BackgroundLeukemia-associated macrophages (LAMs) represent an important cell population within the tumor microenvironment, but little is known about the phenotype, function, and plasticity of these cells. The present study provides an extensive characterization of macrophages in patients with acute myeloid leukemia (AML).MethodsThe phenotype and expression of coregulatory markers were assessed on bone marrow (BM)-derived LAM populations, using multiparametric flow cytometry. BM and blood aspirates were obtained from patients with newly diagnosed acute myeloid leukemia (pAML, n=59), patients in long-term remission (lrAML, n=8), patients with relapsed acute myeloid leukemia (rAML, n=7) and monocyte-derived macrophages of the blood from healthy donors (HD, n=17). LAM subpopulations were correlated with clinical parameters. Using a blocking anti-T-cell immunoreceptor with Ig and ITIM domains (TIGIT) antibody or mouse IgG2α isotype control, we investigated polarization, secretion of cytokines, and phagocytosis on LAMs and healthy monocyte-derived macrophages in vitro.ResultsIn pAML and rAML, M1 LAMs were reduced and the predominant macrophage population consisted of immunosuppressive M2 LAMs defined by expression of CD163, CD204, CD206, and CD86. M2 LAMs in active AML highly expressed inhibitory receptors such as TIGIT, T-cell immunoglobulin and mucin-domain containing-3 protein (TIM-3), and lymphocyte-activation gene 3 (LAG-3). High expression of CD163 was associated with a poor overall survival (OS). In addition, increased frequencies of TIGIT+M2 LAMs were associated with an intermediate or adverse risk according to the European Leukemia Network criteria and the FLT3 ITD mutation. In vitro blockade of TIGIT shifted the polarization of primary LAMs or peripheral blood-derived M2 macrophages toward the M1 phenotype and increased secretion of M1-associated cytokines and chemokines. Moreover, the blockade of TIGIT augmented the anti-CD47-mediated phagocytosis of AML cell lines and primary AML cells.ConclusionOur findings suggest that immunosuppressive TIGIT+M2 LAMs can be redirected into an efficient effector population that may be of direct clinical relevance in the near future.

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Background: The novel coronavirus pandemic has affected Brazil's Santa Catarina State (SC) severely. At the time of writing (24 March 2021), over 764,000 cases and over 9,800 deaths by COVID-19 have been confirmed, hospitals were fully occupied with local news reporting at least 397 people in the waiting list for an ICU bed. Despite initial state-wide measures at the outbreak of the pandemic, the state government passed most responsibilities down to cities local government, leaving them to plan whether and when to apply Non-Pharmaceutical Interventions (NPIs). In an attempt to better inform local policy making, we applied an existing Bayesian algorithm to model the spread of the pandemic in the seven geographic macro-regions of the state. However, as we found that the model was too reactive to change in data trends, here we propose changes to extend the model and improve its forecasting capabilities. Methods: Our four proposed variations of the original method allow accessing data of daily reported infections and take into account under-reporting of cases more explicitly. Two of the proposed versions also attempt to model the delay in test reporting. We simulated weekly forecasting of deaths from the period from 31/05/2020 until 31/01/2021.First week data were used as a cold-start to the algorithm, after which weekly calibrations of the model were able to converge in fewer iterations. Google Mobility data were used as covariates to the model, as well as to estimate of the susceptible population at each simulated run. Findings: The changes made the model significantly less reactive and more rapid in adapting to scenarios after a peak in deaths is observed. Assuming that the cases are under-reported greatly benefited the model in its stability, and modelling retroactively-added data (due to the “hot” nature of the data used) had a negligible impact in performance. Interpretation: Although not as reliable as death statistics, case statistics, when modelled in conjunction with an overestimate parameter, provide a good alternative for improving the forecasting of models, especially in long-range predictions and after the peak of an infection wave.

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