Prevalence of rearrangements in the 22q11.2 region and population-based risk of neuropsychiatric and developmental disorders in a Danish population: a case-cohort study.

BACKGROUND: Although the pathogenic nature of copy number variants (CNVs) on chromosome 22q11.2 has been recognised for decades, unbiased estimates of their population prevalence, mortality, disease risks, and diagnostic trajectories are absent. We aimed to provide the true population prevalence of 22q11.2 CNVs and associated trajectory of disease risk and mortality by use of the unbiased, representative Danish iPSYCH population case cohort. METHODS: This case-cohort study was done on a population of 86 189 individuals selected from the iPSYCH case cohort of 1 472 762 singletons born in Denmark between May 1, 1981, and Dec 31, 2005, who have a known mother from the Danish Civil Registration System, were residents in Denmark at 1 year of age, and enrolled in the iPSYCH Initiative. We used epidemiological methods in conjunction with nationwide hospital registers to analyse the iPSYCH case cohort of individuals with attention-deficit hyperactivity disorder (ADHD), major depressive disorder, schizophrenia, autism, or bipolar disorder and a random population-based sample. The main outcomes assessed were the population prevalence of 22q11.2 rearrangements, and associated unbiased, population-adjusted estimates and 31-year disease risk trajectories for major neuropsychiatric disorders. FINDINGS: Population prevalence in the Danish population was one in 3672 (seven of 25 704 [0·027%; 95% CI 0·012-0·057]) for deletions and one in 1606 (17 of 25 704 [0·066%; 0·040-0·107]) for duplications. Mortality after the age of 1 year among carriers was zero, and hazard ratios for neuropsychiatric disorders ranged from 2·60 to 82·44 for both rearrangements. By the age of 32 years, about 10% of individuals with deletions or duplications had developed ADHD, autism, or intellectual disability, and deletion carriers had higher probability than duplication carriers of co-occurring intellectual disability or epilepsy. INTERPRETATION: The significantly different prevalence of 22q11.2 duplications and deletions indicates distinct selective pressures on these rearrangements. Although risk of congenital abnormalities, developmental delay, and intellectual disability is elevated in deletion carriers, the overall prevalence of neuropsychiatric disorders is higher in duplication carriers, which implies that identification and clinical monitoring should extend beyond congenital traits and into child and adolescent psychiatry. FUNDING: Capital Region's Research Foundation for Mental Health Research, The Lundbeck Foundation, and US National Institutes of Health.

Risk of Psychiatric Disorders Among Individuals With the 22q11.2 Deletion or Duplication: A Danish Nationwide, Register-Based Study.

Importance: Microdeletions and duplications have been described at the 22q11.2 locus. However, little is known about the clinical and epidemiologic consequences at the population level. Objective: To identify indicators of deletions or duplications at the 22q11.2 locus and estimate the incidence rate ratios (IRRs) and absolute risk for psychiatric disorders in clinically identified individuals with 22q11.2 deletion or duplication. Design, Setting, and Participants: A Danish nationwide register study including all individuals recorded in the Danish Cytogenetic Central Register with a 22q11.2 deletion or duplication was performed. A total of 3 768 943 individuals born in Denmark from 1955 to 2012 were followed up during the study period (total follow-up, 57.1 million person-years). Indicators of 22q11.2 deletion or duplication and cumulative incidences were estimated using a nested case-control design that included individuals from the population-based cohort. Survival analysis was used to compare risk of disease in individuals with and without the 22q11.2 deletion or duplication. The study was conducted from May 7, 2015, to August 14, 2016. Exposure: The 22q11.2 deletion or duplication. Main Outcomes and Measures: Indicators for carrying a 22q11.2 deletion or duplication, IRR, and cumulative incidences for psychiatric diagnoses (International Statistical Classification of Diseases and Related Health Problems, 10th Revision, codes F00-F99), including schizophrenia-spectrum disorders, mood disorders, neurotic stress-related and somatoform disorders, and a range of developmental and childhood disorders. Results: Among the 3 768 943 participants, 244 (124 [50.8%] male) and 58 (29 [50.0%] male) individuals were clinically identified with a 22q11.2 deletion or duplication, respectively. Mean (SD) age at diagnosis of any psychiatric disorder was 12.5 (8.3) years for individuals with deletions and 6.1 (0.9) years for duplication carriers. A parental diagnosis of schizophrenia-but not of other psychiatric diagnoses-was associated with a 22q11.2 deletion, and parental psychiatric diagnoses other than schizophrenia were associated with duplication carrier status. Both the 22q11.2 deletion (IRR, 4.24; 95% CI, 3.07-5.67) and duplication (IRR, 4.99; 95% CI, 1.79-10.72) was associated with increased risk of any psychiatric disorders. Furthermore, a highly increased risk of intellectual disability was found for the deletion (IRR, 34.08; 95% CI, 22.39-49.27) and duplication (IRR, 33.86; 95% CI, 8.42-87.87). Furthermore, individuals with the 22q11.2 deletion had an increased risk of several psychiatric disorders under study, for example, pervasive developmental disorders (IRR, 9.45; 95% CI, 5.64-14.69) and childhood autism (IRR, 8.94; 95% CI, 3.21-19.23). Conclusions and Relevance: Individuals with the 22q11.2 deletion or duplication have a significantly increased risk of developing psychiatric disorders. Survival analysis of persons carrying either the 22q11.2 deletion or duplication provides estimates of direct clinical relevance useful to assist clinical ascertainment, genetic counseling, guidance of symptomatic monitoring, and early clinical intervention.

Development and blind clinical validation of a microRNA based predictor of response to treatment with R-CHO(E)P in DLBCL.

MicroRNAs (miRNA) are a group of short noncoding RNAs that regulate gene expression at the posttranscriptional level. It has been shown that microRNAs are independent predictors of outcome in patients with diffuse large B-cell lymphoma (DLBCL) treated with the drug combination R-CHOP. Based on the measured growth inhibition of 60 human cancer cell lines (NCI60) in the presence of doxorubicine, cyclophosphamide, vincristine and etoposide as well as the baseline microRNA expression of the 60 cell lines, a microRNA based response predictor to CHOP was developed. The response predictor consisting of 20 microRNAs was blindly validated in a cohort of 116 de novo DLBCL patients treated with R-CHOP or R-CHOEP as first line treatment. The predicted sensitivity based on diagnostic FFPE samples matched the clinical response, with decreasing sensitivity in poor responders (P = 0.03). When the International Prognostic Index (IPI) was included in the prediction analysis, the separation between responders and non-responders improved (P = 0.001). Thirteen patients developed relapse, and five patients predicted sensitive to their second and third line treatment survived a median 1194 days, while eight patients predicted not sensitive to their second and third line treatment survived a median 187 days (90% CI: 485 days versus 227 days). Among the latter group it was predicted that four would have been sensitive to another second line treatment than the one they received. The predictions were almost the same when diagnostic biopsies were used as when relapse biopsies were used. These preliminary findings warrant testing in a larger cohort of relapse patients to confirm whether the miRNA based predictor can select the optimal second line treatment and increase survival.

Existing data sources for clinical epidemiology: Aarhus University Clinical Trial Candidate Database, Denmark.

Denmark is facing a reduction in clinical trial activity as the pharmaceutical industry has moved trials to low-cost emerging economies. Competitiveness in industry-sponsored clinical research depends on speed, quality, and cost. Because Denmark is widely recognized as a region that generates high quality data, an enhanced ability to attract future trials could be achieved if speed can be improved by taking advantage of the comprehensive national and regional registries. A "single point-of-entry" system has been established to support collaboration between hospitals and industry. When assisting industry in early-stage feasibility assessments, potential trial participants are identified by use of registries to shorten the clinical trial startup times. The Aarhus University Clinical Trial Candidate Database consists of encrypted data from the Danish National Registry of Patients allowing an immediate estimation of the number of patients with a specific discharge diagnosis in each hospital department or outpatient specialist clinic in the Central Denmark Region. The free access to health care, thorough monitoring of patients who are in contact with the health service, completeness of registration at the hospital level, and ability to link all databases are competitive advantages in an increasingly complex clinical trial environment.

Development and validation of a gene expression score that predicts response to fulvestrant in breast cancer patients.

Fulvestrant is a selective estrogen receptor antagonist. Based on the measured growth inhibition of 60 human cancer cell lines (NCI60) in the presence of fulvestrant, as well as the baseline gene expression of the 60 cell lines, a gene expression score that predicts response to fulvestrant was developed. The score is based on 414 genes, 103 of which show increased expression in sensitive cell lines, while 311 show increased expression in the non-responding cell lines. The sensitivity genes primarily sense signaling through estrogen receptor alpha, whereas the resistance genes modulate the PI3K signaling pathway. The latter genes suggest that resistance to fulvestrant can be overcome by drugs targeting the PI3K pathway. The level of this gene expression score and its correlation with fulvestrant response was measured in a panel of 20 breast cancer cell lines. The predicted sensitivity matched the measured sensitivity well (CC = -0.63, P = 0.003). The predictor was applied to tumor biopsies obtained from a Phase II clinical trial. The sensitivity of each patient to treatment with fulvestrant was predicted based on the RNA profile of the biopsy taken before neoadjuvant treatment and without knowledge of the subsequent response. The prediction was then compared to clinical response to show that the responders had a significantly higher sensitivity prediction than the non-responders (P = 0.01). When clinical covariates, tumor grade and estrogen receptor H-score, were included in the prediction, the difference in predicted senstivity between responders and non-responders improved (P = 0.003). Using a pre-defined cutoff to separate patients into predicted sensitive and predicted resistant yielded a positive predictive value of 88% and a negative predictive value of 100% when compared to clinical data. We conclude that pre-screening patients with the new gene expression predictor has the potential to identify those postmenopausal women with locally advanced, estrogen-receptor-positive breast cancer most likely to respond to fulvestrant.

Independent validation of a model using cell line chemosensitivity to predict response to therapy.

BACKGROUND: Methods using cell line microarray and drug sensitivity data to predict patients' chemotherapy response are appealing, but groups may be reluctant to release details to preserve intellectual property. Here we describe a case study to validate predictions while treating the methods as a "black box." METHODS: Medical Prognosis Institute (MPI) constructed cell-line-derived sensitivity scores (SSs) and combined scores (CSs) that incorporate clinical variables. MD Anderson researchers evaluated their predictions. We searched the Gene Expression Omnibus (GEO) to identify validation datasets, and we performed statistical evaluation of the agreement between prediction and clinical observation. RESULTS: We identified 3 suitable datasets: GSE16446 (n = 120; binary outcome), GSE17920 (n = 130; binary outcome), and GSE10255 (n = 161; continuous and time-to-event outcomes). The SS was statistically significantly associated with primary treatment responses for all studies (GSE16446: P = .02; GSE17920: P = .02; GSE10255: P = .02). Dichotomized SSs performed no better than chance for GSE16446 and GSE17920, and categorized SSs did not predict disease-free survival (GSE10255). SSs sometimes improved on predictions using clinical variables (GSE16446: P = .05; GSE17920: P = .31; GSE10255: P = .045), but gains were limited (95% confidence intervals for GSE16446 and GSE17920 include 0). The CS did not predict treatment response for GSE16446 (P = .55), but it did for GSE17920 (P < .001). Coefficients of clinical variables provided by MPI for CSs agree with estimates for GSE17920 better than estimates for GSE16446. CONCLUSIONS: Model predictions were better than chance in all three datasets. However, these scores added little to existing clinical predictors; statistically significant contributions were likely to be too small to change clinical practice. These findings suggest that discovering better predictors will require both cell line data and a clinical training dataset of patient samples.

Expression of microRNAs in the NCI-60 cancer cell-lines.

The NCI-60 panel of 60 human cancer cell-lines of nine different tissues of origin has been extensively characterized in biological, molecular and pharmacological studies. Analyses of data from such studies have provided valuable information for understanding cellular processes and developing strategies for the diagnosis and treatment of cancer. Here, Affymetrix® GeneChip™ miRNA version 1 oligonucleotide microarrays were used to quantify 847 microRNAs to generate an expression dataset of 495 (58.4%) microRNAs that were identified as expressed in at least one cell-line of the NCI-60 panel. Accuracy of the microRNA measurements was partly confirmed by reverse transcription and polymerase chain reaction assays. Similar to that seen among the four existing NCI-60 microRNA datasets, the concordance of the new expression dataset with the other four was modest, with mean Pearson correlation coefficients of 0.37-0.54. In spite of this, comparable results with different datasets were noted in clustering of the cell-lines by their microRNA expression, differential expression of microRNAs by the lines' tissue of origin, and correlation of specific microRNAs with the doubling-time of cells or their radiation sensitivity. Mutation status of the cell-lines for the TP53, PTEN and BRAF but not CDKN2A or KRAS cancer-related genes was found to be associated with changes in expression of specific microRNAs. The microRNA dataset generated here should be valuable to those working in the field of microRNAs as well as in integromic studies of the NCI-60 panel.

A 71-gene signature of TRAIL sensitivity in cancer cells.

TNF-related apoptosis inducing ligand (TRAIL) is a promising anticancer agent because of its ability to selectively induce apoptosis in cancer cells but not in most normal cells. However, some cancer cells are resistant to TRAIL cytotoxicity thereby limiting its therapeutic efficacy. Using genome-wide mRNA expression profiles from the NCI60 panel and their differential sensitivities to TRAIL-induced apoptosis, we have identified 71 genes whose expression levels are systemically higher in TRAIL-sensitive cell lines than resistant lines. The elevated expression of the 71 genes was able to accurately predict TRAIL sensitivity in the NCI60 training set and two test sets consisting of a total of 95 human cancer cell lines. Interestingly, the 71-gene signature is dominated by two functionally related gene families-interferon (IFN)-induced genes and the MHC genes. Consistent with this result, treatment with IFN-γ augmented TRAIL-induced apoptosis. The 71-gene signature could be evaluated clinically for predicting tumor response to TRAIL-related therapies.

Analytical variables influencing the performance of a miRNA based laboratory assay for prediction of relapse in stage I non-small cell lung cancer (NSCLC).

BACKGROUND: Laboratory assays are needed for early stage non-small lung cancer (NSCLC) that can link molecular and clinical heterogeneity to predict relapse after surgical resection. We technically validated two miRNA assays for prediction of relapse in NSCLC. Total RNA from seventy-five formalin-fixed and paraffin-embedded (FFPE) specimens was extracted, labeled and hybridized to Affymetrix miRNA arrays using different RNA input amounts, ATP-mix dilutions, array lots and RNA extraction- and labeling methods in a total of 166 hybridizations. Two combinations of RNA extraction- and labeling methods (assays I and II) were applied to a cohort of 68 early stage NSCLC patients. RESULTS: RNA input amount and RNA extraction- and labeling methods affected signal intensity and the number of detected probes and probe sets, and caused large variation, whereas different ATP-mix dilutions and array lots did not. Leave-one-out accuracies for prediction of relapse were 63% and 73% for the two assays. Prognosticator calls ("no recurrence" or "recurrence") were consistent, independent on RNA amount, ATP-mix dilution, array lots and RNA extraction method. The calls were not robust to changes in labeling method. CONCLUSIONS: In this study, we demonstrate that some analytical conditions such as RNA extraction- and labeling methods are important for the variation in assay performance whereas others are not. Thus, careful optimization that address all analytical steps and variables can improve the accuracy of prediction and facilitate the introduction of microRNA arrays in the clinic for prediction of relapse in stage I non-small cell lung cancer (NSCLC).