Correlation of Socioeconomic and Environmental Factors With Incidence of Crohn Disease in Children and Adolescents: Systematic Review and Meta-Regression.

BACKGROUND: The worldwide incidence of Crohn disease (CD) in childhood and adolescence has an increasing trend, with significant differences between different geographic regions and individual countries. This includes an increase in the incidence of CD in countries and geographic regions where CD was not previously prevalent. In response to the increasing incidence, the pediatric care landscape is facing growing challenges. OBJECTIVE: This systematic review and meta-analysis were undertaken to comprehensively delineate the incidence rates of CD in pediatric populations across different countries and to explore potential influencing factors. METHODS: We performed a systematic review of PubMed and Embase (via Ovid) for studies from January 1, 1970, to December 31, 2019. In addition, a manual search was performed in relevant and previously published reviews. The results were evaluated quantitatively. For this purpose, random effects meta-analyses and meta-regressions were performed to investigate the overall incidence rate and possible factors influencing the incidence. RESULTS: A qualitative synthesis of 74 studies was performed, with 72 studies included in the meta-analyses and 52 in the meta-regressions. The results of our meta-analysis showed significant heterogeneity between the individual studies, which cannot be explained by a sample effect alone. Our findings showed geographical differences in incidence rates, which increased with increasing distance from the equator, although no global temporal trend was apparent. The meta-regression analysis also identified geographic location, UV index, and Human Development Index as significant moderators associated with CD incidence. CONCLUSIONS: Our results suggest that pediatric CD incidence has increased in many countries since 1970 but varies widely with geographic location, which may pose challenges to the respective health care systems. We identified geographic, environmental, and socioeconomic factors that contribute to the observed heterogeneity in incidence rates. These results can serve as a basis for future research. To this end, implementations of internationally standardized and interoperable registries combined with the dissemination of health data through federated networks based on a common data model, such as the Observational Medical Outcomes Partnership, would be beneficial. This would deepen the understanding of CD and promote evidence-based approaches to preventive and interventional strategies as well as inform public health policies aimed at addressing the increasing burden of CD in children and adolescents. TRIAL REGISTRATION: PROSPERO International prospective register of systematic reviews CRD42020168644; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=168644. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2020-037669.

Clonal hematopoiesis and its impact on the aging osteo-hematopoietic niche.

Clonal hematopoiesis (CH) defines a premalignant state predominantly found in older persons that increases the risk of developing hematologic malignancies and age-related inflammatory diseases. However, the risk for malignant transformation or non-malignant disorders is variable and difficult to predict, and defining the clinical relevance of specific candidate driver mutations in individual carriers has proved to be challenging. In addition to the cell-intrinsic mechanisms, mutant cells rely on and alter cell-extrinsic factors from the bone marrow (BM) niche, which complicates the prediction of a mutant cell's fate in a shifting pre-malignant microenvironment. Therefore, identifying the insidious and potentially broad impact of driver mutations on supportive niches and immune function in CH aims to understand the subtle differences that enable driver mutations to yield different clinical outcomes. Here, we review the changes in the aging BM niche and the emerging evidence supporting the concept that CH can progressively alter components of the local BM microenvironment. These alterations may have profound implications for the functionality of the osteo-hematopoietic niche and overall bone health, consequently fostering a conducive environment for the continued development and progression of CH. We also provide an overview of the latest technology developments to study the spatiotemporal dependencies in the CH BM niche, ideally in the context of longitudinal studies following CH over time. Finally, we discuss aspects of CH carrier management in clinical practice, based on work from our group and others.

Continuous therapy response references for BCR::ABL1 monitoring in pediatric chronic myeloid leukemia.

Response to tyrosine kinase inhibitor (TKI) therapy in patients with chronic myeloid leukemia (CML) is monitored by quantification of BCR::ABL1 transcript levels. Milestones for assessing optimal treatment response have been defined in adult CML patients and are applied to children and adolescents although it is questionable whether transferability to pediatric patients is appropriate regarding genetic and clinical differences. Therefore, we analyzed the molecular response kinetics to TKI therapy in 129 pediatric CML patients and investigated whether response assessment based on continuous references can support an early individual therapy adjustment. We applied a moving quantiles approach to establish a high-resolution response target curve and contrasted the median responses in all patients with the median of the ideal target curve obtained from a subgroup of optimal responders. The high-resolution response target curve of the optimal responder group presents a valuable tool for continuous therapy monitoring of individual pediatric CML patients in addition to the fixed milestones. By further comparing BCR::ABL1 transcript levels with BCR::ABL1 fusion gene copy numbers, it is also possible to model the differential dynamics of BCR::ABL1 expression and cell number under therapy. The developed methodology can be transferred to other biomarkers for continuous therapy monitoring.

Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions.

The transfer of new insights from basic or clinical research into clinical routine is usually a lengthy and time-consuming process. Conversely, there are still many barriers to directly provide and use routine data in the context of basic and clinical research. In particular, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings. Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, our work is based on a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: a clinical (i.e., treatment context) perspective focusing on patient-specific data visualization, analysis and outcome prediction and a research perspective focusing on the exploration of pseudonymized data. We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes while enabling seamless integration into a clinical information system or an electronic health record.

Modelling of immune response in chronic myeloid leukemia patients suggests potential for treatment reduction prior to cessation.

Introduction: Discontinuation of tyrosine kinase inhibitor (TKI) treatment is emerging as the main therapy goal for Chronic Myeloid Leukemia (CML) patients. The DESTINY trial showed that TKI dose reduction prior to cessation can lead to an increased number of patients achieving sustained treatment free remission (TFR). However, there has been no systematic investigation to evaluate how dose reduction regimens can further improve the success of TKI stop trials. Methods: Here, we apply an established mathematical model of CML therapy to investigate different TKI dose reduction schemes prior to therapy cessation and evaluate them with respect to the total amount of drug used and the expected TFR success. Results: Our systematic analysis confirms clinical findings that the overall time of TKI treatment is a major determinant of TFR success, while highlighting that lower dose TKI treatment for the same duration is equally sufficient for many patients. Our results further suggest that a stepwise dose reduction prior to TKI cessation can increase the success rate of TFR, while substantially reducing the amount of administered TKI. Discussion: Our findings illustrate the potential of dose reduction schemes prior to treatment cessation and suggest corresponding and clinically testable strategies that are applicable to many CML patients.

A ROS-dependent mechanism promotes CDK2 phosphorylation to drive progression through S phase.

Reactive oxygen species (ROS) at the right concentration promote cell proliferation in cell culture, stem cells, and model organisms. However, the mystery of how ROS signaling is coordinated with cell cycle progression and integrated into the cell cycle control machinery on the molecular level remains unsolved. Here, we report increasing levels of mitochondrial ROS during the cell cycle in human cell lines that target cyclin-dependent kinase 2 (CDK2). Chemical and metabolic interferences with ROS production decrease T-loop phosphorylation on CDK2 and so impede its full activation and thus its efficient DNA replication. ROS regulate CDK2 activity through the oxidation of a conserved cysteine residue near the T-loop, which prevents the binding of the T-loop phosphatase KAP. Together, our data reveal how mitochondrial metabolism is coupled with DNA replication and cell cycle progression via ROS, thereby demonstrating how KAP activity toward CDKs can be cell cycle regulated.

Impact of BCR::ABL1 transcript type on RT-qPCR amplification performance and molecular response to therapy.

Several studies have reported that chronic myeloid leukaemia (CML) patients expressing e14a2 BCR::ABL1 have a faster molecular response to therapy compared to patients expressing e13a2. To explore the reason for this difference we undertook a detailed technical comparison of the commonly used Europe Against Cancer (EAC) BCR::ABL1 reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) assay in European Treatment and Outcome Study (EUTOS) reference laboratories (n = 10). We found the amplification ratio of the e13a2 amplicon was 38% greater than e14a2 (p = 0.015), and the amplification efficiency was 2% greater (P = 0.17). This subtle difference led to measurable transcript-type dependent variation in estimates of residual disease which could be corrected by (i) taking the qPCR amplification efficiency into account, (ii) using alternative RT-qPCR approaches or (iii) droplet digital PCR (ddPCR), a technique which is relatively insensitive to differences in amplification kinetics. In CML patients, higher levels of BCR::ABL1/GUSB were identified at diagnosis for patients expressing e13a2 (n = 67) compared to e14a2 (n = 78) when analysed by RT-qPCR (P = 0.0005) but not ddPCR (P = 0.5). These data indicate that widely used RT-qPCR assays result in subtly different estimates of disease depending on BCR::ABL1 transcript type; these differences are small but may need to be considered for optimal patient management.

Clonal reconstruction from co-occurrence of vector integration sites accurately quantifies expanding clones in vivo.

High transduction rates of viral vectors in gene therapies (GT) and experimental hematopoiesis ensure a high frequency of gene delivery, although multiple integration events can occur in the same cell. Therefore, tracing of integration sites (IS) leads to mis-quantification of the true clonal spectrum and limits safety considerations in GT. Hence, we use correlations between repeated measurements of IS abundances to estimate their mutual similarity and identify clusters of co-occurring IS, for which we assume a clonal origin. We evaluate the performance, robustness and specificity of our methodology using clonal simulations. The reconstruction methods, implemented and provided as an R-package, are further applied to experimental clonal mixes and preclinical models of hematopoietic GT. Our results demonstrate that clonal reconstruction from IS data allows to overcome systematic biases in the clonal quantification as an essential prerequisite for the assessment of safety and long-term efficacy of GT involving integrative vectors.

Reduced adhesion of aged intestinal stem cells contributes to an accelerated clonal drift.

Upon aging, the function of the intestinal epithelium declines with a concomitant increase in aging-related diseases. ISCs play an important role in this process. It is known that ISC clonal dynamics follow a neutral drift model. However, it is not clear whether the drift model is still valid in aged ISCs. Tracking of clonal dynamics by clonal tracing revealed that aged crypts drift into monoclonality substantially faster than young ones. However, ISC tracing experiments, in vivo and ex vivo, implied a similar clonal expansion ability of both young and aged ISCs. Single-cell RNA sequencing for 1,920 high Lgr5 ISCs from young and aged mice revealed increased heterogeneity among subgroups of aged ISCs. Genes associated with cell adhesion were down-regulated in aged ISCs. ISCs of aged mice indeed show weaker adhesion to the matrix. Simulations applying a single cell-based model of the small intestinal crypt demonstrated an accelerated clonal drift at reduced adhesion strength, implying a central role for reduced adhesion for affecting clonal dynamics upon aging.

The Reconstitution Dynamics of Cultivated Hematopoietic Stem Cells and Progenitors Is Independent of Age.

Hematopoietic stem cell transplantation (HSCT) represents the only curative treatment option for numerous hematologic malignancies. While the influence of donor age and the composition of the graft have already been examined in clinical and preclinical studies, little information is available on the extent to which different hematological subpopulations contribute to the dynamics of the reconstitution process and on whether and how these contributions are altered with age. In a murine model of HSCT, we therefore simultaneously tracked different cultivated and transduced hematopoietic stem and progenitor cell (HSPC) populations using a multicolor-coded barcode system (BC32). We studied a series of age-matched and age-mismatched transplantations and compared the influence of age on the reconstitution dynamics. We show that reconstitution from these cultured and assembled grafts was substantially driven by hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) independent of age. The reconstitution patterns were polyclonal and stable in all age groups independently of the variability between individual animals, with higher output rates from MPPs than from HSCs. Our experiments suggest that the dynamics of reconstitution and the contribution of cultured and individually transduced HSPC subpopulations are largely independent of age. Our findings support ongoing efforts to expand the application of HSCT in older individuals as a promising strategy to combat hematological diseases, including gene therapy applications.

Mechanistic models of blood cell fate decisions in the era of single-cell data.

Billions of functionally distinct blood cells emerge from a pool of hematopoietic stem cells in our bodies every day. This progressive differentiation process is hierarchically structured and remarkably robust. We provide an introductory review to mathematical approaches addressing the functional aspects of how lineage choice is potentially implemented on a molecular level. Emerging from studies on the mutual repression of key transcription factors, we illustrate how those simple concepts have been challenged in recent years and subsequently extended. Especially, the analysis of omics data on the single-cell level with computational tools provides descriptive insights on a yet unknown level, while their embedding into a consistent mechanistic and mathematical framework is still incomplete.

How to predict relapse in leukemia using time series data: A comparative in silico study.

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients' time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients.

Does preoperative antibiotic prophylaxis affect sonication-based diagnosis in implant-associated infection?

As culture-negative implant-associated infection denote a diagnostic challenge, sonicate fluid cultures of the explanted endoprosthesis and osteosynthesis components are frequently used. However, the effect of antibiotic treatment on pathogen detection by sonication fluid cultures in implant-associated infection has not been investigated. Thus, the aim of this study was to evaluate the influence of preoperative antibiotic prophylaxis (PAP) and antibiotic therapy (AT) on sonicate fluid cultures in patients with implant-associated infection. In this retrospective study three groups were compared: (i) standard PAP, (ii) AT for at least one day, and (iii) no antibiotics before surgery. For the inclusion criteria, an established diagnostic protocol for implant-associated infection was used. Sonicate fluid cultures were validated by corresponding microbiological and histopathological samples. In 90 patients with single and multiple infections, 114 pathogens were detected. The detection rate by sonicate fluid cultures in patients receiving PAP (n = 27, 29 pathogens), AT before surgery (n = 33, 48 pathogens) and no antibiotics before surgery (n = 30, 37 pathogens) were 86.2%, 81.3%, and 86.5% (p = .778), respectively. Eleven of 114 infectious agents were detected exclusively by sonicate fluid cultures, while conventional tissue culture failed in these cases. PAP and AT do not affect intraoperative cultures in implant-associated infection. It is therefore not recommended to omit antibiotic prophylaxis in patients with implant-associated infection. Algorithms including both sonicate fluid cultures and tissue samples should be used for appropriate microbiological diagnosis of implant-associated infections.

Overlooking the obvious? On the potential of treatment alterations to predict patient-specific therapy response.

Prognostic or therapeutic classification of diseases is often based on clinical or genetic characteristics at diagnosis or response landmarks determined at a certain time point of treatment. On the other hand, there are more and more means, such as molecular markers and sensor data, that allow for quantification of disease or therapeutic parameters over time. Although a general value of time-resolved disease monitoring is widely accepted, the full potential of using the available information on disease and treatment dynamics in the context of outcome prediction or individualized treatment optimization still seems to be, at least partially, overlooked. Within this Perspective, we summarize the conceptual idea of using dynamic information to obtain a better understanding of complex pathophysiological processes within their particular "host environment," which also allows us to intrinsically map patient-specific heterogeneity. Specifically, we discuss to which extent treatment alterations can provide additional information to understand a patient's individual condition and use this information to further adapt the therapeutic strategy. This conceptual discussion is illustrated by using examples from myeloid leukemias to which we recently applied this concept using statistical and mathematical modeling.

Analysis of chronic myeloid leukaemia during deep molecular response by genomic PCR: a traffic light stratification model with impact on treatment-free remission.

This work investigated patient-specific genomic BCR-ABL1 fusions as markers of measurable residual disease (MRD) in chronic myeloid leukaemia, with a focus on relevance to treatment-free remission (TFR) after achievement of deep molecular response (DMR) on tyrosine kinase inhibitor (TKI) therapy. DNA and mRNA BCR-ABL1 measurements by qPCR were compared in 2189 samples (129 patients) and by digital PCR in 1279 sample (62 patients). A high correlation was found at levels of disease above MR4, but there was a poor correlation for samples during DMR. A combination of DNA and RNA MRD measurements resulted in a better prediction of molecular relapse-free survival (MRFS) after TKI stop (n = 17) or scheduled interruption (n = 25). At 18 months after treatment cessation, patients with stopped or interrupted TKI therapy who were DNA negative/RNA negative during DMR maintenance (green group) had an MRFS of 80% and 100%, respectively, compared with those who were DNA positive/RNA negative (MRFS = 57% and 67%, respectively; yellow group) or DNA positive/RNA positive (MRFS = 20% for both cohorts; red group). Thus, we propose a "traffic light" stratification as a TFR predictor based on DNA and mRNA BCR-ABL1 measurements during DMR maintenance before TKI cessation.

Continuous mitotic activity of primitive hematopoietic stem cells in adult mice.

The proliferative activity of aging hematopoietic stem cells (HSCs) is controversially discussed. Inducible fluorescent histone 2B fusion protein (H2B-FP) transgenic mice are important tools for tracking the mitotic history of murine HSCs in label dilution experiments. A recent study proposed that primitive HSCs symmetrically divide only four times to then enter permanent quiescence. We observed that background fluorescence due to leaky H2B-FP expression, occurring in all H2B-FP transgenes independent of label induction, accumulated with age in HSCs with high repopulation potential. We argue that this background had been misinterpreted as stable retention of induced label. We found cell division-independent half-lives of H2B-FPs to be short, which had led to overestimation of HSC divisional activity. Our data do not support abrupt entry of HSCs into permanent quiescence or sudden loss of regeneration potential after four divisions, but show that primitive HSCs of adult mice continue to cycle rarely.

[Registries and studies in the Society for Pediatric Oncology and Hematology (GPOH) - What contribution do they make to progress?] / Register und Studien in der Gesellschaft für Pädiatrische Onkologie und Hämatologie (GPOH) ­ Welchen Beitrag leisten sie zum Fortschritt?

Cancer in children and adolescents under the age of 18 is rare; in 2017, approximately 2220 new cases in Germany were reported to the German Childhood Cancer Registry. The aim of the GPOH has always been to treat as many affected patients as possible in a standardized way, preferably in prospective, controlled studies. The Joint Federal Committee has also laid down this requirement in the paediatric oncology guideline. In a survey among the study chairs of the GPOH, it was determined how the number of clinical trials has changed following the amended drug legislation. In 2002, 33 therapy optimization studies (TOS) of the GPOH were open. Overall, TOS decreased from 33 in 2002 to 2 in 2017. The number of drug trials has increased to 16 by 2017 (almost 1100 patients registered). At the time, the number of clinical registries has increased to 28 with a total of more than 1800 registered patents. This observation shows that the clinical registers have taken on a new significance in paediatric oncology. Three examples are used to examine what contributions registries can make in relation to studies on the treatment of patients and to scientific progress. In summary, the experience gained so far from the examples discussed illustrates that studies and registries mutually represent a meaningful and necessary addition to the study group structure in paediatric oncology.

Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology.

BACKGROUND: Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities. RESULTS: In the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level. CONCLUSIONS: By integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.

Model-Based Inference and Classification of Immunologic Control Mechanisms from TKI Cessation and Dose Reduction in Patients with CML.

Recent clinical findings in patients with chronic myeloid leukemia (CML) suggest that the risk of molecular recurrence after stopping tyrosine kinase inhibitor (TKI) treatment substantially depends on an individual's leukemia-specific immune response. However, it is still not possible to prospectively identify patients that will remain in treatment-free remission (TFR). Here, we used an ordinary differential equation model for CML, which explicitly includes an antileukemic immunologic effect, and applied it to 21 patients with CML for whom BCR-ABL1/ABL1 time courses had been quantified before and after TKI cessation. Immunologic control was conceptually necessary to explain TFR as observed in about half of the patients. Fitting the model simulations to data, we identified patient-specific parameters and classified patients into three different groups according to their predicted immune system configuration ("immunologic landscapes"). While one class of patients required complete CML eradication to achieve TFR, other patients were able to control residual leukemia levels after treatment cessation. Among them were a third class of patients that maintained TFR only if an optimal balance between leukemia abundance and immunologic activation was achieved before treatment cessation. Model simulations further suggested that changes in the BCR-ABL1 dynamics resulting from TKI dose reduction convey information about the patient-specific immune system and allow prediction of outcome after treatment cessation. This inference of individual immunologic configurations based on treatment alterations can also be applied to other cancer types in which the endogenous immune system supports maintenance therapy, long-term disease control, or even cure. SIGNIFICANCE: This mathematical modeling approach provides strong evidence that different immunologic configurations in patients with CML determine their response to therapy cessation and that dose reductions can help to prospectively infer different risk groups.See related commentary by Triche Jr, p. 2083.