Citations

Abstract Traditional Hardy–Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in data sets composed of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false-positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently among the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

AbstractObjectiveTo develop and validate a model to predict seizure freedom in children undergoing cerebral hemispheric surgery for the treatment of drug‐resistant epilepsy.MethodsWe analyzed 1267 hemispheric surgeries performed in pediatric participants across 32 centers and 12 countries to identify predictors of seizure freedom at 3 months after surgery. A multivariate logistic regression model was developed based on 70% of the dataset (training set) and validated on 30% of the dataset (validation set). Missing data were handled using multiple imputation techniques.ResultsOverall, 817 of 1237 (66%) hemispheric surgeries led to seizure freedom (median follow‐up = 24 months), and 1050 of 1237 (85%) were seizure‐free at 12 months after surgery. A simple regression model containing age at seizure onset, presence of generalized seizure semiology, presence of contralateral 18‐fluoro‐2‐deoxyglucose–positron emission tomography hypometabolism, etiologic substrate, and previous nonhemispheric resective surgery is predictive of seizure freedom (area under the curve = .72). A Hemispheric Surgery Outcome Prediction Scale (HOPS) score was devised that can be used to predict seizure freedom.SignificanceChildren most likely to benefit from hemispheric surgery can be selected and counseled through the implementation of a scale derived from a multiple regression model. Importantly, children who are unlikely to experience seizure control can be spared from the complications and deficits associated with this surgery. The HOPS score is likely to help physicians in clinical decision‐making.

Abstract Coronary artery disease (CAD) is a leading cause of death, yet its genetic determinants are not fully elucidated. We report a multi-ethnic genome-wide association study of CAD involving nearly a quarter of a million cases, incorporating the largest cohorts to date of Whites, Blacks, and Hispanics from the Million Veteran Program with existing studies including CARDIoGRAMplusC4D, UK Biobank, and Biobank Japan. We verify substantial and nearly equivalent heritability of CAD across multiple ancestral groups, discover 107 novel loci including the first nine on the X-chromosome, identify the first eight genome-wide significant loci among Blacks and Hispanics, and demonstrate that two common haplotypes are largely responsible for the risk stratification at the well-known 9p21 locus in most populations except those of African origin where both haplotypes are virtually absent. We identify 15 loci for angiographically derived burden of coronary atherosclerosis, which robustly overlap with the strongest and earliest loci reported to date for clinical CAD. Phenome-wide association analyses of novel loci and externally validated polygenic risk scores (PRS) augment signals from the insulin resistance cluster of risk factors and consequences, extend previously established pleiotropic associations of loci with traditional risk factors to include smoking and family history, and confirm a substantially reduced transferability of existing PRS to Blacks. Downstream integrative genomic analyses reinforce the critical role of endothelial, fibroblast, and smooth muscle cells within the coronary vessel wall in CAD susceptibility. Our study highlights the value of a multi-ethnic design in efficiently characterizing the genetic architecture of CAD across all human populations.

Abstract RPA is a critical factor for DNA replication and replication stress response. Surprisingly, we found that chromatin RPA stability is tightly regulated. We report that the GDP/GTP exchange factor DOCK7 acts as a critical replication stress regulator to promote RPA stability on chromatin. DOCK7 is phosphorylated by ATR and then recruited by MDC1 to the chromatin and replication fork during replication stress. DOCK7-mediated Rac1/Cdc42 activation leads to the activation of PAK1, which subsequently phosphorylates RPA1 at S135 and T180 to stabilize chromatin-loaded RPA1 and ensure proper replication stress response. Moreover, DOCK7 is overexpressed in ovarian cancer and depleting DOCK7 sensitizes cancer cells to camptothecin. Taken together, our results highlight a novel role for DOCK7 in regulation of the replication stress response and highlight potential therapeutic targets to overcome chemoresistance in cancer.

Abstract PARP inhibitors induce DNA lesions, the repair of which are highly dependent on homologous recombination (HR), and preferentially kill HR‐ deficient cancers. However, cancer cells have developed several mechanisms to transform HR and confer drug resistance to PARP inhibition. Therefore, there is a great clinical interest in exploring new therapies that induce HR deficiency (HRD), thereby sensitizing cancer cells to PARP inhibitors. Here, we found that GSK2578215A, a high‐selective and effective leucine‐rich repeat kinase 2 (LRRK2) inhibitor, or LRRK2 depletion suppresses HR preventing the recruitment of RAD51 to DNA damage sites through disruption of the interaction of RAD51 and BRCA2. Moreover, LRRK2 inhibition or depletion increases the susceptibility of ovarian cancer cells to Olaparib in vitro and in vivo. In clinical specimens, LRRK2 high expression is high related with advanced clinical characteristics and poor survival of ovarian cancer patients. All these findings indicate ovarian cancers expressing high levels of LRRK2 are more resistant to treatment potentially through promoting HR. Furthermore, combination treatment with an LRRK2 and PARP inhibitor may be a novel strategy to improve the effectiveness of LRRK2 expression ovarian cancers.

Objective An understanding of the etiologic heterogeneity of colorectal cancer (CRC) is critical for improving precision prevention, including individualized screening recommendations and the discovery of novel drug targets and repurposable drug candidates for chemoprevention. Known differences in molecular characteristics and environmental risk factors among tumors arising in different locations of the colorectum suggest partly distinct mechanisms of carcinogenesis. The extent to which the contribution of inherited genetic risk factors for CRC differs by anatomical subsite of the primary tumor has not been examined. Design To identify new anatomical subsite-specific risk loci, we performed genome-wide association study (GWAS) meta-analyses including data of 48 214 CRC cases and 64 159 controls of European ancestry. We characterised effect heterogeneity at CRC risk loci using multinomial modelling. Results We identified 13 loci that reached genome-wide significance (p<5×10 −8 ) and that were not reported by previous GWASs for overall CRC risk. Multiple lines of evidence support candidate genes at several of these loci. We detected substantial heterogeneity between anatomical subsites. Just over half (61) of 109 known and new risk variants showed no evidence for heterogeneity. In contrast, 22 variants showed association with distal CRC (including rectal cancer), but no evidence for association or an attenuated association with proximal CRC. For two loci, there was strong evidence for effects confined to proximal colon cancer. Conclusion Genetic architectures of proximal and distal CRC are partly distinct. Studies of risk factors and mechanisms of carcinogenesis, and precision prevention strategies should take into consideration the anatomical subsite of the tumour.

Abstract Background Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk. Methods Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19–75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50–70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds. Results Among women &lt;50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7–1.0) overall and 0.9 (0.7–1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7–1.3) and 1.2 (0.7–1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases. Conclusion Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.

T-cell clonality testing is integral to the diagnostic work-up of T-cell malignancies; however, current methods lack specificity and sensitivity, which can make the diagnostic process difficult. The recent discovery of a monoclonal antibody (mAb) specific for human TRBC1 will greatly improve the outlook for T-cell malignancy diagnostics. The anti-TRBC1 mAb can be used in flow cytometry immunophenotyping assays to provide a low-cost, robust, and highly specific test that detects clonality of immunophenotypically distinct T-cell populations. Recent studies demonstrate the clinical utility of this approach in several contexts; use of this antibody in appropriately designed flow cytometry panels improves detection of circulating disease in patients with cutaneous T-cell lymphoma, eliminates the need for molecular clonality testing in the context of large granular lymphocyte leukemia, and provides more conclusive results in the context of many other T-cell disorders. It is worth noting that the increased ability to detect discrete clonal T-cell populations means that identification of T-cell clones of uncertain clinical significance (T-CUS) will become more common. This review discusses this new antibody and describes how it defines clonal T-cells. We present and discuss assay design and summarize findings to date about the use of flow cytometry TRBC1 analysis in the field of diagnostics, including lymph node and fluid sample investigations. We also make suggestions about how to apply the assay results in clinical work-ups, including how to interpret and report findings of T-CUS. Finally, we highlight areas that we think will benefit from further research.

Abstract Germline variation and smoking are independently associated with pancreatic ductal adenocarcinoma (PDAC). We conducted genome-wide smoking interaction analysis of PDAC using genotype data from four previous genome-wide association studies in individuals of European ancestry (7,937 cases and 11,774 controls). Examination of expression quantitative trait loci data from the Genotype-Tissue Expression Project followed by colocalization analysis was conducted to determine whether there was support for common SNP(s) underlying the observed associations. Statistical tests were two sided and P &lt; 5 × 10–8 was considered statistically significant. Genome-wide significant evidence of qualitative interaction was identified on chr2q21.3 in intron 5 of the transmembrane protein 163 (TMEM163) and upstream of the cyclin T2 (CCNT2). The most significant SNP using the Empirical Bayes method, in this region that included 45 significantly associated SNPs, was rs1818613 [per allele OR in never smokers 0.87, 95% confidence interval (CI), 0.82–0.93; former smokers 1.00, 95% CI, 0.91–1.07; current smokers 1.25, 95% CI 1.12–1.40, Pinteraction = 3.08 × 10–9). Examination of the Genotype-Tissue Expression Project data demonstrated an expression quantitative trait locus in this region for TMEM163 and CCNT2 in several tissue types. Colocalization analysis supported a shared SNP, rs842357, in high linkage disequilibrium with rs1818613 (r2 = 0. 94) driving both the observed interaction and the expression quantitative trait loci signals. Future studies are needed to confirm and understand the differential biologic mechanisms by smoking status that contribute to our PDAC findings. Significance: This large genome-wide interaction study identifies a susceptibility locus on 2q21.3 that significantly modified PDAC risk by smoking status, providing insight into smoking-associated PDAC, with implications for prevention.

Background: The phase 1b/2 CARTITUDE-1 study (NCT03548207) is evaluating ciltacabtagene autoleucel (cilta-cel; JNJ-68284528; LCAR-B38M CAR-T cells), a chimeric antigen receptor T (CAR-T) cell therapy with 2 B-cell maturation antigen-targeting single-domain antibodies, in patients with relapsed/refractory (R/R) multiple myeloma (MM). Cytokine release syndrome (CRS), a known side effect of CAR-T therapy, can be mild to life-threatening and requires careful monitoring and management. Here, we analyzed CRS and cytokine profiles in CARTITUDE-1. Methods: Eligible patients (aged ≥18 years) had a diagnosis of MM per International Myeloma Working Group criteria, measurable disease, Eastern Cooperative Oncology Group performance status ≤1, received ≥3 prior regimens or were double-refractory to a proteasome inhibitor and immunomodulatory drug, and received an anti-CD38 antibody. Bridging therapy was permitted after apheresis. At 5-7 days prior to cilta-cel infusion, lymphodepletion was performed with cyclophosphamide 300 mg/m2 and fludarabine 30 mg/m2 daily for 3 days. Cilta-cel was administered as a single infusion at a target dose of 0.75×106 (range: 0.5-1.0×106) CAR+ viable T cells/kg. CRS was graded using Lee et al (Blood 2014) in the phase 1b portion and American Society for Transplantation and Cellular Therapy (ASTCT) in the phase 2 portion. In this combined analysis, Lee et al criteria were mapped to ASTCT criteria for patients in the phase 1b portion. Serum samples for cytokine profiling were collected prior to lymphodepletion, prior to cilta-cel infusion and 2 hours post-infusion on Day 1, at regular time points until Day 100, and if CRS was suspected or reported. Results: A total of 97 patients with R/R MM were treated with cilta-cel; median follow-up for this analysis was 8.8 months (range: 1.5-20.4). CRS was reported in 92 (94.8%) patients. A total of 48 (49.5%) patients had grade 1 CRS, 38 (39.2%) had grade 2, 4 (4.1%) had grade 3, and 1 had grade 5 (1.0%); maximum toxicity grade according to the ASTCT consensus grading system could not be derived for 1 patient with CRS in the phase 1b portion. Median time to CRS onset from cilta-cel infusion was 7.0 days (range: 1-12). Median duration of CRS was 4.0 days (range: 1-27, excluding n=1 with 97 days). Supportive measures to treat CRS or CRS symptoms were administered to 87 (89.7%) patients, most commonly tocilizumab (69.1%), acetaminophen (68.0%), corticosteroids (20.6%), and anakinra (18.6%). CRS resolved in 91 (98.9%) patients; the patient with grade 5 CRS/hemophagocytic lymphohistiocytosis died on Day 99 subsequent to sequelae of grade 4 CRS. Across all patients, levels of interleukin (IL)-6, IL-10, and interferon-γ peaked at Days 7-14 post-cilta-cel infusion. Conclusions: CRS after cilta-cel treatment was low grade and manageable in most patients with R/R MM. The low rate of grade ≥3 CRS, median time to CRS onset of 7.0 days, and median duration of 4.0 days suggests outpatient dosing of cilta-cel may be feasible; this approach is being explored in the phase 2 CARTITUDE-2 study (NCT04133636). Disclosures Lin: Gamida Cells: Consultancy; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite, a Gilead Company: Consultancy, Research Funding; Vineti: Consultancy; Takeda: Research Funding; Merck: Research Funding; Legend BioTech: Consultancy; Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; Janssen: Consultancy, Research Funding. Martin:Janssen: Research Funding; AMGEN: Research Funding; Sanofi: Research Funding; Seattle Genetics: Research Funding; GSK: Consultancy. Cohen:Novartis: Other: Patents/Intellectual property licensed, Research Funding; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Takeda,: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Genentech/Roche: Membership on an entity's Board of Directors or advisory committees. Jakubowiak:AbbVie, Amgen, BMS/Celgene, GSK, Janssen, Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive, Juno: Consultancy, Honoraria. Usmani:Celgene: Other; Takeda: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Sanofi: Consultancy, Honoraria, Research Funding; SkylineDX: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Pharmacyclics: Research Funding; GSK: Consultancy, Research Funding; Janssen: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Array Biopharma: Research Funding; Merck: Consultancy, Research Funding; Abbvie: Consultancy; BMS, Celgene: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Amgen: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Incyte: Research Funding. Madduri:AbbVie: Consultancy, Honoraria; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Speaking Engagement, Speakers Bureau; Foundation Medicine: Consultancy, Honoraria; Legend: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Speaking Engagement, Speakers Bureau; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Kinevant: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Speaking Engagement, Speakers Bureau. Stewart:Janssen, BMS, Sanofi-Aventis, GSK: Honoraria; Tempus, Inc., Genomics England LLC: Membership on an entity's Board of Directors or advisory committees. Singh:Janssen: Current Employment. Zudaire:Janssen: Current Employment. Yeh:Janssen: Current Employment. Allred:Janssen: Current Employment. Olyslager:Janssen: Current Employment. Banerjee:Janssen: Current Employment. Goldberg:Johnson & Johnson: Current Employment, Current equity holder in publicly-traded company. Schecter:Janssen: Current Employment. Jackson:Memorial Sloan Kettering Cancer Center: Consultancy; Janssen: Current Employment. Deraedt:Janssen: Current Employment, Current equity holder in publicly-traded company. Geng:Legend Biotech USA Inc.: Current Employment. Wu:Legend Biotech USA Inc.: Current Employment. Carrasco:Legend Biotech USA Inc.: Current Employment. Akram:Legend Biotech USA Inc.: Current Employment. Hossain:Legend Biotech USA Inc.: Current Employment. Wang:Legend Biotech USA Inc.: Current Employment. Berdeja:CRISPR Therapeutics: Consultancy, Research Funding; EMD Sorono: Research Funding; Teva: Research Funding; Servier: Consultancy; Vivolux: Research Funding; Abbvie: Research Funding; Novartis: Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Karyopharm: Consultancy; Kesios: Research Funding; Kite Pharma: Consultancy; Legend: Consultancy; Genentech, Inc.: Research Funding; BMS: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Cellularity: Research Funding; Constellation: Research Funding; Glenmark: Research Funding; CURIS: Research Funding; Bioclinica: Consultancy; Amgen: Consultancy, Research Funding; Bluebird: Research Funding; Poseida: Research Funding; Acetylon: Research Funding; Prothena: Consultancy; Lilly: Research Funding. Jagannath:BMS, Janssen, Karyopharm, Legend Biotech, Sanofi, Takeda: Consultancy.