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OBJECTIVE: Improved treatment landscape has led to better outcomes for paediatric acute lymphoblastic leukemia (ALL) survivors. As the number of survivors increase, we need to elucidate the long-term quality of life (QoL) and domains of complaints in these patients. Furthermore, the main priorities of these patients need to be clarified. We assessed long-term QoL outcomes of survivors of childhood ALL compared to matched population controls. METHODS: QoL data were collected from survivors recruited in France and Belgium between 2012 and 2017, including the Short Form Health Survey (SF-12) and the Quality of Life Systemic Inventory (QLSI). The Wilcoxon test was used to compare SF-12 scale scores between survivors and matched population controls. For the QLSI, comparisons were mainly descriptive. RESULTS: One hundred and eighty-six survivors (mean age: 27.6 years; range: 18.1-52.8) at follow-up completed QoL measures, amongst whom 180 were matched to controls. Overall, survivors had higher QoL on all SF12 scale scores, indicating that they had better functioning compared to controls. Statistically significant differences on the SF12 were observed for Vitality, Social Functioning, Role Limitations due to Emotional Problems and Mental Health scales. QLSI outcomes suggested that survivors were happier than controls with Couple and Social Relations. Controls were unhappiest compared to survivors with Money, Love life, Self-esteem, Nutrition and Paid Work. CONCLUSIONS: Our findings suggest that survivors of childhood ALL have better QoL outcomes on some domains compared to the general population, specifically around social and emotional functioning, and that they tend to prioritize their relationships more. Interventions for improving QoL outcomes, might build on existing positive experiences with family, friends and partners. ispartof: PSYCHO-ONCOLOGY vol:31 issue:12 pages:2159-2168 ispartof: location:England status: published

Background Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models. Methods RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted. Results Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 +/- 0.1 and 0.7 +/- 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC >= 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups. Conclusion Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice. We received fnancial support from the Research Foundation Flanders (1861219N to BO), Al Thrasher Early Career Grant, academic investigatorinitiated grants from AbbVie and charity support from “Cycling for the smile of a child”. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. We appreciate the participation of all patients and their families in this study. We are grateful to all unmentioned clinicians, nurses, and lab colleagues. We thank Yanick Crow for helping us with certain analyses and Yves Vandeputte who organised the charity support from “Cycling for the smile of a child”.

Juvenile-onset recurrent respiratory papillomatosis (JRRP) is a rare and debilitating childhood disease that presents with recurrent growth of papillomas in the upper airway. Two common human papillomaviruses (HPVs), HPV-6 and -11, are implicated in most cases, but it is still not understood why only a small proportion of children develop JRRP following exposure to these common viruses. We report 2 siblings with a syndromic form of JRRP associated with mild dermatologic abnormalities. Whole-exome sequencing of the patients revealed a private homozygous mutation in NLRP1, encoding Nucleotide-Binding Domain Leucine-Rich Repeat Family Pyrin Domain-Containing 1. We find the NLRP1 mutant allele to be gain of function (GOF) for inflammasome activation, as demonstrated by the induction of inflammasome complex oligomerization and IL-1β secretion in an overexpression system. Moreover, patient-derived keratinocytes secrete elevated levels of IL-1β at baseline. Finally, both patients displayed elevated levels of inflammasome-induced cytokines in the serum. Six NLRP1 GOF mutations have previously been described to underlie 3 allelic Mendelian diseases with differing phenotypes and modes of inheritance. Our results demonstrate that an autosomal recessive, syndromic form of JRRP can be associated with an NLRP1 GOF mutation., United States Department of Health and Human Services; National Institutes of Health (NIH); NIH National Center for Advancing Translational Sciences (NCATS); French National Research Agency (ANR); Integrative Biology of Emerging Infectious Diseases Laboratoire d'Excellence; French Cancer Institute; Strategic Positioning Fund on Genetic Orphan Diseases from A* STAR, Singapore; Jeffrey Modell Foundation; Bijzonder Onderzoeksfonds-Tenure Grant; Cure-AID; European Union ERA-Net for Research Programmes on Rare Diseases; H2020; European Union (European Union); National Research Foundation; St. Giles Foundation; Rockefeller University; Institut National de la Sante et de la Recherche Medicale (Inserm); Paris Descartes University; Shapiro-Silverberg Fund for the Advancement of Translational Research; American Philosophical Society Daland Fellowship in Clinical Investigation; NIH National Center for Research Resources (NCRR)