Bibliographic record
Abstract
Pediatric brain tumors represent a diverse group of tumors with distinct clinical and molecular features. Despite the use of surgery, chemotherapy, or radiotherapy, survival rates for many patients remain poor, underscoring the urgent need for more effective and less toxic therapies. A growing area of research is immunotherapy, which harnesses the body’s immune system to recognize and eliminate tumor cells. Among these, oncolytic viruses (OVs)—either naturally occurring or genetically engineered—are capable of directly infecting and lysing tumor cells while simultaneously stimulating anti-tumor immune responses. Increasing evidence shows the therapeutic potential of OVs against various types of cancer, leading to their investigation for the treatment of pediatric brain tumors. In this thesis, we evaluated the oncolytic potential of both natural and modified OVs against a range of pediatric brain tumor cell types. Each virus displayed distinct tumor preferences, which could be used as predictive biomarkers of therapeutic potential. These findings highlight the importance of tailoring OV selection to specific tumor profiles. The second part's key focus of the thesis was the shift from direct oncolysis to immune activation as the driver of improved patient outcomes. Building on previous observations, we investigated combination strategies pairing OVs with other immunotherapies. Thus, the combination potential of oncolytic viruses with genetically modified T-cells was explored against highly aggressive tumors such as diffuse midline gliomas. First, the combination with modified T-cells sensitive to metabolic changes, known as TEG, showed enhanced tumor killing under specific conditions. Using the knowledge gained, oncolytic viruses were then combined with chimeric antigen receptor (CAR) T-cells. This combination improved both tumor clearance and CAR T-cell functionality. Overall, the findings presented throughout the thesis highlighted the therapeutic potential of oncolytic viruses against pediatric brain tumors. While also providing insights on their combination with other immunotherapies, such as CAR T-cells. Altogether, this thesis enriches the understanding of viral-based immunotherapies. And provides evidence of a more effective therapeutic option for the treatment of pediatric brain tumors. Overall, this thesis demonstrates the therapeutic potential of OVs as standalone agents and in synergy with engineered T-cells. These findings advance the understanding of viral-based immunotherapies and support their development as versatile and more effective treatment options for pediatric brain tumors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".