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• Genetic Causes of Growth Disorders

  Description of the disorder: A very common disorder presenting to pediatricians/pediatric endocrinologists is childhood growth failure. Sometimes the cause is evident, for example, growth hormone deficiency. In other children, the etiology remains unknown despite extensive evaluation, resulting in the unhelpful diagnosis of severe idiopathic short stature (SISS). These conditions are quite heterogeneous, including children with isolated growth failure and others who also have other abnormalities such as developmental delay or a constellation of congenital anomalies (syndromic short stature). Sometimes, the disorder appears to primarily affect the growth plate, which drives skeletal growth and thereby determines overall body proportions, whereas in other children, the disorder affects skeletal and non-skeletal tissues equally. Some cases of SISS have a polygenic inheritance while others appear to follow a Mendelian inheritance model, recessive, dominant or X-linked. Very recently, genome-wide analysis for copy number variants (CNVs) and whole-exome sequencing have begun to identify some of the molecular etiologies of these disorders.1 Identifying the molecular etiology of growth disorders has clinical and scientific value. Clinically, identifying a molecular cause prevents extensive further testing and may direct anticipatory care for associated medical problems. For example, we recently studied aggrecan (ACAN) gene mutations in families with autosomal dominant short stature and accelerated skeletal maturation. These mutations affect both growth plate cartilage, causing linear growth failure, and also articular cartilage, causing osteochondritis dissecans and early-onset osteoarthritis.1 Etiological classification of idiopathic growth failure allows more precise characterization of prognosis and response to treatment, which are currently highly imprecise because of the locus heterogeneity. In some cases, finding the genetic etiology points to a novel treatment approach that targets the specific molecular pathway involved. The proposed project is central to the main focus of our group, the Section on Growth and Development, NICHD. Our primary goal is to investigate cellular and molecular mechanisms governing childhood growth and to gain insight into the many human genetic disorders causing childhood growth failure. The proposed project is well suited for the intramural program because it takes advantage of Clinical Center expertise to phenotype subjects with SISS. Study subjects: We will study subjects with SISS and nuclear family members. SISS will be defined by height SDS < -2.5 for age without evident cause after routine evaluation including: growth hormone axis evaluation; thyroid function testing; celiac disease screening; urinalysis; CBC; chemistry; karyotype (girls, for Turner syndrome); and testing for single gene defects based on the clinical evaluation (for example, SHOX or Noonan-associated genes). Candidate families will include isolated growth disorders and growth disorders that are accompanied by congenital anomalies, developmental delay, or other syndromic short stature. Strong preference will be given to subjects with a severe phenotype and a pedigree that indicates a Mendelian inheritance. Multiple independent families with the same phenotype will have priority. The pool of applicants for recruitment is large, and we receive many inquiries by emails and phone consultations from pediatric endocrinologists for advice regarding diagnosis and management of unusual growth disorders, including familial disease. Often these families are seeking further evaluation and are willing to participate in a research study. From this pool, we will be selecting pedigrees with very favorable Mendelian characteristics, for example de novo dominant occurrences where two normal parents have a child with SISS, and the child grows up to be an adult who passes this phenotype on to multiple grandchildren in the next generation. Subjects and family members will be brought to the NIH Clinical Center (NIHCC) for outpatient evaluation. Participants will be evaluated by pediatric endocrinology fellows (as part of our training program) and by senior staff to establish the clinical findings and construct a pedigree. Subjects will receive additional biochemical and imaging studies at the Clinical Center to complete the phenotyping and assign affected status. The growth abnormality will be evaluated by assessing body proportions, relative organ size, and skeletal imaging as indicated. Associated clinical abnormalities beyond altered growth will be characterized with the help of other Clinical Center subspecialists. Subjects and family members will be evaluated by SNP microarray and whole-exome sequencing. We anticipate 4-5 persons for each of 16-20 families, for a total of 72-90 whole-exome sequences. Half of the families will be recruited and studied within the first year and half in the second year. We will use freshly collected peripheral blood as the DNA source for SNP array and NextGen Sequencing. Analytic approach: The candidate genes will be chosen based on 1) inheritance state consistency, 2) population frequency in the ESP and UDP databases, and 3) predictions of deleteriousness. We will use VarSifter and the B road Institute Integrated Genome Viewer to filter and visualize these data. The genetic model will be dependent on the family's pedigree. For a simple trio, we will explore variants using genetic models including autosomal recessive, de novo (dominant), compound heterozygous, deletion/point mutation recessive, and X-linked (male only). The candidate variants will be identified using Boolean logic sets in VarSifter following intramural NHGRI/UDP methods. We will also use SNP microarray data to identify copy number variations, complete/single copy deletions, duplications, non-paternity, consanguinity for homozygosity mapping, uniparental isodisomy, mosaicism, and segregation patterns (bed file generation for use in VarSifter filter work). After a list of candidate sequence variants has been generated, annotation will include using the Exome Variant Server, Polyphen-2, MutationTaster, Sift, and CADD predictions of deleteriousness. Biological laboratory data will be included to prioritize candidate variants. Our group has expertise in the molecular mechanisms regulating both skeletal growth2,3 and growth of other tissues4,5, which may be helpful in this phase of the analysis. The most promising candidate mutations will be confirmed by Sanger sequencing and studied functionally, in vitro and/or in vivo. For mutations that affect skeletal growth, we will use experimental systems related to growth plate cartilage. For in vitro studies, we have experience transfecting chondrocyte cell lines, such as ATDC5, and primary chondrocytes. We will determine whether the mutation alters protein and/or cell function. In vivo studies can be used to explore pathophysiology. We have recently successfully used a new approach, the CAS9/CRISPR system to knockout multiple loci in mice (unpublished), which can be used again in the future to create mouse models efficiently. 1J Clin Endocrinol Metab, 2014 (PMID: 24762113) 2J Mol Endocrinol, 2014 (PMID: 24740736) 3Hum Mol Genet, 2012 (PMID: 22914739) 4Proc Natl Acad Sci U S A, 2013 (PMID: 23530192) 5Endocr Rev, 2011 (PMID: 21441345)

  Study phs001617

• PD-L1 blockade immunotherapy rewires cancer-induced emergency myelopoiesis

  HSPCs were sorted as CD34+ cells from bone marrow of patients with Hodgkin Lymphoma (HL). Control samples were isolated before initiating treatment.These were compared to samples from two additional HL patients who had relapsed and were being treated with nivolumab. Nivolumab was administered at a dose of 240 mg every two weeks, with BM samples collected the day before the scheduled dose. The patients receiving nivolumab were in remission at the time of BM collection. RNA was extracted using Arcturus™ PicoPure™ RNA Isolation Kit (Thermo Fisher Scientific, #12204-01) ` instructions. Paired-end sequencing was reads were generated for each sample in the Illumina Novaseq 6000 system.

  Dataset EGAD00001015403

• EORTC study on the evolution of driver mutations and MGMT promotor methylation in glioblastomas treated with standard of care: Correlation with survival and impact on trial design

  Precision medicine trials in glioblastoma should be conducted at tumor recurrence. However, second surgery for recurrent GBMs is not routinely performed and therefore molecular data is predominantly derived from primary samples. This study aims to establish the frequency of driver changes at tumor recurrence.

  Dataset EGAD00001004593

• Whole-genome bisulfite sequencing

  Raw reads from whole-genome bisulfite sequencing. Whole-genome bisulfite sequencing library preparations and Illumina sequencing of DNA samples from TET2 mutation carriers (Ly9, Ly11, Ly14, Id1) and their age-matched controls (Ly8, Ly10, Ly13, Id2, Id3) was done as a service at BGI (BGI Tech Solutions Co., Ltd., China). Bisulfite treatment was done with EZ DNA Methylation-Gold Kit (Zymo Research, CA, USA) for 300-400bp size-range fragments with methylated adapters in 5' and 3' ends. Sequencing was done with the HiSeq X-Ten platform using paired-end 150 base-pair read length.

  Dataset EGAD00001004779

• mRNA-seq data from ALL patients with NUP214::ABL1 disease

  Dataset contains mRNA-sequencing data (fastq files) from 2 patients with NUP214::ABL1 disease (one T-ALL and one B-ALL patient). Patient samples (peripheral blood or bone marrow) were taken at diagnosis (B-ALL) or relapse (T-ALL). mRNA was extracted from lymphoblasts and underwent paired-end sequencing (75bp) using the Illumina NextSeq platform. Transcriptomic data was used for identification of gene fusions, SNVs and InDels. mRNA-seq data for an additional B-ALL patient with NUP214::ABL1 disease utilised in this study has already been published (EGAS00001006460).

  Dataset EGAD50000001395

• Bladder Cancer Organoids as a Functional System to Model Different Disease Stages and Therapy Response

  The study 'Bladder Cancer Organoids as a Functional System to Model Different Disease Stages and Therapy Response' reports the genomic characterization at the single allele level upon Whole Exome Sequencing of Bladder Cancer (BLCa) Patient Tumor (PT) samples obtained from 18 patients and corresponding Patient Derived Organoids (PDOs), implementing the SPICE pipeline (Ciani Y et al). Our cohort included 21 PTs and 22 PDOs. In this study, we generated a unique biobank resource of BLCa organoids that encompasses a broad spectrum of disease stages. We demonstrated that PDOs retain cancer heterogeneity and mutational burden. Furthermore, we observed that BLCa PDO can have different morphologies which may reflect tumor-specific features. Indeed, a solid morphology may be associated with more advanced tumors, as supported by the high PDO genomic burden and high primary tumor stage. Moreover, with our drug screening pipeline, we demonstrated the clinical relevance and feasibility of using a BLCa PDO-based drug assay for comparing the standard of care (SOC) drugs with compounds not currently in use for BLCa. Response to SOC was highly heterogeneous with approximately 50% of non-muscle invasive bladder cancer and 33% of muscle invasive bladder cancer PDOs showing no or minimal response. The most effective targeted therapy was lapatinib. We used an integrative analysis of drug response profiles with matched PDO genomic analysis to determine enrichment thresholds for candidate markers of therapy resistance and sensitivity. Importantly, by assessing the clinical history of longitudinally sampled cases, the clonal evolution of the disease could be determined and matched with PDO drug response profiles.

  Study phs003149

• Recurrent DNMT3B gene rearrangements are associated with unfavorable outcome in dicentric (9;20)-positive pediatric BCP-ALL

  We provide clinical data sets of Array CGH, targeted RNA-seq, total RNA-seq, whole genome bisulfite (WGBS) and whole genome DNA sequencing (WGS) obtained from bone marrow (BM) or peripheral blood (PB) mononuclear cells of 57 pediatric patients with dicentric chromosome dic(9;20) positive Acute lymphocytic leukemia (ALL), from which in 6 cases DNMT3B gene rearrangement was identified. This data is complemented by total RNA-seq and WGBS of samples from 4 additional ALL patients with a t(12;21) translocation and ETV6-RUNX1 gene fusion. DNA was isolated from BM or PB B-lymphocytes using Qiagen QIAamp DNA Blood Midi Kit to perform i) Array CGH of 58 dic(9;20) positive samples by hybridizing 500ng DNA using a Agilent 400K SurePrint G3 Custom CGH Human Genome Microarray (e-Array design 84704) ii) WGBS of 6 DNMT3B rearrangement positive samples and 4 ETV6-RUNX1 positive samples using Tecan TrueMethyl oxBS-Seq module for library preparation and Illumina NovaSeq 6000 platform to run 2x151 cycles iii) WGS of DNMT3B rearrangement positive samples using Illumina Lotus DNA Library Prep Kit followed by sequencing running 2x160 cycles on an Illumina NovaSeq 6000 platform. RNA was isolated from PB B-lymphocytes using the PerkinElmer Chemagic 360 instrument, followed by i) targeted RNA-seq of 56 dic(9;20) positive samples prepared using Illumina TruSight RNA Pan-Cancer Panel and sequenced on an Illumina MiSeq platform running 2x75 cycles ii) total RNA-seq of 6 DNMT3B rearrangement positive samples and 4 ETV6-RUNX1 positive samples utilizing TruSeq Stranded Total RNA Library Prep Gold kit and running 2x100 cycles on an Illumina NovaSeq 6000 platform.

  Dataset EGAD00001011122

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW4_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW4_M

  Dataset EGAD00001001834

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW49_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW49_M

  Dataset EGAD00001001831

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW49_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW49_C

  Dataset EGAD00001001829

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW47_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW47_M

  Dataset EGAD00001001828

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW47_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW47_F

  Dataset EGAD00001001827

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW47_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW47_C

  Dataset EGAD00001001826

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW46_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW46_M

  Dataset EGAD00001001825

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW46_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW46_F

  Dataset EGAD00001001824

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW46_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW46_C

  Dataset EGAD00001001823

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW3_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW3_M

  Dataset EGAD00001001822

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW3_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW3_F

  Dataset EGAD00001001821

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW3_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW3_C

  Dataset EGAD00001001820

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW38_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW38_M

  Dataset EGAD00001001819

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW38_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW38_F

  Dataset EGAD00001001818

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW38_C

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW38_C

  Dataset EGAD00001001817

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: MW32_M

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: MW32_M

  Dataset EGAD00001001816

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB33_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB33_F

  Dataset EGAD00001001731

• WGS of 50 Intellectual Disability (ID) trios with Complete Genomics Sequencing. Dataset of sample: BvB31_F

  50 trios were whole genome sequenced with Complete Genomics to a depth of 80x. For each trio the child was affected with severe ID, and the parents were unaffected. All trios were negative for array, targeted gene and whole exome screening. Dataset consists of sample: BvB31_F

  Dataset EGAD00001001728