TRANSCRIPTIONAL PROFILING OF GBM INVASIVE GENES IDENTIFIES EFFECTIVE INHIBITORS OF THE LIM KINASE-COFILIN PATHWAY
Bibliographic record
Abstract
BACKGROUND: The malignant astrocytoma is a highly proliferative and invasive neoplasm that infiltrates diffusely into regions of normal brain rendering total surgical resection impossible and effective local radiation therapy difficult. For truly significant advances to be made in the treatment of patients with malignant astrocytoma, we must develop a greater understanding of the molecular machinery driving invasion and identify novel treatment targets. METHODS: We recently compared gene-expression data of 400 invasion/migration genes by performing microarray analysis comparing 10 normal brain samples versus 51 mesenchymal GBM, which are highly invasive, and of poor prognosis. RESULTS: We identified over 141 significant genes with a 1.5 fold change (p-value <0.05, and a false discovery rate (q < 0.05) compared to normal brain. Of the 141 genes, the cofilin pathway, which disassembles actin filaments (namely LIMK1, LIMK2, CFL, CAP1) was highly up-regulated compared to normal brain. Furthermore we identified up-regulation of LIM domain kinase 1 and 2 (LIMK1/2) that phosphorylates and inactivates cofilin (CFL) in an additional data set comparing normal brain to GBM. Using a well-characterized tissue microarray of 20 GBM patients for which a central and a matched periphery sample was available we undertook immunohistochemical (IHC) analysis of LIMK1 and pCFL. We observed significant expression of LIMK1 in the periphery of the GBM compared to the center (11/20 in the periphery versus 2/20 in the center). Previously, we established that pCFL plays an integral role in GBM invasion. Consequently we explored how to target this pathway using small molecule inhibitors. We tested the LIMK1 and LIMK2 inhibitors BMS-5 and Cucurbitacin I. Both BMS-5 and Cucurbitacin I inhibited p-CFL at the protein level at 10uM and 100nM respectively in U87 cells. Using the MTT cell viability assay, we observed significant decrease in cell viability in U87 and T98G cells using doses of 10-20uM BMS-5 and 100nM-10uM Cucurbitacin I while no cytotoxic effect was seen in normal human astrocytes that lack LIMK expression. Both BMS-5 (10uM) and Cucurbitacin I (100nM) promoted increased adhesion in U87 and T98G cells. Interestingly, BMS-5 and Cucurbitacin I reduced migration and invasion of both U87 and T98G cells. CONCLUSIONS: Taken together, the use of LIMK inhibitors to target the invasive machinery in GBM are attractive candidates to move forward with in preclinical trials. SECONDARY CATEGORY: Tumor Biology.
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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 | 0.000 | 0.001 |
| 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".