Targeting Bioinformatics Predicted Biomarkers Associated with Cell Proliferation and Migration for Treating Gliomas: Preclinical Studies in a GL261 Mouse Model
Bibliographic record
Abstract
We previously reported on the experimental validation of several in silico-predicted glioma biomarkers (e.g., Plexin-B2 (PLXNB2), SLIT3, and Spondin-1 (SPON1)) that were found to be higher in human high-grade gliomas (HGGs). In this study, we validated their therapeutic potential by investigating antibody therapies against these three biomarkers in a preclinical mouse GL261 high-grade glioma model. Efficacies for antibody therapies against these biomarkers were assessed by survival and tumor volumes, biomarker expressions, cell invasion and proliferation, and bioinformatics gene/protein associations. Antibodies against PLXNB2, SLIT3, or SPON1 were effective in significantly reducing tumor volumes and increasing animal survival. With immunohistochemistry (IHC), these biomarkers were highly expressed in human HGGs, as well as in mice tumors. From IHC, CD44v6 was significantly decreased for all three antibody treatments, compared to UT GL261 tumors. Bioinformatics suggested that targeting either PLXNB2 or SPON1 may have a major effect on HGG cell migration and invasion (validated with CD44v6 IHC), whereas targeting SLIT3, in addition to affecting cell invasion, may also affect cell proliferation (not validated with Ki67 IHC). These results indicate that targeting these three biomarkers could add to the therapeutic arsenal against high-grade gliomas and that antibodies against them could be considered for clinical translation.
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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".