Utilizing the rare and unique patient samples provided by Pattern Medicine, LLC, MD Anderson will molecularly characterize rare cancer samples, and potentially generate a collection of preclinical models to enable identification of novel therapeutic targets and concepts, as well as evaluation of innovative treatment strategies.
The goal was to complete whole exome sequencing (WES) and RNA sequencing (RNA-Seq) on patient derived xenograft samples. Cancer in children is rare with approximately 15,700 new cases diagnosed annually in children 21 years or younger in the U.S. Through use of multimodality therapy (surgery, radiation therapy, and aggressive chemotherapy), 70% of patients will be 'cured' of their disease, and 5-year Event-Free Survival (EFS) exceeds 80%. Consequently, the number of patients that can be enrolled in phase I/II clinical trials is small, and most patients will have been extensively treated, hence drug/radiation resistant. Thus, preclinical studies that accurately translate into effective clinical therapy are an essential component of pediatric drug development. Our group has contributed to studies in the Pediatric Preclinical Testing Program (PPTP) and Pediatric Preclinical Testing Consortium (PPTC) that have led to clinical studies through Children's Oncology Group (COG). Of importance, we have developed and characterized over 330 Patient Derived Xenografts (PDX), developed from tumors both at diagnosis and relapse, that can be used to facilitate pediatric drug development as directed by FDA under the Research to Accelerate Cures and Equity for Children Act (RACE for Children Act).
Study ID: CALGB-80802NCT Number: NCT01015833 Alliance for Clinical Trials in OncologySubmitted in collaboration with the NCTN/NCORP Data ArchiveTrial Title: NCTN: Phase III Randomized Study of Sorafenib plus Doxorubicin versus Sorafenib in Patients with Advanced Hepatocellular Carcinoma (HCC) (CALGB-80802)Trial Description: This randomized phase III trial studies sorafenib tosylate and doxorubicin hydrochloride to see how well they work compared with sorafenib tosylate alone in treating patients with liver cancer that has spread to nearby tissue or lymph nodes or has spread to other places in the body. Sorafenib tosylate may stop the growth of tumor cells by blocking some of the enzymes needed for cell growth and by blocking blood flow to the tumor. Drugs used in chemotherapy, such as doxorubicin hydrochloride, work in different ways to stop the growth of tumor cells, either by killing the cells or by stopping them from dividing. It is not yet known whether giving sorafenib tosylate together with doxorubicin hydrochloride is more effective than sorafenib tosylate alone in treating liver cancer. Data presented in the following publication(s): PMID 31486832 Disease type(s): Gastrointestinal Neoplasm - Liver and Hepatobiliary CancerClinical Dataset(s) included:NCT01015833-D1 (PMID 31486832): Dataset NCT01015833-D1 is the only dataset associated with PubMed ID 31486832. This dataset contains baseline characteristics, treatment arm and number of cycles, treatment discontinuation reasons, hepatitis status, toxicity, and survival data.You are encouraged to view the PDF data dictionaries found in the “Documents” tab of the dbGaP study page, which may contain additional information regarding the data or the associated publication(s).
Pancreatic ductal adenocarcinoma (PDAC) contains a desmoplastic stroma that promotes the aggressive biology of this disease. While the varied mechanisms by which fibrillar collagens contribute to PDAC have been defined, little is known about the contribution of transmembrane collagens. Among this class of collagens, Collagen XVII uniquely contains an extensive intracellular domain that connects intracellular cytoplasmic components with the extracellular matrix. Collagen XVII is best characterized as the defining component of the type I hemidesmosome, an anchoring complex that attaches epithelial cells to the underlying basement membrane. In this study, we characterize the expression of collagen XVII in PDAC and the mechanisms by which it contributes to PDAC biology. We demonstrate that crosstalk between PDAC cells and cancer associated fibroblasts drive collagen XVII expression in vitro and xenograft tumor models in vivo. Genetic depletion of collagen XVII in low-passage patient derived cell lines diminishes their metastatic potential and ability to form tumors in mice. Whole transcriptome sequencing of tumors formed by cells with diminished collagen XVII levels revealed dramatically lower levels of PIK3R5, a regulatory subunit of class IB phosphatidylinositol-3-kinase (PI3K) that interacts with G protein-coupled receptors. Consistent with this, collagen XVII-deficient tumors exhibited lower levels of phosphorylated AKT, a downstream effector of PI3K. The studies highlight the importance of collagen XVII signaling in PDAC and define a novel mechanism by which collagen XVII mediates activation of PI3K.
DNA was isolated from pre-treatment and mid-treatment plasma samples and matched germline samples from patients undergoing chemoradiation therapy for stage IIB-IIIB non-small cell lung cancer. Sequencing libraries were hybrid captured using custom selector pools designed to target genes frequently mutated in non-small cell lung cancer prior to ultra-deep next generation sequencing.
Understanding the constituents of the pancreatic ductal adenocarcinoma (PDAC) microenvironment may illuminate more efficacious targets for treating this aggressive malignancy. However, the compositional changes occurring in untreated and treated PDAC relative to both normal and inflamed pancreas have not been well characterized. To provide a more comprehensive understanding of the PDAC tumor microenvironment relative to the non-cancerous pancreas, we performed single-cell expression profiling (10X Genomics Chromium) on samples of upfront resectable PDAC, FOLFIRINOX-based treated PDAC, chronic pancreatitis, and normal pancreas specimens from 19 patients. Spatial gene expression profiling (10X Genomics Visium) was performed to gain additional insight into cell populations. Our analysis revealed that fibroblasts are central mediators of cell-cell communication in both inflamed and malignant settings and defined unique roles for these cell populations in different disease contexts.
Data Access NOTE: Please refer to the "Authorized Access" section below for information about how access to the data from this accession differs from many other dbGaP accessions.Objectives: To compare response at one year post transplant between participants receiving dendritic cell/myeloma vaccine along with granulocyte macrophage colony-stimulating factor (GM-CSF) and lenalidomide maintenance therapy to those receiving lenalidomide maintenance therapy with or without GM-CSF.Background: The advent of combination biologic therapy for multiple myeloma (MM) has significantly improved patient outcomes. Induction therapy followed by consolidation with autologous hematopoietic stem cell transplantation (auto-HCT) prolongs progression-free survival in patients with MM. The addition of post-transplant lenalidomide maintenance therapy further extends the duration of remission. However, the vast majority of patients ultimately experience disease progression due to the emergence of resistant disease. The development of strategies to enhance the durability of post-transplant response remains a major unmet need in the field. Since MM is associated with immune suppression, the development of cancer vaccines is being investigated in MM with the goal of stimulating tumor-specific immunity. Dendritic cells (DC) are the most potent antigen-presenting cells that prominently express costimulatory signaling necessary for optimal T-cell activation, cross-presentation of antigen to reactive CD8 T cells, and the development of primary immunity. The BMT CTN developed a personalized cancer vaccine in which patient-derived tumor cells are fused with autologous monocyte-derived DCs. The BMT CTN-1401 study examined the efficacy of the DC/MM fusion vaccine in conjunction with lenalidomide maintenance therapy after auto-HCT, compared with maintenance alone.Participants: Participants with symptomatic MM were eligible if they were 18-70 years old, were candidates for auto-HCT, had a Karnofsky Performance status of ≥70%, had >20% plasma cells in the bone marrow aspirate differential A total of 140 participants were randomized. 68 participants were randomized to the vaccine arm, 37 to the lenalidomide plus GM-CSF arm, and 35 to the lenalidomide alone arm.Design: BMT CTN-1401 was a phase II, multicenter, open label trial. All participants underwent bone marrow aspiration following enrollment. Myeloma cells were isolated, preserved, and for participants randomized to the vaccine arm, used for vaccine generation. All participants received standard-of-care induction therapy and auto-HCT conditioned with melphalan. Participants were randomized approximately 2 months post-transplant. Participants were randomized in a 2:1:1 ratio to the vaccine arm (DC/MM vaccine, GM-CSF, plus lenalidomide), or one of two control arms (lenalidomide plus GM-CSF or lenalidomide alone). All participants began maintenance lenalidomide between day 90 and 100 post-transplant at a dose of 10 mg a day for 28 days of a 28-day cycle. On day 1 of cycles 2, 3, and 4 of lenalidomide maintenance, patients assigned to the vaccine arm underwent subcutaneous administration of the DC/MM fusion vaccine along with 100 μg GM-CSF administered subcutaneously adjacent to the vaccine site on days 1 to 4. Participants randomized to the control arms received either lenalidomide alone or lenalidomide with GM-CSF at the same dose and schedule as the vaccine arm.The primary outcome was a comparison of participants in complete response (CR)/stringent CR (sCR) at 1-year post-transplant between the vaccine and control arms. The primary immunologic endpoint of the study was a comparison of the percentage of circulating multiple myeloma–reactive T cells between participants in the vaccine and control arms. PFS (progression-free survival) and OS (overall survival) were secondary endpoints of the study.Conclusions: There was not a statistically significant difference in the percentage of participants achieving complete response 1-year post-transplant in the vaccine arm compared to the control arms.
Study ID: E5204NCT Number: NCT00303628 ECOG-ACRIN Cancer Research GroupSubmitted in collaboration with the NCTN/NCORP Data ArchiveTrial Title: NCTN: Randomized Phase III Study of Postoperative Oxaliplatin, 5-Fluorouracil and Leucovorin vs Oxaliplatin, 5-Fluorouracil, Leucovorin and Bevacizumab for Patients with Stage II or III Rectal Cancer Receiving Pre-operative Chemoradiation (E5204)Trial Description: Drugs used in chemotherapy, such as oxaliplatin, leucovorin, and fluorouracil, work in different ways to stop the growth of tumor cells, either by killing the cells or by stopping them from dividing. Monoclonal antibodies, such as bevacizumab, can block tumor growth in different ways. Some block the ability of tumor cells to grow and spread. Others find tumor cells and help kill them or carry tumor-killing substances to them. Bevacizumab may also stop the growth of tumor cells by blocking blood flow to the tumor. Giving more than one drug (combination chemotherapy) together with bevacizumab after surgery may kill any tumor cells that remain after surgery. It is not yet known whether oxaliplatin, leucovorin, and fluorouracil is more effective with or without bevacizumab in treating rectal cancer. This randomized phase III trial is studying combination chemotherapy to see how well it works with or without bevacizumab in treating patients who have had surgery for stage II or stage III rectal cancer. Clinical data presented in the following publication(s):PMID 31852811 Disease type(s): Gastrointestinal Neoplasm - Colorectal CancerClinical dataset(s) included:NCT00303628-D1 (PMID 31852811): There are five different datasets for PMID 31852811 from trial E5204. This dataset, NCT00303628-D1, contains baseline characteristics, stratification factors, surgery information, treatment, second primary cancer and efficacy data. NCT00303628-D2 (PMID 31852811): There are five different datasets for PMID 31852811 from trial E5204. This dataset, NCT00303628-D2, contains toxicity data. NCT00303628-D3 (PMID 31852811): There are five different datasets for PMID 31852811 from trial E5204. This dataset, NCT00303628-D3, contains PRO Bowel function/uniscale data. NCT00303628-D4 (PMID 31852811): There are five different datasets for PMID 31852811 from trial E5204. This dataset, NCT00303628-D4, contains PRO FACT-Diarrhea Data. NCT00303628-D5 (PMID 31852811): There are five different datasets for PMID 31852811 from trial E5204. This dataset, NCT00303628-D5, contains PRO FACT/GOG-Ntx Data. You are encouraged to view the PDF data dictionaries found in the “Documents” tab of the dbGaP study page, which may contain additional information regarding the data or the associated publication(s).
This dataset contains targeted next-generation sequencing data generated with the Illumina TruSight Oncology 500 (TSO500) assay from six gastrointestinal stromal tumors (GIST) and matched patient-derived xenograft (PDX) models established at Fox Chase Cancer Center (Philadelphia, PA). GIST are most commonly driven by activating mutations in KIT or PDGFRA, and this study focused on KIT-mutant tumors. All PDXs were derived from metastatic patient tumors previously treated with one or more tyrosine kinase inhibitors (TKIs). PDXs were maintained through series of passages and represents in vivo models of differential TKI sensitivity. The dataset represents a detailed comparison of mutational profiles between primary tumors and corresponding PDXs across passages, enabling studies of genomic stability, resistance mechanisms, and the evaluation of novel therapeutic strategies in GIST.
The eMERGE (electronic MEdical Records and GEnomics) network enrolled ~25,000 individuals not typically involved in research in a prospective cohort study across 10 sites. The network developed methods to return cross-ancestry polygenic risk scores, monogenic risks, family history, and clinical risk assessments via a genome-informed risk assessment (GIRA) report along with recommended actions. The GIRAs include summary care recommendations for 11 conditions, education pages, and clinical laboratory reports and was returned to participants, their providers, and their EHR. The network is examining the uptake of care recommendations after return of results.Children's Hospital of Philadelphia (CHOP)The Center for Applied Genomics (CAG) is a specialized Center of Emphasis at the Children's Hospital of Philadelphia (CHOP) with the primary goal of translating basic research findings to medical innovations. The mission of CAG is to develop new and better ways to diagnose and treat children affected by rare and complex medical disorders. We aim to discover genetic causes for the most prevalent diseases of childhood including ADHD, asthma, autism, diabetes, epilepsy, obesity, schizophrenia, pediatric cancer, and a range of rare diseases. Ultimately, our objective is to generate new diagnostic tests and to guide physicians to the most appropriate therapies. Our participants are recruited from the CHOP Health Care Network, which includes over 50 sites throughout Pennsylvania and New Jersey, 100% of which use Epic EMR system. This constitutes the country's largest pediatric integrated delivery system, supporting >1.1 million visits/year within the primary and specialty care networks, community-based inpatient pediatric and neonatal programs, a rehabilitation hospital, and a home care service. It provides acute- and well-care to patients in urban, rural, and suburban settings.Cincinnati Children's Hospital Medical Center (CCHMC)CCHMC has proposed ambitious initiatives for eMERGE IV. The major goals are to generate, return as appropriate along with clinical recommendations, individualized genomic risk assessments that include polygenic risk scores for 2500 minority participants ascertained on dyads (mother/child) and extended to families (dyad + biologic father, siblings). The informatics initiatives include integration of electronic medical records and development, implementation and evaluation of clinical decision support at two different hospital systems, one pediatric and one adult. Participant preferences, education needs, and ethical and social implications will be studied throughout the 5 year period. Columbia UniversityColumbia University in partnership with the New York-Presbyterian hospital has a long and strong tradition of patient-centered research and community engagement in the urban, medically underserved communities of Washington Heights and Harlem in Northern Manhattan, and has established Precision Medicine as a major university-wide initiative (PMI), of which eMERGE is a vital program. We possess broad and deep expertise in clinical genetics, genomics, informatics, EHR phenotyping, systems biology, public health, and ELSI, including return of genetic results and secondary findings to diverse patients. Columbia will share lessons learned in linking EHR and sequence data for genomic decision support, developing scalable genomic education methods and assessing the impact of returning genetic results on patients, families, and clinicians. We are poised to contribute novel comprehensive risk prediction and management solutions for diverse patients as part of eMERGE IV efforts.Icahn School of Medicine at Mt. Sinai (Mt. Sinai)Mt. Sinai investigators have been at the center of many NHGRI-led genomic research (GSP, PAGE) and genomic medicine (eMERGE, CSER, IGNITE) consortia. Building on our decade-long track record in genomic research, clinical trials, and genomic medicine in diverse, underserved patients from New York City, we propose to develop new frameworks to bring genomic risk into clinical care to promote health equity. In addition to genomic discovery and risk prediction, we are creating analytical and data resources, and digital health applications tailored to diverse populations, as part of the eMERGE IV Network. The Mt. Sinai team is composed of clinicians and scientists who work closely with community members and industry partners to develop flexible and innovative strategies to bring leading-edge research into clinical care. Having recently announced a large institutional investment to scale the integration of genomics and artificial intelligence into patient care, Mt. Sinai is poised to contribute to eMERGE IV's mission to implement and learn from genomic risk assessment in clinical care. Mt. Sinai's team will contribute to progress in multiple areas across eMERGE, including developing and deploying electronic phenotypes; discovering new genotype-phenotype associations; and facilitating the communication of genomic results to patients and their healthcare providers.Mass General BrighamThe eMERGE IV Clinical Center at Mass General Brigham leverages a large biobank, clinical data in the electronic health records (EHR) for >6 million patients from the largest integrated health care provider in New England, advanced bioinformatics expertise, prior leadership in PRS development, established expertise in returning genomics results, and experience using information technology to transform clinical processes and assess medical, behavioral and economic outcomes. We have developed highly specific machine learning algorithms for e-phenotypes and implemented eMERGE algorithms for multiple phenotypes and have considerable experience in developing decision support tools and analyzing outcomes that can be leveraged to study the integration of PRS data into clinical practice. We will test the hypothesis that PRS will allow us to stratify eMERGE participants based on genetic risk for common complex traits. We will focus on coronary artery disease, atrial fibrillation, type 2 diabetes, colorectal cancer and depression in GWAS populations and validate multi-ethnic PRS in the Mass General Brigham Biobank and eMERGE III dataset. We will conduct an analysis of the effect of disclosing results to high risk participants to determine how personalized results changed patient outcomes in laboratory values, risk reduction behaviors, or health care utilization.Mayo Clinic (Mayo)The goal of Mayo eMERGE investigators is to leverage the electronic health record (EHR) for genomic discovery and to implement genomic medicine to improve health care outcomes, working closely with other eMERGE sites and the Mayo Center for Individualized Medicine. In eMERGE I we used genotype data and electronic phenotyping algorithms for genomic discovery. In eMERGE II we conducted a clinical trial of disclosing a polygenic risk score (PRS) for coronary heart disease (CHD) and placed pharmacogenomic variants in the EHR with linkage to clinical decision support (CDS). In eMERGE III, we returned actionable findings from targeted sequencing of 68 genes and collaborated with a Federally Qualified Health Center (FQHC) to bring genomic medicine to an under-represented population. In eMERGE IV, as part of the Genomic Risk Assessment & Management Network, we will test the hypothesis that individuals at high polygenic risk for common diseases will have a higher uptake of risk reduction measures than those at lower risk. We will build on our prior work in genomic discovery, EHR integration and return of results (RoR) to develop and validate PRS and combine these with clinical risk scores and family history to compute comprehensive risk profiles for 2500 individuals. Our prior work in disclosing a PRS for CHD to research participants as part of a comprehensive risk profile enables us to efficiently expand the approach to other common diseases that pose a significant public health burden. To assess the utility of PRSs, we will recruit 1800 adults and 200 adolescents from Rochester MN and 500 adults from Phoenix AZ. DNA will be sent for CLIA-certified genomic testing and we will calculate ancestry specific PRSs for each participant. Individualized risk estimates will be reported to participants and health care providers (HCPs) and placed in the EHR with linkage to CDS that includes guideline-based risk-management information. For participants who are at significantly higher polygenic risk of developing at least 1 of the diseases, results will be disclosed in-person by a HCP followed by assessment of outcomes including new tests ordered, risk reduction measures, and changes in modifiable risk factors. We will examine the economic, behavioral and psychosocial implications of returning PRS. Our focus on PRS for common cardiovascular diseases brings genomic medicine to the population. We will continue our partnership with Mountain Park Health Center, a FQHC serving low-income, minority communities in Phoenix AZ, to address concerns about health disparities in the context of genomic medicine.Northwestern University (NU)Northwestern University is dedicated to translational genomics, with a strong focus on unified, interdisciplinary research bridging the informatics, clinical, basic science, and bioethics domains. Over the phases of eMERGE, Northwestern has applied this interdisciplinary approach to study the feasibility, utility, outcomes, and effectiveness of EHR-linked genomic information as a member of the network. Particular areas of focus include: novel informatics approaches to integrate genomic results into EHR systems; development of open-source, EHR-based phenotyping algorithms to empower translational research across a variety of clinical domains; and studies of participant, patient, and provider interaction with EHR-based genomic results. In phase IV of eMERGE, Northwestern is building on this expertise to develop, deploy, and evaluate systems of EHR-based, genomics-guided clinical risk assessment and management tools across a variety of common, impactful conditions. Northwestern's cross-disciplinary approach to this work is empowered by an innovative smartphone app to consolidate outreach, enrollment, engagement, return of results, and dissemination of health education materials surrounding genetic risk and risk management. Focusing recruitment on community-based clinics, Northwestern aims to engage participants from communities that, though not traditionally included in academic research settings, could benefit significantly from these risk management approaches. By combining expertise in informatics and EHR integration with innovative return-of-results strategies rooted in behavior theory, Northwestern aims to provide valuable data on accuracy, utility, and uptake of genetic risk assessment as the field moves towards integrating genetic risk scores into clinical practice.University of Alabama at Birmingham (UAB)Our eMERGE team aims to bring our experience and expertise in research with medically underserved (>75%) populations to identify genomic signatures of common diseases, conduct genomic risk assessments within the clinical workflow and where appropriate, pre-treat at-risk patients with common diseases including diabetes, chronic obstructive pulmonary disease, obesity, coronary artery disease and prostate cancer. Our aims will also allow us to develop genomic risk assessments, and address challenges involved in bringing genomic medicine into routine medical care. Working with the eMERGE network, we will select the eleven diseases of public health impact and select polygenic risk scores for clinical implementation. We will conduct an ethical legal social implications (ELSI) study, to explore patient perspectives on the use of family history and polygenic risk scores for estimating disease risk among Alabama Genomic Health Initiative (a statewide cohort) participants. The results will inform the development of consent, educational materials, and a communication strategy to enhance recruitment and retention of eMERGE participants. Recruiting over 75% of patients from medically underserved communities, we will incorporate genomic risk, family history, and clinical data to identify high-risk patients, recommending risk reduction recommendations in concordance with practice guidelines. For high-risk patients, we will deploy clinical decision support and present the genomic risk assessments and risk reduction recommendations. We will assess three outcomes: uptake of recommendations (implementation outcome), adherence to clinic visits (engagement outcome), and surrogates of disease/control (clinical outcome; e.g. blood sugar, cholesterol). We bring our expertise and experience to collaborate with the eMERGE investigative team to leverage the power of genomics to prevent disease.University of Washington (UW)The eMERGE IV Northwest partnership includes clinicians, scientists, and professionals in other fields from University of Washington (UW) Medicine, UW Health Sciences, the Fred Hutchinson Cancer Research Center, and the Brotman Baty Institute for Precision Medicine. The eMERGE IV Northwest partnership aims to evaluate the use of genomic information in the health care of diverse participants at the University of UW. Our site is expected to lead work on the prediction of colon cancer, glaucoma, and cardiovascular disease; other conditions expected to be studied include diabetes, liver disease, high cholesterol, and osteoporosis. Working with our eMERGE colleagues, we will aggregate, into a single score that informs about the risk of a disease, a participant's polygenic test information, which looks at variants from across the genome, as well as other known risk factors, such as personal and family medical history, environmental and social determinants, and physical and lab results. This score would let clinicians know which patients might benefit from screenings or other interventions to reduce the risk of disease or make an early diagnosis. In addition to experts specializing in each of the medical conditions that are the focus of the Northwest site, leaders in health economics, and in the ethical and societal aspects of genomics and health care will also be part of the research team. Medical, as well as economic, outcomes will be measured. Stakeholder engagement will be encouraged to obtain comments from participants and their families.Vanderbilt University Medical Center (VUMC)Vanderbilt University Medical Center has embraced Personalized Health and Healthcare as a key pillar of our Research Strategic Plan. This institutional commitment has taken multiple forms including the development of key infrastructure resources (the institutional Biobank (BioVU); the program in preemptive pharmacogenetic testing; the genotyping and sequencing core) and support of faculty in key disciplines such as genomics, informatics, ethics, privacy science, and clinical pharmacology. VUMC's team has contributed importantly to progress in multiple areas across eMERGE, including developing and deploying phenotype algorithms; developing and managing the genotype quality control pipeline for the network (eMERGE I); discovering new genotype-phenotype relations in VUMC and cross-network datasets; actively participating in the PGx clinical implementation project, community consultation, and return of results initiatives; developing methods to resolve tensions between data access and individual privacy; developing sampling methodology for the CERC survey; and developing new software tools for the field, including for developing and validating phenotypes, de-identification and for the phenome-wide association study paradigm (“PheWAS”)eMERGE Network Coordinating Center (CC)Vanderbilt University Medical Center has been the coordinating center of the eMERGE Network since inception. During this phase of eMERGE we have partnered with the Broad Institute to to add expertise in large-scale genotyping, cloud computing, and genomic data analysis and management, and with Duke University to leverage its proficiency in the implementation of patient-oriented family history tools and delivery of risk assessments. To meet the needs of the Network, the CC continues to serve NHGRI, the Network, and its members in all coordination, communication, and collaboration functions while providing a nationally visible home for eMERGE authored tools and scientific productivity. The CC is also assisting the Network with selecting and validating polygenic risk scores (PRS) for approximately eleven complex diseases and developing genomic risk assessments (GRAs) which combine the PRS with other clinical risks, family history, and monogenic risks. Finally, The CC will coordinate a prospective study for 25,000 participants which calculates clinical and genomic risks for participants and their providers. In addition to providing the genotyping and PRS reporting services, we will develop with the Network scalable EHR-based methods to assess uptake of risk-reduction recommendations and clinical impact.
