Refine
Year of publication
- 2019 (3)
Document Type
- Article (3)
Language
- English (3)
Has Fulltext
- yes (3)
Is part of the Bibliography
- no (3)
Keywords
- glioma (2)
- HSV (1)
- SUMO1-3 (1)
- SUMOylation (1)
- artificial intelligence (1)
- checkpoint proteins (1)
- convolution neural network (1)
- deep neural network (1)
- glioblastoma (GBM) (1)
- multiplex immunofluorescence (1)
Institute
- Medizin (3)
Immunotherapy with oncolytic herpes simplex virus-1 therapy offers an innovative, targeted, less-toxic approach for treating brain tumors. However, a major obstacle in maximizing oncolytic virotherapy is a lack of comprehensive understanding of the underlying mechanisms that unfold in CNS tumors/associated microenvironments after infusion of virus. We demonstrate that our multiplex biomarker screening platform comprehensively informs changes in both topographical location and functional states of resident/infiltrating immune cells that play a role in neuropathology after treatment with HSV G207 in a pediatric Phase 1 patient. Using this approach, we identified robust infiltration of CD8+ T cells suggesting activation of the immune response following virotherapy; however there was a corresponding upregulation of checkpoint proteins PD-1, PD-L1, CTLA-4, and IDO revealing a potential role for checkpoint inhibitors. Such work may ultimately lead to an understanding of the governing pathobiology of tumors, thereby fostering development of novel therapeutics tailored to produce optimal responses.
Protein SUMOylation is a dynamic post-translational modification which is involved in a diverse set of physiologic processes throughout the cell. Of note, SUMOylation also plays a role in the pathobiology of a myriad of cancers, one of which is glioblastoma (GBM). Accordingly, herein, we review core aspects of SUMOylation as it relates to GBM and in so doing highlight putative methods/modalities capable of therapeutically engaging the pathway for treatment of this deadly neoplasm.
Purpose: Artificial intelligence (AI) has accelerated novel discoveries across multiple disciplines including medicine. Clinical medicine suffers from a lack of AI-based applications, potentially due to lack of awareness of AI methodology. Future collaboration between computer scientists and clinicians is critical to maximize the benefits of transformative technology in this field for patients. To illustrate, we describe AI-based advances in the diagnosis and management of gliomas, the most common primary central nervous system (CNS) malignancy.
Methods: Presented is a succinct description of foundational concepts of AI approaches and their relevance to clinical medicine, geared toward clinicians without computer science backgrounds. We also review novel AI approaches in the diagnosis and management of glioma.
Results: Novel AI approaches in gliomas have been developed to predict the grading and genomics from imaging, automate the diagnosis from histopathology, and provide insight into prognosis.
Conclusion: Novel AI approaches offer acceptable performance in gliomas. Further investigation is necessary to improve the methodology and determine the full clinical utility of these novel approaches.