Dataset Information

Complement Component 3 Adapts the Cerebrospinal Fluid for Leptomeningeal Metastasis.

ABSTRACT: We molecularly dissected leptomeningeal metastasis, or spread of cancer to the cerebrospinal fluid (CSF), which is a frequent and fatal condition mediated by unknown mechanisms. We selected lung and breast cancer cell lines for the ability to infiltrate and grow in CSF, a remarkably acellular, mitogen-poor metastasis microenvironment. Complement component 3 (C3) was upregulated in four leptomeningeal metastatic models and proved necessary for cancer growth within the leptomeningeal space. In human disease, cancer cells within the CSF produced C3 in correlation with clinical course. C3 expression in primary tumors was predictive of leptomeningeal relapse. Mechanistically, we found that cancer-cell-derived C3 activates the C3a receptor in the choroid plexus epithelium to disrupt the blood-CSF barrier. This effect allows plasma components, including amphiregulin, and other mitogens to enter the CSF and promote cancer cell growth. Pharmacologic interference with C3 signaling proved therapeutically beneficial in suppressing leptomeningeal metastasis in these preclinical models.

SUBMITTER: Boire A

PROVIDER: S-EPMC5405733 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Complement Component 3 Adapts the Cerebrospinal Fluid for Leptomeningeal Metastasis.

Boire Adrienne A Zou Yilong Y Shieh Jason J Macalinao Danilo G DG Pentsova Elena E Massagué Joan J

Cell 20170301 6

We molecularly dissected leptomeningeal metastasis, or spread of cancer to the cerebrospinal fluid (CSF), which is a frequent and fatal condition mediated by unknown mechanisms. We selected lung and breast cancer cell lines for the ability to infiltrate and grow in CSF, a remarkably acellular, mitogen-poor metastasis microenvironment. Complement component 3 (C3) was upregulated in four leptomeningeal metastatic models and proved necessary for cancer growth within the leptomeningeal space. In hum ...[more]

PMID: 28283064

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Project description:Background: Leptomeningeal metastasis (LM) has frequently been observed in patients with lung adenocarcinoma. So far, its diagnosis and disease course monitoring are still extremely difficult. Moreover, there is no effective treatment regimen for LM due to a lack knowledge on the molecular mechanism of LM. This study aimed to identify LM-related cerebrospinal fluid (CSF) miRNAs, which have potential value for diagnosing and monitoring LM and exploring the molecular mechanism. Methods: CSF miRNAs were screened and verified by microarray analysis and quantitative real-time PCR (qRT-PCR) in LM patients with lung adenocarcinoma and non-LM controls, and the diagnostic performance of candidate miRNAs was evaluated. Then, candidate miRNAs in matched CSF samples from LM patients at diagnosis, after initial therapy, at relapse, and after salvage therapy, were analyzed to assess the relationship between CSF miRNAs and LM disease course. The effect of candidate miRNAs on proliferation, invasion, and migration of lung adenocarcinoma cell lines was assessed. The targeted genes of the candidate miRNA were predicted by TargetScan, miRDB, and miRTarbase online analysis tools. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the functional categories of predicted target genes. Results: CSF miR-7975, miR-7977, and miR-7641 were screened and verified to be statistically significantly up-regulated in LM patients compared to non-LM controls. The three miRNAs, when combined, exhibited optimal diagnostic performance. Longitudinal data of CSF miR-7975 and miR-7977 correlated well with clinical courses of LM. Overexpression of miR-7977 promoted proliferation, migration, and invasion of lung adenocarcinoma cells. Moreover, 385 targeted genes of miR-7977 were predicted and were involved in various pathways related to cancer metastasis. Conclusions: This study offers insights for future research of CSF miRNAs as robust tools for diagnosing and monitoring LM. It also reveals a novel pathway for exploration of underlying mechanisms of LM.

Project description:BackgroundIt is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.ObjectiveThis study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.MethodThe cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.ResultsWith respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.ConclusionA deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer's primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.

Dataset Information

Complement Component 3 Adapts the Cerebrospinal Fluid for Leptomeningeal Metastasis.

Publications

Complement Component 3 Adapts the Cerebrospinal Fluid for Leptomeningeal Metastasis.

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