Tumors from 173 GBM patients were analysed for somatic mutations to generate a personalized peptide vaccine targeting tumor-specific neoantigens. Exome libraries for 173 glioblastoma tumors and matched normal DNA were sequenced on Illumina platform, alongside whole transcriptomes from the tumor samples. Paper Abstract: Current treatment outcome of patients with glioblastoma (GBM) remains poor. Following standard therapy, recurrence is universal with limited survival. GBM tumors from 173 patients were analysed for somatic mutations to generate a personalized peptide vaccine targeting tumor-specific neoantigens. Among all patients, including 70 treated prior to progression (primary) and 103 treated after progression (recurrent), the median overall survival from first diagnosis was 31.9 months (95% CI: 25.0-36.5). Side effects were infrequent and were predominantly grade 1 or 2. A vaccine-induced immune response to at least one of the vaccinated peptides was detected in blood samples of 87 of 99 (88%) monitored patients. T-cell responses to vaccinated neoepitope peptides were durable in most patients. Significantly prolonged survival was observed for patients with multiple vaccine-induced immune responses (53 months) compared to those with no/low induced responses (27 months; P=0.03). Altogether, our results highlight that the application of personalized neoantigen-targeting peptide vaccine is feasible and represents a promising potential treatment option for GBM patients.
Patients with small-cell lung cancer (SCLC) have an exceptionally poor prognosis, calling for improved real-time non-invasive biomarkers of therapeutic response. We performed targeted error-correction sequencing on 171 serial plasmas and matched white blood cell (WBC) DNA from 33 patients with metastatic SCLC who received systemic treatment with chemotherapy or immunotherapy-containing regimens. Tumor-derived sequence alterations and plasma aneuploidy were evaluated serially and combined to assess changes in total cell-free tumor load (cfTL). Longitudinal dynamic changes in cfTL were monitored to determine circulating cell-free tumor DNA (ctDNA) molecular response during therapy. Combined tiered analyses of tumor-derived sequence alterations and plasma aneuploidy allowed for assessment of ctDNA molecular response in all patients. Molecular responses captured the therapeutic effect and long-term clinical outcomes in a more accurate and rapid manner compared to radiographic imaging. Patients with sustained molecular responses had longer overall (log-rank p=0.0006) and progression-free (log-rank p<0.0001) survival, with molecular responses detected on average 4 weeks earlier than imaging. ctDNA analyses provide a rapid approach for the assessment of early on-therapy molecular responses and have important implications for the management of patients with SCLC, including the development of improved strategies for real-time tumor burden monitoring.
Five hundred ng to 1 ug of genomic DNA was submitted to The Centre for Applied Genomics (TCAG) at The Hospital for Sick Children for genomic library preparation and whole genome sequencing. DNA samples were quantified using the Qubit High Sensitivity Assay and purity was assessed using the Nanodrop OD 260/280 ratio. Approximately 500-700 ng of DNA was used as input material for library preparation using the Illumina TruSeq PCR-free DNA Library Prep Kit following the manufacturer’s recommended protocol. In brief, DNA was fragmented to 400 bp on average using sonication on a Covaris LE220 instrument. Fragmented DNA was end-repaired, A-tailed and indexed TruSeq Illumina adapters with overhang-T were added to the DNA. Libraries were then validated on a Bioanalyzer DNA High Sensitivity chip to check for size and absence of primer dimers and quantified by qPCR using Kapa Library Quantification Illumina/ABI Prism Kit protocol (KAPA Biosystems). Validated libraries were pooled in equimolar quantities and paired-end sequenced on an Illumina HiSeq X platform following Illumina’s recommended protocol to generate paired-end reads of 150-bases in length and an average depth of 40X. FASTQ files were aligned to the hg19 reference genome using BWA-mem v0.78. PCR duplicates were marked with Picard MarkDuplicates v1.1.08, and base recalibration and realignment was performed using GATK v2.8.1.
The Resource for Genetic Epidemiology Research on Aging (GERA) Cohort was created by a RC2 "Grand Opportunity" grant that was awarded to the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) and the UCSF Institute for Human Genetics (AG036607; Schaefer/Risch, PIs). The RC2 project enabled genome-wide SNP genotyping (GWAS) to be conducted on a cohort of over 100,000 adults who are members of the Kaiser Permanente Medical Care Plan, Northern California Region (KPNC), and participating in its RPGEH. The purpose of the RPGEH is to facilitate research on the genetic and environmental factors that affect health and disease by linking together clinical data from electronic health records, survey data on demographic and behavioral factors, and environmental data from various sources, with genetic data derived from biospecimens collected from participants. At the time of the award of the RC2 project in late 2009, the RPGEH had established a cohort of about 140,000 individuals who had answered a detailed survey, provided saliva samples for extraction of DNA, and given broad consent for the use of their data in studies of health and disease. To maximize the diversity of the resulting sample, the GERA cohort was formed by including all racial and ethnic minority participants with saliva samples (N = 20,925; 19%); the remaining participants were drawn sequentially and randomly from white non-Hispanic participants (89,341; 81%). A total of 110,266 participant samples were included to ensure that at least 100,000 were successfully assayed. The resulting GERA cohort is 42% male, 58% female, and ranges in age from 18 to over 100 years old with an average age of 63 years at the time of the RPGEH survey (2007). The sample is ethnically diverse, generally well-educated with above average income. Approximately 69% of the participants are married or living with a partner. Length of membership in KPNC averages 23.5 years. UCSF and RPGEH investigators worked with the genomics company Affymetrix to design four custom microarrays for genotyping each of the four major race-ethnicity groups included in the GERA Cohort, described in detail in Hoffmann et al., 2011a and 2011b. Following genotyping and quality control procedures, and after removal of invalid, discordant, or withdrawn samples, about 103,000 participants were successfully genotyped. The resulting genotypic data were linked to survey data and data abstracted from the electronic medical records. As described below, all RPGEH participants were mailed new consent forms with explicit discussion of the placement of data in the NIH-maintained dbGaP. About 77% of participants returned completed consent forms, resulting in a final sample size of 78,486 participants in the GERA Cohort with data for deposit into dbGaP. Origins of the RPGEH GERA Cohort The goal in creating the RPGEH GERA cohort was to create a large, multiethnic, and comprehensive population-based resource for research into the genetic and environmental basis of common age-related diseases and their treatment, and factors influencing healthy aging and longevity. The GERA Cohort consists of a diverse cohort of more than 100,000 adults who are members of the Kaiser Permanente Medical Care Plan, Northern California Region (KPNC), and participating in its Research Program on Genes, Environment and Health (RPGEH). KPNC is an integrated health care delivery system with a population of about 3.3 million people in northern California. The membership of KPNC is representative of the general population in the 14 county area in which facilities are located, although the membership is underrepresented for the extremes of income at both ends of the spectrum. The RPGEH utilizes the longitudinal electronic health records (EHR) of KPNC to obtain clinical, laboratory, imaging and pharmacy information on all cohort members, to which personal demographic, behavioral and health characteristics have been added through member surveys. The GERA Cohort comprises a subsample of the RPGEH participant cohort, and was created through the RC2 award from the NIA, NIMH, and NIH Common Fund as described above. GERA Study Design The GERA Cohort is a subsample, as described above, of the longitudinal cohort enrolled in the Kaiser Permanente RPGEH. The RPGEH cohort includes about 400,000 survey participants of whom about 200,000 have provided broad consent and a sample of saliva or blood for use in studies of genetic and environmental factors in health and disease. The GERA Cohort was developed from a mailed survey sent to all adult members of KPNC who had been members for two years or more in 2007. All survey respondents were contacted and asked to complete a consent form; those who completed consent forms were asked to provide a saliva sample. Additional male participants were added to the RPGEH through inclusion of the Northern California sample of the California Men's Health Study (CMHS) cohort of about 40,000 men from KPNC, ages 45-69 years old at the time of the CMHS survey in 2002-2003. The CMHS participants contributed about 15,400 saliva samples to the RPGEH and were eligible for inclusion in the GERA Cohort. CMHS participants were included according to the same sampling design as for the RPGEH cohort as a whole. Specifically, all minority participants were selected for inclusion in order to maximize representation of minorities in the GERA Cohort, and Non-Hispanic White participants were selected at random to complete the sample of 110,266 GERA Cohort participants. GERA Genotypic Data High-density genotyping was conducted at UCSF using custom designed Affymetrix Axiom arrays, as described in Hoffmann et al. (2011a; 2011b). To maximize genome-wide coverage of common and less common variants, four specific arrays were designed for individuals of Non-Hispanic White (EUR), East Asian (EAS), African-American (AFR), and Latino (LAT) race/ethnicity. There was broad overlap among the SNPs on the arrays, which were designed using a hybrid greedy imputation algorithm (Hoffmann et al., 2011b) applied to genotype information validated by Affymetrix from the 1000 Genomes Project. However, in order to capture low frequency variants specific to particular race-ethnicity groups, SNP content varies between arrays. A more detailed description of the process of genotyping and results is included in Genotyping of DNA Samples. Description of the analyses of population structure and development of principal components for adjustment of population structure is included in Population Structure Analysis. GERA Phenotypic Data RPGEH and CMHS Survey Data. The sources of data on demographic and behavioral factors deposited in dbGaP for the GERA Cohort are the RPGEH and CMHS surveys. Data on common demographic factors such as gender, race/ethnicity, marital status, and education and on behavioral factors such as smoking, alcohol consumption, and body mass index, have been cleaned, edited, reconciled between the two surveys, and compiled into summary indices, where appropriate, for deposition into dbGaP. A more complete description of the survey variables is included in Survey Variables Documentation. Please note that the terms of use of the GERA Cohort Data, as specified in the Data Use Certification (DUC), prohibit the use of survey variables as outcomes in analyses. For example, a genome-wide association study (GWAS) of education or smoking is not permitted as specified by the DUC. Only health conditions can be used as outcome variables in analyses. Health Conditions derived from Kaiser Permanente Electronic Medical Records. Data on the occurrence of health conditions in participants in the GERA Cohort have been derived from summarizing ICD-9 coded diagnoses in Kaiser Permanente's electronic medical records. An algorithm that aggregates specific ICD-9 codes into appropriate diagnostic groups for selected conditions is applied to outpatient and inpatient databases; see Disease and Conditions Definitions Documentation for details. The criterion for including a condition as "present" for a participant is the occurrence of two or more diagnoses within a diagnostic category occurring on separate days. Two or more is used as the criterion in order to reduce false positives due to mistakes or rule-out diagnoses. When compared with validated disease registries, the criterion of 2+ diagnoses yields high specificity and good sensitivity. ICD-9 codes in the electronic records are specified in several ways. For outpatient visits occurring during the period 1995 to 2006, diagnoses were assigned by the treating physician who endorsed specific diagnoses on an optically scanned list that varied by specialty. Beginning in 2006 with the advent of an integrated, fully electronic medical record, outpatient diagnoses are made by physicians/ providers using a pull down menu. Discharge diagnoses from inpatient stays are specified by physicians and coded by specially trained coders. Databases of ICD-9 codes for diagnoses assigned at outpatient visits, or as one of the discharge diagnoses following inpatient stays, are complete and available for all KPNC members dating back to 1995. Although the average length of KPNC membership among GERA cohort members is 23.5 years in 2007, not all have been members since 1995, so the history for some conditions, such as those that are not chronic or recurrent, may not be complete for all cohort members. The year of first membership in KPNC is included as a variable in the list of survey variables, enabling investigators to estimate the number of years of observation of each Cohort member. RPGEH Access and Collaborations Website and Procedures The RPGEH maintains a web portal for inquiries and applications for collaboration and access to data. The url is: https://rpgehportal.kaiser.org/. RPGEH has an application process and an Access Review Committee that reviews applications for collaboration and use. For more details, please contact RPGEH through the website.
The NINDS Stroke Genetics Network (SiGN) is a large international collaboration designed to detect genetic variants that predispose to subtypes of ischemic stroke. The study implements a genome wide association study (GWAS) methodology with all stroke cases undergoing phenotypic and stroke-subtype classification using the same web-based Causative Classification of Stroke (CCS) system, with data entered by trained and certified adjudicators at participating Research Centers (GRC's). SiGN includes ischemic stroke cases from 24 GRC's, 13 from the US and 11 from Europe. Each GRC has access to well-characterized ischemic stroke cases in which extensive phenotype data and high-quality DNA was available. Genome-wide data was available for many cases and for those without, new genome-wide genotyping, including exome chip genotyping of rare variants, was done through the Center for Inherited Diseases Research (CIDR). To maximize power for subtype analyses, genotyping resources were allocated almost exclusively to cases. With few exceptions, controls were drawn from studies with publicly available genome-wide data.
The Northern Manhattan Family Study is a sub-study of the Northern Manhattan Study (NOMAS), a population-based cohort study investigating stroke and stroke risk factors among individuals from three ethnic groups residing in northern Manhattan. The overall goal for the Northern Manhattan Family Study was to determine the genetic factors associated with stroke precursor phenotypes. A subset of Caribbean Hispanic probands from the NOMAS Study were selected for inclusion in the family study. Selected probands were at high risk for cardiovascular disease, defined as either 1. Having a sibling with a history of myocardial infarction or stroke, or 2. Having two of the following three risk phenotypes above the 75th percentile in NOMAS; maximal carotid plaque thickness, left ventricular mass or homocysteine level. Probands for the family study were enrolled in northern Manhattan and family members were enrolled at two locations: New York at Columbia University and in the Dominican Republic (DR) at the Clinicas Corazones Unidos in Santo Domingo. A subset of Caribbean Hispanic NOMAS participants were included as a replication sample.
Adolescent idiopathic scoliosis (AIS) is the most common pediatric spinal deformity. Although the Bracing in AIS Trial (BrAIST) recently demonstrated the effectiveness of bracing for preventing scoliosis progression in some patients, more than 20,000 children undergo major spinal fusion surgery at an annual cost of $3 billion. Spinal fusion surgery is a major operation with considerable risks and complications. Accurate methods of predicting curve progression are needed to develop personalized prevention strategies for those at high risk and to eliminate screening and treatment of those at low risk of progression. Previously identified risk factors for scoliosis curve progression include sex, age of onset, curve type, and presence of an underlying disorder. However, currently available algorithms for predicting AIS curve progression are inaccurate, possibly because the role of genetic factors has been largely unexplored. Because there is little a priori knowledge of the genetic variants involved in AIS pathology, an unbiased genome-wide approach is likely to provide the best opportunity to comprehensively identify disease-associated genes. This is a multicenter exome sequencing study of extreme cases with severe scoliosis.
The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. The goals for this project are to conduct exome sequencing for novel dilated cardiomyopathy (DCM) gene discovery in families with DCM. These families have already been sequenced for 15 DCM genes, accounting for approximately 75% of known genetic cause, without rare coding variants identified.
This study is part of the Molecular Genetics of Schizophrenia (MGS) genome wide association study (GWAS) of 3,972 cases (2,686 EA and 1,286 AA) and 3,629 controls (2,656 EA and 973 AA) (analyzed sample remaining after quality control exclusions), comprised of European ancestry (EA) and African American (AA) samples. We genotyped about half of the EA sample and almost all of the AA sample under the auspices of the Genetic Association Information Network (GAIN) with the Affymetrix 6.0 platform at the Broad Institute. The remainder of the included sample was also genotyped with the Affymetrix 6.0 platform at the Broad Institute, and we refer to this component as the nonGAIN sample. Cases met criteria for schizophrenia (SCZ) or schizoaffective disorder (SA) per the Diagnostic and Statistical Manual of Mental Disorders version IV (DSM-IV) for all three collections (SGI, MGS1, and MGS2) comprising these cases. However, for the older SGI collection, codes for the secondary diagnoses refer to the older DSM-III-R version. Controls were screened briefly and excluded if they endorsed a history of these illnesses.
Objective: To identify genetic variants associated with response to lithium prophylaxis treatment. Materials: 294 bipolar I (BPI) patients receiving lithium treatment, a subset of 1761 bipolar I (BPI) patients recruited by the Taiwan Bipolar Consortium. Lithium response was assessed based on the Alda Scale. Methods: Responders to lithium treatment were defined if the patients' Alda Scale > 4, 5, 6, and 7. Genome-wide association (GWA) analysis was carried out on the 294 BPI patients for the 4 definitions of responders. The SNPs showing strongest association in the GWAS were then tested for association in a replication sample of 100 patients and further tested in a prospective follow-up series of 24 patients. Results: Two SNPs on GADL1 showed the strongest associations in the four GWA analyses and in the replication set of 100 patients. These two SNPs had a sensitivity of 0.93 for predicting lithium response and differentiated between the good and poor responders in the follow-up cohort. Summary: These genetic variants are associated with response to lithium prophylaxis treatment for BPI.
