A trojan horse for treatment of brain tumors

AbstractOptimal treatment of brain metastases is often hindered by limitations in diagnostic capabilities. To meet these challenges, we generated genome-scale DNA methylomes of the three most frequent types of brain metastases: melanoma, breast, and lung cancers (n=96). Using supervised machine learning and integration of multiple DNA methylomes from normal, primary, and metastatic tumor specimens (n=1,860), we unraveled epigenetic signatures specific to each type of metastatic brain tumor and constructed a three-step DNA methylation-based classifier (BrainMETH) that categorizes brain metastases according to the tissue of origin and therapeutically-relevant subtypes. BrainMETH predictions were supported by routine histopathologic evaluation. We further characterized and validated the most predictive genomic regions in a large cohort of brain tumors (n=165) using quantitative methylation-specific PCR. Our study highlights the importance of brain tumor-defining epigenetic alterations, which can be utilized to further develop DNA methylation profiling as a critical tool in the histomolecular stratification of patients with brain metastases.

Download Full-text

Convection enhanced delivery of boronated EGF as a molecular targeting agent for neutron capture therapy of brain tumors

Journal of Neuro-Oncology ◽

10.1007/s11060-009-9945-x ◽

2009 ◽

Vol 95 (3) ◽

pp. 355-365 ◽

Cited By ~ 38

Author(s):

Weilian Yang ◽

Rolf F. Barth ◽

Gong Wu ◽

Tianyao Huo ◽

Werner Tjarks ◽

...

Keyword(s):

Brain Tumors ◽

Neutron Capture ◽

Molecular Targeting ◽

Neutron Capture Therapy ◽

Convection Enhanced Delivery ◽

Molecular Targeting Agent

Download Full-text

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

Strahlentherapie und Onkologie ◽

10.1007/s00066-020-01626-8 ◽

2020 ◽

Vol 196 (10) ◽

pp. 856-867 ◽

Cited By ~ 1

Author(s):

Martin Kocher ◽

Maximilian I. Ruge ◽

Norbert Galldiks ◽

Philipp Lohmann

Keyword(s):

Machine Learning ◽

Brain Tumors ◽

Brain Metastases ◽

Imaging Techniques ◽

Machine Learning Algorithms ◽

Radiotherapy Planning ◽

Who Grade ◽

Large Variability ◽

Malignant Brain Tumors ◽

Integrated Diagnosis

Abstract Background Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors. Methods This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases. Results Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods. Conclusion Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.

Download Full-text

GENE-03. SPECIFIC SIGNATURES OF microRNA IN CEREBROSPINAL FLUID OF PATIENTS WITH PRIMARY BRAIN TUMORS AND METASTASES

Neuro-Oncology ◽

10.1093/neuonc/noz175.405 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi98-vi98

Author(s):

Radim Jancalek ◽

Martin Smrcka ◽

Alena Kopkova ◽

Jiri Sana ◽

Marek Vecera ◽

...

Keyword(s):

Cerebrospinal Fluid ◽

Czech Republic ◽

Brain Tumor ◽

Brain Tumors ◽

Brain Metastases ◽

Independent Set ◽

Low Grade ◽

Primary Brain Tumors ◽

Csf Analysis ◽

Different Levels

Abstract Cerebrospinal fluid (CSF) baths extracellular environment of the central nervous system, and thus, it is ideal source of tumor diagnostic biomarkers like microRNAs (miRNAs), short non-coding RNAs involved in the pathogenesis of many cancers. As dysregulated levels of brain tumor specific miRNAs have been already observed in CSF, analysis of CSF miRNAs in brain tumor patients might help to develop new diagnostic platform. Next-Generation sequencing (NGS) was performed for analysis of small RNAs in 89 CSF samples taken from 32 glioblastomas (GBM), 14 low-grade gliomas (LGG), 11 meningiomas, 13 brain metastases and 19 non-tumor donors. Subsequently, according to NGS results levels of 10 miRNAs were measured in independent set of CSF samples (41 GBM, 44 meningiomas, 12 brain metastases and 20 non-tumor donors) using TaqMan Advanced miRNA Assays. NGS analysis revealed 22, 12 and 35 CSF miRNAs with significantly different levels in GBM, meningiomas, and brain metastases (adj.p < 0.0005, adj.p < 0.01, and adj.p < 0.005) respectively, in comparison with non-tumor CSF samples. Subsequent validation of selected CSF miRNAs has confirmed different levels of 7 miRNAs in GBM, 2 in meningiomas, and 2 in brain metastases compared to non-tumors. Panel of miR-30e-5p and miR-140-5p was able to distinguish brain metastases with 65% sensitivity and 100% specificity compared to non-tumor samples (AUC = 0.8167); panel of miR-21-3p and miR-196-5p classified metastatic patients with 78% sensitivity and 92 % specificity in comparison to GBM (AUC = 0.90854) and with 75% sensitivity and 83% specificity compared to meningiomas (AUC = 0.84848). We have observed that CSFs from patients with various primary brain tumors and metastases are characterized by specific miRNA signatures. This work was supported by the Ministry of Health, Czech Republic grant nr. NV18-03-00398 and the Ministry of Education, Youth and Sports, Czech Republic under the project CEITEC 2020 (LQ1601).

Download Full-text

90-gene signature assay for tissue origin diagnosis of brain metastases

Journal of Translational Medicine ◽

10.1186/s12967-019-2082-1 ◽

2019 ◽

Vol 17 (1) ◽

Cited By ~ 3

Author(s):

Yulong Zheng ◽

Yongfeng Ding ◽

Qifeng Wang ◽

Yifeng Sun ◽

Xiaodong Teng ◽

...

Keyword(s):

Gene Expression ◽

Brain Tumors ◽

Brain Metastases ◽

Expression Profiles ◽

Gene Expression Signature ◽

Similarity Score ◽

Primary Site ◽

Tumor Type ◽

Metastatic Brain Tumors ◽

Expression Signature

Abstract Background Brain metastases (BM) are the most common intracranial tumors. 2–14% of BM patients present with unknown primary site despite intensive evaluations. This study aims to evaluate the performance of a 90-gene expression signature in determining the primary sites for BM samples. Methods The sequence-based gene expression profiles of 708 primary brain tumors (PBT) collected from The Cancer Genome Atlas (TCGA) database were analyzed by the 90-gene expression signature, with a similarity score for each of 21 common tumor types. We then used Optimal Binning algorithm to generate a threshold for separating PBT from BM. Eighteen PBT samples were analyzed to substantiate the reliability of the threshold. In addition, the performance of the 90-gene expression signature for molecular classification of metastatic brain tumors was validated in a cohort of 48 BM samples with the known origin. For each BM sample, the tumor type with the highest similarity score was considered tissue of origin. When a sample was diagnosed as PBT, but the similarity score below the threshold, the second prediction was considered as the primary site. Results A threshold of the similarity score, 70, was identified to discriminate PBT from BM (PBT: > 70, BM: ≤ 70) with an accuracy of 99% (703/708, 95% CI 98–100%). The 90-gene expression signature was further validated with 18 PBT and 44 BM samples. The results of 18 PBT samples matched reference diagnosis with a concordance rate of 100%, and all similarity scores were above the threshold. Of 44 BM samples, the 90-gene expression signature accurately predicted primary sites in 89% (39/44, 95% CI 75–96%) of the cases. Conclusions Our findings demonstrated the potential that the 90-gene expression signature could serve as a powerful tool for accurately identifying the primary sites of metastatic brain tumors.

Download Full-text

Epigenetic Classifiers for Precision Diagnosis of Brain Tumors

Epigenetics Insights ◽

10.1177/2516865719840284 ◽

2019 ◽

Vol 12 ◽

pp. 251686571984028 ◽

Cited By ~ 1

Author(s):

Javier IJ Orozco ◽

Ayla O Manughian-Peter ◽

Matthew P Salomon ◽

Diego M Marzese

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Brain Tumors ◽

Brain Metastases ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Malignant Neoplasms ◽

Cancer Breast ◽

Wide Range ◽

Breast Cancer Brain Metastases

DNA methylation profiling has proven to be a powerful analytical tool, which can accurately identify the tissue of origin of a wide range of benign and malignant neoplasms. Using microarray-based profiling and supervised machine learning algorithms, we and other groups have recently unraveled DNA methylation signatures capable of aiding the histomolecular diagnosis of different tumor types. We have explored the methylomes of metastatic brain tumors from patients with lung cancer, breast cancer, and cutaneous melanoma and primary brain neoplasms to build epigenetic classifiers. Our brain metastasis methylation (BrainMETH) classifier has the ability to determine the type of brain tumor, the origin of the metastases, and the clinical-therapeutic subtype for patients with breast cancer brain metastases. To facilitate the translation of these epigenetic classifiers into clinical practice, we selected and validated the most informative genomic regions utilizing quantitative methylation-specific polymerase chain reaction (qMSP). We believe that the refinement, expansion, integration, and clinical validation of BrainMETH and other recently developed epigenetic classifiers will significantly contribute to the development of more comprehensive and accurate systems for the personalized management of patients with brain metastases.

Download Full-text

Unique histopathologic features of brain metastases from hepatocellular carcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.4_suppl.169 ◽

2013 ◽

Vol 31 (4_suppl) ◽

pp. 169-169

Author(s):

Yu Yun Shao ◽

Min-Shu Hsieh ◽

Chung-Yi Huang ◽

Li-Chun Lu ◽

Chih-Hung Hsu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Brain Metastasis ◽

Brain Tumors ◽

Brain Metastases ◽

Lung Metastasis ◽

Positive Staining ◽

Metastatic Brain Tumors ◽

Tumor Tissues ◽

Metastatic Lung ◽

Histopathologic Features

169 Background: In the era of anti-angiogenic therapy as treatment for advanced hepatocellular carcinoma (HCC), the incidence and importance of brain metastases are increasing. We aimed to study their histopathologic features. Methods: We searched for all patients who were diagnosed to have HCC with brain metastasis from 1999 to 2010 at National Taiwan University Hospital, Taipei, Taiwan. Patients who had HCC with lung metastasis were also included for comparison. Patients with available tissues of both primary and metastatic tumors were enrolled in this study. Tumor slides from paired primary and metastatic HCCs were stained by H and E, and immunohistochemically stained for CK7, p53, Ki67, vimentin, Hes1, and c-Met. The expressions of CK7, p53, and vimentin were graded according to percentages of positive staining, but those of Hes1 and c-Met were recorded as an H score, which was defined as intensity (0, 1, 2, or 3) × percentages of positive staining. Results: A total of 14 patients had available tumor tissues of both primary and metastatic brain tumors. Another 21 patients had tumor tissues of both primary and metastatic lung tumors. The metastatic brain tumors, comparing to the metastatic lung tumors, had significantly more bizarre dilated vessels (86% vs. 14%, p < 0.001), hyaline globules (50% vs. 5%, p = 0.003), higher Hes1 H scores (mean, 245 vs. 131, p = 0.001), and lower c-Met H scores (mean, 15.4 vs. 38.1, p = 0.046). Tumor necrosis also tended to be more common among metastatic brain tumors (93% vs. 62%, p = 0.056). On the contrary, the above differences were not identified between the primary tumors which later developed brain metastasis and those which later developed lung metastasis. When disease progressed from primary liver to brain metastasis, mitosis counts (p = 0.034) and bizarre dilated vessels (p = 0.020) significantly increased, and necrosis (p = 0.059) tended to be more common. Conclusions: Metastatic brain tumors from HCC had unique histopathologic features compared to primary liver tumors or lung metastases. The increased Hes1 expression and decreased c-Met expression in HCC brain metastasis should be further explored. (This study was supported by the grant of NSC101-2314-B-002 -141, 100CAP1020-2 & NTUH.101-N1965.)

Download Full-text