Frontiers in oncology
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Frontiers in oncology · Jan 2019
ReviewUnderstanding the Mechanisms of Resistance to CAR T-Cell Therapy in Malignancies.
Taking advantage of the immune system to exert an antitumor effect is currently a novel approach in cancer therapy. Adoptive transfer of T cells engineered to express chimeric antigen receptors (CARs) targeting a desired antigen has shown extraordinary antitumor activity, especially in refractory and relapsed B-cell malignancies. ⋯ However, with the widespread use of CAR T-cell therapy, problems of resistance and relapse are starting to be considered. This review provides a comprehensive picture of the mechanisms of resistance to CAR T-cell therapy from three aspects, namely, CAR T-cell factors, tumor factors, and tumor microenvironment factors, offering insights for improving CAR T-cell therapy.
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Cutaneous T cell lymphomas (CTCL) are a heterogeneous group of malignancies characterized by the expansion of a malignant T cell clone. Chimeric Antigen Receptor (CAR) T cell therapy has shown impressive results for the treatment of B-cell tumors, but several challenges have prevented this approach in the context of T cell lymphoma. These challenges include the possibilities of fratricide due to shared T-cell antigens, T cell immunodeficiency, and CAR transduction of malignant cells if CAR T are manufactured in the autologous setting. In this review, we discuss these and other challenges in detail and summarize the approaches currently in development to overcome these challenges and offer cellular targeting of T cell lymphomas.
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Frontiers in oncology · Jan 2019
The Role of 5-ALA in Low-Grade Gliomas and the Influence of Antiepileptic Drugs on Intraoperative Fluorescence.
Objectives: Intraoperative tumor visualization with 5-aminolevulinic acid (5-ALA) induced protoporphyrin IX (PpIX) fluorescence is widely applied for improved resection of high-grade gliomas. However, visible fluorescence is present only in a minority of low-grade gliomas (LGGs) according to current literature. Nowadays, antiepileptic drugs (AEDs) are frequently administered to LGG patients prior to surgery. ⋯ Thus, visible fluorescence was significantly more common in patients without AEDs compared to patients with preoperative AED intake (OR = 0,15 (CI 95% 0.012-1.07), p = 0.046). Conclusions: Our study shows a markedly higher rate of visible fluorescence in a series of LGGs compared to current literature. According to our preliminary data, preoperative intake of AEDs seems to reduce the presence of visible fluorescence in such tumors and should thus be taken into account in the clinical setting.
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Frontiers in oncology · Jan 2019
Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics.
Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients. Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0 T magnetic resonance imaging (MRI) examination. ⋯ Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, and 0.851 in internal validation cohort, and 0.714, 0.952, and 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. DCA demonstrated good clinical use of radiomic nomogram. Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.
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Frontiers in oncology · Jan 2019
Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer.
Purpose: To investigate whether a combination of radiomics and automatic machine learning applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of primary breast cancer can non-invasively predict axillary sentinel lymph node (SLN) metastasis. Methods: 62 patients who received a DCE-MRI breast scan were enrolled. Tumor resection and sentinel lymph node (SLN) biopsy were performed within 1 week after the DCE-MRI examination. ⋯ In the validation set, with respect to the accuracy and MSE, the SVM demonstrated the highest performance, with an accuracy, AUC, sensitivity (for positive SLN), specificity (for positive SLN) and Mean Squared Error (MSE) of 0.85, 0.83, 0.71, 1, 0.26, respectively. Conclusions: We demonstrated the feasibility of combining artificial intelligence and radiomics from DCE-MRI of primary tumors to predict axillary SLN metastasis in breast cancer. This non-invasive approach could be very promising in application.