Immunotherapy for Glioblastoma: Current Progress and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it