Advancing neuro-oncology of glial tumors from big data and multidisciplinary studies

Multidisciplinary studies for glial tumors has produced an enormous amount of information including imaging, histology, and a large cohort of molecular data (i.e. genomics, epigenomics, metabolomics, proteomics, etc.). The big data resources are made possible through open access that offers great potential for new biomarker or therapeutic intervention via deep-learning and/or machine learning for integrated multi-omics analysis. An equally important effort to define the hallmarks of glial tumors will also advance precision neuro-oncology and inform patient-specific therapeutics. This review summarizes past studies regarding tumor classification, hallmarks of cancer, and hypothetical mechanisms. Leveraging on advanced big data approaches and ongoing cross-disciplinary endeavors, this review also discusses how to integrate multiple layers of big data toward the goal of precision medicine.

In addition to basic research of cancer biology, the results from integrated multi-omics analysis will highlight biological processes and potential candidates as biomarkers or therapeutic targets. Ultimately, these collective resources built upon an armamentarium of accessible data can re-form clinical and molecular data to stratify patient-tailored therapy.

We envision that a comprehensive understanding of the link between molecular signatures, tumor locations, and patients' history will identify a molecular taxonomy of glial tumors to advance the improvements in early diagnosis, prevention, and treatment.