Effects of germline DHFR and FPGS variants on methotrexate metabolism and relapse of leukemia.

Methotrexate (MTX) during maintenance therapy is essential for curing acute lymphoblastic leukemia (ALL), but dosing strategies aiming at adequate treatment intensity are challenged by interindividual differences in drug disposition. To evaluate genetic factors associated with MTX metabolism, we performed a genome-wide association study in 447 ALL cases from the Nordic Society for Pediatric Haematology and Oncology ALL2008 study, validating results in an independent set of 196 patients. The intergenic single-nucleotide polymorphism rs1382539, located in a regulatory element of DHFR, was associated with increased levels of short-chain MTX polyglutamates (P = 1.1 × 10-8) related to suppression of enhancer activity, whereas rs35789560 in FPGS (p.R466C, P = 5.6 × 10-9) was associated with decreased levels of long-chain MTX polyglutamates through reduced catalytic activity. Furthermore, the FPGS variant was linked with increased relapse risk (P = .044). These findings show a genetic basis for interpatient variability in MTX response and could be used to improve future dosing algorithms.

Acute central nervous system toxicity during treatment of pediatric acute lymphoblastic leukemia: phenotypes, risk factors and genotypes.

Central nervous system (CNS) toxicity is common at diagnosis and during treatment of pediatric acute lymphoblastic leukemia (ALL). We studied CNS toxicity in 1,464 children aged 1.0-17.9 years, diagnosed with ALL and treated according to the Nordic Society of Pediatric Hematology and Oncology ALL2008 protocol. Genome-wide association studies, and a candidate single-nucleotide polymorphism (SNP; n=19) study were performed in 1,166 patients. Findings were validated in an independent Australian cohort of children with ALL (n=797) in whom two phenotypes were evaluated: diverse CNS toxicities (n=103) and methotrexate-related CNS toxicity (n=48). In total, 135/1,464 (9.2%) patients experienced CNS toxicity for a cumulative incidence of 8.7% (95% confidence interval: 7.31-10.20) at 12 months from diagnosis. Patients aged ≥10 years had a higher risk of CNS toxicity than had younger patients (16.3% vs. 7.4%; P<0.001). The most common CNS toxicities were posterior reversible encephalopathy syndrome (n=52, 43 with seizures), sinus venous thrombosis (n=28, 9 with seizures), and isolated seizures (n=16). The most significant SNP identified by the genome-wide association studies did not reach genomic significance (lowest P-value: 1.11x10-6), but several were annotated in genes regulating neuronal functions. In candidate SNP analysis, ATXN1 rs68082256, related to epilepsy, was associated with seizures in patients <10 years (P=0.01). ATXN1 rs68082256 was validated in the Australian cohort with diverse CNS toxicities (P=0.04). The role of ATXN1 as well as the novel SNP in neurotoxicity in pediatric ALL should be further explored.

Characteristics of white blood cell count in acute lymphoblastic leukemia: A COST LEGEND phenotype-genotype study.

BACKGROUND: White blood cell count (WBC) as a measure of extramedullary leukemic cell survival is a well-known prognostic factor in acute lymphoblastic leukemia (ALL), but its biology, including impact of host genome variants, is poorly understood. METHODS: We included patients treated with the Nordic Society of Paediatric Haematology and Oncology (NOPHO) ALL-2008 protocol (N = 2347, 72% were genotyped by Illumina Omni2.5exome-8-Bead chip) aged 1-45 years, diagnosed with B-cell precursor (BCP-) or T-cell ALL (T-ALL) to investigate the variation in WBC. Spline functions of WBC were fitted correcting for association with age across ALL subgroups of immunophenotypes and karyotypes. The residuals between spline WBC and actual WBC were used to identify WBC-associated germline genetic variants in a genome-wide association study (GWAS) while adjusting for age and ALL subtype associations. RESULTS: We observed an overall inverse correlation between age and WBC, which was stronger for the selected patient subgroups of immunophenotype and karyotypes (ρBCP-ALL  = -.17, ρT-ALL  = -.19; p < 3 × 10-4 ). Spline functions fitted to age, immunophenotype, and karyotype explained WBC variation better than age alone (ρ = .43, p << 2 × 10-6 ). However, when the spline-adjusted WBC residuals were used as phenotype, no GWAS significant associations were found. Based on available annotation, the top 50 genetic variants suggested effects on signal transduction, translation initiation, cell development, and proliferation. CONCLUSION: These results indicate that host genome variants do not strongly influence WBC across ALL subsets, and future studies of why some patients are more prone to hyperleukocytosis should be performed within specific ALL subsets that apply more complex analyses to capture potential germline variant interactions and impact on WBC.

The stepwise evolution of the exome during acquisition of docetaxel resistance in breast cancer cells.

BACKGROUND: Resistance to taxane-based therapy in breast cancer patients is a major clinical problem that may be addressed through insight of the genomic alterations leading to taxane resistance in breast cancer cells. In the current study we used whole exome sequencing to discover somatic genomic alterations, evolving across evolutionary stages during the acquisition of docetaxel resistance in breast cancer cell lines. RESULTS: Two human breast cancer in vitro models (MCF-7 and MDA-MB-231) of the step-wise acquisition of docetaxel resistance were developed by exposing cells to 18 gradually increasing concentrations of docetaxel. Whole exome sequencing performed at five successive stages during this process was used to identify single point mutational events, insertions/deletions and copy number alterations associated with the acquisition of docetaxel resistance. Acquired coding variation undergoing positive selection and harboring characteristics likely to be functional were further prioritized using network-based approaches. A number of genomic changes were found to be undergoing evolutionary selection, some of which were likely to be functional. Of the five stages of progression toward resistance, most resistance relevant genomic variation appeared to arise midway towards fully resistant cells corresponding to passage 31 (5 nM docetaxel) for MDA-MB-231 and passage 16 (1.2 nM docetaxel) for MCF-7, and where the cells also exhibited a period of reduced growth rate or arrest, respectively. MCF-7 cell acquired several copy number gains on chromosome 7, including ABC transporter genes, including ABCB1 and ABCB4, as well as DMTF1, CLDN12, CROT, and SRI. For MDA-MB-231 numerous copy number losses on chromosome X involving more than 30 genes was observed. Of these genes, CASK, POLA1, PRDX4, MED14 and PIGA were highly prioritized by the applied network-based gene ranking approach. At higher docetaxel concentration MCF-7 subclones exhibited a copy number loss in E2F4, and the gene encoding this important transcription factor was down-regulated in MCF-7 resistant cells. CONCLUSIONS: Our study of the evolution of acquired docetaxel resistance identified several genomic changes that might explain development of docetaxel resistance. Interestingly, the most relevant resistance-associated changes appeared to originate midway through the evolution towards fully resistant cell lines. Our data suggest that no single genomic event sufficiently predicts resistance to docetaxel, but require genomic alterations affecting multiple pathways that in concert establish the final resistance stage.

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning.

Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients' lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71-0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-ß signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.

Biomarkers and Coronary Microvascular Dysfunction in Women With Angina and No Obstructive Coronary Artery Disease.

Background: Coronary microvascular dysfunction (CMD) is a major cause of ischemia with no obstructed coronary arteries. Objectives: The authors sought to assess protein biomarker signature for CMD. Methods: We quantified 184 unique cardiovascular proteins with proximity extension assay in 1,471 women with angina and no obstructive coronary artery disease characterized for CMD by coronary flow velocity reserve (CFVR) by transthoracic echo Doppler. We performed Pearson's correlations of CFVR and each of the 184 biomarkers, and principal component analyses and weighted correlation network analysis to identify clusters linked to CMD. For prediction of CMD (CFVR < 2.25), we applied logistic regression and machine learning algorithms (least absolute shrinkage and selection operator, random forest, extreme gradient boosting, and adaptive boosting) in discovery and validation cohorts. Results: Sixty-one biomarkers were correlated with CFVR with strongest correlations for renin (REN), growth differentiation factor 15, brain natriuretic protein (BNP), N-terminal-proBNP (NT-proBNP), and adrenomedullin (ADM) (all P < 1e-06). Two principal components with highest loading on BNP/NTproBNP and interleukin 6, respectively, were strongly associated with low CFVR. Weighted correlation network analysis identified 2 clusters associated with low CFVR reflecting involvement of hypertension/vascular function and immune modulation. The best prediction model for CFVR <2.25 using clinical data had area under the receiver operating characteristic curve (ROC-AUC) of 0.61 (95% CI: 0.56-0.66). ROC-AUC was 0.66 (95% CI: 0.62-0.71) with addition of biomarkers (P for model improvement = 0.01). Stringent two-layer cross-validated machine learning models had ROC-AUC ranging from 0.58 to 0.66; the most predictive biomarkers were REN, BNP, NT-proBNP, growth differentiation factor 15, and ADM. Conclusions: CMD was associated with pathways particularly involving inflammation (interleukin 6), blood pressure (REN, ADM), and ventricular remodeling (BNP/NT-proBNP) independently of clinical risk factors. Model prediction improved with biomarkers, but prediction remained moderate.

Polygenic risk score-analysis of thromboembolism in patients with acute lymphoblastic leukemia.

INTRODUCTION: Thromboembolism (TE) is a common and serious toxicity of acute lymphoblastic leukemia (ALL) treatment, but studies of genetic predisposition have been underpowered with conflicting results. We tested whether TE in ALL and TE in the general adult population have a shared genetic etiology. MATERIALS AND METHODS: We prospectively registered TE events and collected germline DNA in patients 1.0-45.9 years in the Nordic Society of Pediatric Hematology and Oncology (NOPHO) ALL2008 study (7/2008-7/2016). Based on summary statistics from two large genome-wide association studies (GWAS) on venous TE in adults (the International Network of VENous Thromboembolism Clinical Research Networks (INVENT) consortium and the UK Biobank), we performed polygenic risk score (PRS) analysis on TE development in the NOPHO cohort, progressively expanding the PRS by increasing the p-value threshold of single nucleotide polymorphism (SNP) inclusion. RESULTS AND CONCLUSION: Eighty-nine of 1252 patients with ALL developed TE, 2.5 year cumulative incidence 7.2%. PRS of genome-wide significant SNPs from the INVENT and UK Biobank data were not significantly associated with TE, HR 1.16 (p 0.14) and 1.02 (p 0.86), respectively. Expanding PRS by increasing p-value threshold did not reveal polygenic overlap. However, subgroup analysis of adolescents 10.0-17.9 years (n = 231), revealed significant polygenic overlap with the INVENT GWAS. The best fit PRS, including 16,144 SNPs, was associated with TE with HR 1.76 (95% CI 1.23-2.52, empirical p-value 0.02). Our results support an underlying genetic predisposition for TE in adolescents with ALL and should be explored further in future TE risk prediction models.

Data integration for prediction of weight loss in randomized controlled dietary trials.

Diet is an important component in weight management strategies, but heterogeneous responses to the same diet make it difficult to foresee individual weight-loss outcomes. Omics-based technologies now allow for analysis of multiple factors for weight loss prediction at the individual level. Here, we classify weight loss responders (N = 106) and non-responders (N = 97) of overweight non-diabetic middle-aged Danes to two earlier reported dietary trials over 8 weeks. Random forest models integrated gut microbiome, host genetics, urine metabolome, measures of physiology and anthropometrics measured prior to any dietary intervention to identify individual predisposing features of weight loss in combination with diet. The most predictive models for weight loss included features of diet, gut bacterial species and urine metabolites (ROC-AUC: 0.84-0.88) compared to a diet-only model (ROC-AUC: 0.62). A model ensemble integrating multi-omics identified 64% of the non-responders with 80% confidence. Such models will be useful to assist in selecting appropriate weight management strategies, as individual predisposition to diet response varies.

Candidate single nucleotide polymorphisms and thromboembolism in acute lymphoblastic leukemia - A NOPHO ALL2008 study.

INTRODUCTION: Thromboembolism is a serious toxicity of acute lymphoblastic leukemia treatment, and contributes to substantial morbidity and mortality. Several single nucleotide polymorphisms have been associated with thromboembolism in the general population; however, their impact in patients with acute lymphoblastic leukemia, particularly in children, remains uncertain. MATERIALS AND METHODS: We collected constitutional DNA and prospectively registered thromboembolic events in 1252 patients, 1-45 years, with acute lymphoblastic leukemia included in the Nordic Society of Pediatric Hematology and Oncology ALL2008 protocol in the Nordic and Baltic countries (7/2008-7/2016). Based on previously published data and a priori power calculations, we selected four single nucleotide polymorphisms: F5 rs6025, F11 rs2036914, FGG rs2066865, and ABO rs8176719. RESULTS: The 2.5 year cumulative incidence of thromboembolism was 7.1% (95% confidence interval (CI) 5.6-8.5). F11 rs2036914 was associated with thromboembolism (hazard ratio (HR) 1.52, 95%CI 1.11-2.07) and there was a borderline significant association for FGG rs2066865 (HR 1.37, 95%CI 0.99-1.91), but no association for ABO rs8176719 or F5 rs6025 in multiple cox regression. A genetic risk score based on F11 rs2036914 and FGG rs2066865 was associated with thromboembolism (HR 1.45 per risk allele, 95%CI 1.15-1.81), the association was strongest in adolescents 10.0-17.9 years (HR 1.64). CONCLUSION: If validated, a F11 rs2036914/FGG rs2066865 risk prediction model should be tested as a stratification tool for prevention of thromboembolism in patients with acute lymphoblastic leukemia.