Advances in Experimental Targeted Therapy and Immunotherapy for Patients with Glioblastoma Multiforme
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) represents the most malignant primary brain tumor in adults with generally dismal prognosis, early clinical deterioration and high mortality. GBM is extremely invasive, characterized by intense and aberrant vascularization and high resistance to multimodal treatment. Standard therapy (surgery, radiotherapy and chemotherapy with temozolomide) has very limited effectiveness, with median overall survival of patients no longer than 15 months. Progress in genetics and epigenetics of GBM over the past decade has revealed various aberrations in cellular signaling pathways, the tumor microenvironment, and pathological angiogenesis. A number of targeted anticancer drugs, such as small-molecule kinase inhibitors and monoclonal antibodies, have been evaluated in clinical trials with newly-diagnosed, as well as recurrent GBM. Unfortunately, to date, only a single anti-angiogenic agent, bevacizumab, has been approved for the treatment of recurrent GBM in the USA and Canada. The novel possibilities of cancer immunotherapy, especially immune checkpoint inhibitors, are being evaluated in clinical trials of patients with GBM. The most recent clinical experiences with targeted therapy as well as immunotherapy of GBM are given in this review. The relative lack of success of some of these approaches recently revealed in well-designed randomized clinical trials is also discussed.
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.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".