Background We previously reported an association between household chemical exposures and an increased risk of paediatric-onset multiple sclerosis. Methods Using a case–control paediatric multiple sclerosis study, gene–environment interaction between exposure to household chemicals and genotypes for risk of paediatric-onset multiple sclerosis was estimated. Genetic risk factors of interest included the two major HLA multiple sclerosis risk factors, the presence of DRB1*15 and the absence of A*02, and multiple sclerosis risk variants within the metabolic pathways of common household toxic chemicals, including IL-6 (rs2069852), BCL-2 (rs2187163) and NFKB1 (rs7665090). Results 490 paediatric-onset multiple sclerosis cases and 716 controls were included in the analyses. Exposures to insect repellent for ticks or mosquitos (OR 1.47, 95% CI 1.06 to 2.04, p=0.019), weed control products (OR 2.15, 95% CI 1.51 to 3.07, p<0.001) and plant/tree insect or disease control products (OR 3.25, 95% CI 1.92 to 5.49, p<0.001) were associated with increased odds of paediatric-onset multiple sclerosis. There was significant additive interaction between exposure to weed control products and NFKB1 SNP GG (attributable proportions (AP) 0.48, 95% CI 0.10 to 0.87), and exposure to plant or disease control products and absence of HLA-A*02 (AP 0.56; 95% CI 0.03 to 1.08). There was a multiplicative interaction between exposure to weed control products and NFKB1 SNP GG genotype (OR 2.30, 95% CI 1.00 to 5.30) but not for other exposures and risk variants. No interactions were found with IL-6 and BCL-2 SNP GG genotypes. Conclusions The presence of gene–environment interactions with household toxins supports their possible causal role in paediatric-onset multiple sclerosis.
ABSTRACT Background The COVID‐19 pandemic underscored the need for rapid and accurate diagnostic tools. In August 2020, the Abbott BinaxNOW COVID‐19 Antigen Card test became available as a timely and affordable alternative for SARS‐CoV‐2 molecular testing, but its performance may vary due to factors including timing and symptomatology. This study evaluates BinaxNOW diagnostic performance in diverse epidemiological contexts. Methods Using RT‐PCR as reference, we assessed performance of the BinaxNOW COVID‐19 test for SARS‐CoV‐2 detection in anterior nasal swabs from participants of two studies in Puerto Rico from December 2020 to May 2023. Test performance was assessed by days post symptom onset, collection strategy, vaccination status, symptomatology, repeated testing, and RT‐PCR cycle threshold (Ct) values. Results BinaxNOW demonstrated an overall sensitivity of 84.1% and specificity of 98.8%. Sensitivity peaked within 1–6 days after symptom onset (93.2%) and was higher for symptomatic (86.3%) than asymptomatic (67.3%) participants. Sensitivity declined over the course of infection, dropping from 96.3% in the initial test to 48.4% in testing performed 7–14 days later. BinaxNOW showed 99.5% sensitivity in participants with low Ct values (≤ 25) but lower sensitivity (18.2%) for participants with higher Cts (36–40). Conclusions BinaxNOW demonstrated high sensitivity and specificity, particularly in early‐stage infections and symptomatic participants. In situations where test sensitivity is crucial for clinical decision‐making, nucleic acid amplification tests are preferred. These findings highlight the importance of considering clinical and epidemiological context when interpreting test results and emphasize the need for ongoing research to adapt testing strategies to emerging SARS‐CoV‐2 variants.
Abstract The high prevalence of autoimmune hypothyroidism (AIHT) - more than 5% in human populations - provides a unique opportunity to unlock the most complete picture to date of genetic loci that underlie systemic and organ-specific autoimmunity. Using a meta-analysis of 81,718 AIHT cases in FinnGen and the UK Biobank, we dissect associations along axes of thyroid dysfunction and autoimmunity. This largest-to-date scan of hypothyroidism identifies 418 independent associations (p < 5x10− 8), more than half of which have not previously been documented in thyroid disease. In 48 of these, a protein-coding variant is the lead SNP or is highly correlated (r2 > 0.95) with the lead SNP at the locus, including low-frequency coding variants at LAG3, ZAP70, TG, TNFSF11, IRF3, S1PR4, HABP2, ZNF429 as well as established variants at ADCY7, IFIH1 and TYK2. The variants at LAG3 (P67T), ZAP70 (T155M), and TG (Q655X) are highly enriched in Finland and functional experiments in T-cells demonstrate that the ZAP70:T155M allele reduces T-cell activation. By employing a large-scale scan of non-thyroid autoimmunity and a published meta-analysis of TSH levels, we use a Bayesian classifier to dissect the associated loci into distinct groupings and from this estimate, a significant proportion are involved in systemic (i.e., general to multiple autoimmune conditions) autoimmunity (34%) and another subset in thyroid-specific dysfunction (17%). By comparing these association results further to other common disease endpoints, we identify a noteworthy overlap with skin cancer, with 10% of AIHT loci showing a consistent but opposite pattern of association where alleles that increase the risk of hypothyroidism have protective effects for skin cancer. The association results, including genes encoding checkpoint inhibitors and other genes affecting protein levels of PD1, bolster the causal role of natural variation in autoimmunity influencing cancer outcomes.
Checkpoint inhibitors (CPIs) have significantly enhanced cancer treatment, yet formation of antidrug antibodies (ADA) can reduce drug efficacy and lead to increased immune toxicity.1 Identifying biomarkers predictive of ADA formation is crucial to optimize administration of CPIs. Human leukocyte antigen (HLA) genes are responsible for presentation of antigens to T cells and harbor substantial inter-patient variation. A recent study2 identified allelic variation in the HLA-DRB1 gene as a risk factor for ADA formation in CPI. For non-CPI-directed cancer immunotherapies (CITs), aggregated clinical data is scarce, and the role of HLA in ADA formation remains largely unexplored.
Methods
To investigate associations between HLA alleles and ADA formation, we established a harmonized data mart from 23 early-phase CIT trials, encompassing 12 molecules with diverse mode of actions and various cancer indications. This comprehensive dataset includes a total of 3568 patients, both CPI-naïve and CPI-experienced, and features clinical and two-field class I and II HLA alleles imputed from genotyping arrays.3 Treatment-induced ADA status was determined using a standardized set of definitions based on therapeutic antibody-specific titers collected longitudinally.4 We adopted an HLA-wide approach5 to assess associations with persistent ADA formation, focusing on a patient population of European ethnicity to account for differences in HLA allele frequencies and correcting for potential confounders of ADA such as age and gender.
Results
Associations with the HLA alleles are summarized on a per molecule basis. After Bonferroni correction for HLA alleles, no statistically significant association was identified. However, the results for CPI as a standard-of-care combination therapy showed an odds ratio of 1.84 (CI 95% 1.02–3.21) for the DRB1*01:01 variant, consistent with the main finding previously reported in a set of larger atezolizumab monotherapy trials.2 Furthermore, consistency in the ranking of HLA alleles associated with ADA across the different therapeutic antibodies was low, with Spearman correlations ranging from 0.02 to 0.15.
Conclusions
This dataset has enabled a systematic investigation of the HLA region's role in ADA formation linked to non-standard CITs. Low correlations amongst rankings of the associations suggest that genetic predisposition to ADA formation is likely molecule-specific. However, these results may also be attributed to low statistical power and other confounding factors. As a next step, we aim to integrate in-silico prediction algorithms6 for T cell epitopes to factor in the immunogenicity cascade and elucidate the differences seen across the therapeutic antibodies.
Acknowledgements
Members of the Enhanced Data Insights and Sharing community at Genentech and Roche who developed, curated, and integrated data for this work in the Cancer Immunotherapy Data Mart.
References
van Brummelen EM, Ros W, Wolbink G, Beijnen JH, Schellens JH. Antidrug antibody formation in oncology: clinical relevance and challenges. Oncologist 2016 Oct;21(10):1260–1268.doi: 10.1634/theoncologist.2016-0061. Epub 2016 Jul 20. PMID: 27440064;PMCID: PMC5061540. Hammer C, Ruppel J, Kamen L, Hunkapiller J, Mellman I, Quarmby V. Allelic variation in HLA-DRB1 is associated with development of antidrug antibodies in cancer patients treated with atezolizumab that are neutralizing in vitro. Clin Transl Sci 2022 Jun;15(6):1393–1399. doi: 10.1111/cts.13264. Epub 2022 Apr 8. PMID: 35263013; PMCID: PMC9199883. Zheng X, Shen J, Cox C, Wakefield JC, Ehm MG, Nelson MR, Weir BS. HIBAG--HLA genotype imputation with attribute bagging. Pharmacogenomics J 2014 Apr;14(2):192–200. doi: 10.1038/tpj.2013.18. Epub 2013 May 28. PMID: 23712092; PMCID: PMC3772955. Shankar G, Devanarayan V, Amaravadi L, Barrett YC, Bowsher R, Finco-Kent D, Fiscella M, Gorovits B, Kirschner S, Moxness M, Parish T, Quarmby V, Smith H, Smith W, Zuckerman LA, Koren E. Recommendations for the validation of immunoassays used for detection of host antibodies against biotechnology products. J Pharm Biomed Anal 2008 Dec 15;48(5):1267–81.doi: 10.1016/j.jpba.2008.09.020. Epub 2008 Sep 19. PMID: 18993008. Migdal M, Ruan DF, Forrest WF, Horowitz A, Hammer C. MiDAS-meaningful immunogenetic data at scale. PLoS Comput Biol 2021 Jul 6;17(7):e1009131.doi: 10.1371/journal.pcbi.1009131. PMID: 34228721; PMCID: PMC8284797. Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res 2020 Jul 2;48(W1):W449-W454. doi: 10.1093/nar/gkaa379. PMID: 32406916; PMCID: PMC7319546.
To use the case-only gene-environment (G [Formula: see text] E) interaction study design to estimate interaction between pregnancy before onset of MS symptoms and established genetic risk factors for MS among White adult females.We studied 2,497 female MS cases from 4 cohorts in the United States, Sweden, and Norway with clinical, reproductive, and genetic data. Pregnancy exposure was defined in 2 ways: (1) [Formula: see text] live birth pregnancy before onset of MS symptoms and (2) parity before onset of MS symptoms. We estimated interaction between pregnancy exposure and established genetic risk variants, including a weighted genetic risk score and both HLA and non-HLA variants, using logistic regression and proportional odds regression within each cohort. Within-cohort associations were combined using inverse variance meta-analyses with random effects. The case-only G × E independence assumption was tested in 7,067 individuals without MS.Evidence for interaction between pregnancy exposure and established genetic risk variants, including the strongly associated HLA-DRB1*15:01 allele and a weighted genetic risk score, was not observed. Results from sensitivity analyses were consistent with observed results.Our findings indicate that pregnancy before symptom onset does not modify the risk of MS in genetically susceptible White females.
We previously reported a relationship between air pollutants and increased risk of pediatric-onset multiple sclerosis (POMS). Ozone is an air pollutant that may play a role in multiple sclerosis (MS) pathoetiology.
Abstract Background The COVID-19 pandemic underscored the need for rapid and accurate diagnostic tools. In August 2020, the Abbot BinaxNOW COVID-19 Antigen Card test became available as a timely and affordable alternative for SARS-CoV-2 molecular testing, but its performance may vary due to factors including timing and symptomatology. This study evaluates BinaxNOW diagnostic performance in diverse epidemiological contexts. Methods Using RT-PCR as reference, we assessed performance of the BinaxNOW COVID-19 test for SARS-CoV-2 detection in anterior nasal swabs from participants of two studies in Puerto Rico from December 2020 to May 2023. Test performance was assessed by days post symptom onset, collection strategy, vaccination status, symptomatology, repeated testing, and RT-PCR cycle threshold (Ct) values. Results BinaxNOW demonstrated an overall sensitivity of 84.1% and specificity of 98.8%. Sensitivity peaked within 1–6 days after symptom onset (93.2%) and was higher for symptomatic (86.3%) than asymptomatic (67.3%) participants. Sensitivity declined over the course of infection, dropping from 96.3% in the initial test to 48.4% in testing performed 7–14 days later. BinaxNOW showed 99.5% sensitivity in participants with low Ct values (≤25) but lower sensitivity (18.2%) for participants with higher Cts (36–40). Conclusions BinaxNOW demonstrated high sensitivity and specificity, particularly in early-stage infections and symptomatic participants. In situations where test sensitivity is crucial for clinical decision- making, nucleic acid amplification tests are preferred. These findings highlight the importance of considering clinical and epidemiological context when interpreting test results and emphasize the need for ongoing research to adapt testing strategies to emerging SARS-CoV-2 variants.
DNA methylation is an epigenetic mark that is influenced by environmental factors and is associated with changes to gene expression and phenotypes. It may link environmental exposures to disease etiology or indicate important gene pathways involved in disease pathogenesis. We identified genomic regions that are differentially methylated in T cells of patients with relapsing remitting multiple sclerosis (MS) compared to healthy controls. DNA methylation was assessed at 450,000 genomic sites in CD4+ and CD8+ T cells purified from peripheral blood of 94 women with MS and 94 healthy women, and differentially methylated regions were identified using bumphunter. Differential DNA methylation was observed near four loci: MOG/ZFP57, HLA-DRB1, NINJ2/LOC100049716, and SLFN12. Increased methylation of the first exon of the SLFN12 gene was observed in both T cell subtypes and remained present after restricting analyses to samples from patients who had never been on treatment or had been off treatment for more than 2.5 years. Genes near the regions of differential methylation in T cells were assessed for differential expression in whole blood samples from a separate population of 1,329 women with MS and 97 healthy women. Gene expression of HLA-DRB1, NINJ2, and SLFN12 was observed to be decreased in whole blood in MS patients compared to controls. We conclude that T cells from MS patients display regions of differential DNA methylation compared to controls, and corresponding gene expression differences are observed in whole blood. Two of the genes that showed both methylation and expression differences, NINJ2 and SLFN12, have not previously been implicated in MS. SLFN12 is a particularly compelling target of further research, as this gene is known to be down-regulated during T cell activation and up-regulated by type I interferons (IFNs), which are used to treat MS.
Metadata of KPNC samples included in analysis in manuscript entitled: Evidence supports causal association between allele-specific vitamin D receptor binding and multiple sclerosis among Europeans. Complete KPNC data can be accessed by contacting Lisa Barcellos (lbarcellos@berkeley.edu) and Lynn Hollyer (lhollyer@berkeley.edu). Requests will be reviewed by the IRB and data can be shared upon approval.
