Judgments of effort exerted by others are influenced by received rewards

Estimating invested effort is a core dimension for evaluating own and others’ actions, and views on the relationship between effort and rewards are deeply ingrained in various societal attitudes. Internal representations of effort, however, are inherently noisy, e.g. due to the variability of sensorimotor and visceral responses to physical exertion. The uncertainty in effort judgments is further aggravated when there is no direct access to the internal representations of exertion – such as when estimating the effort of another person. Bayesian cue integration suggests that this uncertainty can be resolved by incorporating additional cues that are predictive of effort, e.g. received rewards. We hypothesized that judgments about the effort spent on a task will be influenced by the magnitude of received rewards. Additionally, we surmised that such influence might further depend on individual beliefs regarding the relationship between hard work and prosperity, as exemplified by a conservative work ethic. To test these predictions, participants performed an effortful task interleaved with a partner and were informed about the obtained reward before rating either their own or the partner’s effort. We show that higher rewards led to higher estimations of exerted effort in self-judgments, and this effect was even more pronounced for other-judgments. In both types of judgment, computational modelling revealed that reward information and sensorimotor markers of exertion were combined in a Bayes-optimal manner in order to reduce uncertainty. Remarkably, the extent to which rewards influenced effort judgments was associated with conservative world-views, indicating links between this phenomenon and general beliefs about the relationship between effort and earnings in society

Targeting Survivin with YM155 (Sepantronium Bromide): A novel therapeutic strategy for paediatric acute myeloid leukaemia

Despite aggressive chemotherapy, approximately one-third of children with acute myeloid leukaemia(AML) relapse. More effective treatments are urgently needed. Survivin is an inhibitor-of-apoptosis protein with key roles in regulating cell division, proliferation and apoptosis. Furthermore, high expression of Survivin has been associated with poor clinical outcome in AML. The Survivin suppressant YM155 (Sepantronium Bromide) has pre-clinical activity against a range of solid cancers and leukemias, although data in AML is limited. Therefore, we undertook a comprehensive pre-clinical evaluation of YM155 in paediatric AML. YM155 potently inhibited cell viability in a diverse panel of AML cell lines. All paediatric cell lines were particularly sensitive, with a median IC50 of 0.038 mu M. Cell cycle analyses demonstrated concentration-dependent increases in a sub-G1 population with YM155 treatment, suggestive of apoptosis that was subsequently confirmed by an increase in annexin-V positivity. YM155-mediated apoptosis was confirmed across a panel of 8 diagnostic bone marrow samples from children with AML. Consistent with the proposed mechanism of action, YM155 treatment was associated with down-regulation of Survivin mRNA and protein expression and induction of DNA damage

Targeted Next-Gen Sequencing for Detecting MLL Gene Fusions in Leukemia

Mixed Lineage Leukemia (MLL) gene rearrangements characterize approximately 70% of infant and 10% of adult and therapy-related leukemia. Conventional clinical diagnostics, including cytogenetics and fluorescence in situ hybridization (FISH) fail to detect MLL translocation partner genes (TPGs) in many patients. Long-Distance Inverse (LDI)-PCR, the 'gold standard' technique that is used to characterize MLL breakpoints is laborious and requires a large input of genomic DNA (gDNA). To overcome the limitations of current techniques, a targeted Next-Generation Sequencing (NGS) approach that requires low RNA input was tested. Anchored Multiplex PCR based enrichment (AMP-E) was used to rapidly identify a broad range of MLL fusions in patient specimens. Libraries generated using Archer® FusionPlex® Heme and Myeloid panels were sequenced using the Illumina platform. Diagnostic specimens (n=39) from pediatric leukemia patients were tested with AMP-E and validated by LDI-PCR. In concordance with LDI-PCR, the AMP-E method successfully identified TPGs without prior knowledge. AMP-E identified 10 different MLL fusions in the 39 samples. Only two specimens were discordant; AMP-E successfully identified a MLL-MLLT1 fusion where LDI-PCR had failed to determine the breakpoint, whilst a MLL-MLLT3 fusion was not detected by AMP-E due to low expression of the fusion transcript. Sensitivity assays demonstrated that AMP-E can detect MLL-AFF1 in MV4-11 cell dilutions of 10-7 and transcripts down to 0.005 copies/ng.This study demonstrates a Next-Gen Sequencing methodology with improved sensitivity compared to current diagnostic methods for MLL-rearranged leukemia. Furthermore, this assay rapidly and reliably identifies MLL partner genes and patient-specific fusion sequences that could be used for monitoring minimal residual disease (MRD)

Spatial and temporal homogeneity of driver mutations in diffuse intrinsic pontine glioma

Diffuse Intrinsic Pontine Gliomas (DIPGs) are deadly paediatric brain tumours where needle biopsies help guide diagnosis and targeted therapies. To address spatial heterogeneity, here we analyse 134 specimens from various neuroanatomical structures of whole autopsy brains from nine DIPG patients. Evolutionary reconstruction indicates histone 3 (H3) K27M - including H3.2K27M - mutations potentially arise first and are invariably associated with specific, high-fidelity obligate partners throughout the tumour and its spread, from diagnosis to end-stage disease, suggesting mutual need for tumorigenesis. These H3K27M ubiquitously-associated mutations involve alterations in TP53 cell-cycle (TP53/PPM1D) or specific growth factor pathways (ACVR1/PIK3R1). Later oncogenic alterations arise in sub-clones and often affect the PI3K pathway. Our findings are consistent with early tumour spread outside the brainstem including the cerebrum. The spatial and temporal homogeneity of main driver mutations in DIPG implies they will be captured by limited biopsies and emphasizes the need to develop therapies specifically targeting obligate oncohistone partnerships

Development of a targeted sequencing approach to identify prognostic, predictive and diagnostic markers in paediatric solid tumours

The implementation of personalised medicine in childhood cancers has been limited by a lack of clinically validated multi-target sequencing approaches specific for paediatric solid tumours. In order to support innovative clinical trials in high-risk patients with unmet need, we have developed a clinically relevant targeted sequencing panel spanning 311 kb and comprising 78 genes involved in childhood cancers. A total of 132 samples were used for the validation of the panel, including Horizon Discovery cell blends (n=4), cell lines (n=15), formalin-fixed paraffin embedded (FFPE, n≥83) and fresh frozen tissue (FF, n=30) patient samples. Cell blends containing known single nucleotide variants (SNVs, n=528) and small insertion-deletions (indels n=108) were used to define panel sensitivities of ≥98% for SNVs and ≥83% for indels [95% CI] and panel specificity of ≥98% [95% CI] for SNVs. FFPE samples performed comparably to FF samples (n=15 paired). Of 95 well-characterised genetic abnormalities in 33 clinical specimens and 13 cell lines (including SNVs, indels, amplifications, rearrangements and chromosome losses), 94 (98.9%) were detected by our approach. We have validated a robust and practical methodology to guide clinical management of children with solid tumours based on their molecular profiles. Our work demonstrates the value of targeted gene sequencing in the development of precision medicine strategies in paediatric oncology

Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages.

Importance Approaches are needed to stratify individuals in early psychosis stages beyond positive symptom severity to investigate specificity related to affective and normative variation and to validate solutions with premorbid, longitudinal, and genetic risk measures. Objective To use machine learning techniques to cluster, compare, and combine subgroup solutions using clinical and brain structural imaging data from early psychosis and depression stages. Design, Setting, and Participants A multisite, naturalistic, longitudinal cohort study (10 sites in 5 European countries; including major follow-up intervals at 9 and 18 months) with a referred patient sample of those with clinical high risk for psychosis (CHR-P), recent-onset psychosis (ROP), recent-onset depression (ROD), and healthy controls were recruited between February 1, 2014, to July 1, 2019. Data were analyzed between January 2020 and January 2022. Main Outcomes and Measures A nonnegative matrix factorization technique separately decomposed clinical (287 variables) and parcellated brain structural volume (204 gray, white, and cerebrospinal fluid regions) data across CHR-P, ROP, ROD, and healthy controls study groups. Stability criteria determined cluster number using nested cross-validation. Validation targets were compared across subgroup solutions (premorbid, longitudinal, and schizophrenia polygenic risk scores). Multiclass supervised machine learning produced a transferable solution to the validation sample. Results There were a total of 749 individuals in the discovery group and 610 individuals in the validation group. Individuals included those with CHR-P (n = 287), ROP (n = 323), ROD (n = 285), and healthy controls (n = 464), The mean (SD) age was 25.1 (5.9) years, and 702 (51.7%) were female. A clinical 4-dimensional solution separated individuals based on positive symptoms, negative symptoms, depression, and functioning, demonstrating associations with all validation targets. Brain clustering revealed a subgroup with distributed brain volume reductions associated with negative symptoms, reduced performance IQ, and increased schizophrenia polygenic risk scores. Multilevel results distinguished between normative and illness-related brain differences. Subgroup results were largely validated in the external sample. Conclusions and Relevance The results of this longitudinal cohort study provide stratifications beyond the expression of positive symptoms that cut across illness stages and diagnoses. Clinical results suggest the importance of negative symptoms, depression, and functioning. Brain results suggest substantial overlap across illness stages and normative variation, which may highlight a vulnerability signature independent from specific presentations. Premorbid, longitudinal, and genetic risk validation suggested clinical importance of the subgroups to preventive treatments