A Statistical Testing Strategy Accounting for Random and Nonrandom (Skewed) X-Chromosome Inactivation Identifies Lung Cancer Susceptibility Loci among Smokers.

INTRODUCTION: Lung cancer is the most common cancer worldwide in mortality and the second in incidence. Epidemiological studies found a higher lung cancer risk for smoking women in comparison to men, but these sex differences, irrespective of smoking habits, remain controversial. One of the hypotheses concerns the genetic contribution of the sex chromosomes. However, while genome-wide association studies identified many lung cancer susceptibility loci, these analyses have excluded X-linked loci. METHODS: To account for nongenetic factors, we first presented an association test based on an additive-multiplicative hazard model accounting for random/nonrandom X-inactivation process. A simulation study was performed to investigate the properties of the proposed test as compared with the Wald test from a Cox model with random X-inactivation process and the partial likelihood ratio test proposed by Xu et al. accounting for nonrandom X-inactivation process. Then, we performed an X chromosome-wide association study on 9,261 individuals from the population-based cohort CARTaGENE to identify susceptibility loci for lung cancer among current and past smokers. We adjusted for the PLCOm2012 lung cancer risk score used in screening programs. RESULTS: Simulation results show the good behavior of the proposed test in terms of power and type I error probability as compared to the Xu et al. and the Wald test. Using the proposed test statistic and adjusting for the PLCOm2012 score, the X chromosome-wide statistical analysis identified two SNPs in low-linkage disequilibrium located in the IL1RAPL1 (IL-1 R accessory protein-like) gene: rs12558491 (p = 2.75×10-9) and rs12835699 (p = 1.26×10-6). For both SNPs, the minor allele was associated with lower lung cancer risk. Adjusting for multiple testing, no signal was detected using the Wald or the Xu et al. likelihood ratio tests. CONCLUSION: By taking into account smoking behavior and the X-inactivation process, the investigation of the X chromosome has shed a new light on the association between X-linked loci and lung cancer. We identified two loci associated with lung cancer located in the IL1RAPL1 gene. This finding would have been overlooked by examining only results from other test statistics.

Analyzing cohort studies with interval-censored data: A new model-based linear rank-type test.

To compare two or more survival distributions with interval-censored data, various nonparametric tests have been proposed. Some are based on the G ρ $$ {G}^{\rho } $$ -family introduced by Harrington and Fleming (1991) that allows flexibility for situations in which the hazard ratio decreases monotonically to unity. However, it is unclear how to choose the appropriate value of the parameter ρ $$ \rho $$ . In this work, we propose a novel linear rank-type test for analyzing interval-censored data that derived from a proportional reversed hazard model. We show its relationship with decreasing hazard ratio. This test statistic provides an alternative to the G ρ $$ {G}^{\rho } $$ -based test statistics by bypassing the choice of the ρ $$ \rho $$ parameter. Simulation results show its good behavior. Two studies on breast cancer and drug users illustrate its practical uses and highlight findings that would have been overlooked if other tests had been used. The test is easy to implement with standard software and can be used for a wide range of situations with interval-censored data to test the equality of survival distributions between two or more independent groups.

Normal sex and age-specific parameters in a multi-ethnic population: a cardiovascular magnetic resonance study of the Canadian Alliance for Healthy Hearts and Minds cohort.

BACKGROUND: Despite the growing utility of cardiovascular magnetic resonance (CMR) for cardiac morphology and function, sex and age-specific normal reference values derived from large, multi-ethnic data sets are lacking. Furthermore, most available studies use a simplified tracing methodology. Using a large cohort of participants without history of cardiovascular disease (CVD) or risk factors from the Canadian Alliance for Healthy Heart and Minds, we sought to establish a robust set of reference values for ventricular and atrial parameters using an anatomically correct contouring method, and to determine the influence of age and sex on ventricular parameters. METHODS AND RESULTS: Participants (n = 3206, 65% females; age 55.2 ± 8.4 years for females and 55.1 ± 8.8 years for men) underwent CMR using standard methods for quantitative measurements of cardiac parameters. Normal ventricular and atrial reference values are provided: (1) for males and females, (2) stratified by four age categories, and (3) for different races/ethnicities. Values are reported as absolute, indexed to body surface area, or height. Ventricular volumes and mass were significantly larger for males than females (p < 0.001). Ventricular ejection fraction was significantly diminished in males as compared to females (p < 0.001). Indexed left ventricular (LV) end-systolic, end-diastolic volumes, mass and right ventricular (RV) parameters significantly decreased as age increased for both sexes (p < 0.001). For females, but not men, mean LV and RVEF significantly increased with age (p < 0.001). CONCLUSION: Using anatomically correct contouring methodology, we provide accurate sex and age-specific normal reference values for CMR parameters derived from the largest, multi-ethnic population free of CVD to date. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, NCT02220582. Registered 20 August 2014-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02220582 .

Epidemiological characteristics of the COVID-19 spring outbreak in Quebec, Canada: a population-based study.

BACKGROUND: By mid-July 2020, more than 108,000 COVID-19 cases had been diagnosed in Canada with more than half in the province of Quebec. In this context, we launched a study to analyze the epidemiological characteristics and the socio-economic impact of the spring outbreak in the population. METHOD: We conducted an online survey of the participants of the CARTaGENE population-based cohort, composed of middle-aged and older adults. We collected information on socio-demographic, lifestyle, health condition, COVID-19 related symptoms and COVID-19 testing. We studied the association between these factors and two outcomes: the status of having been tested for SARS-CoV-2 and the status of having received a positive test. These associations were measured with univariate and multivariate analyses using a hybrid tree-based regression model. RESULTS: Among the 8,129 respondents from the CARTaGENE cohort, 649 were tested for COVID-19 and 41 were positive. Medical workers and individuals having a contact with a COVID-19 patient had the highest probabilities of being tested (32% and 42.4%, respectively) and of being positive (17.2% and 13.0%, respectively) among those tested. Approximately 8% of the participants declared that they have experienced at least one of the four COVID-19 related symptoms chosen by the Public Health authorities (fever, cough, dyspnea, anosmia) but were not tested. Results from the tree-based model analyses adjusted on exposure factors showed that the combination of dyspnea, dry cough and fever was highly associated with being tested whereas anosmia, fever, and headache were the most discriminant factors for having a positive test among those tested. During the spring outbreak, more than one third of the participants have experienced a decrease in access to health services. There were gender and age differences in the socio-economic and emotional impacts of the pandemic. CONCLUSION: We have shown some discrepancies between the symptoms associated with being tested and being positive. In particular, the anosmia is a major discriminant symptom for positivity whereas ear-nose-throat symptoms seem not to be COVID-19 related. The results also emphasize the need of increasing the accessibility of testing for the general population.

Assessing the effect of genetic markers on drug immunogenicity from a mechanistic model-based approach.

BACKGROUND: With the growth in use of biotherapic drugs in various medical fields, the occurrence of anti-drug antibodies represents nowadays a serious issue. This immune response against a drug can be due either to pre-existing antibodies or to the novel production of antibodies from B-cell clones by a fraction of the exposed subjects. Identifying genetic markers associated with the immunogenicity of biotherapeutic drugs may provide new opportunities for risk stratification before the introduction of the drug. However, real-world investigations should take into account that the population under study is a mixture of pre-immune, immune-reactive and immune-tolerant subjects. METHOD: In this work, we propose a novel test for assessing the effect of genetic markers on drug immunogenicity taking into account that the population under study is a mixed one. This test statistic is derived from a novel two-part semiparametric improper survival model which relies on immunological mechanistic considerations. RESULTS: Simulation results show the good behavior of the proposed statistic as compared to a two-part logrank test. In a study on drug immunogenicity, our results highlighted findings that would have been discarded when considering classical tests. CONCLUSION: We propose a novel test that can be used for analyzing drug immunogenicity and is easy to implement with standard softwares. This test is also applicable for situations where one wants to test the equality of improper survival distributions of semi-continuous outcomes between two or more independent groups.

A test for comparing current status survival data with crossing hazard functions and its application to immunogenicity of biotherapeutics.

Several tests have been recently implemented in the nonparametric comparison of current status survival data. However, they are not suited for the situation of crossing hazards. In this setting, we propose a new test specifically designed for crossing hazards alternatives. The proposed test is compared to classical implemented tests through simulations mimicking crossing hazards situations with various schemes of censoring. The results show that the proposed test has a correct type I error and generally outperforms the existing methods. The application of the proposed test on a real dataset on immunogenicity of interferon-ß among multiple sclerosis patients highlights the interest of the proposed test.

Bagging survival tree procedure for variable selection and prediction in the presence of nonsusceptible patients.

BACKGROUND: For clinical genomic studies with high-dimensional datasets, tree-based ensemble methods offer a powerful solution for variable selection and prediction taking into account the complex interrelationships between explanatory variables. One of the key component of the tree-building process is the splitting criterion. For survival data, the classical splitting criterion is the Logrank statistic. However, the presence of a fraction of nonsusceptible patients in the studied population advocates for considering a criterion tailored to this peculiar situation. RESULTS: We propose a bagging survival tree procedure for variable selection and prediction where the survival tree-building process relies on a splitting criterion that explicitly focuses on time-to-event survival distribution among susceptible patients. A simulation study shows that our method achieves good performance for the variable selection and prediction. Different criteria for evaluating the importance of the explanatory variables and the prediction performance are reported. Our procedure is illustrated on a genomic dataset with gene expression measurements from early breast cancer patients. CONCLUSIONS: In the presence of nonsusceptible patients among the studied population, our procedure represents an efficient way to select event-related explanatory covariates with potential higher-order interaction and identify homogeneous groups of susceptible patients.

A Bagged, Partially Linear, Tree-Based Regression Procedure for Prediction and Variable Selection.

OBJECTIVES: In genomics, variable selection and prediction accounting for the complex interrelationships between explanatory variables represent major challenges. Tree-based methods are powerful alternatives to classical regression models. We have recently proposed the generalized, partially linear, tree-based regression (GPLTR) procedure that integrates the advantages of generalized linear regression (allowing the incorporation of confounding variables) and of tree-based models. In this work, we use bagging to address a classical concern of tree-based methods: their instability. METHODS: We present a bagged GPLTR procedure and three scores for variable importance. The prediction accuracy and the performance of the scores are assessed by simulation. The use of this procedure is exemplified by the analysis of a lung cancer data set. The aim is to predict the epidermal growth factor receptor (EGFR) mutation based on gene expression measurements, taking into account the ethnicity (confounder variable) and perform variable selection. RESULTS: The procedure performs well in terms of prediction accuracy. The scores differentiate predictive variables from noise variables. Based on a lung adenocarcinoma data set, the procedure achieves good predictive performance for EGFR mutation and selects relevant genes. CONCLUSION: The proposed bagged GPLTR procedure performs well for prediction and variable selection.

Reduction of microRNA 122 expression in IFNL3 CT/TT carriers and during progression of fibrosis in patients with chronic hepatitis C.

UNLABELLED: The microRNA miR-122 is highly expressed in the liver and stimulates hepatitis C virus (HCV) replication in vitro. IFNL3 (lambda-3 interferon gene) polymorphisms and the expression of miR-122 have been associated with sustained virological response (SVR) to treatment with pegylated interferon plus ribavirin in patients with chronic hepatitis C (CHC). We investigated, in vivo, the relationship between miR-122 expression, IFNL3 polymorphism, fibrosis, and response to PEG-IFN plus ribavirin. Pretreatment liver biopsy specimens and serum samples from 133 patients with CHC were included. Sixty-six patients achieved SVR, and 64 failed to respond to the treatment (43 nonresponders [NR] and 21 relapsers [RR]). All stages of fibrosis were represented, with 39, 50, 23, and 19 patients, respectively, having Metavir scores of F1, F2, F3, and F4. miR-122 expression was assessed by real-time quantitative PCR (RT-qPCR) and IFNL3 rs12979860 by direct sequencing. Hepatic miR-122 expression was higher in patients with the IFNL3 CC genotype than in those with the IFNL3 CT or TT genotype, in all patients (P = 0.025), and in NRs plus RRs (P = 0.013). Increased hepatic miR-122 was more strongly associated with complete early virological response (cEVR) (P = 0.003) than with SVR (P = 0.016). In multivariate analysis, increased hepatic miR-122 was only associated with the IFNL3 CC genotype. miR-122 was decreased in patients with advanced fibrosis (Metavir scores of F3 and F4) compared to its levels in patients with mild and moderate fibrosis (F1 and F2) (P = 0.01). Serum and hepatic expression of miR-122 were not associated. The association between miR-122 and IFNL3 was stronger than the association between miR-122 and response to treatment. miR-122 may play a role in the early viral decline that is dependent on IFNL3 and the innate immune response. IMPORTANCE: miR-122 plays a crucial role during HCV infection. Moreover, it was reported that miR-122 binding within the HCV genome stimulates its replication. Moreover, miR-122 is highly expressed within hepatocytes, where it regulates many cellular pathways. A reduction of miR-122 expression has been suggested to be associated with responsiveness to IFN-based therapy in patients with chronic hepatitis C. Several independent genome-wide association studies reported a strong association between IFNL3 polymorphism and responsiveness to IFN-based therapy. We report here a strong association between the expression of miR-122 and IFNL3 polymorphism that is independent of the response to the treatment. Our data suggest that modification of miR-122 expression may play an important role in the molecular mechanism associated with IFNL3 polymorphism. Moreover, we report a reduction of miR-122 at more advanced stages of fibrosis in patients with chronic hepatitis C.

Patterns of chromosomal copy-number alterations in intrahepatic cholangiocarcinoma.

BACKGROUND: Intrahepatic cholangiocarcinomas (ICC) are relatively rare malignant tumors associated with a poor prognosis. Recent studies using genome-wide sequencing technologies have mainly focused on identifying new driver mutations. There is nevertheless a need to investigate the spectrum of copy number aberrations in order to identify potential target genes in the altered chromosomal regions. The aim of this study was to characterize the patterns of chromosomal copy-number alterations (CNAs) in ICC. METHODS: 53 patients having ICC with frozen material were selected. In 47 cases, DNA hybridization has been performed on a genomewide SNP array. A procedure with a segmentation step and a calling step classified genomic regions into copy-number aberration states. We identified the exclusively amplified and deleted recurrent genomic areas. These areas are those showing the highest estimated propensity level for copy loss (resp. copy gain) together with the lowest level for copy gain (resp. copy loss). We investigated ICC clustering. We analyzed the relationships between CNAs and clinico-pathological characteristics. RESULTS: The overall genomic profile of ICC showed many alterations with higher rates for the deletions. Exclusively deleted genomic areas were 1p, 3p and 14q. The main exclusively amplified genomic areas were 1q, 7p, 7q and 8q. Based on the exclusively deleted/amplified genomic areas, a clustering analysis identified three tumors groups: the first group characterized by copy loss of 1p and copy gain of 7p, the second group characterized by 1p and 3p copy losses without 7p copy gain, the last group characterized mainly by very few CNAs. From univariate analyses, the number of tumors, the size of the largest tumor and the stage were significantly associated with shorter time recurrence. We found no relationship between the number of altered cytobands or tumor groups and time to recurrence. CONCLUSION: This study describes the spectrum of chromosomal aberrations across the whole genome. Some of the recurrent exclusive CNAs harbor candidate target genes. Despite the absence of correlation between CNAs and clinico-pathological characteristics, the co-occurence of 7p gain and 1p loss in a subgroup of patients may suggest a differential activation of EGFR and its downstream pathways, which may have a potential effect on targeted therapies.

Influence of trial duration on the bias of the estimated treatment effect in clinical trials when individual heterogeneity is ignored.

In randomized clinical trials where the times to event of two treatment groups are compared under a proportional hazards assumption, it has been established that omitting prognostic factors from the model entails an underestimation of the hazards ratio. Heterogeneity due to unobserved covariates in cancer patient populations is a concern since genomic investigations have revealed molecular and clinical heterogeneity in these populations. In HIV prevention trials, heterogeneity is unavoidable and has been shown to decrease the treatment effect over time. This article assesses the influence of trial duration on the bias of the estimated hazards ratio resulting from omitting covariates from the Cox analysis. The true model is defined by including an unobserved random frailty term in the individual hazard that reflects the omitted covariate. Three frailty distributions are investigated: gamma, log-normal, and binary, and the asymptotic bias of the hazards ratio estimator is calculated. We show that the attenuation of the treatment effect resulting from unobserved heterogeneity strongly increases with trial duration, especially for continuous frailties that are likely to reflect omitted covariates, as they are often encountered in practice. The possibility of interpreting the long-term decrease in treatment effects as a bias induced by heterogeneity and trial duration is illustrated by a trial in oncology where adjuvant chemotherapy in stage 1B NSCLC was investigated.

Intertumor heterogeneity of non-small-cell lung carcinomas revealed by multiplexed mutation profiling and integrative genomics.

Non-small-cell lung cancer (NSCLC) is a heterogeneous disease, with a burden of genomic alterations exceeding most other tumors. The goal of our study was to evaluate the frequencies of co-occurring mutations and copy-number aberrations (CNAs) within the same tumor and to evaluate their potential clinical impact. Mass-spectrometry based mutation profiling using a customized lung cancer panel evaluating 214 mutations across 26 key NSCLC genes was performed on 230 nonsquamous NSCLC and integrated with genome-wide CNAs and clinical variables. Among the 138 cases having at least one mutation, one-third (41, 29.7%) showed two or more mutations, either in the same gene (double mutation) or in different genes (co-mutations). In epidermal growth factor receptor (EGFR) mutant cancers, there was a double mutation in 18% and co-mutations in the following genes: TP53 (10%), PIK3CA (8%), STK11 (6%) and MET (4%). Significant relationships were detected between EGFR mutation and 1p, 7p copy gains (harboring the EGFR gene) as well as 13q copy loss. KRAS mutation was significantly related with 1q gain and 3q loss. For Stage I, tumors harboring at least one mutation or PIK3CA mutation were significantly correlated with poor prognosis (p-value = 0.02). When combining CNAs and mutational status, patients having both KRAS mutation and the highest related CNA (3q22.3 copy loss) showed a significant poorer prognosis (p-value = 0.03). Our study highlights the clinical relevance of studying tumor complexity by integrative genomic analysis and the need for developing assays that broadly screen for both "actionable" mutations and copy-number alterations to improve precision of stratified treatment approaches.

A discrimination index for selecting markers of tumor growth dynamic across multiple cancer studies with a cure fraction.

To identify genomic markers with consistent effect on tumor dynamics across multiple cancer series, discrimination indices based on proportional hazards models can be used since they do not depend heavily on the sample size. However, the underlying assumption of proportionality of the hazards does not always hold, especially when the studied population is a mixture of cured and uncured patients, like in early-stage cancers. We propose a novel index that quantifies the capability of a genomic marker to separate uncured patients, according to their time-to-event outcomes. It allows to identify genomic markers characterizing tumor growth dynamic across multiple studies. Simulation results show that our index performs better than classical indices based on the Cox model. It is neither affected by the sample size nor the cure rate fraction. In a cross-study of early-stage breast cancers, the index allows to select genomic markers with a potential consistent effect on tumor growth dynamics.

Evaluation of the accuracy of the PLCOm2012 6-year lung cancer risk prediction model among smokers in the CARTaGENE population-based cohort.

BACKGROUND: The PLCOm2012 prediction tool for risk of lung cancer has been proposed for a pilot program for lung cancer screening in Quebec, but has not been validated in this population. We sought to validate PLCOm2012 in a cohort of Quebec residents, and to determine the hypothetical performance of different screening strategies. METHODS: We included smokers without a history of lung cancer from the population-based CARTaGENE cohort. To assess PLCOm2012 calibration and discrimination, we determined the ratio of expected to observed number of cases, as well as the sensitivity, specificity and positive predictive values of different risk thresholds. To assess the performance of screening strategies if applied between Jan. 1, 1998, and Dec. 31, 2015, we tested different thresholds of the PLCOm2012 detection of lung cancer over 6 years (1.51%, 1.70% and 2.00%), the criteria of Quebec's pilot program (for people aged 55-74 yr and 50-74 yr) and recommendations from 2021 United States and 2016 Canada guidelines. We assessed shift and serial scenarios of screening, whereby eligibility was assessed annually or every 6 years, respectively. RESULTS: Among 11 652 participants, 176 (1.51%) lung cancers were diagnosed in 6 years. The PLCOm2012 tool underestimated the number of cases (expected-to-observed ratio 0.68, 95% confidence interval [CI] 0.59-0.79), but the discrimination was good (C-statistic 0.727, 95% CI 0.679-0.770). From a threshold of 1.51% to 2.00%, sensitivities ranged from 52.3% (95% CI 44.6%-59.8%) to 44.9% (95% CI 37.4%-52.6%), specificities ranged from 81.6% (95% CI 80.8%-82.3%) to 87.7% (95% CI 87.0%-88.3%) and positive predictive values ranged from 4.2% (95% CI 3.4%-5.1%) to 5.3% (95% CI 4.2%-6.5%). Overall, 8938 participants had sufficient data to test performance of screening strategies. If eligibility was estimated annually, Quebec pilot criteria would have detected fewer cancers than PLCOm2012 at a 2.00% threshold (48.3% v. 50.2%) for a similar number of scans per detected cancer. If eligibility was estimated every 6 years, up to 26 fewer lung cancers would have been detected; however, this scenario led to higher positive predictive values (highest for PLCOm2012 with a 2.00% threshold at 6.0%, 95% CI 4.8%-7.3%). INTERPRETATION: In a cohort of Quebec smokers, the PLCOm2012 risk prediction tool had good discrimination in detecting lung cancer, but it may be helpful to adjust the intercept to improve calibration. The implementation of risk prediction models in some of the provinces of Canada should be done with caution.

Provincial variation in colorectal cancer screening adherence in Canada; evidence from the Canadian Partnership for Tomorrow's Health.

Introduction: Although colorectal cancer (CRC) screening program is proven to reduce CRC incidence and mortality, understanding patterns and predictors of suboptimal adherence in screening program requires further investigation in Canada. Methods: We used self-reported data from five regional cohorts of the Canadian Partnership for Tomorrow's Health (CanPath), namely the BC Generations Project (BCGP), Alberta's Tomorrow Project (ATP), the Ontario Health Study (OHS), Quebec's CARTaGENE, and the Atlantic Partnership for Tomorrow's Health Study (Atlantic PATH). We stratified participants into the following four risk categories: 1) age 50-74 years, 2) family history in a first-degree relative, 3) personal history of chronic inflammatory bowel disease and/or polyps, and 4) co-existence of personal risk and family history. Multivariable logistic regression was used to identify predictors of adherence to the screening guidelines. Results: Adherence to CRC screening varied considerably between regions, ranging from 16.6% in CARTaGENE to 47.7% in OHS. Compared to the largest cohort OHS, the likelihood of non-adherence to CRC screening was significantly higher in BCGP (OR 1.15, 95% CI 1.11-1.19), the Atlantic PATH (OR 1.90, 95% CI 1.82-1.99) and CARTaGENE (OR 5.10, 95% CI 4.85-5.36). Low physical activity, current smoking, presence of personal risk, family history of CRC significantly reduced the likelihood of adherence to screening recommendations. Discussion/conclusion: Compared to the national target of ≥ 60% for participation in CRC screening, adherence to regular CRC screening was suboptimal in this cohort of Canadians and varied by region. Further efforts are needed to identify the specific barriers to screening adherence in different provinces and across risk categories.

Response to Biologic Drugs in Patients With Rheumatoid Arthritis and Antidrug Antibodies.

Importance: There are conflicting data on the association of antidrug antibodies with response to biologic disease-modifying antirheumatic drugs (bDMARDs) in rheumatoid arthritis (RA). Objective: To analyze the association of antidrug antibodies with response to treatment for RA. Design, Setting, and Participants: This cohort study analyzed data from the ABI-RA (Anti-Biopharmaceutical Immunization: Prediction and Analysis of Clinical Relevance to Minimize the Risk of Immunization in Rheumatoid Arthritis Patients) multicentric, open, prospective study of patients with RA from 27 recruiting centers in 4 European countries (France, Italy, the Netherlands, and the UK). Eligible patients were 18 years or older, had RA diagnosis, and were initiating a new bDMARD. Recruitment spanned from March 3, 2014, to June 21, 2016. The study was completed in June 2018, and data were analyzed in June 2022. Exposures: Patients were treated with a new bDMARD: adalimumab, infliximab (grouped as anti-tumor necrosis factor [TNF] monoclonal antibodies [mAbs]), etanercept, tocilizumab, and rituximab according to the choice of the treating physician. Main Outcomes and Measures: The primary outcome was the association of antidrug antibody positivity with EULAR (European Alliance of Associations for Rheumatology; formerly, European League Against Rheumatism) response to treatment at month 12 assessed through univariate logistic regression. The secondary end points were the EULAR response at month 6 and at visits from month 6 to months 15 to 18 using generalized estimating equation models. Detection of antidrug antibody serum levels was performed at months 1, 3, 6, 12, and 15 to 18 using electrochemiluminescence (Meso Scale Discovery) and drug concentration for anti-TNF mAbs, and etanercept in the serum was measured using enzyme-linked immunosorbent assay. Results: Of the 254 patients recruited, 230 (mean [SD] age, 54.3 [13.7] years; 177 females [77.0%]) were analyzed. At month 12, antidrug antibody positivity was 38.2% in patients who were treated with anti-TNF mAbs, 6.1% with etanercept, 50.0% with rituximab, and 20.0% with tocilizumab. There was an inverse association between antidrug antibody positivity (odds ratio [OR], 0.19; 95% CI, 0.09-0.38; P < .001) directed against all biologic drugs and EULAR response at month 12. Analyzing all the visits starting at month 6 using generalized estimating equation models confirmed the inverse association between antidrug antibody positivity and EULAR response (OR, 0.35; 95% CI, 0.18-0.65; P < .001). A similar association was found for tocilizumab alone (OR, 0.18; 95% CI, 0.04-0.83; P = .03). In the multivariable analysis, antidrug antibodies, body mass index, and rheumatoid factor were independently inversely associated with response to treatment. There was a significantly higher drug concentration of anti-TNF mAbs in patients with antidrug antibody-negative vs antidrug antibody-positive status (mean difference, -9.6 [95% CI, -12.4 to -6.9] mg/L; P < 001). Drug concentrations of etanercept (mean difference, 0.70 [95% CI, 0.2-1.2] mg/L; P = .005) and adalimumab (mean difference, 1.8 [95% CI, 0.4-3.2] mg/L; P = .01) were lower in nonresponders vs responders. Methotrexate comedication at baseline was inversely associated with antidrug antibodies (OR, 0.50; 95% CI, 0.25-1.00; P = .05). Conclusions and Relevance: Results of this prospective cohort study suggest an association between antidrug antibodies and nonresponse to bDMARDs in patients with RA. Monitoring antidrug antibodies could be considered in the treatment of these patients, particularly nonresponders to biologic RA drugs.

IL28B polymorphism is associated with treatment response in patients with genotype 4 chronic hepatitis C.

BACKGROUND & AIMS: Polymorphisms in the region of the interleukin (IL)28B gene have been associated with pegylated-interferon (PEG-IFN) and ribavirin treatment response mainly in genotype 1 HCV infections. However, there are few data on HCV genotype 4 (HCV-4) infection. We evaluated, in a unique well-characterized cohort of HCV-4 patients, the association of IL28B polymorphism with response to treatment or liver disease severity. METHODS: This study included 164 HCV-4 patients from different ethnic groups (Egyptian, European, and Sub-Saharan African). Among these patients, 82 were studied for response and 160 for disease severity. Free DNA extracted from all the 164 patient's serum samples was analyzed by direct sequencing of the SNP rs12979860 of IL28B. Genetic and bio-clinical features from patients having sustained virological response (43 SVR patients) and from those who did not respond to treatment or had a relapse after the end of the treatment (39 NR patients) were compared. IL28B polymorphism was compared between the 78 patients with mild fibrosis (Metavir score F0-F1) and the 82 with advanced fibrosis (F2-F4). RESULTS: Our data showed a better treatment response rate of the C allele of the IL28B gene SNP rs12979860 (p=0.0008). The response rates were 81.8%, 46.5%, and 29.4% for genotype CC, CT, and TT, respectively. No significant relationship was found between rs12979860 and the severity of the disease. CONCLUSIONS: The SNP rs12979860 is strongly associated with SVR in patients infected with HCV-4, but not with liver disease severity. Analysis of IL28B genotype might be used to guide treatment for these patients.

Socio-Demographic Factors Associated With COVID-19 Vaccine Hesitancy Among Middle-Aged Adults During the Quebec's Vaccination Campaign.

Introduction: The objective of this study was to characterize the combinations of demographic and socioeconomic characteristics associated to the unwillingness to receive the COVID-19 vaccines during the 2021 Quebec's vaccination campaign. Materials and Methods: In March-June 2021, we conducted an online survey of the participants of the CARTaGENE population-based cohort, composed of middle-aged and older adults. After comparing the vaccinated and unvaccinated participants, we investigated vaccine hesitancy among participants who were unvaccinated. For identifying homogeneous groups of individuals with respect to vaccine hesitancy, we used a machine learning approach based on a hybrid tree-based model. Results: Among the 6,105 participants of the vaccine cohort, 3,553 (58.2%) had at least one dose of COVID-19 vaccine. Among the 2,552 participants, 221 (8.7%) did not want to be vaccinated (91) or were uncertain (130). The median age for the unvaccinated participants was 59.3 years [IQR 54.7-63.9]. The optimal hybrid tree-based model identified seven groups. Individuals having a household income lower than $100,000 and being born outside of Canada had the highest rate of vaccine hesitancy (28% [95% CI 19.8-36.3]). For those born in Canada, the vaccine hesitancy rate among the individuals who have a household income below $50,000 before the pandemic or are Non-retired was of 12.1% [95% CI 8.7-15.5] and 10.6% [95% CI 7.6-13.7], respectively. For the participants with a high household income before the pandemic (more than $100,000) and a low level of education, those who experienced a loss of income during the pandemic had a high level of hesitancy (19.2% [8.5-29.9]) whereas others who did not experience a loss of income had a lower level of hesitancy (6.0% [2.8-9.2]). For the other groups, the level of hesitancy was low of around 3% (3.2% [95% CI 1.9-4.4] and 3.4% [95% CI 1.5-5.2]). Discussion: Public health initiatives to tackle vaccine hesitancy should take into account these socio-economic determinants and deliver personalized messages toward people having socio-economic difficulties and/or being part of socio-cultural minorities.

Five-year absolute risk estimates of colorectal cancer based on CCRAT model and polygenic risk scores: A validation study using the Quebec population-based cohort CARTaGENE.

The objective was to evaluate the predictive performance of the Colorectal Cancer Risk Assessment Tool (CCRAT) and three polygenic risk scores (Hsu et al., 2015; Law et al., 2019, Archambault et al., 2020) to predict the occurrence of colorectal cancer at five years in a Quebec population-based cohort. By using the CARTaGENE cohort, we computed the absolute risk of colorectal cancer with the CCRAT model, the polygenic risk scores (PRS) and combined clinico-genetic models (CCRAT + PRS). We also tailored the CCRAT model by using the marginal age-specific colorectal incidence rates in Canada and the risk score distribution. We reported the calibration and the discrimination. Performances of the PRSs, combined and tailored CCRAT models were compared to the original CCRAT model. The expected-to-observed ratio of the original CCRAT model was 0.54 [0.43-0.68]. The c-index was 74.79 [68.3-80.5]. The tailored CCRAT model improved the expected-to-observed ratio (0.74 [0.59-0.94]) and c-index (76.39 [69.7-82.1]). All PRS improved the expected-to-observed ratios (around 0.83, confidence intervals including one). PRSs' c-indexes were not significantly different from CCRAT models. Results from the combined models were close to those from the PRS models, Archambault combined model's c-index being significantly higher than the original and tailored CCRAT models (78.67 [70.8-86.5]; p < 0.001 and p = 0.028, respectively). In this Quebec cohort, CCRAT model has a good discrimination with a poor calibration. While the tailored CCRAT provides some gain in calibration, clinico-genetic models improved both calibration and discrimination. However, better calibrations must be obtained before a practical use among the inhabitants of Quebec province.

Machine learning predicts response to TNF inhibitors in rheumatoid arthritis: results on the ESPOIR and ABIRISK cohorts.

OBJECTIVES: Around 30% of patients with rheumatoid arthritis (RA) do not respond to tumour necrosis factor inhibitors (TNFi). We aimed to predict patient response to TNFi using machine learning on simple clinical and biological data. METHODS: We used data from the RA ESPOIR cohort to train our models. The endpoints were the EULAR response and the change in Disease Activity Score (DAS28). We compared the performances of multiple models (linear regression, random forest, XGBoost and CatBoost) on the training set and cross-validated them using the area under the receiver operating characteristic curve (AUROC) or the mean squared error. The best model was then evaluated on a replication cohort (ABIRISK). RESULTS: We included 161 patients from ESPOIR and 118 patients from ABIRISK. The key selected features were DAS28, lymphocytes, ALT (aspartate aminotransferase), neutrophils, age, weight, and smoking status. When predicting EULAR response, CatBoost achieved the best performances of the four tested models. It reached an AUROC of 0.72 (0.68-0.73) on the train set (ESPOIR). Better results were obtained on the train set when etanercept and monoclonal antibodies were analysed separately. On the test set (ABIRISK), these models respectively achieved on AUROC of 0.70 (0.57-0.82) and 0.71 (0.55-0.86). Two decision thresholds were tested. The first prioritised a high confidence in identifying responders and yielded a confidence up to 90% for predicting response. The second prioritised a high confidence in identifying inadequate responders and yielded a confidence up to 70% for predicting non-response. The change in DAS28 was predicted with an average error of 1.1 DAS28 points. CONCLUSION: The machine learning models developed allowed predicting patient response to TNFi exclusively using data available in clinical routine.