Abstract 4699: Identification of novel potential biomarkers of response to sunitinib in glioblastoma
Bibliographic record
Abstract
Abstract Introduction: Glioblastoma multiforme (GBM) is the most advanced and aggressive form of primary malignant brain tumors in adults. O6-Methylguanine methyltransferase (MGMT) is a DNA repair protein well known for its role in resistance to temozolomide used in standard treatment of patients newly diagnosed with GBM. Sunitinib is an oral multitargeting receptor tyrosine kinase (RTK) inhibitor with antiangiogenic and antiproliferative activities targeting several RTKs. Our group has previously shown the role of MGMT as a negative regulator of angiogenesis and invasion and the differential antiproliferative effect of sunitinib based on expression of MGMT in GBM. Recent gene expression profiling (GEP) studies showed that besides its effects on RTKs, sunitinib affects the expression level of other genes at the transcriptional level in different cancer types. We hypothesized that: (i) the mechanism of action of sunitinib may not be limited to direct inhibition of its known targets (RTKs) in GBM and (ii) expression of MGMT may affect response of GBM tumor cells to sunitinib. We performed GEP analysis to identify genes that could be potentially modulated in response to sunitinib in GBM cell lines isogenic for MGMT. Empty vector (EV)-transfected T98/EV cells with constitutive expression of MGMT and its knockdown counterpart T98/MGMT-shRNA were treated with sunitinib or vehicle control. Treatment of T98/EV and T98/MGMT-shRNA cell lines with sunitinib significantly changed the expression of over 2,000 gene transcripts (analysis performed at Genome Quebec Innovation center, McGill University). Lists of significant genes were fitted into previously known biochemical pathways, available among the canonical pathways from various databases provided by ConsensusPathDB (CPDB). Strikingly, immune system was among the top significantly enriched pathways (p-value<10-3), with some immune pathways not previously reported for response to sunitinib. Conclusion: Our study opens new avenues to further understand the mechanisms of action of sunitinib, investigate its immunomodulatory effects, and identify biomarkers for selection of patients who might benefit from the combination of antiangiogenic and immunebased strategies in GBM. Funding Acknowledgment: Cancer Research Society Operating Grant Competition #22716, and Higher Education Quality Enhancement Project (HEQEP)- Window -4 (Grant Id. CP-4023), University Grant Commission (UGC), Bangladesh. Citation Format: Abu Shadat Mohammod Noman, Bassam Abdulkarim, Siham Sabri. Identification of novel potential biomarkers of response to sunitinib in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4699.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".