1248 results for ""halted+early"+"slower-than-anticipated+enrollment"+hospitalized+oral+adaptive+platform"

in 10.70 milliseconds.

• National Eye Institute Glaucoma Human Genetics Collaboration (NEIGHBOR) Consortium Glaucoma Genome-Wide Association Study: Whole Exome Resequencing in Glaucoma

  This is a study of primary open angle glaucoma (POAG) conducted through exome sequencing of cases and comparison of variant frequencies with general population frequencies available in dbGAP and controlled for sequencing platform artifact to minimize false positives. POAG is an intraocular pressure (IOP) related progressive optic neuropathy that ultimately leads to blindness. This study builds upon the efforts of an on-going collaborative consortium that studied 2,170 POAG cases and 2,347 controls with a unified definition of POAG (the NEIGHBOR consortium: NEI Glaucoma Human genetic collaBORation). The case definition for NEIGHBOR was harmonized with an additional 976 cases and 1140 controls from the NHGRI supported GENEVA (gene-environment) study of glaucoma (GLAUGEN) (NIH/NHGRI U01HG004728, Pasquale PI). In NEIGHBOR, cases and controls were recruited from ophthalmology clinics and were examined by ophthalmologists. For cases, the clinical exam included intraocular pressure measurements, optic nerve assessment and visual field evaluation. Controls had no family history of glaucoma, normal intraocular pressure and normal optic nerves. Cases and controls were also drawn from two clinical trial populations: Advanced Glaucoma Intervention Study (AGIS, NEI U10EY006827, D. Gaasterland PI) and Collaborative Initial Glaucoma Treatment Study (CIGTS, NEI U10 EY009149, P. Lichter PI). The Glaucoma Exome Sequencing study has one Principal Investigator: Theresa Gaasterland (UCSD) and two Co-Investigators: Robert Weinreb, MD, and Kang Zhang, MD, PhD, all of whom are part of the NEIGHBOR, subsequently NEIGHBORHOOD, consortium, which in turn has two Co-Principal Investigators: J. Wiggs (Harvard, MEEI), and M. Hauser (Duke). NEIGHBOR collaborators who contributed samples and/or expertise to the Glaucoma Exome Sequencing study included the following: Harvard Medical School (Massachusetts Eye and Ear Infirmary) (J. Wiggs, L. Pasquale); Duke University Medical Center (M. Hauser, E. Hauser, R. Allingham, S. Schmidt); University of Michigan (J. Richards, S. Moroi, P. Lichter); University of Miami (M. Pericak-Vance, R. Lee, D. Budenz); Vanderbilt University (J. Haines); University of California San Diego (K. Zhang, R. Weinreb; T. Gaasterland); University of Pittsburgh (J. Schuman, G. Wollenstein); University of West Virginia (A. Realini, J. Charlton, S. Zareparsi); Johns Hopkins University (D. Friedman); Stanford University (D. Vollrath, K. Singh), Eye Doctors of Washington (D. Gaasterland), Marshfield Clinic (Cathy McCarty). Hemin Chin serves as the NEI Staff Collaborator. This national collaborative study is supported by multiple NIH grants: NEI R01 EY015543 (Allingham); NEI U10 EY006827 (D. Gaasterland); NHLBI R01 HL073389 (E. Hauser); NEI R01 EY13315 (M. Hauser); NEI U10 EY009149 (Lichter); NEI R01 EY015473 (Pasquale); NEI U10 EY012118 (Pericak-Vance); NEI R03 EY015682 (Realini); NEI R01 EY011671 (Richards); NEI R01 EY09580 (Richards); NEI R01 EY013178 (Schuman); NEI R01 EY015872 (Wiggs); NEI R01 EY009847 (Wiggs); NEI R01 EY010886 (Wiggs); NEI R01 EY144428 (Zhang); NEI R01 EY144448 (Zhang); NEI R01 EY18660 (Zhang). Funding support for genotyping through exome sequencing, which was performed at the University of California, San Diego, was provided by the National Eye Institute (RC2 EY020678-01).

  Study phs000558

• NHLBI TOPMed: Pediatric Cardiac Genomics Consortium (PCGC)'s Congenital Heart Disease Biobank

  The Pediatric Cardiovascular Genetics Consortium (PCGC) proposes to define genetic causes for congenital heart defects (CHD) as part of the TOPMed Program are the most common form of heart disease in childhood and are also the most prevalent form of birth defects, occurring in 2-3% of live births. The PCGC has recruited and clinically characterized = 10,000 CHD probands and parents (CHD trios). From whole exome sequencing (WES) of >2800 CHD trios, we identified a substantial enrichment of damaging de novo mutations in genes important for cardiogenesis, particularly implicating histone modifier enzyme gene defects. Analysis of whole genome sequencing (WGS) of 350 probands with CHD unexplained by WES and their parents preliminarily implicated de novo mutations in enhancers of genes previously shown to cause CHD in mouse knock out models. Sequencing of RNA (RNAseq) from discarded cardiac tissues from CHD probands has revealed likely causal allele-specific expression (ASE) as well as biallelic loss of expression (LOE). We have also discovered de novo epimutations, differentially methylated regions (DMRs), some with underlying de novo DNA variation, that are detectable in peripheral blood leukocytes and appear to underlie 10% of CHD. Of note, these assorted 'omic' approaches have enabled one another, both for attributing causality and assessing functional impact. Based on these extensive preliminary data, we hypothesize that PCGC probands with uninformative exomic analyses (WES-negative) harbor de novo genetic and/or epigenetic mutations in critical regulatory elements that participate in developmental expression of cardiac genes. To identify these etiologies, we propose analyses of WGS in 1000 WES-negative CHD trios, prioritizing those with probands with banked CHD tissues (n=78), one damaging variant in a recessive CHD gene, and older fathers (age>45). We also request WGS for 230 probands, for whom we have cardiac tissues but not parental DNAs. We request RNAseq for 308 cardiac tissues. For DNA methylation, which TOPMed will offer through the Illumina 850k array platform, we are requesting analysis of DNAs from peripheral blood leukocytes for all probands for whom WGS will be performed (1000 from trios, 230 singletons) as well as DNAs from cardiac tissues (n=308) to pair with the WGS, RNAseq and blood DNA methylation data. We will use existing resources and capabilities of the PCGC to confirm relevant mutations and those of its companion consortium in the Bench to Bassinet Program, the Cardiovascular Development Consortium, to inform analyses of non-coding mutations as well as to perform confirmatory functional genomics studies using cell and animal models. We expect that the studies resulting from data generated through TOPMed will provide novel insights into the molecular basis for CHD and fundamental knowledge about genes and pathways involved in cardiac development. Aside from being relevant to CHD, we anticipate that our findings will inform the understanding of later-onset cardiovascular diseases, including some arising in adulthood.

  Study phs001735

• Maintenance of Brain Tumor Profile on Organotypic Brain Slice Culture (OBSC)

  We used uncultured tumor resection tissue from an NF2 mutation-driven grade II meningioma (MG-II) to investigate whether tumors engrafted onto organotypic brain slice cultures (OBSCs) maintained the genetic profile of the parent tumor. We prepared three groups of samples for whole exome sequencing (WES) from the same pool of dissociated MG-II tissue. Group HT is the original uncultured human tumor tissue (n = 3). Group CL is the parent tumor tissue expanded in standard in vitro cell culture until the minimum number of cells required for WES had grown. Because of initial cell loss and subsequent clonal expansion, this required six passages over the span of approximately one month (n = 3). Group BSHT is the uncultured tumor resection tissue engrafted onto OBSCs and subsequently dissected from the OBSCs at the conclusion of our standardized assay length (4 days; n = 4). WES analysis showed that tumor tissue engrafted onto OBSCs maintained a significant genetic resemblance to the parent tumor, while tumor tissue expanded in vitro displayed a distinctly different profile. Furthermore, the mutational profiles of all four BSHT biological replicates were markedly similar, indicating that each OBSC-engrafted tumor indeed contained a representative sample of the original patient tumor. A closer look at the hallmark NF2 mutations existing within all samples revealed that while all samples from the original tumor (HT1-3) and the OBSC-engrafted tumor (BSHT1-4) maintain the frame shift deletion at V24, this mutation was lost in all samples expanded in vitro (CL1-3) and replaced by mutations in other areas. Together, this data suggests that the rapid assay design and tumor-accommodating niche of our OBSC platform enables effective maintenance of the original patient tumor profile. The BBsplit algorithm from the BBtools suite was run on all samples to eliminate rat DNA contamination in the BSHT samples as well as to account for any biases that may result as a part of that process. Only the reads that were binned to the human reference were used for subsequent analysis. Reads were then mapped to the GRCh38 version of the human genome with BWA v0.7.17 and realigned together with ABRA2 v.23. Quality control was implemented using the GATK/Picard v4.1.7.0 toolkit. Somatic variants were called for each sample using the MuTect2 algorithm v4.1.7.0. Variants were merged into a single cohort variant call file and then converted to MAF via vcf2maf v1.6.21 tool. Variants were annotated using VEP v87. To identify mutations with potentially high biological impact, multiple filtering steps were applied to somatic mutation calling. First, we selected only the somatic variants that passed all filters from the MuTect2 FilterMutectCalls algorithm and second, only high/moderate impact (change coding) variants as defined by the VEP annotation were further analyzed. Over 1900 single nucleotide variants (SNVs) were detected across all samples. Figures that summarized the results were generated using maftools.

  Study phs003268

• INCLUDE Data Hub: NDA GUIDs for Down Syndrome Research

  The INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) Project is an NIH-wide collaboration that seeks to improve health and quality of life for people with Down syndrome. The INCLUDE Project Data Coordinating Center (DCC) and partners created the INCLUDE Data Hub, a centralized data resource that allows access to large-scale clinical and multi-omics datasets specific to Down syndrome and supports collaborative, cloud-based analysis to accelerate scientific discoveries related to Down syndrome and its co-occurring conditions. The INCLUDE Data Hub includes an innovative researcher data repository, a portal, and a cloud-based platform for data analysis. The portal provides access to de-identified demographics and clinical metadata, as well as multi-omics datasets, including whole genome sequences, transcriptomes, proteomes, and metabolomes. The INCLUDE Data Hub uses a 'registered access' model, whereby users can access the portal and associated data after agreeing to terms of use. From the Data Hub, users can also reach 'controlled access' data (e.g., whole genome sequences) only for those studies for which they have obtained approval by an NIH Data Access Committee through the corresponding dbGaP study. A thoughtful linkage strategy to identify duplicate participants across studies enables researchers to merge multiple datasets and data types to create richer datasets and reduce costly redundancies in large-scale data generation (e.g., -omics) while mitigating potential risks introduced by linkage.Based on recommendations from the NICHD Office of Data Science and Sharing, which completed a comprehensive assessment of existing Privacy Protecting Record Linkage (PPRL) and associated data governance solutions, the INCLUDE DCC and the associated NIH Steering Committee have implemented NIMH Data Archive Global Unique Identifiers (NDA GUIDs) in the INCLUDE Data Hub. The NDA GUID Tool is a government-owned and operated PPRL technology that enables researchers to link data from multiple sources to the same individual without revealing personally identifiable information (PII). With the NDA GUID Tool, the same participant information will return the same GUID whenever or wherever it is entered. The GUID itself is not personally identifiable information or protected health information. Sharing data with NDA GUIDs allows approved researchers to link together data on a single participant, even if the data were collected at different locations or through different studies or are distributed from different data repositories. This dbGaP study enables researchers to access an INCLUDE GUID Mapping File that includes all available NDA GUIDs and their associations to study-specific Participant IDs across studies in the INCLUDE Data Hub. This dbGaP study only distributes NDA GUIDs associated with the INCLUDE Data Hub datasets. To access the associated controlled-access datasets, add the corresponding dbGaP studies to your Data Access Request. A list of these studies is available on the INCLUDE Data Hub Portal: https://portal.includedcc.org/studies.

  Study phs003678

• Genome-Wide Association of Type 2 Diabetes in Africans: The AADM Study

  The Africa America Diabetes Mellitus (AADM) study is a genetic epidemiological study of type 2 diabetes in Sub-Saharan Africa. Study participants were enrolled through university medical centers in Nigeria, Ghana, and Kenya. Ethical approval for the study was obtained from the Institutional Review Board (IRB) of each participating institution. All subjects provided written informed consent for the collection of samples and subsequent analysis. The case definition of type 2 diabetes was based on the American Diabetes Association (ADA) criteria. After providing informed consent, participants underwent the same enrollment procedures, which included collection of demographic information, medical history, clinical examination and a blood draw. Genome-wide SNP genotyping was done on either the Axiom™ PanAFR SNP array (n=1,808) or the Multi-Ethnic Global Array (MEGA) (n=3,423). After appropriate quality control, in silico imputation was done using the African Genome Resources Haplotype Reference Panel (at the Sanger Imputation Service). Imputed genotypes were filtered for variants with minor allele frequency (MAF)≥ 0.01 and information score (info) ≥ 0.3 for genetic association analysis. Genome-wide association analysis between type 2 diabetes and the imputed genotype dosages was done using a generalized linear mixed model, which adjusted for age, gender, body mass index, the genetic relatedness matrix and the first three principal components (PCs) of the genotypes.Metabolomics profiling of plasma samples of type 2 diabetes (T2D) cases and controls in Nigerians (West Africa) was done in the AADM Study. Plasma metabolites were measured in a total of 580 individuals (N=310 for the discovery phase and N=270 for the replication stage) using the global/untargeted approach on the Metabolon platform and following the manufacturer's standard operation protocols. The analytic methods are described in detail in Doumatey et al. [Genome Med 2024]. The measured metabolites level represented by peak areas are relative values. The peak area data were batch-normalized to remove the instrument batch effects (batch variability) and the batch-normalized data correspond to the median-scaled raw data. For each identified metabolite, the minimum value across all batches in the batch-normalized was imputed for the missing values. The batch-normalized and imputed data are natural log-transformed and consist of 1116 metabolites for the discovery cohort and 1071 metabolites for the replication. Welch's two-sample t-test on the log-transformed data was used to identify metabolites differentially expressed between T2D cases and controls . All other statistical analyses conducted on both the replication and discovery cohorts used the log-transformed data. To merge the discovery and replication, the same quality control samples (bridge samples) were run with each batch of the experimental samples in both cohorts and used to correct for additional variability and uniformize the procedures. The resulting merged data is the QC-normalized and imputed data that contains only metabolites that were common to both cohorts and successfully bridged for all batches (n= 891 metabolites).

  Study phs001844

• Kids First: Whole Genome Sequencing in Structural Defects of the Neural Tube

  Myelomeningocele (aka meningomyelocele, MM) is the most severe form of spina bifida, a neural tube defect (NTD) in humans and the most common CNS birth defect. MM is considered a genetically complex disease, and occurs in 3.72/10,000 live US birth, and is partly preventable with prenatal folate, but the genetic basis and the mechanisms by which folate work to reduce disease incidence remain obscure. MM is associated nearly uniformly with prenatal hydrocephalus and the Arnold-Chiari malformation, as well as paraplegia and lifelong neuromotor disability. The genes for several rare syndromic forms of NTDs are known, but the causes for the majority with sporadic MM remain unknown. Despite the importance of MM, most previous research has been limited to targeted sequencing and association studies of folate metabolism genes, or very small-scale exome sequencing. We hypothesize that de novo mutations (DNMs) produce likely gene disrupting (LGD) events that contribution to MM risk. Using conservative estimates of between 50-100 recurrently mutated discoverable genes contributing to risk, and our preliminary data demonstrating an excess of LGD DNMs in MM compared with control individuals, we estimate that with a cohort size of 1000 trios, we should uncover between 5-20 new recurrently mutated genes underlying MM, with minimal false-discovery. With this in mind, we formed the Spina Bifida Sequencing Consortium, and established a platform for data and sample sharing. Preliminary analysis of our first batch of 100 trios analyzed by WGS from GMKF suggests a wealth of important gene mutations. We have embarked on a new recruitment effort of an additional cohort of 400 new simplex MM trios, in collaboration with the US Spina Bifida Association, consented trios to allow for data sharing, and have performed detailed sample quality control. We also have preliminary data that use of dried bloodspot DNA from trios performs comparably to whole blood DNA, so will be happy to swap saliva for bloodspot recruitment at NIH's preference. This cohort is now half-way assembled, with the remaining cohort to be ascertained in the next 6 months. We have established a workflow for de novo SNP/INDEL/SV detection from WGS and have ample computer storage and nodes to see the project to completion. We also plan to continue recruitment into the future with the goal of 2000 trios in the next 5 years. We propose a detailed bioinformatics workflow to identify gene mutations within a statistical framework, considering detailed scRNA expression profiling from developing mammalian neural tube, and have developed a robust functional validation workflow using Xenopus and mouse gene targeting. Our project has the potential to uncover a host of causes for this most common of the CNS birth defects, paving the way for future breakthroughs in detection, treatment, and prevention.

  Study phs002591

• What is metadata?

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

  Documentation submission/metadata/what-is-metadata

• WES data (cfTrack study) from NSCLC patients and OC patients

  We also collected samples from 8 NSCLC patients and 4 ovarian cancer patients and. For all 8 NSCLC patients, a tumor biopsy sample, a WBC sample, and three plasma samples were collected. For all 4 ovarian cancer patients, a WBC sample and two serum samples were collected. We collected tumor tissue sample from one ovarian cancer patient (OV4). The cfDNA was extracted from their plasma samples using the QIAamp circulating nucleic acid kit from QIAGEN (Germantown, MD). For serum cfDNA, ampure XP beads size selection was further performed to eliminate gDNA contamination. In brief, 0.5 volume of beads were first added to the cfDNA samples. After incubation, the supernatant was transferred to a new tube and an additional 2.0 volume of beads were added. After 80% ethanol wash, cfDNA was eluted from the beads. FA assays (Agilent Technologies) were performed to rule out the contamination of gDNA in the size selected samples. The cfDNA WES library of all patients and the genomic DNA WES library of the 4 ovarian cancer patients were constructed with the SureSelect XT HS kit from Agilent Technologies (Santa Clara, CA) according to the manufacturer’s protocol. In brief, 10ng of cfDNA was used as input material. After end repair/dA-tailing of cfDNA, the adaptor was ligated. The ligation product was purified with Ampure XP beads (Beckman-Coulter, Atlanta, GA) and the adaptor-ligated library was amplified with index primer in 10-cycle PCR. The amplified library was purified again with Ampure XP beads, and the amount of amplified DNA was measured using the Qubit 1xdsDNA HS assay kit (ThermoFisher, Waltham, MA). 700-1000 ng of DNA sample was hybridized to the capture library and pulled down by streptavidin-coated beads. After washing the beads, the DNA library captured on the beads was re-amplified with 10-cycle PCR. The final libraries were purified by Ampure XP beads. The library concentration was measured by Qubit, and the quality was further examined with Agilent Bioanalyzer before the final step of 2x150bp paired-end sequencing at an average coverage of 200. Whole-exome capture libraries of genomic DNA from the 8 NSCLC patients were constructed via Roche SeqCap EZ Exome V6 (Roche). Enriched exome libraries were sequenced on the Illumina HiSeq 3000 platform (Illumina) to generate 2x100bp paired-end reads at an average coverage of 200.

  Dataset EGAD00001008454

• National Cancer Institute (NCI) Genome Wide Association Study (GWAS) of Lung Cancer in Never Smokers

  A genomewide study of lung cancer in never smokers Abstract and specific aims In the United States, lung cancer incidence and mortality rates have been steadily declining over the past decade, following decline in the prevalence of tobacco smoking. However, lung cancer remains the leading cause of cancer death, killing more patients than breast, colon, and prostate cancers combined. Although tobacco smoke is the predominant risk factor for development of lung cancer, some patients develop the disease without a history of tobacco smoking. About 10 - 15% of all lung cancers occur in lilfetime never smokers. This figure will increase as the proportion of never smokers increases in the population. Even at present rates, lung cancer in never smokers, if considered a separate disease, is 6th to 8th top cause of cancer death. The growing number of never smokers in the USA and other countries emphasizes the importance of understanding the epidemiology and biology underlying lung cancer in this group. Genetic polymorphisms associated with the risk of lung cancer in never smokers are expected to overlap with those associated with the risk of lung cancer in ever smokers only partially. Epidemiological, molecular and clinical data suggest that molecular mechanisms of LC may differ in smokers and non-smokers, implying that lung cancer in never smokers is a different disease compared to the lung cancer in smokers. One can expect that there should be stronger genetic component in the control lung cancer in never smokers because effects of the genetic factors in never smokers are unmasked by the lack of tobacco smoke exposure. The genetic epidemiology of lung cancer in never smokers has not been well explored, largely because of difficulties in accruing the needed sample size for association studies. We propose a multicenter (total 14 sites from the US and Europe) genomewide association study of lung cancer in never smokers with the following specific aims: Aim 1: To identify candidate SNPs influencing risk for lung cancer in never smokers using Discovery sample. In the Discovery phase we will genotype 1256 Caucasian cases and 1365 age- and gender-matched never smoker controls using the Illumina Human660W-Quad platform. In addition, we will include in the analysis 284 cases and 175 matched controls already genotyped on the 610Quad platform. In this phase we will only include the study sites that have collected blood specimens (MDACC, Mayo Clinic, Karmanos Cancer Institute, The University of Liverpool Cancer research Centre, Institute of Cancer Research in Sutton, and Lunenfeld Research Institute in Toronto, Canada). All the samples will be sent to the independent lab for genotyping, to reduce site-specific technical artifacts. The final sample will consist of 1540 cases and 1540 controls matched by study site. Aim 2: To perform the second phase (validation) analysis of significant SNPs identified in aim 1 using an independent set of cases and controls. SNPs associated with risk at the significance level of 0.01 or below in the discovery set will be included in the replication phase. The proposed threshold guarantees an adequate power to retain SNPs with the typical effect size of 1.3. We plan to carry 6000-7000 SNPs for validation. The independent replication set will include 800 cases and 800 controls, mostly from sites that collected tissue (Mayo Clinic, Karmanos Cancer Institute, UT Southwestern) or buccal specimens (UCLA), but also blood samples (Imperial College London, University of Pennsylvana, German Cancer Research Center, Heidelberg, National Research Center for Environment and Health, Neuherberg, Carmel Medical Center, Haifa). We will then perform a joint analysis to test the significance of the SNPs identified in the first stage using a stringent critical p-value of 10-7. There will be 2340 cases and 2340 controls in the joint set. Based on our experience with GWAS in smokers and assuming that genetic component in lung cancer risk in never smokers can be higher than genetic component in smokers, we expect to identify about 5-10 candidate regions associated with lung cancer risk in never smokers. Aim 3: To identify and explore pathways associated with the risk of lung cancer in never smokers. Results of the number of studies on the molecular mechanisms and drug response suggest that lung cancer in never smokers is a different disease and different pathways will be associated with lung cancer risk in non-smokers and smokers. To identify pathways and molecular functions associated with lung cancer risk in never smokers we will apply Ingenuity and DAVID bioinformatics tools. We will use at least 300 top candidate genes identified in joint and discovery analysis. The reason why we select rather large number of candidate genes for functional annotation is two-fold: 1. Both algorithms are looking for enrichment of pathways and function by most significant genes and they produce statistically robust results only when number of genes is relatively high. 2. Despite the fact that this study will be largest possible for never smokers we still are underpowered to detect SNPs with relatively small effect size. But though those SNPs will not reach genome wide level of significance they will tend to be on the top of the list. In other words genes from the gray zone (significant on individual level and non-significant for genome wide level) are expected to be enriched by true discoveries. True discoveries are likely to be associated with limited number of pathways / functions while false positives are expected to be uniformly distributed across functions and pathways. Therefore significant clustering of the gene to a given function will suggest that that those genes are true discoveries. This is the first GWAS aiming at identifying the genetic control of susceptibility to lung cancer in Caucasian never smokers. We will combine the available resources from the multiple sites to achieve the sample size sufficient for this study. The study will identify genetic architecture of the predisposition to the lung cancer in never smokers.

  Study phs000634

• Genome-Wide Predictors of Treatment-Related Toxicities in SWOG S0221 Trial

  When undergoing treatment for breast cancer, many women experience severe side effects, some of which result in treatment-related death and some that can persist for years. Little is understood regarding factors that may predict drug toxicities. Pharmacogenetics, the investigation of variants in candidate genes in drug metabolism pathways, has been used to determine susceptibility to treatment-related toxicities, as well as to cancer recurrence. Although there have been some strong and important findings using this approach, such as identification of single nucleotide polymorphisms (SNPs) that predispose to side effects associated with thiopurines and irinotecan, there has been less progress in assessment of genetic variants that predispose to toxicities resulting from the multi-drug regimen commonly used to treat breast cancer, anthracyclines (A), cyclophosphamide (C), and taxanes (T). These difficulties in identification of key gene variants may be due to the complex metabolic pathways of these drugs, the lack of rate limiting enzymes in the processes, or the limitations of single SNP analysis, rather than capturing all of the variability across the genes. In addition to drug metabolism pathways, however, there may be a number of constitutional factors or other processes that affect damage to normal tissues in the course of chemotherapy, some known or hypothesized, such as DNA repair and oxidative stress pathways, and others not yet discovered. However, there have been no clear candidate genes that account for a large part of the variation in drug or treatment response, and there are likely important genes that influence sensitivity of cells to chemotherapy through unknown pathways which have not yet been identified or hypothesized. The present technological capabilities to screen the entire genome for variants that discriminate populations allows the opportunity to identify these as yet unknown pathways, and open the doors to exciting new avenues of research into mechanisms that had not been previously considered. We conducted a genome-wide scan (GWAS) in a clinical trial (S0221), in which women with high risk breast cancer were treated with different dosing schedules of C, A and T. Blood specimens were collected and banked, and DNA extracted for genotyping on the Illumina OMNI 1M platform. The GWAS data were used to examine genetic variants significantly associated with grades 3 and 4 toxicities, including peripheral neuropathy recorded during the T segment. In S0221, toxicities were graded according to the NCI Common Toxicity Criteria for Adverse Events (CTCAE). The neurotoxicity is predominantly a distal sensory neuropathy, which is characterized by pain, numbness, tingling, and reduced functional capacity in the extremities. Other symptoms include parasthesias, ataxia, impaired vibration and joint position sense, and loss of tendon reflexes. By using a GWAS approach, it is likely that important pathways not previously considered can be revealed as important in susceptibility to treatment-related toxicities, identifying those at greatest risk for alternate drugs or dose reduction, and opening new areas of research for prevention of taxanes-related neuropathy among patients receiving chemotherapy for breast cancer.

  Study phs001428