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INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 369 nominally genome-wide significant loci ( P < 5 × 10 −8 ) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 360 loci for which replication data were available, 241 loci influencing surface area and 66 influencing thickness remained significant after replication, with 237 loci passing multiple testing correction ( P < 8.3 × 10 −10 ; 187 influencing surface area and 50 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation ( r G = −0.32, SE = 0.05, P = 6.5 × 10 −12 ), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 46 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function. Identifying genetic influences on human cortical structure. ( A ) Measurement of cortical surface area and thickness from MRI. ( B ) Genomic locations of common genetic variants that influence global and regional cortical structure. ( C ) Our results support the radial unit hypothesis that the expansion of cortical surface area is driven by proliferating neural progenitor cells. ( D ) Cortical surface area shows genetic correlation with psychiatric and cognitive traits. Error bars indicate SE. IMAGE CREDITS: (A) K. COURTNEY; (C) M. R. GLASS

TPS11120 Background: Currently there is no biomarker to predict specific benefit from DNA-damaging anthracycline and cyclophosphamide-based chemotherapy in the clinic. Loss of the Fanconi anemia/BRCA (FA/BRCA) DNA-damage response pathway occurs in approximately 25% of breast cancer and results in sensitivity to DNA-damaging agents. We therefore developed an assay to detect loss of the FA/BRCA pathway, for the purpose of predicting benefit from chemotherapy. Methods: 21 FA patient samples were analyzed to identify genetic processes associated with loss of the FA/BRCA pathway. Unsupervised hierarchical clustering was then performed using 60 BRCA1/2 mutant and 47 sporadic tumor samples and a molecular subgroup was identified that was defined by the molecular processes representing loss of the FA/BRCA pathway. A 44-gene DNA Damage response deficient (DDRD) assay was developed that could identify this subgroup from formalin fixed, paraffin embedded (FFPE) samples in the clinic. Results: In a publicly available independent cohort of 204 patients, the assay predicted response to neoadjuvant DNA-damaging chemotherapy (5-fluorouracil, anthracycline and cyclophosphamide) with an odds ratio of 4.01, (95% Cl:1.69-9.54). We also analysed samples from an independent cohort of 114 node-negative breast cancer patients treated with adjuvant 5-fluorouracil, epirubicin and cyclophosphamide treatment at the Northern Ireland Cancer Centre. The DDRD assay significantly predicted 5-year relapse free survival with a hazard ratio of 0.27 (95% Cl:0.10-0.83). The assay was not predictive of survival in patients who did not receive chemotherapy. Conclusions: An FFPE tissue-based assay that detects loss of the FA/BRCA pathway has been developed and independently validated as a predictor of response and prognosis following DNA damaging anthracycline/cyclophosphamide-based chemotherapy in the neoadjuvant and adjuvant settings.

Prediction models aim to use available data to predict a health state or outcome that has not yet been observed. Prediction is primarily relevant to clinical practice, but is also used in research, and administration. While prediction modeling involves estimating the relationship between patient factors and outcomes, it is distinct from casual inference. Prediction modeling thus requires unique considerations for development, validation, and updating. This document represents an effort from editors at 31 respiratory, sleep, and critical care medicine journals to consolidate contemporary best practices and recommendations related to prediction study design, conduct, and reporting. Herein, we address issues commonly encountered in submissions to our various journals. Key topics include considerations for selecting predictor variables, operationalizing variables, dealing with missing data, the importance of appropriate validation, model performance measures and their interpretation, and good reporting practices. Supplemental discussion covers emerging topics such as model fairness, competing risks, pitfalls of “modifiable risk factors”, measurement error, and risk for bias. This guidance is not meant to be overly prescriptive; we acknowledge that every study is different, and no set of rules will fit all cases. Additional best practices can be found in the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, to which we refer readers for further details.

Background: Avoiding early hospital readmission after a diagnosis of atrial fibrillation (AF) lowers healthcare costs. Purpose: This investigation pilot tested the process of using a Connected Health (CH) communication platform in AF care management. Subjects: A self-selected group of patients (n=18, 56% male, mean age=69 years, SD =12.6) diagnosed with hemodynamically stable AF and an uncontrolled ventricular response with heart rates > 110 bpm were recruited. MethodS: Eligibility criteria included a recent AF related emergency department or outpatient clinic visit and the new prescription of a rate control medication. This included AV node modifying medications (either a beta blocker or nondihydropyradine calcium channel blocker) at a starting dose determined by the supervising physician. A care management team (CMT) coordinated by a nurse practitioner provided individualized telephone medical management with no planned clinical visits during a period of early recovery. Patients were equipped with the AliveCor TM Kardia electrocardiogram (ECG) home monitoring device and a supporting mobile device application. Patients returned home with medical guidance from the CMT and ECG recording instructions. Heart rate and rhythm were monitored remotely by the NP and the medication dose was adjusted to reach a target resting heart rate <110 bpm. Patients were managed for 3-weeks before being discharged back to primary care or outpatient cardiology for consideration of a rate versus rhythm control strategy if spontaneous restoration of sinus rhythm had not occurred. ResultS: Patients recorded an average of 50 ( SD =40.0) ECG tracings with a mean of 13 ( SD =3.79) related CMT telephone contacts. The mean resting heart rate was significantly lower ( p <0.05) at the completion of the intervention (72.9 bpm, SD =17.31) compared to baseline (102.3 bpm, SD =27.17). All of the AF patients achieved adequate heart rate control or spontaneously returned to sinus rhythm. However, 4 patients (22%) required a related emergency room visit and unplanned hospitalization. Conclusion: In this small feasibility study, an electronic healthcare device (biosensor) paired with a mobile-heath application provided a practical remote monitoring communication platform to support tele-management, but the intervention did not prevent hospital readmission. Future Directions: Further research is needed to identify factors that will improve the process of applying CH to AF care management.

442 Background: Patients (pts) with BCG-unresponsive NMIBC are at significant risk for disease recurrence and progression. While cystectomy can be curative, many such pts are unwilling or unable given the attendant morbidity of surgery. To date, effective salvage intravesical therapies in this setting remain lacking. Nadofaragene firadenovec is a novel intravesical gene-mediated therapy that delivers the human IFNα2b gene resulting in IFNα2b expression that provided durable responses in a previous Phase 2 trial. Herein, we report the results from a follow-up Phase III trial of this agent for pts with BCG unresponsive NMIBC. Methods: This multicenter, open-label Phase 3 study investigated nadofaragene for high-grade NMIBC (CIS ± Ta/T1, or Ta/T1 alone) unresponsive to BCG. Nadofaragene (3X1011 vp/mL [75 mL]) was administered once every 3 months for up 4 doses in the initial 12 months, with additional dosing at the investigator’s discretion. The primary endpoint was complete response (CR) at any time in pts with carcinoma in situ (CIS). Results: A total of 157 pts (safety population, n=157; efficacy population, n=151) were enrolled. Among pts with CIS (n=103), 55 (53.4%) (95% CI, 43.3-63.3) achieved CR, all by month 3 after treatment. Of these 55 CIS CR pts, 25 (45.5%) remained free of high-grade recurrence at month 12, confirmed on protocol-mandated biopsy. For pts with HG Ta/T1 alone, 35 (72.9%) and 21 (43.8%) were free from recurrence at 3 and 12mos, respectively. Most treatment-emergent adverse events (TEAEs) were transient in nature: instillation site discharge 33.1%; fatigue 23.6%; bladder spasm 19.7%; micturition urgency 17.8%; hematuria 16.6%. There were 2 Gr4 TEAEs (sepsis and anaphylactic reaction, neither related to study drug) and no Gr5 TEAEs. Conclusions: Nadofaragene achieved CR in 53.4% of pts with BCG-unresponsive CIS, and was well tolerated. Responses were noted early and remained durable to one year. These data represent a potentially significant management advancement for a historically difficult to treat disease state. Clinical trial information: NCT02773849.

Abstract Eating disorders and substance use disorders frequently co‐occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [ r g ], twin‐based = 0.23‐0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome‐wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance‐use‐related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism‐based genetic correlations between eating disorder‐ and substance‐use‐related phenotypes. Significant positive genetic associations emerged between AUD and AN ( r g = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN ( r g = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating ( r g = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating ( r gs = −0.19 to −0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co‐varying for major depressive disorder loci. The patterns of association between eating disorder‐ and substance‐use‐related phenotypes highlights the potentially complex and substance‐specific relationships among these behaviors.