Introduction: Stratification of patients according to the individual sensory phenotype has been suggested a promising method to identify responders for pain treatment. However, many state-of-the-art sensory testing procedures are expensive or time-consuming. Objectives: Therefore, this study aimed to present a selection of easy-to-use bedside devices. Methods: In total, 73 patients (39 m/34 f) and 20 controls (11 m/9 f) received a standardized laboratory quantitative sensory testing (QST) and a bedside-QST. In addition, 50 patients were tested by a group of nonexperienced investigators to address the impact of training. The sensitivity, specificity, and receiver-operating characteristics were analyzed for each bedside-QST parameter as compared to laboratory QST. Furthermore, the patients' individual sensory phenotype (ie, cluster) was determined using laboratory QST, to select bedside-QST parameters most indicative for a correct cluster allocation. Results: The bedside-QST parameters “loss of cold perception to 22°C metal,” “hypersensitivity towards 45°C metal,” “loss of tactile perception to Q-tip and 0.7 mm CMS hair,” as well as “the allodynia sum score” indicated good sensitivity and specificity (ie, ≳70%). Results of interrater variability indicated that training is necessary for individual parameters (ie, CMS 0.7). For the cluster assessment, the respective bedside quantitative sensory testing (QST) parameter combination indicated the following agreements as compared to laboratory QST stratification: excellent for “sensory loss” (area under the curve [AUC] = 0.91), good for “thermal hyperalgesia” (AUC = 0.83), and fair for “mechanical hyperalgesia” (AUC = 0.75). Conclusion: This study presents a selection of bedside parameters to identify the individual sensory phenotype as cost and time efficient as possible.

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The processes leading from disturbed B-cell development to adult B-cell progenitor acute lymphoblastic leukemia (BCP-ALL) remain poorly understood. Here, we describe Irf4−/− mice as prone to developing BCP-ALL with age. Irf4−/− preB-I cells exhibited impaired differentiation but enhanced proliferation in response to IL-7, along with reduced retention in the IL-7 providing bone marrow niche due to decreased CXCL12 responsiveness. Thus selected, preB-I cells acquired Jak3 mutations, probably following irregular AID activity, resulting in malignant transformation. We demonstrate heightened IL-7 sensitivity due to Jak3 mutants, devise a model to explain it, and describe structural and functional similarities to Jak2 mutations often occurring in human Ph-like ALL. Finally, targeting JAK signaling with Ruxolitinib in vivo prolonged survival of mice bearing established Irf4−/− leukemia. Intriguingly, organ infiltration including leukemic meningeosis was selectively reduced without affecting blood blast counts. In this work, we present spontaneous leukemogenesis following IRF4 deficiency with potential implications for high-risk BCP-ALL in adult humans.

Current classifications (WHO-HAEM5 / ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes which are defined by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by RNA-Seq is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning based classifier using RNA-Seq expression data to allocate BCP-ALL samples to 21 defined molecular subtypes. Trained on n=1,869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n=1,018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). "High confidence predictions" were achieved in 84.6% of samples with 99.7% accuracy. Only 1.2% of samples remained "unclassified". ALLCatchR outperformed existing tools and identified novel candidates in previously unassigned samples. We established a novel RNA-Seq reference of human B-lymphopoiesis. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory, which identified shared pattenrs of proximity of BCP-ALL subtypes to normal lymphopoiesis stages. ALLCatchR sustains RNA-Seq routine application in BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocations and novel insights into underlying developmental trajectories.