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The integrated stress response (ISR) is a critical mediator of cancer cell survival, and targeting the ISR inhibits tumor progression. Here, we have shown that activating transcription factor 4 (ATF4), a master transcriptional effector of the ISR, protects transformed cells against anoikis - a specialized form of apoptosis - following matrix detachment and also contributes to tumor metastatic properties. Upon loss of attachment, ATF4 activated a coordinated program of cytoprotective autophagy and antioxidant responses, including induced expression of the major antioxidant enzyme heme oxygenase 1 (HO-1). HO-1 upregulation was the result of simultaneous activation of ATF4 and the transcription factor NRF2, which converged on the HO1 promoter. Increased levels of HO-1 ameliorated oxidative stress and cell death. ATF4-deficient human fibrosarcoma cells were unable to colonize the lungs in a murine model, and reconstitution of ATF4 or HO-1 expression in ATF4-deficient cells blocked anoikis and rescued tumor lung colonization. HO-1 expression was higher in human primary and metastatic tumors compared with noncancerous tissue. Moreover, HO-1 expression correlated with reduced overall survival of patients with lung adenocarcinoma and glioblastoma. These results establish HO-1 as a mediator of ATF4-dependent anoikis resistance and tumor metastasis and suggest ATF4 and HO-1 as potential targets for therapeutic intervention in solid tumors.

Metastasis is a critical event affecting breast cancer patient survival. To identify molecules contributing to the metastatic process, we analysed The Cancer Genome Atlas (TCGA) breast cancer data and identified 41 genes whose expression is inversely correlated with survival. Here we show that GABA A receptor alpha3 (Gabra3), normally exclusively expressed in adult brain, is also expressed in breast cancer, with high expression of Gabra3 being inversely correlated with breast cancer survival. We demonstrate that Gabra3 activates the AKT pathway to promote breast cancer cell migration, invasion and metastasis. Importantly, we find an A-to-I RNA-edited form of Gabra3 only in non-invasive breast cancers and show that edited Gabra3 suppresses breast cancer cell invasion and metastasis. A-to-I-edited Gabra3 has reduced cell surface expression and suppresses the activation of AKT required for cell migration and invasion. Our study demonstrates a significant role for mRNA-edited Gabra3 in breast cancer metastasis. GABRA3, a subunit of the GABA receptor, is often highly expressed in brain metastasis and breast cancers. Here, the authors demonstrated that GABRA3 activates AKT to promote breast cancer cell invasion and that the A-to-I edited form of GABRA3, specifically expressed in noninvasive breast cancers, can suppress the function of wild type GABRA3.

Objectives There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging. Methods Five hundred forty-five lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training, validation, and testing set in a 7:2:1 ratio. Model performance was compared with four junior and three senior radiologists on the test set. Results Compared with junior radiologists averaged, the final ensemble model combining MR imaging and clinical variables had a higher test accuracy (0.87 vs 0.64, p  < 0.001) and specificity (0.92 vs 0.64, p  < 0.001) with comparable sensitivity (0.75 vs 0.63, p  = 0.407). Against the senior radiologists averaged, the final ensemble model also had a higher test accuracy (0.87 vs 0.74, p  = 0.033) and specificity (0.92 vs 0.70, p  < 0.001) with comparable sensitivity (0.75 vs 0.83, p  = 0.557). Assisted by the model’s probabilities, the junior radiologists achieved a higher average test accuracy (0.77 vs 0.64, Δ = 0.13, p  < 0.001) and specificity (0.81 vs 0.64, Δ = 0.17, p  < 0.001) with unchanged sensitivity (0.69 vs 0.63, Δ = 0.06, p  = 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Δ = 0.11, p  = 0.005) but similar accuracy (0.77 vs 0.74, Δ = 0.03, p  = 0.409) and sensitivity (0.69 vs 0.83, Δ = -0.146, p  = 0.097) when compared with the senior radiologists. Conclusions These results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance. Key Points • Artificial Intelligence based on deep learning can assess the nature of ovarian lesions on routine MRI with higher accuracy and specificity than radiologists. • Assisted by the deep learning model’s probabilities, junior radiologists achieved better performance that matched those of senior radiologists.

Abstract Objectives Ionized calcium binding adaptor molecule 1 (IBA1), a marker of microglia/macrophages, has not been investigated in human hematopathologic contexts. We evaluated its expression in mature and immature neoplasms of monocytic/histiocytic and dendritic cell (DC) origin. Methods Immunohistochemistry for IBA1, CD14, CD68, and CD163 was performed on a total of 114 cases, including a spectrum of monocytic/histiocytic and DC neoplasms (20 tissue based and 59 bone marrow based) and several nonhistiocytic/monocytic/DC neoplasms as control groups (15 tissue based and 20 bone marrow based). Results IBA1 expression was observed in all types of mature tissue-based histiocytic/DC neoplasms (20/20) but not in the corresponding control group (0/15). In bone marrow–based cases, IBA1 was expressed in most acute myeloid leukemias (AMLs) with monocytic differentiation (48/53), both blastic plasmacytoid dendritic cell neoplasms (2/2), and all chronic myelomonocytic leukemias (4/4), while it was positive in only one nonmonocytic AML (1/15) and none of the acute lymphoblastic leukemias (0/5). Collectively, IBA1 showed much higher sensitivity and specificity (93.7%, 97.1%) compared with CD14 (65.4%, 88.2%), CD68 (74.4%, 74.2%), and CD163 (52.6%, 90.6%). Conclusions IBA1 is a novel, highly sensitive, and specific marker for diagnosing neoplasms of monocytic/histiocytic and DC origin.

Glomus tumor is a benign mesenchymal neoplasm comprising less than 2% of soft tissue tumors. It is composed of cells resembling modified smooth muscle cells of the normal glomus body. The glomus body, a thermoregulator, is a specialized form of arteriovenous anastomosis localized in dermal and precoccygeal soft tissue. Although glomus tumors are rare neoplasms, clinical misdiagnosis of many of these lesions as hemangiomas or venous malformations makes an accurate assessment of their actual prevalence difficult. A malignant counterpart of this lesion exists but is extremely rare.

PurposeThe ability to reliably distinguish benign from malignant solid liver lesions on ultrasonography can increase access, decrease costs, and help to better triage patients for biopsy. In this study, we used deep learning to differentiate benign from malignant focal solid liver lesions based on their ultrasound appearance.MethodsAmong the 596 patients who met the inclusion criteria, there were 911 images of individual liver lesions, of which 535 were malignant and 376 were benign. Our training set contained 660 lesions augmented dynamically during training for a total of 330,000 images; our test set contained 79 images. A neural network with ResNet50 architecture was fine-tuned using pre-trained weights on ImageNet. Non-cystic liver lesions with definite diagnosis by histopathology or MRI were included. Accuracy of the final model was compared with expert interpretation. Two separate datasets were used in training and evaluation, one with all lesions and one with lesions deemed to be of uncertain diagnosis based on the Code Abdomen rating system.ResultsOur model trained on the complete set of all lesions achieved a test accuracy of 0.84 (95% CI 0.74–0.90) compared to expert 1 with a test accuracy of 0.80 (95% CI 0.70–0.87) and expert 2 with a test accuracy of 0.73 (95% CI 0.63–0.82). Our model trained on the uncertain set of lesions achieved a test accuracy of 0.79 (95% CI 0.69–0.87) compared to expert 1 with a test accuracy of 0.70 (95% CI 0.59–0.78) and expert 2 with a test accuracy of 0.66 (95% CI 0.55–0.75). On the uncertain dataset, compared to all experts averaged, the model had higher test accuracy (0.79 vs. 0.68, p = 0.025).ConclusionDeep learning algorithms proposed in the current study improve differentiation of benign from malignant ultrasound-captured solid liver lesions and perform comparably to expert radiologists. Deep learning tools can potentially be used to improve the accuracy and efficiency of clinical workflows.

Histiocytic sarcoma (HS) is a rare yet lethal malignancy with no established standard of care therapies. A lack of pre-clinical models limits our understanding of HS pathogenesis and identification of therapeutic targets. Canine HS shares multiple clinical and genetic similarities with human HS, supporting its use as a unique translational model. Prior studies have investigated the immunogenicity of HS. Although increased tumor infiltrating lymphocyte (TIL) density is associated with favorable outcomes in canine HS, virtually all canine patients eventually succumb to progressive disease consistent with ultimate failure of anti-tumor immunity. To investigate potential regulators of the immune tumor microenvironment (TME), we undertook a comparative transcriptional approach of three long-lived cases of canine pulmonary HS with heavy T cell infiltrate and three short-lived cases of splenic HS that lacked significant T cell inflammation and compared these data to corresponding grossly normal tissues from dogs undergoing necropsy. This comparison identified PDCD1, encoding the immune checkpoint PD-1, and SPP1, encoding the secreted pro-tumorigenic protein osteopontin, as positive differentially expressed genes (DEGs) in canine HS. TXNIP, encoding the tumor suppressor TXNIP, was the most significant negative DEG. Comparative transcriptomic studies revealed conservation of enriched (including SPP1 ) and depleted (including TXNIP ) DEGs between canine and human HS patients. Immunohistochemistry demonstrated osteopontin in the TMEs of canine and human HS. Collectively, we uncover PD-1, osteopontin, and TXNIP as putative actionable targets in HS and further establish canine HS as a preclinical platform to screen novel immunotherapeutic approaches for this deadly disease.

PurposeClear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, and necrosis (SSIGN) score of ccRCC using MRI-based radiomics.MethodsA multicenter cohort of 364 histopathologically confirmed ccRCC patients (272 low [< 4] and 92 high [≥ 4] SSIGN score) with preoperative T2-weighted and T1-contrast-enhanced MRI were retrospectively identified and divided into training (254 patients) and testing sets (110 patients). The performance of a manually optimized radiomics model was assessed by measuring accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUROC), and area under precision-recall curve (AUPRC) on an independent test set, which was not included in model training. Lastly, its performance was compared to that of a machine learning pipeline, Tree-Based Pipeline Optimization Tool (TPOT).ResultsThe manually optimized radiomics model using Random Forest classification and Analysis of Variance feature selection methods achieved an AUROC of 0.89, AUPRC of 0.81, accuracy of 0.89 (95% CI 0.816–0.937), specificity of 0.95 (95% CI 0.875–0.984), and sensitivity of 0.72 (95% CI 0.537–0.852) on the test set. The TPOT using Extra Trees Classifier achieved an AUROC of 0.94, AUPRC of 0.83, accuracy of 0.89 (95% CI 0.816–0.937), specificity of 0.95 (95% CI 0.875–0.984), and sensitivity of 0.72 (95% CI 0.537–0.852) on the test set.ConclusionPreoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its promise as a prognostic tool that can be used in conjunction with diagnostic markers.

Objectives Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. Methods An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. Results A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients’ to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks ( p < 0.0001). Conclusions Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. Key Point • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

In 2018, the consensus meeting for the WHO Classification of Tumours of the Eye decided that conjunctival mucoepidermoid carcinoma should be reclassified as adenosquamous carcinoma, as this represented a better morphological fit. To examine the applicability of this terminology, we studied the clinical, histopathological, immunohistochemical and molecular pathology of 14 cases that were originally diagnosed as conjunctival mucoepidermoid carcinoma. There were 7 (50%) females and 7 (50%) males. The median age was 64 years. The left eye was affected in 8 and the right eye in 6 patients. In-situ carcinoma was present in 11/14 (79%) cases and comprised in-situ squamous cell carcinoma (SCC) and conjunctival intraepithelial neoplasia with mucinous differentiation (CIN-Muc). Invasive carcinoma was present in 11/14 (79%) cases. Group 1 (1/11 cases, 9%) comprised invasive SCC only. Group 2 (6/11 cases, 55%) comprised SCC with mucinous differentiation, manifesting as scattered intracellular mucin, occasionally together with intercellular mucin, with no evidence of true glandular differentiation. Group 3 (3/11 cases. 27%) comprised true adenosquamous carcinoma. Group 4 (1/11 cases, 9%) comprised pure adenocarcinoma. Thirteen of 14 cases (93%) underwent FISH for MAML2 translocation and none were rearranged. Two cases harboured high-risk HPV (type 16 and 18). The combined findings confirm that all lesions in our study were not mucoepidermoid carcinoma, but represented predominantly SCC with mucinous differentiation and adenosquamous carcinoma. We, therefore, recommend future revision of the WHO classification to include SCC with mucinous differentiation alongside adenosquamous carcinoma.