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 Table of Contents  
REVIEW
Year : 2018  |  Volume : 4  |  Issue : 4  |  Page : 95-101

Glioma Research in the era of medical big data


1 Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
2 Department of Pathology, The Preston Robert Tisch Brain Tumor Center At Duke, Duke University Medical Center, Durham, NC, USA

Date of Submission09-Aug-2018
Date of Acceptance20-Aug-2018
Date of Web Publication31-Aug-2018

Correspondence Address:
Dr. Xuejun Li
Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha 410008, Hunan
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ctm.ctm_26_18

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  Abstract 


Glioma is the most common type of malignant tumor of the central nervous system. Studies of the biological mechanism behind its occurrence and development have significant importance in better understanding glioma. In the era of multiomics and big data, glioma research is undergoing a paradigm shift from traditional standardized medical models to precision medicine. Big data in glioma research is in its infancy, and as such, there are exciting opportunities to take advantage of. However, there are also major challenges that must be addressed. This review introduces a series of changes in glioma research brought about by medical big data. It further elaborates on how big data bioinformatic analyzes can contribute to the identification of molecular targets, leading to molecular pathological diagnosis and the promotion of precision medicine of glioma.

Keywords: Big data, glioma, molecular pathology, multiomics, precision medicine


How to cite this article:
Wang F, Pirozzi CJ, Li X. Glioma Research in the era of medical big data. Cancer Transl Med 2018;4:95-101

How to cite this URL:
Wang F, Pirozzi CJ, Li X. Glioma Research in the era of medical big data. Cancer Transl Med [serial online] 2018 [cited 2018 Sep 19];4:95-101. Available from: http://www.cancertm.com/text.asp?2018/4/4/95/240296




  Introduction Top


Gliomas are a major disease that threatens human health and are the most common primary tumors of the central nervous system (CNS), accounting for about 50% of all CNS tumors.[1] Gliomas are fast-growing tumors that grow invasively into the surrounding brain parenchyma. For moderate-to-high grade-gliomas, a complete surgical resection is particularly difficult due to the invasiveness of the tumors. Therapy for these patients usually includes radiotherapy and chemotherapy, which can prolong the survival of patients but can also cause a decreased quality of life due to the damage of normal brain tissue.[2],[3] Therefore, fundamentally improving prognosis remains a huge challenge for neurosurgeons. In addition, researches regarding the mechanism of glioma development and the search for effective treatment methods have been highly investigated by clinicians and oncologists. In the field of glioma diagnosis and treatment, whether it is the epidemiological investigation of tumors, mechanistic research, clinical diagnosis and treatment, prevention and monitoring, clinical trial research, research and development of new drugs, the collection, management, and analysis of data is a fundamental component. The deep excavation of clinical and scientific research data provides new ideas and new methods for glioma research and continuously promotes the rapid development of glioma research.

With the completion of the Human Genome Project, we are now able to research and understand the developmental mechanisms of cancer on a molecular level.[4],[5] The use of large-scale data analysis technology to predict and diagnose tumors and their subtypes as well as to build a genetic relationship control network has far-reaching significance. The study of glioma in the era of big data has distinct features separate from traditional scientific research models.[6],[7] Therefore, efficient use of medical data will be the key to promoting basic research in glioma. Recently, oncology genomic research has produced a large amount of data. The emergence of these big data provides an opportunity for the discovery of glioma molecular targets, but the full exploitation, integration, and utilization of big data also pose great challenges. This article introduces the evolution of multiomics data for glioma research. Specifically, we explain bioinformatics analysis of big data for the discovery of glioma molecular targets and highlight potential opportunities and challenges of glioma research in the era of big data.


  Big Data Brings About a Change in Oncology Research Top


Big data represents another subversive technological revolution in the information technology (IT) industry after cloud computing and the internet of things.[8],[9] With the partial breakthrough of related technologies such as machine learning and artificial intelligence, the application of big data technology has also progressed in recent years. Compared to conventional data mining technologies of the 1990s, today's big data technology is becoming more and more mature and continues to evolve.[10],[11]

Currently, big data is widely used in the fields of biology, medicine, finance, e-commerce, energy, and transportation. The occurrence and development of tumors contain complex genetic molecular mechanisms that are influenced by the interaction between intrinsic genes and the external environment. Basic research in oncology requires integration of data from the cellular molecular layer to genomics, transcriptomics, epigenomics, proteomics, and metabolomics, necessitating the need for big data technology. Genomics, transcriptomics, epigenetics, proteomics, and metabolomics in cancer research can be collectively referred to as high-throughput omics [Figure 1]. The big data generated by high-throughput omics research generally have characteristics that are referred to as “4V:” the volume of data (volume), the variety of data (variety), the value of further digging (value), and the speed of retrieval (velocity).[12] The use of big data technology to analyze this high-throughput, large sample data may help to find tumor molecular targets.[13]

In March 2013, the American Society of Clinical Oncology announced a project called CancerLinQ that uses big data to assist cancer treatment.[14],[15] CancerLinQ is a “fast learning system” that allows researchers to access and analyze the medical records of anonymous cancer patients. The project aims to collect data on the diagnosis and treatment of cancer patients worldwide to improve the clinical diagnosis and treatment model of cancer, to improve the quality of treatment of patients, to evaluate the advantages and disadvantages of diagnosis and treatment methods, and to promote the development of clinical research. The CEO's Roundtable on Cancer, a nonprofit organization involving multiple pharmaceutical companies, announced the launch of the Project Data Sphere in 2014.[16],[17] The program created a third-stage cancer clinical trial data sharing and analysis platform, and initial data sets were provided by AstraZeneca, Bayer, Celgene, Memorial Sloan-Kettering Cancer Center, Pfizer, Sanofi, among other institutions. All identifiers were removed from the data and have been uniformly numbered for free use by life science companies, hospitals, medical institutions, and independent researchers. Researchers can access analytical tools built into the platform or insert data into self-developed analysis software.

In August 2014, International Business Machines Corporation collaborated with the Memorial Sloan-Kettering Cancer Center through the Watson Artificial Intelligence System to extract a large amount of clinical data and screen millions of recorded text, journal articles, as well as clinical trial reports.[18] By integrating such information, the system was able to provide recommendations on individualized treatment options. This is a major breakthrough in the medical field, as it links big data technology and artificial intelligence technology.[19] Compared with traditional scientific research, bio-big data based on clinical information allows for large-scale sample accumulation. Through the collection and analysis of massive information and data, molecular classification can be more accurately performed.

The field of basic oncology research has made great achievements, particularly with regard to cancer genomics, the development of high-throughput sequencing technology, and the maturity of big data networks. The Cancer Genome  Atlas More Details (TCGA) program has analyzed the genomes of > 30 human tumor types using chip-based high-throughput nucleic acid, protein detection technology, and global nucleic acid analysis.[20],[21] A $100 million pilot project started in 2006, TCGA is the largest component of the International Cancer Genome Alliance, a consortium of scientists from 16 countries, whose goal is to capture, characterize, and analyze human cancers, providing large-scale, multi-variant molecular features, and rapid data for cancer researchers. The project has identified nearly 10 million cancer-related genetic mutations, and > 500 papers have been published based on TCGA research data.[22]


  Big Data and Glioma Diagnosis and Treatment Top


Big data's role in understanding the molecular mechanism of glioma

The traditional research model for the identification of disease biomarkers consists of studying the literature and studying the relationship between biosignaling pathway molecules. The use of next-generation sequencing technology and big data technology to analyze the gene regulation mechanisms at the system level is one of the more important research methods of genomics in the new era. It is superior to the single gene research models of the past and can conduct comprehensive, systematic, and accurate quantitative analysis on the expression regulation network of multiple-related genes in the organism and the whole genome. Researchers can now use big data platforms to conduct batch analyzes of relevant literature worldwide [Table 1]. This performs various bioinformatics analysis and literature comparisons in batches and obtains the latest research progress with a large amount of information at the molecular network level. In general, this in-depth analysis focuses on the identification of the most relevant and promising candidate biomarkers for gliomas.
Figure 1: The multiple levels and multiomics of oncology research

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Table 1: The commonly used database in glioma research

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The commonly used database in glioma research

Oncomine (www.oncomine.org) is one of the representations of cancer integrated data-mining platform, which aims to (a) promote genome-wide expression profiling and (b) to mine cancer gene information for discovery.[23] To date, the database has collected samples from 715 gene expression datasets and 86,733 cancer tissues and normal tissues.[24] Oncomine has the most comprehensive mutation spectrum, gene expression data, and tumor biomarkers. The full use of this type of big data will provide opportunities for the exploration of tumor molecular targets and targeted drug development.

TCGA program, cosponsored by the National Cancer Institute and the National Human Genome Research Institute, initially sequenced the glioblastoma genome and has now completed the genomic sequencing of many tumor types. Researchers can explore new molecular targets utilizing multiomics data, such as oncomine and TCGA [Figure 2]. Based on the GeneBank database, Thirumurthi et al.[25] analyzed the sequence of the human Sift6 gene regulatory region and found that MDM2 regulates the AKT1 signaling pathway by mediating SIRT6 phosphorylation, thereby promoting the development of malignant glioma. By detecting and evaluating tumor-driven genes and genomic library information, we can clarify the gene mutation control mechanism of glioma occurrence. This approach provides a feasible and practical method for predicting the risk of tumorigenesis, which will be the development trend of future malignant tumor prediction.

Big data's role in the molecular pathological diagnosis of glioma

With the rapid development of molecular biology and high-throughput genomics technology, it has been recognized that heterogeneity exists within tumors.[26],[27] Scholars from the Salk Institute of Biology have confirmed through a mouse glioblastoma model that the introduction of genetic variation in neural stem cells and more mature glial cells may lead to glioma formation.[28] Both glial mother cells and differentiated astrocytes have potential to contribute to GBM, with the difference in tumor formation depending on the heterogeneity of their cell of origin. Based on gene expression of human GBM, there has been documented correlations with normal brain cell types, suggesting that heterogeneity of the cell of origin may affect the final GBM subtype.[29],[30]
Figure 2: The architecture of data mining

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The heterogeneity present in glioma represents a large obstacle that causes difficulty in further improving the clinical diagnosis and treatment.[31] For example, glioblastoma is diverse and complex in its clinical manifestations, pathological morphology, and gene and protein profiles. With such heterogeneity, there are inevitably inconsistencies in tumor pathology and biological behavior (invasiveness, drug resistance, rate of recurrence, sensitivity to radiotherapy, and chemotherapy). In view of the above pathological classification of glioma, traditional histopathological examination has obvious limitations and bias and is far from meeting the needs of modern medical research.

In the field of molecular pathology, TCGA is also a very important data resource. The project team published two academic papers in 2010, one of which used gene expression data to cluster malignant glioma patients into four categories and attempted to characterize these four types of patients with clinical and other data.[32] Another paper described the use of gene methylation data for cluster analysis with patients divided into three categories. A third paper described the use of gene methylation data to divide patients into three categories. The research team based their categorization on a large number of pathological analyzes and verification performed on the patient samples.[33] In these studies, TCGA researchers further divided GBM into four subtypes based on gene expression profiles: preneuronal (26%), neuron (17%), classic (27%), and interstitial type (29%). They were also classified as G-CIMP positive and negative based on the genomic CpG island methylation phenotype. Each of these subtypes has its own unique clinical manifestations and molecular characteristics such as the prognosis of anterior neuron type, in which G-CIMP positive and isocitrate dehydrogenase (IDH) gene mutations are common, a classical epidermal growth factor receptor (EGFR) amplification, the worst interstitial prognosis. In lower-grade gliomas (WHO I-III grade gliomas), 1p/19q co-deletion, IDH mutation, and TERTp (telomerase reverse transcriptase gene promoter region) mutations can be used as prognostic factors. In 2015, Eckel-Passow et al.[34] published 615 LGG sequencing results in N Engl J Med, where they classified LGG into five categories: three positive (29%), IDH, TERTp double mutation (5%), single IDH mutation (45%), triple negative (7%), and single TERTp mutation (10%). Among them, the three positive and double mutations were shown to have a good prognosis and the three negative and single TERT mutations were shown to have a poor prognosis.[34] The TCGA project published a comprehensive analysis of GBM and LGG in Cell. Although the IDH1 mutation is mostly G-CIMP positive, 6% are still G-CIMP negative and this part of the tumor is more likely to progress; while in IDH1 wild-type, 6% patients have similar hair cell type astrocytoma with good prognosis.[35] The TCGA team also reported that the TERT mutation is mutually exclusive with the ATRX mutation. They emphasized the important role of the Ras-Raf-MEK, P53, and PI3K/AKT/mTOR signaling pathways in the development of glioma.

Professor Tao Jiang (Tiantan Hospital, Beijing) established the 1st high-throughput NGS sequencing big data network for glioma within the Chinese population, which has been named the Chinese Glioma Genome Atlas (CGGA).[36],[37] The CGGA took the lead in establishing a glioma diagnosis and treatment model for EGFR/PDGFRA modules. The EGFR module samples have been shown to be associated with high malignancy and poor clinical prognosis. These patients should choose EGFR-targeted drug nimotuzumab. The CGGA project also found that miR-181d can downregulate the expression level of 06-methylguanine-DNA methyltransferase and predict the chemosensitivity of patients to the temozolomide.

A full-scale glioma fusion gene profile, including 214 fusion genes, was constructed for the first time by whole transcriptome sequencing. It shows that the PTPRZ1-MET fusion gene occurs in 15% of secondary glioblastoma, which is a malignant progression of the tumor. The PTPRZ1-MET fusion gene is a key driver of malignant progression of the tumor, leading to a reduction in the median survival of patients from 8 months to 4 months. The researchers also found that PLB-1001 can target the kinase activity of the fusion gene. In addition, the study also found that many potential driving mutations are only present in glioma samples and have not been found in other tumors.[38] All of the above studies provide an extremely important molecular basis for molecular pathological diagnosis and personalized treatment of glioma.

Big data guides glioma precision medicine

The rapid development of big data technology applications has greatly promoted the individualized treatment of malignant tumors. Using a modern medical platform to comprehensively integrate patient data (basic clinical information, multimodal image data, and multiomics data), analysis and use of these data can help clinicians develop optimal treatment options with the most effective drugs to achieve precision medicine.[39],[40] Precision medicine is a “customized” medical model for individual patients. Under this model, medical decision-making and implementation are formulated for individual patient characteristics based on genetic, molecular, and/or cytological information.[41] In the precise treatment of glioma, the collection and mining of multimodal image data can best represent the trend of using big data technology to promote clinical precision medicine.

Genomic sequencing techniques including whole-genome sequencing, exon sequencing, transcriptomics sequencing, and amino acid sequencing can be used to detect glioma occurrence and relevant mutations. Big data combined with classical glioma mutations can create a more sophisticated and accurate glioma diagnostic system. This will further drive mutations to link with related targeted drugs to better guide the precise treatment of gliomas. As the cost of genome sequencing has decreased, many research institutions or organizations, including TCGA, have conducted precise medical research on gliomas.[42]

Different imaging techniques can analyze the shape and function of the brain from multiple media and multiple levels. Therefore, it is important to integrate the advantages of different technologies.[43] Through the organic integration of the above various image data through big data technology, the structure and functional status of glioma and brain tissue can be well characterized, which can effectively guide the diagnosis and treatment of glioma under noninvasive conditions, minimize brain damage, and improve the diagnosis and treatment accuracy of glioma.[44] At present, with the fusion of big data and multimodal imaging, research has gradually shifted from simple structural and functional analysis to network-based structural and functional integration.

The massive data collected by multimodal imaging technology can be used to delineate the whole brain structure and functional connection state. Graph theory methods can further reveal the organizational construction and topological forms of these neural networks. This kind of calculation and analysis framework has been gradually applied toward the accurate diagnosis and treatment of glioma. Revealing the working mechanism of the human brain and the pathological mechanism of glioma occurrence and development from the perspective of system biology can provide a new imaging reference for accurate surgery and prognosis evaluation of glioma. Multimodal brain image fusion and brain network construction are supported by different models of big data algorithms. Multimodal brain image fusion includes an asymmetric algorithm for integrating optimization algorithms and data integration of various modal data; brain network construction includes graph theory and small world network analysis.[45] Both need to build corresponding analyzes, processing software, and establish a big data clinical center with powerful computing capabilities so as to effectively collect, mine, and utilize massive data.


  The Challenge of Glioma Diagnosis and Treatment in the Era of Big Data Top


At present, medical data is characterized by complexity, fragmentation, incompatibility, and nonintegrity, which makes it difficult for clinicians and researchers to access and use. Therefore, the standardization of medical data has become a major topic of interest. First, medical data for a patient with glioma can come from different hospitals and departments, with reports and records that are often unstructured. At the same time, data are stored in various incompatible systems such as Hospital Information Management System, Laboratory Information Management System, or Picture Archiving and Communication System. In addition, strict privacy regulations limit the exchange of data.[46] For these reasons, it is very difficult to acquire the complete collection of medical data of cancer patients, making it more difficult to achieve multidisciplinary consultation of the multidisciplinary team amidst thousands of pieces of medical data. At present, we should further strengthen exchanges and cooperation between various disciplines, conduct standardized multicenter massive data collection (including basic biomedical information and clinical information), and research new types of data processing tools and methods including data analysis frameworks and software systems, thereby improving the efficiency of data resource utilization.

Second, while big data technology affects medical behaviors and methods, it also brings a series of privacy, security, and ethical issues. In the process of collecting, processing, and applying medical data, leakage will inevitably occur. On the one hand, there are multiple nodes in the hospital's internal business that can access the data, which will cause privacy leakage in the hospital's internal information system; on the other hand, leakage may occur in the information platform transmission process including scientific research. Therefore, utilization of these data requires processes and trainings on the interaction with regional data platforms. The US government introduced the Federal Health Insurance Portability and Accountability Act (HIPAA) in 1996.[47],[48] The bill is highly relevant to the analysis of big data. HIPAA requires any medical institution or life insurance organization to ensure the security of all tagged data when storing, processing, and transmitting personal health information.[49] However, China lacks relevant privacy regulations for big data applications and laws and regulations lag in the development of advanced technologies. With the development of medical big data technology, privacy and security issues will become more and more serious. Only through relevant technical means, laws, and regulations to build a complete and efficient security protection mechanism, we can solve these important problems.

At present, medical big data is a vast and complicated area of interest, involving not only clinical medicine, basic medicine, medical administrative institutions but also various disciplines such as IT and biostatistics. This requires implementing big data strategies at the national level. It requires investments in infrastructure, cutting-edge analytical tools, advanced digital technologies, highly qualified interdisciplinary teams, and data security and privacy protection. The cost of diagnosis and treatment of patients, as well as the cost of training clinicians is also increasing. The basis of big data application lies in a large amount of comprehensive data extraction and analysis. The comprehensive data of clinical patients (clinical medical records, laboratory tests, imaging examinations, pathological examinations, genomics, and molecular biology information.) will also increase the patient medical costs.[50],[51] Individualized treatment of tumors cannot fully realize the so-called standard patterning and structuring. Individualized treatment still requires the inquiry, review, and judgment of clinicians, which undoubtedly increases the cost of teaching and application of clinicians.


  Conclusion Top


In summary, the advent of the era of big data poses a challenge to the traditional glioma research model. In the face of data fragmentation and privacy protection, it also brings significant opportunities for the diagnosis and treatment of glioma. It will affect the way we prevent and cure diseases and will reshape the hospital's clinical management and research investment models. The development of big data has shifted people's awareness of glioma from the cell level to the molecular level. This paradigm shift opens up new opportunities for clinicians to accurately predict and diagnose gliomas and find new genetic targets. It also offers more possibilities for the development and use of new targeted drugs, individualized treatments, and real-time monitoring of gliomas. In addition, this represents new opportunities for drug development, individualized treatment, and real-time monitoring of gliomas. The current treatment of glioma will also change from traditional surgical resection to comprehensive diagnosis and personalized treatment. The prevention, detection, diagnosis, treatment, and rehabilitation of glioma patients will be refreshed by the arrival of the era of big data.

Financial support and sponsorship

This work was supported financially by grants from the National Natural Science Foundation of China (81472594 & 81770781) and the Fundamental Research Funds for the Central Universities of Central South University (2018zzts043).

Conflicts of interest

There are no conflicts of interest.



 
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