Immunotherapy for Glioblastoma: Current Progress and Challenges
Bibliographic record
Abstract
Glioblastoma is a highly lethal brain cancer with a median survival rate of less than 15 months when treated with the current standard of care, which consists of surgery, radiotherapy and chemotherapy. With the recent success of immunotherapy in other aggressive cancers such as advanced melanoma and advanced non-small cell lung cancer, glioblastoma has been brought to the forefront of immunotherapy research. Resistance to therapy has been a major challenge across a multitude of experimental candidates and no immunotherapies have been approved for glioblastoma to-date. Intra- and inter-tumoral heterogeneity, an inherently immunosuppressive environment and tumor plasticity remain barriers to be overcome. Moreover, the unique tissue-specific interactions between the central nervous system and the peripheral immune system present an additional challenge for immune-based therapies. Nevertheless, there is sufficient evidence that these challenges may be overcome, and immunotherapy continues to be actively pursued in glioblastoma. Herein, we review the primary ongoing immunotherapy candidates for glioblastoma with a focus on immune checkpoint inhibitors, myeloid-targeted therapies, vaccines and chimeric antigen receptor (CAR) immunotherapies. We further provide insight on mechanisms of resistance and how our understanding of these mechanisms may pave the way for more effective immunotherapeutics against glioblastoma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".