ATPS-28COMBINED DRUG SCREENING AND PHOSPHOPROTEOMICS IDENTIFIES CANDIDATE BRAIN TUMOR THERAPEUTICS IN PRIMARY HUMAN BRAIN TUMOR-INITIATING CELLS
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor with a very grim prognosis for the patients. "Therapeutic Targeting of Glioblastoma" is a new pan-Canadian research team of the Terry Fox Research Institute and the Canadian Stem Cell Network funded to discover efficacious therapeutics for GBM. We use our collection of over 100 primary brain tumor-initiating lines (BTICs) that are subjected to drug screening by over 1500 compounds. Multiple compounds that exhibit nanomolar cytotoxicity towards all of the BTIC lines are prioritized based upon their potency, novelty for GBM, BBB penetration and clinical status. These drugs are currently undergoing efficacy testing in an orthotopic xenograft model as single agents and in combination with TMZ. We also use phosphoproteomics as a complementary strategy to better understand BTIC signaling, identify novel targets and mechanisms of drug resistance. Phosphotyrosine characterization of 14 BTIC lines revealed heterogeneous activation of multiple RTKs in different BTIC lines, whereas non-receptor kinases were found equally phosphorylated in all BTIC lines. Moreover, we have performed phosphoproteomic analysis of 3 matching BTIC lines, tumors and xenograft samples to identify shared phospho-targets. Our results demonstrated that EGFR is the only RTK in common in matched line-tumor-xenograft samples. Other RTKs such as PDGFRA and EPH receptors, were only activated in BTIC lines, suggesting that targeting these proteins may have limited efficacy. In contrast to RTKs, multiple non-receptor kinases were activated in matched line-tumor-xenograft samples. To validate the biological relevance of the identified shared targets, we are currently using siRNA knockdown of selected candidate proteins to assess the effect on cell viability, migration and invasion in vitro and eventually in vivo. In conclusion, we anticipate that our combined drug screening and phosphoproteomics approach will generate promising clinical candidates as well as shed light on GBM biology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".