Loss of G0/G1 switch gene 2 (G0S2) promotes disease progression and drug resistance in chronic myeloid leukaemia (CML) by disrupting glycerophospholipid metabolism

Abstract Tyrosine kinase inhibitors (TKIs) targeting BCR::ABL1 have turned chronic myeloid leukaemia (CML) from a fatal disease into a manageable condition for most patients. Despite improved survival, targeting drug‐resistant leukaemia stem cells (LSCs) remains a challenge for curative CML therapy. Aberrant lipid metabolism can have a large impact on membrane dynamics, cell survival and therapeutic responses in cancer. While ceramide and sphingolipid levels were previously correlated with TKI response in CML, the role of lipid metabolism in TKI resistance is not well understood. We have identified downregulation of a critical regulator of lipid metabolism, G0/G1 switch gene 2 (G0S2), in multiple scenarios of TKI resistance, including (1) BCR::ABL1 kinase‐independent TKI resistance, (2) progression of CML from the chronic to the blast phase of the disease, and (3) in CML versus normal myeloid progenitors. Accordingly, CML patients with low G0S2 expression levels had a worse overall survival. G0S2 downregulation in CML was not a result of promoter hypermethylation or BCR::ABL1 kinase activity, but was rather due to transcriptional repression by MYC. Using CML cell lines, patient samples and G0s2 knockout (G0s2−/−) mice, we demonstrate a tumour suppressor role for G0S2 in CML and TKI resistance. Our data suggest that reduced G0S2 protein expression in CML disrupts glycerophospholipid metabolism, correlating with a block of differentiation that renders CML cells resistant to therapy. Altogether, our data unravel a new role for G0S2 in regulating myeloid differentiation and TKI response in CML, and suggest that restoring G0S2 may have clinical utility

Relationship between executive function, attachment style, and psychotic like experiences in typically developing youth

Psychotic like experiences (PLE's) are common in the general population, particularly during adolescence, which has generated interest in how PLE's emerge, and the extent to which they reflect either risk for, or resilience to, psychosis. The "attachment-developmental-cognitive" (ADC) model is one effort to model the effect of risk factors on PLEs. The ADC model proposes attachment insecurity as an early environmental insult that can contribute to altered neurodevelopment, increasing the likelihood of PLE's and psychosis. In particular, early-life attachment disruptions may negatively impact numerous aspects of executive function (EF), including behavioral inhibition and emotion regulation. Yet despite the relationship of disrupted attachment to EF impairments, no studies have examined how these factors may combine to contribute to PLE's in adolescents. Here, we examined the relative contributions of daily-life EF and attachment difficulties (avoidance and anxiety) to PLEs in typically developing youth (N=52; ages 10-21). We found that EF deficits and high attachment insecurity both accounted for a significant proportion of the variance in PLE's, and interacted to predict PLE manifestation. Specifically, positive PLEs were predicted by greater trouble monitoring behavioral impact, less difficulty completing tasks, greater difficulty regulating emotional reactions, greater difficulty controlling impulses and higher attachment anxiety. Negative PLEs were predicted by greater difficulty in alternating attention, transitioning across situations, and regulating emotional reactions as well as higher attachment anxiety. These results are consistent with the ADC model, providing evidence that early-life attachment disruptions may impact behavioral regulation and emotional control, which together may contribute to PLEs

A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study

Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions