The Impact of Age on Outcome of Embryonal and Alveolar Rhabdomyosarcoma Patients.:A Multicenter Study

Background: The prognosis of rhabdomyosarcoma (RMS) in children and adolescents has improved since the introduction of multi-agent chemotherapy. However, outcome data of adults with RMS are scarce. This multicenter retrospective study investigated the effect of age on outcome of RMS. Patients and Methods: Data were collected from three Dutch University Medical Centers between 1977-2009. The effect of age and clinical prognostic factors on relapse-free and disease-specific survival (DSS) were analyzed. Results: Age as a continuous variable predicted poor survival in multivariate analysis. Five-year DSS was highest for non-metastatic embryonal RMS, followed by non-metastatic alveolar RMS and was poor in metastatic disease. Higher age correlated with unfavorable histological subtype (alveolar RMS) and with metastatic disease at presentation in embryonal RMS. In non-metastatic embryonal RMS and in all alveolar RMS, higher age was an adverse prognostic factor of outcome. Conclusion: This study indicates that age is a negative predictor of survival in patients with embryonal and alveolar RMS

Repertoire sequencing of B cells elucidates the role of UNG and mismatch repair proteins in somatic hypermutation in humans

The generation of high-affinity antibodies depends on somatic hypermutation (SHM). SHM is initiated by the activation-induced cytidine deaminase (AID), which generates uracil (U) lesions in the B-cell receptor (BCR) encoding genes. Error-prone processing of U lesions creates a typical spectrum of point mutations during SHM. The aim of this study was to determine the molecular mechanism of SHM in humans; currently available knowledge is limited by the number of mutations analyzed per patient. We collected a unique cohort of 10 well-defined patients with bi-allelic mutations in genes involved in base excision repair (BER) (UNG) or mismatch repair (MMR) (MSH2, MSH6, or PMS2) and are the first to present next-generation sequencing (NGS) data of the BCR, allowing us to study SHM extensively in humans. Analysis using ARGalaxy revealed selective skewing of SHM mutation patterns specific for each genetic defect, which are in line with the five-pathway model of SHM that was recently proposed based on mice data. However, trans-species comparison revealed differences in the role of PMS2 and MSH2 in strand targeting between mice and man. In conclusion, our results indicate a role for UNG, MSH2, MSH6, and PMS2 in the generation of SHM in humans comparable to their function in mice. However, we observed differences in strand targeting between humans and mice, emphasizing the importance of studying molecular mechanisms in a human setting. The here developed method combining NGS and ARGalaxy analysis of BCR mutation data forms the basis for efficient SHM analyses of other immune deficiencies

Recognition of genetic predisposition in pediatric cancer patients : An easy-to-use selection tool

Genetic predisposition for childhood cancer is under diagnosed. Identifying these patients may lead to therapy adjustments in case of syndrome-related increased toxicity or resistant disease and syndrome-specific screening programs may lead to early detection of a further independent malignancy. Cancer surveillance might also be warranted for affected relatives and detection of a genetic mutation can allow for reproductive counseling. Here we present an easy-to-use selection tool, based on a systematic review of pediatric cancer predisposing syndromes, to identify patients who may benefit from genetic counseling. The selection tool involves five questions concerning family history, the type of malignancy, multiple primary malignancies, specific features and excessive toxicity, which results in the selection of those patients that may benefit from referral to a clinical geneticist

Progress against non-Hodgkin's lymphoma in children and young adolescents in the Netherlands since 1990: Stable incidence, improved survival and lower mortality

Background: With epidemiologic analyses of population-based trends in incidence and outcomes, we ascertained progress against non-Hodgkin's lymphoma (NHL) in children and young adolescents in the Netherlands since 1990. Methods: Tumour characteristics were extracted from the Netherlands Cancer Registry for patients aged <18 years at diagnosis, between 1990 and 2015. Mortality data for 1980–2016 were derived from Statistics Netherlands. NHL subtypes comprised lymphoblastic lymphoma (LBL), Burkitt lymphoma (BL), diffuse large B-cell lymphoma (DLBCL) and anaplastic large cell lymphoma (ALCL). Time trends in incidence and mortality rates and 5-year overall survival (OS) rates were evaluated by average annual percentage change (AAPC) analyses and parametric survival models, respectively. Results: Overall incidence of NHL remained stable at 11 per million person-years (AAPC -0.2%, p = 0.68), with a marked decrease among children of 5–9 years (AAPC -2.6%, p < 0.01), especially among those with BL. Treatment regimens comprised less radiotherapy over time, especially for LBL and BL. Since 2004, most 15–17-year-old patients with NHL have been treated at a paediatric oncology centre. Five-year OS improved from 71% in 1990–94 to 87% in 2010–15 (p < 0.01), the most gain has been achieved in patients with DLBCL and ALCL from 60% and 73%, respectively, to both 90%. Population-based mortality from NHL decreased significantly towards 1.4 per million person-years (AAPC -4.2%, p < 0.01). Conclusions: This population-based epidemiological study exhibited significant progress against childhood and young adolescent NHL in the Netherlands since 1990, before the advent of a national paediatric oncologic centre in 2018: incidence decreased among children of 5–9 years, survival improved, and mortality steadily decreased over time

High Prevalence of Constitutional Mismatch Repair Deficiency in a Pediatric T-cell Lymphoblastic Lymphoma Cohort

This study describes the clinical characteristics of a complete Dutch T-cell lymphoblastic lymphoma (T-LBL) cohort, including second primary malignancies and comorbidities. We show that over 10% of patients in this complete T-LBL cohort have been diagnosed with a cancer predisposition syndrome (CPS), consisting almost exclusively of constitutional mismatch repair deficiency (CMMRD). The clinical characteristics of sporadic T-LBL patients were compared with T-LBL patients that have been diagnosed with CMMRD. This shows that disease presentation is comparable but that disease localization in CMMRD patients might be more localized. The percentage of CPS seems reliable considering the completeness of the cohort of Dutch T-LBL patients and might even be an underestimation (possibility of undiagnosed CPS patients in cohort). As the frequency of an underlying predisposition syndrome among T-LBL patients may be underestimated at present, we advocate for screening all pediatric T-LBL patients for the presence of germline mutations in mismatch repair genes

The Impact of Age on Outcome of Embryonal and Alveolar Rhabdomyosarcoma Patients.: A Multicenter Study

Background: The prognosis of rhabdomyosarcoma (RMS) in children and adolescents has improved since the introduction of multi-agent chemotherapy. However, outcome data of adults with RMS are scarce. This multicenter retrospective study investigated the effect of age on outcome of RMS. Patients and Methods: Data were collected from three Dutch University Medical Centers between 1977-2009. The effect of age and clinical prognostic factors on relapse-free and disease-specific survival (DSS) were analyzed. Results: Age as a continuous variable predicted poor survival in multivariate analysis. Five-year DSS was highest for non-metastatic embryonal RMS, followed by non-metastatic alveolar RMS and was poor in metastatic disease. Higher age correlated with unfavorable histological subtype (alveolar RMS) and with metastatic disease at presentation in embryonal RMS. In non-metastatic embryonal RMS and in all alveolar RMS, higher age was an adverse prognostic factor of outcome. Conclusion: This study indicates that age is a negative predictor of survival in patients with embryonal and alveolar RMS

Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients

INTRODUCTION: Sarcopenia is a muscle disease that involves loss of muscle strength and physical function and is associated with adverse health effects. Even though sarcopenia has attracted increasing attention in the literature, many research findings have not yet been translated into clinical practice. In this article, we aim to validate a deep learning neural network for automated segmentation of L3 CT slices and aim to explore the potential for clinical utilization of such a tool for clinical practice. MATERIALS AND METHODS: A deep learning neural network was trained on a multi-centre collection of 3413 abdominal cancer surgery subjects to automatically segment muscle, subcutaneous and visceral adipose tissue at the L3 lumbar vertebral level. 536 Polytrauma subjects were used as an independent test set to show generalizability. The Dice Similarity Coefficient was calculated to validate the geometric similarity. Quantitative agreement was quantified using Bland-Altman's Limits of Agreement interval and Lin's Concordance Correlation Coefficient. To determine the potential clinical usability, randomly selected segmentation images were presented to a panel of experienced clinicians to rate on a Likert scale. RESULTS: Deep learning results gave excellent agreement versus a human expert operator for all of the body composition indices, with Concordance Correlation Coefficient for skeletal muscle index of 0.92, Skeletal muscle radiation attenuation 0.94, Visceral Adipose Tissue index 0.99 and Subcutaneous Adipose Tissue Index 0.99. Triple-blinded visual assessment of segmentation by clinicians correlated only to the Dice coefficient, but had no association to quantitative body composition metrics which were accurate irrespective of clinicians' visual rating. CONCLUSION: A deep learning method for automatic segmentation of truncal muscle, visceral and subcutaneous adipose tissue on individual L3 CT slices has been independently validated against expert human-generated results for an enlarged polytrauma registry dataset. Time efficiency, consistency and high accuracy relative to human experts suggest that quantitative body composition analysis with deep learning should is a promising tool for clinical application in a hospital setting