Biology subjects

El-Kenawi, A.

Publications and source records attributed to El-Kenawi, A..

5 recordsLinked to original sources

bioRxiv2021

Macrophage-derived cholesterol contributes to therapeutic resistance in prostate cancer

Tumor-associated macrophages are key immune cells associated with cancer progression. Here we sought to determine the role of macrophages in castration-resistant prostate cancer (CRPC) using a syngeneic model that reflected the mutational landscape of the disease. A transcriptomic analysis of CRPC tumors following macrophage depletion revealed lower molecular signatures for steroid and bile acid synthesis, indicating potential perturbation of cholesterol metabolism. Since cholesterol is the precursor of the five major classes of steroid hormones, we reasoned that macrophages were regulating androgen biosynthesis within the prostate tumor microenvironment. Indeed, macrophage depletion reduced the levels of androgens within prostate tumors and restricted androgen receptor (AR) nuclear localization in vitro and in vivo. Macrophages were cholesterol rich and had the ability to transfer cholesterol to tumor cells in vitro, and AR nuclear translocation was inhibited by activation of Liver X Receptor (LXR)-{beta}, the master regulator of cholesterol homeostasis. Finally, combining macrophage depletion with androgen deprivation therapy increased survival, supporting the therapeutic potential of targeting macrophages in CRPC.

cancer biology↗

bioRxiv2021

Spatial Clustering of CD68+ Tumor Associated Macrophages with Tumor Cells is Associated with Worse Overall Survival in Metastatic Clear Cell Renal Cell Carcinoma

Immune infiltration is typically quantified using cellular density, not accounting for cellular clustering. Tumor-associated macrophages (TAM) activate oncogenic signaling through paracrine interactions with tumor cells, which may be better reflected by local cellular clustering than global density metrics. Using multiplex immunohistochemistry and digital pathologic analysis we quantified cellular density and cellular clustering for myeloid cell markers in 129 regions of interest from 55 samples from 35 patients with metastatic ccRCC. CD68+ cells were found to be clustered with tumor cells and dispersed from stromal cells, while CD163+ and CD206+ cells were found to be clustered with stromal cells and dispersed from tumor cells. CD68+ density was not associated with OS, while high tumor/CD68+ cell clustering was associated with significantly worse OS. These novel findings would not have been identified if immune infiltrate was assessed using cellular density alone, highlighting the importance of including spatial analysis in studies of immune cell infiltration of tumors. SIGNIFICANCEIncreased clustering of CD68+ TAMs and tumor cells was associated with worse overall survival for patients with metastatic ccRCC. This effect would not have been identified if immune infiltrate was assessed using cell density alone, highlighting the importance of including spatial analysis in studies of immune cell infiltration of tumors.

cancer biology↗

bioRxiv2019

Lymph Nodes Inhibit T-cell Effector Functions Locally by Establishing Acidic Niches

Lymph nodes are an essential component of the adaptive immune response where antigen-presenting cells are closely housed with their cognate effector cells. Protection of lymph node resident cells from activated immune cells in such close quarters would need to be robust and reversible. Effector functions of T-cells are profoundly and reversibly inhibited by an acidic microenvironment. The underlying mechanisms of this inhibition are unknown, but may relate to glycolysis, which is obligatory for expression of effector functions. Here, we demonstrate that acidification rapidly and potently inhibits monocarboxylate transporter-dependent lactic acid efflux, which dually inhibits glycolysis by end-product accumulation and by reducing cytoplasmic pH. Based on the robustness of these responses, we propose that acid-evoked T-cell inhibition is physiologically important, and that lymph nodes are a natural site for such modulation. Using multiple imaging techniques, we show that paracortical T-zones of lymph nodes are highly acidic. We further show that T-cells can be activated by dendritic cells at low pH, and their effector functions are restored rapidly upon increasing pH. Thus, we describe a novel physiological mechanism whereby activated T-cells are kept in stasis by acidosis whilst resident in lymph nodes.

physiology↗

bioRxiv2018

Acidity promotes tumor progression by altering macrophage phenotype in prostate cancer

Tumors rapidly ferment glucose to lactic acid even in the presence of oxygen, and coupling high glycolysis with poor perfusion leads to extracellular acidification. Here we demonstrate that acidity, independent from lactate, augments the pro-tumor phenotype of macrophages. We used zwitterionic buffers to show that activating macrophages at pH 6.8 in vitro enhanced an IL-4-driven phenotype as measured by gene expression, cytokine profiling, and functional assays. These results were recapitulated in vivo wherein neutralizing intratumoral acidity reduced the pro-tumor phenotype of macrophages, while also decreasing tumor incidence and invasion in the TRAMP model of prostate cancer. These results were recapitulated using an in silico mathematical model that simulate macrophage responses to environmental signals. By turning off acid-induced cellular responses, our in silico mathematical modeling shows that acid-resistant macrophages can limit tumor progression. In summary, this study suggests that tumor acidity contributes to prostate carcinogenesis by altering the state of macrophage activation.

immunology↗

bioRxiv2017

Combining radiomics and mathematical modeling to elucidate mechanisms of resistance to immune checkpoint blockade in non-small cell lung cancer

Immune therapies have shown promise in a number of cancers, and clinical trials using the anti-PD-L1/PD-1 checkpoint inhibitor in lung cancer have been successful for a number of patients. However, some patients either do not respond to the treatment or have cancer recurrence after an initial response. It is not clear which patients might fall into these categories or what mechanisms are responsible for treatment failure. To explore the different underlying biological mechanisms of resistance, we created a spatially explicit mathematical model with a modular framework. This construction enables different potential mechanisms to be turned on and off in order to adjust specific tumor and tissue interactions to match a specific patient's disease. In parallel, we developed a software suite to identify significant computed tomography (CT) imaging features correlated with outcome using data from an anti-PDL-1 checkpoint inhibitor clinical trial for lung cancer and a tool that extracts these features from both patient CT images and \"virtual CT\" images created from the cellular density profile of the model. The combination of our two toolkits provides a framework that feeds patient data through an iterative pipeline to identify predictive imaging features associated with outcome, whilst at the same time proposing hypotheses about the underlying resistance mechanisms.

cancer biology↗