EXPLORING THE INTERPLAY BETWEEN BRAIN TUMOUR INITIATING CELLS AND THE TUMOUR MICROENVIRONMENT: POTENTIAL TARGETING STRATEGIES FOR GLIOBLASTOMA
Bibliographic record
Abstract
Abstract The aggressive biology and poor response to therapy resulting in relapse of patients with GBM is attributed to the existence of Brain Tumour Initiating Cells (BTICs) with stem cell properties. Targeting BTICs in isolation of their surrounding tumour microenvironment (TME) has proven ineffective. Determining the molecular effects of the TME on BTICs is an evolving area that requires more focus. OUR APPROACH: Using innovative patient-derived organoid co-culture models paired with an in vivo zebrafish xenograft system our multidisciplinary team is exploring the biology of BTICs in the context of their microenvironment in search for novel therapeutic options that will benefit GBM patients. RESULTS: Our data supports an important role for activated cancer associated fibroblasts and endothelial cells in driving BTIC expansion and resistance to therapy. We further show that these effects, at least in part, are attributed to changes in the composition of the TME. Using semi-conducting conjugated polymer nanoparticles designed to selectively disrupt microenvironment interactions with BTICs, we show a reduction in anti-apoptotic and proliferation markers and a decreased self-renewal in functional assays. In vitro and in vivo drug response assays reveal this combined approach sensitizes to standard of care Temozolomide. CONCLUSIONS: Our data reveals novel aspects of the TME that play an essential role in the biology of GBM and in resistance to therapy. We propose that this offers unique opportunities for drug targeting that may improve patient outcomes for this aggressive disease.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".