Abstract B223: Protein kinase C iota as a target for glioblastoma therapy
Bibliographic record
Abstract
Abstract In spite of advances in surgery, radiation and chemotherapy, glioblastoma is still one of the deadliest forms of cancer. Two key features of the malignant nature of glioblastoma are its abnormal proliferation and its ability to invade both locally and to distant sites within the central nervous system. The phosphoinositide 3-kinase pathway is frequently activated by oncogenic mutations in glioblastoma, leading to activation of multiple downstream signaling molecules including protein kinase C iota (PKCι). Stable suppression of PKCι in glioblastoma cells with a short hairpin RNA caused a significant decrease in the proliferation of glioblastoma cells along with increased actin stress fiber formation and decreased cell motility and invasion. Live cell imaging was used to further assess the role of PKCι in glioblastoma cell motility and proliferation. While control glioblastoma cells form a coordinated leading edge lamellipodia and migrate substantial distances, cells stably depleted of PKCι show a loss of the ability to coordinate the formation of a functional leading edge lamellipodia and instead generate projections from all sides of the cell. These cells are therefore unable to move in a coordinated fashion. In addition live cell imaging showed that while glioblastoma cells round up and initiate mitosis, they are significantly impaired in their ability to complete mitosis. These effects on motility and mitosis were also seen when PKCι activity was inhibited with a myristoylated pseudosubstrate peptide. PKCι is therefore a promising new therapeutic target for glioblastoma. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):B223.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".