Patient-centred outcomes and effect of disease progression on health status in patients with newly diagnosed advanced ovarian cancer and a BRCA mutation receiving maintenance olaparib or placebo (SOLO1): a randomised, phase 3 trial.

BACKGROUND: In the phase 3 SOLO1 trial, maintenance olaparib provided a significant progression-free survival benefit versus placebo in patients with newly diagnosed, advanced ovarian cancer and a BRCA mutation in response after platinum-based chemotherapy. We analysed health-related quality of life (HRQOL) and patient-centred outcomes in SOLO1, and the effect of radiological disease progression on health status. METHODS: SOLO1 is a randomised, double-blind, international trial done in 118 centres and 15 countries. Eligible patients were aged 18 years or older; had an Eastern Cooperative Oncology Group performance status score of 0-1; had newly diagnosed, advanced, high-grade serous or endometrioid ovarian cancer, primary peritoneal cancer, or fallopian tube cancer with a BRCA mutation; and were in clinical complete or partial response to platinum-based chemotherapy. Patients were randomly assigned (2:1) to either 300 mg olaparib tablets or placebo twice per day using an interactive voice and web response system and were treated for up to 2 years. Treatment assignment was masked for patients and for clinicians giving the interventions, and those collecting and analysing the data. Randomisation was stratified by response to platinum-based chemotherapy (clinical complete or partial response). HRQOL was a secondary endpoint and the prespecified primary HRQOL endpoint was the change from baseline in the Functional Assessment of Cancer Therapy-Ovarian Cancer Trial Outcome Index (TOI) score for the first 24 months. TOI scores range from 0 to 100 (higher scores indicated better HRQOL), with a clinically meaningful difference defined as a difference of at least 10 points. Prespecified exploratory endpoints were quality-adjusted progression-free survival and time without significant symptoms of toxicity (TWiST). HRQOL endpoints were analysed in all randomly assigned patients. The trial is ongoing but closed to new participants. This trial is registered with ClinicalTrials.gov, NCT01844986. FINDINGS: Between Sept 3, 2013, and March 6, 2015, 1084 patients were enrolled. 693 patients were ineligible, leaving 391 eligible patients who were randomly assigned to olaparib (n=260) or placebo (n=131; one placebo patient withdrew before receiving any study treatment), with a median duration of follow-up of 40·7 months (IQR 34·9-42·9) for olaparib and 41·2 months (32·2-41·6) for placebo. There was no clinically meaningful change in TOI score at 24 months within or between the olaparib and placebo groups (adjusted mean change in score from baseline over 24 months was 0·30 points [95% CI -0·72 to 1·32] in the olaparib group vs 3·30 points [1·84 to 4·76] in the placebo group; between-group difference of -3·00, 95% CI -4·78 to -1·22; p=0·0010). Mean quality-adjusted progression-free survival (olaparib 29·75 months [95% CI 28·20-31·63] vs placebo 17·58 [15·05-20·18]; difference 12·17 months [95% CI 9·07-15·11], p<0·0001) and the mean duration of TWiST (olaparib 33·15 months [95% CI 30·82-35·49] vs placebo 20·24 months [17·36-23·11]; difference 12·92 months [95% CI 9·30-16·54]; p<0·0001) were significantly longer with olaparib than with placebo. INTERPRETATION: The substantial progression-free survival benefit provided by maintenance olaparib in the newly diagnosed setting was achieved with no detrimental effect on patients' HRQOL and was supported by clinically meaningful quality-adjusted progression-free survival and TWiST benefits with maintenance olaparib versus placebo. FUNDING: AstraZeneca and Merck Sharp & Dohme.

Risk Prediction Using Bayesian Networks: An Immunotherapy Case Study in Patients With Metastatic Renal Cell Carcinoma.

PURPOSE: To address the need for more accurate risk stratification models for cancer immuno-oncology, this study aimed to develop a machine-learned Bayesian network model (BNM) for predicting outcomes in patients with metastatic renal cell carcinoma (mRCC) being treated with immunotherapy. METHODS: Patient-level data from the randomized, phase III CheckMate 025 clinical trial comparing nivolumab with everolimus for second-line treatment in patients with mRCC were used to develop the BNM. Outcomes of interest were overall survival (OS), all-cause adverse events, and treatment-related adverse events (TRAE) over 36 months after treatment initiation. External validation of the model's predictions for OS was conducted using data from select centers from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC). RESULTS: Areas under the receiver operating characteristic curve (AUCs) for BNM-based classification of OS using baseline data were 0.74, 0.71, and 0.68 over months 12, 24, and 36, respectively. AUC for OS at 12 months increased to 0.86 when treatment response and progression status in year 1 were included as predictors; progression and response at 12 months were highly prognostic of all outcomes over the 36-month period. AUCs for adverse events and treatment-related adverse events were approximately 0.6 at 12 months but increased to approximately 0.7 by 36 months. Sensitivity analysis comparing the BNM with machine learning classifiers showed comparable performance. Test AUC on IMDC data for 12-month OS was 0.71 despite several variable imbalances. Notably, the BNM outperformed the IMDC risk score alone. CONCLUSION: The validated BNM performed well at prediction using baseline data, particularly with the inclusion of response and progression at 12 months. Additionally, the results suggest that 12 months of follow-up data alone may be sufficient to inform long-term survival projections in patients with mRCC.

Prospective investigation of autism and genotype-phenotype correlations in 22q13 deletion syndrome and SHANK3 deficiency.

BACKGROUND: 22q13 deletion syndrome, also known as Phelan-McDermid syndrome, is a neurodevelopmental disorder characterized by intellectual disability, hypotonia, delayed or absent speech, and autistic features. SHANK3 has been identified as the critical gene in the neurological and behavioral aspects of this syndrome. The phenotype of SHANK3 deficiency has been described primarily from case studies, with limited evaluation of behavioral and cognitive deficits. The present study used a prospective design and inter-disciplinary clinical evaluations to assess patients with SHANK3 deficiency, with the goal of providing a comprehensive picture of the medical and behavioral profile of the syndrome. METHODS: A serially ascertained sample of patients with SHANK3 deficiency (n = 32) was evaluated by a team of child psychiatrists, neurologists, clinical geneticists, molecular geneticists and psychologists. Patients were evaluated for autism spectrum disorder using the Autism Diagnostic Interview-Revised and the Autism Diagnostic Observation Schedule-G. RESULTS: Thirty participants with 22q13.3 deletions ranging in size from 101 kb to 8.45 Mb and two participants with de novo SHANK3 mutations were included. The sample was characterized by high rates of autism spectrum disorder: 27 (84%) met criteria for autism spectrum disorder and 24 (75%) for autistic disorder. Most patients (77%) exhibited severe to profound intellectual disability and only five (19%) used some words spontaneously to communicate. Dysmorphic features, hypotonia, gait disturbance, recurring upper respiratory tract infections, gastroesophageal reflux and seizures were also common. Analysis of genotype-phenotype correlations indicated that larger deletions were associated with increased levels of dysmorphic features, medical comorbidities and social communication impairments related to autism. Analyses of individuals with small deletions or point mutations identified features related to SHANK3 haploinsufficiency, including ASD, seizures and abnormal EEG, hypotonia, sleep disturbances, abnormal brain MRI, gastroesophageal reflux, and certain dysmorphic features. CONCLUSIONS: This study supports findings from previous research on the severity of intellectual, motor, and speech impairments seen in SHANK3 deficiency, and highlights the prominence of autism spectrum disorder in the syndrome. Limitations of existing evaluation tools are discussed, along with the need for natural history studies to inform clinical monitoring and treatment development in SHANK3 deficiency.

Pathway-based analysis of a genome-wide case-control association study of rheumatoid arthritis.

Evaluation of the association between single-nucleotide polymorphisms (SNPs) and disease outcomes is widely used to identify genetic risk factors for complex diseases. Although this analysis paradigm has made significant progress in many genetic studies, many challenges remain, such as the requirement of a large sample size to achieve adequate power. Here we use rheumatoid arthritis (RA) as an example and explore a new analysis strategy: pathway-based analysis to search for related genes and SNPs contributing to the disease.We first propose the application of measure of explained variation to quantify the predictive ability of a given SNP. We then use gene set enrichment analysis to evaluate enrichment of specific pathways, where pathways, are considered enriched if they consist of genes that are associated with the phenotype of interest above and beyond is expected by chance. The results are also compared with score tests for association analysis by adjusting for population stratification.Our study identified some significantly enriched pathways, such as "cell adhesion molecules," which are known to play a key role in RA. Our results showed that pathway-based analysis may identify other biologically interesting loci (e.g., rs1018361) related to RA: the gene (CTLA4) closest to this marker has previously been shown to be associated with RA and the gene is in the significant pathways we identified, even though the marker has not reached genome-wide significance in univariate single-marker analysis.

Using a higher criticism statistic to detect modest effects in a genome-wide study of rheumatoid arthritis.

In high-dimensional studies such as genome-wide association studies, the correction for multiple testing in order to control total type I error results in decreased power to detect modest effects. We present a new analytical approach based on the higher criticism statistic that allows identification of the presence of modest effects. We apply our method to the genome-wide study of rheumatoid arthritis provided in the Genetic Analysis Workshop 16 Problem 1 data set. There is evidence for unknown bias in this study that could be explained by the presence of undetected modest effects. We compared the asymptotic and empirical thresholds for the higher criticism statistic. Using the asymptotic threshold we detected the presence of modest effects genome-wide. We also detected modest effects using 90th percentile of the empirical null distribution as a threshold; however, there is no such evidence when the 95th and 99th percentiles were used. While the higher criticism method suggests that there is some evidence for modest effects, interpreting individual single-nucleotide polymorphisms with significant higher criticism statistics is of undermined value. The goal of higher criticism is to alert the researcher that genetic effects remain to be discovered and to promote the use of more targeted and powerful studies to detect the remaining effects.

Penetrance of HNPCC-related cancers in a retrolective cohort of 12 large Newfoundland families carrying a MSH2 founder mutation: an evaluation using modified segregation models.

BACKGROUND: Accurate risk (penetrance) estimates for associated phenotypes in carriers of a major disease gene are important for genetic counselling of at-risk individuals. Population-specific estimates of penetrance are often needed as well. Families ascertained from high-risk disease clinics provide substantial data to estimate penetrance of a disease gene, but these estimates must be adjusted for possible specific sources of bias. METHODS: A cohort of 12 independently ascertained HNPCC families harbouring a founder MSH2 mutation was identified from a cancer genetics clinic in St. John's, Newfoundland, Canada. Carrier status was known for 247 family members but phenotype information on up to 85 additional relatives with unknown carrier status was available; using modified segregation models these additional individuals could be included in the analyses. Three HNPCC-related phenotypes were evaluated as age at diagnosis of: any HNPCC cancer (first cancer), colorectal cancer (CRC), and endometrial cancer (EC) for females. RESULTS: Lifetime (age 70) risk estimates for male and female carriers were similar for developing any HNPCC cancer (Males = 98.2%, 95% Confidence Interval (CI) = (93.8%, 99.9%); Females = 92.8%, 95% CI = (82.4%, 99.1%)) but female carriers experienced substantially reduced lifetime risk for developing CRC compared to male carriers (Females = 38.9%, 95% CI = (24.2%, 62.1%); Males = 84.5%, 95% CI = (67.3%, 91.3%)). Female non-carriers had very low lifetime risk for these two outcomes while male non-carriers had lifetime risks intermediate to the female carriers and non-carriers. Female carriers had a lifetime risk of developing EC of 82.4%. Relative risks for developing any HNPCC cancer (carriers relative to non-carriers) were substantially greater for females compared to their male counterparts (Females = 54.8, 95%CI = (4.4, 379.8); Males = 9.7, 95% CI = (0.3, 23.8)). Relative risks for developing CRC at age 70 were substantially greater for females compared to their male counterparts (Females = 23.7, 95%CI = (5.6, 137.9); Males = 6.8%, 95% CI = (2.3, 66.2)). However, the risk of developing CRC decreased with age among both genders. CONCLUSION: The proposed modified segregation-based models used to estimate age-specific risks for HNPCC phenotypes can reduce bias due to ascertainment and missing genotype information as well as provide estimates of absolute and relative risks.

Filtering genes for cluster and network analysis.

BACKGROUND: Prior to cluster analysis or genetic network analysis it is customary to filter, or remove genes considered to be irrelevant from the set of genes to be analyzed. Often genes whose variation across samples is less than an arbitrary threshold value are deleted. This can improve interpretability and reduce bias. RESULTS: This paper introduces modular models for representing network structure in order to study the relative effects of different filtering methods. We show that cluster analysis and principal components are strongly affected by filtering. Filtering methods intended specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. To study more realistic situations, we analyze simulated "real" data based on well-characterized E. coli and S. cerevisiae regulatory networks. CONCLUSION: The methods introduced apply very generally, to any similarity matrix describing gene expression. One of the proposed methods, SUMCOV, performed well for all models simulated.

Sparse canonical correlation analysis with application to genomic data integration.

Large scale genomic studies with multiple phenotypic or genotypic measures may require the identification of complex multivariate relationships. In multivariate analysis a common way to inspect the relationship between two sets of variables based on their correlation is canonical correlation analysis, which determines linear combinations of all variables of each type with maximal correlation between the two linear combinations. However, in high dimensional data analysis, when the number of variables under consideration exceeds tens of thousands, linear combinations of the entire sets of features may lack biological plausibility and interpretability. In addition, insufficient sample size may lead to computational problems, inaccurate estimates of parameters and non-generalizable results. These problems may be solved by selecting sparse subsets of variables, i.e. obtaining sparse loadings in the linear combinations of variables of each type. In this paper we present Sparse Canonical Correlation Analysis (SCCA) which examines the relationships between two types of variables and provides sparse solutions that include only small subsets of variables of each type by maximizing the correlation between the subsets of variables of different types while performing variable selection. We also present an extension of SCCA--adaptive SCCA. We evaluate their properties using simulated data and illustrate practical use by applying both methods to the study of natural variation in human gene expression.

Multivariate analysis of complex gene expression and clinical phenotypes with genetic marker data.

This paper summarizes contributions to group 12 of the 15th Genetic Analysis Workshop. The papers in this group focused on multivariate methods and applications for the analysis of molecular data including genotypic data as well as gene expression microarray measurements and clinical phenotypes. A range of multivariate techniques have been employed to extract signals from the multi-feature data sets that were provided by the workshop organizers. The methods included data reduction techniques such as principal component analysis and cluster analysis; latent variable models including structural equations and item response modeling; joint multivariate modeling techniques as well as multivariate visualization tools. This summary paper categorizes and discusses individual contributions with regard to multiple classifications of multivariate methods. Given the wide variety in the data considered, the objectives of the analysis and the methods applied, direct comparison of the results of the various papers is difficult. However, the group was able to make many interesting comparisons and parallels between the various approaches. In summary, there was a consensus among authors in group 12 that the genetic research community should continue to draw experiences from other fields such as statistics, econometrics, chemometrics, computer science and linear systems theory.

Genome-wide sparse canonical correlation of gene expression with genotypes.

There is a growing interest in studying natural variation in human gene expression. Studies mapping genetic determinants of expression profiles are often carried out considering the expression of one gene at a time, an approach that is computationally intensive and may be prone to high false-discovery rate because the number of genes under consideration often exceeds tens of thousands. We present an exploratory method for investigating such data and apply it to the data provided as Problem 1 of Genetic Analysis Workshop 15 (GAW15). In multivariate analysis, canonical correlation analysis is a common way to inspect the relationship between two sets of variables based on their correlation. It determines linear combinations of all variables from each data set such that the correlation between the two linear combinations is maximized. However, due to the large number of genes, linear combinations involving all single-nucleotide polymorphism (SNP) loci and gene expression phenotypes lack biological plausibility and interpretability. We introduce sparse canonical correlation analysis, which examines the relationships of many genetic loci and gene expression phenotypes by providing sparse linear combinations that include only a small subset of loci and gene expression phenotypes. These correlated sets of variables are sufficiently small for biological interpretability and further investigation. Applying this method to the GAW15 Problem 1 data, we identified groups of 41 loci and 150 gene expressions with the highest between-group correlation of 43%.

Impact of normalization and filtering on linkage analysis of gene expression data.

Using the Problem 1 data set made available for Genetic Analysis Workshop 15, we assessed sensitivity of linkage results to a correlation-based feature extraction method as well as to different normalization procedures applied to the raw Affymetrix gene expression microarray data. The impact of these procedures on heritability estimates and on expression quantitative trait loci are investigated. The filtering algorithm we propose in this paper ranks genes based on the total absolute correlation of each gene with all other genes on the array and has the potential to extract features that may play role in functional pathways and gene networks. Our results showed that the normalization and filtering algorithms can have a profound influence on genetic analysis of gene expression data.