Asparaginase-Associated Pancreatitis in Acute Lymphoblastic Leukemia : Results From the NOPHO ALL2008 Treatment of Patients 1-45 Years of Age

PURPOSE Asparaginase-associated pancreatitis (AAP) is common in patients with acute lymphoblastic leukemia (ALL), but risk differences across age groups both in relation to first-time AAP and after asparaginase re-exposure have not been explored. PATIENTS AND METHODS We prospectively registered AAP (n = 168) during treatment of 2,448 consecutive ALL patients aged 1.0-45.9 years diagnosed from July 2008 to October 2018 and treated according to the Nordic Society of Pediatric Hematology and Oncology (NOPHO) ALL2008 protocol. RESULTS Compared with patients aged 1.0-9.9 years, adjusted AAP hazard ratios (HRa) were associated with higher age with almost identical HRa (1.6; 95% CI, 1.1 to 2.3; P = .02) for adolescents (10.0-17.9 years) and adults (18.0-45.9 years). The day 280 cumulative incidences of AAP were 7.0% for children (1.0-9.9 years: 95% CI, 5.4 to 8.6), 10.1% for adolescents (10.0 to 17.9 years: 95% CI, 7.0 to 13.3), and 11.0% for adults (18.0-45.9 years: 95% CI, 7.1 to 14.9; P = .03). Adolescents had increased odds of both acute (odds ratio [OR], 5.2; 95% CI, 2.1 to 13.2; P = .0005) and persisting complications (OR, 6.7; 95% CI, 2.4 to 18.4; P = .0002) compared with children (1.0-9.9 years), whereas adults had increased odds of only persisting complications (OR, 4.1; 95% CI, 1.4 to 11.8; P = .01). Fifteen of 34 asparaginase-rechallenged patients developed a second AAP. Asparaginase was truncated in 17/21 patients with AAP who subsequently developed leukemic relapse, but neither AAP nor the asparaginase truncation was associated with increased risk of relapse. CONCLUSION Older children and adults had similar AAP risk, whereas morbidity was most pronounced among adolescents. Asparaginase re-exposure should be considered only for patients with an anticipated high risk of leukemic relapse, because multiple studies strongly indicate that reduction of asparaginase treatment intensity increases the risk of relapse. (C) 2019 by American Society of Clinical OncologyPeer reviewe

An exploratory phenome wide association study linking asthma and liver disease genetic variants to electronic health records from the Estonian Biobank

<div><p>The Estonian Biobank, governed by the Institute of Genomics at the University of Tartu (Biobank), has stored genetic material/DNA and continuously collected data since 2002 on a total of 52,274 individuals representing ~5% of the Estonian adult population and is increasing. To explore the utility of data available in the Biobank, we conducted a phenome-wide association study (PheWAS) in two areas of interest to healthcare researchers; asthma and liver disease. We used 11 asthma and 13 liver disease-associated single nucleotide polymorphisms (SNPs), identified from published genome-wide association studies, to test our ability to detect established associations. We confirmed 2 asthma and 5 liver disease associated variants at nominal significance and directionally consistent with published results. We found 2 associations that were opposite to what was published before (rs4374383:AA increases risk of NASH/NAFLD, rs11597086 increases ALT level). Three SNP-diagnosis pairs passed the phenome-wide significance threshold: rs9273349 and E06 (thyroiditis, p = 5.50x10^-8); rs9273349 and E10 (type-1 diabetes, p = 2.60x10^-7); and rs2281135 and K76 (non-alcoholic liver diseases, including NAFLD, p = 4.10x10^-7). We have validated our approach and confirmed the quality of the data for these conditions. Importantly, we demonstrate that the extensive amount of genetic and medical information from the Estonian Biobank can be successfully utilized for scientific research.</p></div

Publisher Correction: Stroke genetics informs drug discovery and risk prediction across ancestries (Nature, (2022), 611, 7934, (115-123), 10.1038/s41586-022-05165-3)

In the version of this article initially published, the name of the PRECISE4Q Consortium was misspelled as “PRECISEQ” and has now been amended in the HTML and PDF versions of the article. Further, data in the first column of Supplementary Table 55 were mistakenly shifted and have been corrected in the file accompanying the HTML version of the article

Comparison of Antibody Repertoires Produced by HIV-1 Infection, Other Chronic and Acute Infections, and Systemic Autoimmune Disease

Background Antibodies (Abs) produced during HIV-1 infection rarely neutralize a broad range of viral isolates; only eight broadly-neutralizing (bNt) monoclonal (M)Abs have been isolated. Yet, to be effective, an HIV-1 vaccine may have to elicit the essential features of these MAbs. The V genes of all of these bNt MAbs are highly somatically mutated, and the VH genes of five of them encode a long (≥20 aa) third complementarity-determining region (CDR-H3). This led us to question whether long CDR-H3s and high levels of somatic mutation (SM) are a preferred feature of anti-HIV bNt MAbs, or if other adaptive immune responses elicit them in general. Methodology and Principal Findings We assembled a VH-gene sequence database from over 700 human MAbs of known antigen specificity isolated from chronic (viral) infections (ChI), acute (bacterial and viral) infections (AcI), and systemic autoimmune diseases (SAD), and compared their CDR-H3 length, number of SMs and germline VH-gene usage. We found that anti-HIV Abs, regardless of their neutralization breadth, tended to have long CDR-H3s and high numbers of SMs. However, these features were also common among Abs associated with other chronic viral infections. In contrast, Abs from acute viral infections (but not bacterial infections) tended to have relatively short CDR-H3s and a low number of SMs, whereas SAD Abs were generally intermediate in CDR-H3 length and number of SMs. Analysis of VH gene usage showed that ChI Abs also tended to favor distal germline VH-genes (particularly VH1-69), especially in Abs bearing long CDR-H3s. Conclusions and Significance The striking difference between the Abs produced during chronic vs. acute viral infection suggests that Abs bearing long CDR-H3s, high levels of SM and VH1-69 gene usage may be preferentially selected during persistent infection

Track A Basic Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138319/1/jia218438.pd

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p