the DAC Portal, you will encounter three primary tabs on the homepage: DAC: Contains information about Data Access Committees (DACs). Policies: Provides insights into linked policies. Datasets: Displays datasets and relevant details. Within each tab, you'll find a comprehensive list of the objects you manage, these being grouped by type (DAC, Policies, Datasets), as well as by your role (member/admin). Whilst the lists give you a quick look, to check how things are connected, we've added a table at the bottom DACs and policies. DACs Let’s check which policies are linked to a specific DAC: Go to "My DACs." Select a DAC. Click on "EDIT" to see more details. You are now on this page: Scroll down to see the linked policies. Click on “List of linked policies of this DAC” and you will see a list of all policies linked to your selected DAC. Policies Do you want to view a list of datasets connected to a particular policy? Follow the same steps mentioned earlier, but head to the policy tab this time. In the policy tab, you'll find a list of all your policies. Here, you may notice two different icons next to the policy ID (EGAP): DAC Icon (): This represents the DAC. Hover your mouse over the icon to see the DAC ID. Dataset icon (): This indicates datasets falling under that policy. The number next to the icon tells you how many datasets are linked to the policy. For example, if you see "2" next to the icon, it means there are two datasets linked to that policy. If you want to check the linked datasets and their relevant information, simply click on a specific policy. You'll then find the "List of linked datasets of this policy" at the bottom of the page. Datasets Finally, in the dataset tab, by default you will see a list of all the datasets you can manage with all your DACs and policies. Yet, we've included two handy ways to organise them: DAC vs. policy: You can group by datasets, either by DAC or policy. Released vs. unreleased: You can sort out datasets based on their release status. Feel free to experiment with both options! For instance, if you want to see which DACs have unreleased datasets, simply select DAC and unreleased, and you'll get the details you need! Deprecation Do you have a bunch of metadata objects like DACs and policies that you don’t need anymore? This section shows you how to get rid of them! But what does "deprecation" mean for EGA? It's basically changing the status of a metadata object to "deprecated," which means we won't be using it in the future. In simple terms, it's like saying these objects are no longer useful. However, because we believe in making metadata FAIR, once an object has a persistent identifier, we can't just delete it. So, instead of deleting, we deprecate it. Here's a helpful tip! If you want to make a metadata object disappear from the DAC Portal, deprecate it. You won’t see it in the portal anymore! Let’s say you want to deprecate a DAC. Let’s do it! Go to "My DACs." Select a DAC. Click "EDIT" to see more details. You are now in this page: Click on the Deprecate button It will then appear a message. There are two options here: Your DAC is not linked to any policy, hence it’s ready to be deprecated. Click on “Yes. Confirm” to deprecate your DAC object! Your DAC is linked to at least one policy, and you need to either: Deprecate the linked policy first, or Link the policy to a different DAC Now, let’s say you don’t want to get rid of the policy altogether, but you want to change the DAC it’s linked to. Here's how: Go to the policy tab. Find the policy you want to change (for example, EGAP50000000019). Choose a new DAC to link it to. Click on Update. See the Edit Policy section for more details. After ensuring that the DAC we want to deprecate isn’t linked to any policy, return to the "My DACs" section and follow the steps outlined previously. This will lead you to a confirmation message. You can only deprecate DACs and Policies. To deprecate a dataset, please contact our Helpdesk team. Check more information on how deprecation works here! User Preferences We have implemented email notifications in the DAC Portal. Here’s the complete list: DACs: Pending requests Approved by Helpdesk Rejected by Helpdesk DAC Invitation Requesters: Data access request approved Data access request denied Permissions revoked Upcoming expiration date As a DAC member, you will be able to decide whether you want to receive the DAC notifications or not. For that, go to the top-right corner menu, select User Preference. You will be able to decide whether you want to receive notifications for: Approved by Helpdesk Rejected by Helpdesk DAC Invitation For pending requests, you will be able to select how often do you want to receive the notification: Daily Weekly Fortnightly As a DAC member, you are responsible for managing data access requests. Consequently, you will receive notifications for pending requests. If you prefer not to manage these requests, please arrange to be removed as a contact for your DAC. Here's a tip! Do you have a pending request that you don’t want to receive a notification? Add a DAC comment! The EGA understands that a request resolution can take some time, for this reason, if you add a comment (make sure you save it by clicking the APPLY button!) we will filter those requests at the time of sending the notification! DAC API - A programmatic approach In addition to the new DAC Portal, we are excited to announce the release of the DAC API. This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can click the button below. Check out the DAC API specification!

Documentation access/data-access-committee/dac-portal

Aberrant Oligoclonal Hematopoiesis in Remission AML and Relapse from Rare Cells Genomically Resembling Leukemic Blasts

Acute myelogenous leukemia (AML) is a hematological malignancy that often responds well to initial treatment in which patients achieve a complete remission (CR). Nevertheless, most patients will relapse after achieving a CR, which is often fatal. This minimal residual disease (MRD) that remains after CR is likely the source of relapse and so improved measurement and characterization of these residual cells is critical. Employing droplet digital PCR (ddPCR), we were able to measure mutated alleles of many recurrently mutated genes in bone marrow samples acquired during CR from AML patients at levels as low as 0.002% variant allele frequency. Most of these gene mutations persisted in CR but at very variable and gene-dependent levels. Most AML cases involved residual aberrant oligoclonal hematopoiesis, and rare cell populations (as few as 1 in 15,000) were detected that were genomically similar to the AML blast populations at that dominated at diagnosis and were fully clonally represented at relapse. From a clinical perspective, the mutant allele frequency correlated closely with relapse-free and overall survival.

Study phs001408

Broad utility of ultrasensitive analysis of ctDNA dynamics across solid tumors treated with immunotherapy

Prior studies have suggested the biomarker potential of plasma circulating tumor DNA (ctDNA) in cancer patients treated with immune checkpoint inhibitors (ICIs). In order to characterize the potential role of ultrasensitive ctDNA detection in the management of these patients we have performed tumor whole-genome-informed, ultrasensitive ctDNA analysis—tracking approximately 1,800 tumor-specific mutations per patient—in a retrospective cohort (n=136) and a prospective validation cohort (n=66) across 24 cancer types treated with ICIs alone or in combination with bispecific antibodies or immune cell engagers. Analyzing 1,455 longitudinal samples, we found that ctDNA molecular response measured as early as 3 weeks after treatment initiation correlated with improved progression-free and overall survival, while ctDNA clearance at any time strongly correlated with radiological response and prolonged survival. Additionally, ctDNA dynamics distinguished true progression from pseudoprogression and predicted outcomes in patients receiving continued immunotherapy beyond initial progression. This study highlights the broader applicability of ultrasensitive ctDNA as a biomarker across cancer types and immunotherapy modalities.

Study EGAS50000001333

12255 results for "free coins ultimate team Visit Buyfc26coins.com for latest FC 26 coins news..G9vn"

in 42.12 milliseconds.