Bordering and Debordering Across Time. Refugees and Asylum Seekers Facing Chronopolitics

With these contributes we investigate how refugees and asylum seekers deal with the politics of time that run within asylum policies, including the different production of temporal regimes along diverse types of migrant centres. By bringing together papers and case studies that map the trajectories of time undertaken by individuals in their daily routines and life experiences, we attempt to set up a reflection on the notion of a ‘landscape of time’. If, on the one hand, the interactions between spatial confinement and temporality of immigration controls realise multiple forms of (im)mobility of refugees and asylum seekers; on the other hand, the practices of routinisation, acceleration, stasis and waiting – exercised by individuals both inside and outside the centres and across the borders – can also be read as a tactic aimed at claiming time; a time which is differently experienced, according to the current system of social and civil stratification. Therefore, moving within these premises, we focus on how the ‘chronopolitics’ of the asylum and reception system affect daily lives and biographical trajectories of refugees and asylum seekers, both during their experience of migration and within the so called ‘reception system’

Longitudinal evolution of the immune suppressive glioma microenvironment in different synchronous lesions during treatment

The role of immune suppression in glioma progression has been clearly established.1 We and others have recently demonstrated that myeloid cells play a major role in the tumor microenvironment of glioblastoma (GBM) patients,2,3 and that not only bone marrow-derived macrophages (BMDMs) have a higher intrinsic immune suppressive ability compared to resident microglial cells (MG), but also that this ability greatly increases going from the periphery to the tumor core.3 In lower grade gliomas (grades II and III), a much lower amount of BMDM is present, devoid of immune suppressive ability.3 We present here a longitudinal analysis of the immune infiltrate in a patient with a synchronous occurrence of GBM in the left temporal lobe, and a low-grade glioma (LGG) in the right frontal lobe, with discordant isocitrate dehydrogenase (IDH)-mutational status,4 followed by two GBM relapse

Anaplastic astrocytoma (AA) and glioblastoma (GBM): a real-life experience in Padua Neuro-Oncology Center

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Annexin 2A sustains glioblastoma cell dissemination and proliferation.

Glioblastoma (GBM) is the most devastating tumor of the brain, characterized by an almost inevitable tendency to recur after intensive treatments and a fatal prognosis. Indeed, despite recent technical improvements in GBM surgery, the complete eradication of cancer cell disseminated outside the tumor mass still remains a crucial issue for glioma patients management. In this context, Annexin 2A (ANXA2) is a phospholipid-binding protein expressed in a variety of cell types, whose expression has been recently associated with cell dissemination and metastasis in many cancer types, thus making ANXA2 an attractive putative regulator of cell invasion also in GBM.Here we show that ANXA2 is over-expressed in GBM and positively correlates with tumor aggressiveness and patient survival. In particular, we associate the expression of ANXA2 to a mesenchymal and metastatic phenotype of GBM tumors. Moreover, we functionally characterized the effects exerted by ANXA2 inhibition in primary GBM cultures, demonstrating its ability to sustain cell migration, matrix invasion, cytoskeletal remodeling and proliferation. Finally, we were able to generate an ANXA2-dependent gene signature with a significant prognostic potential in different cohorts of solid tumor patients, including GBM.In conclusion, we demonstrate that ANXA2 acts at multiple levels in determining the disseminating and aggressive behaviour of GBM cells, thus proving its potential as a possible target and strong prognostic factor in the future management of GBM patients

Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage

Background: Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Machine learning (ML) defines modern data analysis techniques allowing accurate subject-based risk stratifications. We aimed at developing and testing different ML models to predict shunt-dependent hydrocephalus after aneurysmal SAH. Methods: We consulted electronic records of patients with aneurysmal SAH treated at our institution between January 2013 and March 2019. We selected variables for the models according to the results of the previous works on this topic. We trained and tested four ML algorithms on three datasets: one containing binary variables, one considering variables associated with shunt-dependency after an explorative analysis, and one including all variables. For each model, we calculated AUROC, specificity, sensitivity, accuracy, PPV, and also, on the validation set, the NPV and the Matthews correlation coefficient (ϕ). Results: Three hundred eighty-six patients were included. Fifty patients (12.9%) developed shunt-dependency after a mean follow-up of 19.7 (± 12.6) months. Complete information was retrieved for 32 variables, used to train the models. The best models were selected based on the performances on the validation set and were achieved with a distributed random forest model considering 21 variables, with a ϕ = 0.59, AUC = 0.88; sensitivity and specificity of 0.73 (C.I.: 0.39–0.94) and 0.92 (C.I.: 0.84–0.97), respectively; PPV = 0.59 (0.38–0.77); and NPV = 0.96 (0.90–0.98). Accuracy was 0.90 (0.82–0.95). Conclusions: Machine learning prognostic models allow accurate predictions with a large number of variables and a more subject-oriented prognosis. We identified a single best distributed random forest model, with an excellent prognostic capacity (ϕ = 0.58), which could be especially helpful in identifying low-risk patients for shunt-dependency

Association between thyroid function and regorafenib efficacy in patients with relapsed wild-type IDH glioblastoma: a large multicenter study

Purpose: Regorafenib demonstrated encouraging results in recurrent glioblastoma patients. Some studies showed that changes in circulating thyroid hormones (fT3, fT4, fT3/fT4 ratio) can be considered as prognostic factors in patients with various types of tumors. We designed this study to investigate the relationship between baseline thyroid variables and outcome in IDH-wild type GBM patients who were treated with regorafenib. Methods: This multicenter retrospective study included recurrent IDH-wild-type glioblastoma patients treated with regorafenib. Only patients with baseline thyroid function values (TSH, fT3, fT4, fT3/fT4 ratio) available were evaluated. RANO criteria were used to analyze neuroradiological response. Survival curves were estimated using the Kaplan–Meier method. The relationships between baseline thyroid variables (TSH, fT3, fT4, fT3/fT4) and survival (PFS, OS) were investigated with Cox regression models. Results: From November 2015 to April 2022, 134 recurrent IDH-wildtype GBM patients were treated with regorafenib and 128 of these had information on baseline thyroid function value. Median follow-up was 8 months (IQR 4.7–14.0). Objective Response Rate was 9% and Disease Control Rate was 40.9%. Median PFS was 2.7 months (95%CI 2.2–3.6) and median OS was 10.0 months (95%CI 7.0–13.0). Lower baseline TSH value in the blood was correlated with a higher rate of disease progression to regorafenib (p = 0.04). Multivariable analyses suggested a non-linear relationship between PFS (p = 0.01) and OS (p = 0.03) with baseline fT3/fT4 ratio. Conclusion: In recurrent wild-type IDH glioblastoma patients, baseline fT3/fT4 ratio showed a non-linear relationship with survival, with different impacts across the spectrum of fT3/fT4 ratio. Moreover, baseline TSH may be a predictor of regorafenib activity

Targeting of immunosuppressive myeloid cells from glioblastoma patients by modulation of size and surface charge of lipid nanocapsules

Background: Myeloid derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs) are two of the major players involved in the inhibition of anti-tumor immune response in cancer patients, leading to poor prognosis. Selective targeting of myeloid cells has therefore become an attractive therapeutic strategy to relieve immunosuppression and, in this frame, we previously demonstrated that lipid nanocapsules (LNCs) loaded with lauroyl-modified gemcitabine efficiently target monocytic MDSCs in melanoma patients. In this study, we investigated the impact of the physico-chemical characteristics of LNCs, namely size and surface potential, towards immunosuppressive cell targeting. We exploited myeloid cells isolated from glioblastoma patients, which play a relevant role in the immunosuppression, to demonstrate that tailored nanosystems can target not only tumor cells but also tumor-promoting cells, thus constituting an efficient system that could be used to inhibit their function. Results: The incorporation of different LNC formulations with a size of 100 nm, carrying overall positive, neutral or negative charge, was evaluated on leukocytes and tumor-infiltrating cells freshly isolated from glioblastoma patients. We observed that the maximum LNC uptake was obtained in monocytes with neutral 100 nm LNCs, while positively charged 100 nm LNCs were more effective on macrophages and tumor cells, maintaining at low level the incorporation by T cells. The mechanism of uptake was elucidated, demonstrating that LNCs are incorporated mainly by caveolae-mediated endocytosis. Conclusions: We demonstrated that LNCs can be directed towards immunosuppressive cells by simply modulating their size and charge thus providing a novel approach to exploit nanosystems for anticancer treatment in the frame of immunotherapy.[Figure not available: see fulltext.

Myeloid Diagnostic and Prognostic Markers of Immune Suppression in the Blood of Glioma Patients.

Although gliomas are confined to the central nervous system, their negative influence over the immune system extends to peripheral circulation. The immune suppression exerted by myeloid cells can affect both response to therapy and disease outcome. We analyzed the expansion of several myeloid parameters in the blood of low- and high-grade gliomas and assessed their relevance as biomarkers of disease and clinical outcome. Methods: Peripheral blood was obtained from 134 low- and high-grade glioma patients. CD14+, CD14+/p-STAT3+, CD14+/PD-L1+, CD15+ cells and four myeloid-derived suppressor cell (MDSC) subsets, were evaluated by flow cytometry. Arginase-1 (ARG1) quantity and activity was determined in the plasma. Multivariable logistic regression model was used to obtain a diagnostic score to discriminate glioma patients from healthy controls and between each glioma grade. A glioblastoma prognostic model was determined by multiple Cox regression using clinical and myeloid parameters. Results: Changes in myeloid parameters associated with immune suppression allowed to define a diagnostic score calculating the risk of being a glioma patient. The same parameters, together with age, permit to calculate the risk score in differentiating each glioma grade. A prognostic model for glioblastoma patients stemmed out from a Cox multiple analysis, highlighting the role of MDSC, p-STAT3, and ARG1 activity together with clinical parameters in predicting patient's outcome. Conclusions: This work emphasizes the role of systemic immune suppression carried out by myeloid cells in gliomas. The identification of biomarkers associated with immune landscape, diagnosis, and outcome of glioblastoma patients lays the ground for their clinical use