Abstract 3069: Investigation of pharmacodynamic and predictive biomarkers to define response to proteasome inhibitor marizomib in glioma
Bibliographic record
Abstract
Abstract Proteasome inhibitors (PIs) have been employed with clinical success in multiple myeloma, but have been much less effective in solid tumors, despite the central role of the proteasome in controlling cellular metabolism. Marizomib (MRZ) is a novel second generation proteasome inhibitor which binds irreversibly to and inhibits the enzymatic activity of all three subunits of the proteasome. The unique ability of MRZ among PIs to cross the blood-brain barrier, combined with its pan-proteasome activity, suggest that MRZ may have distinct therapeutic advantages over the approved PIs in the treatment of glioma. Preclinical studies with MRZ have demonstrated anti-tumor activity in intracranial glioma studies, and MRZ is currently being evaluated in a Phase I clinical trial in WHO Grade IV recurrent glioma in combination with bevacizumab (NCT02330562). The aim of this study was to identify pharmacodynamic and predictive biomarkers of response to marizomib in glioma patients. Analysis of the pharmacodynamic profile of MRZ in packed whole blood from MRZ-treated glioma patients demonstrated >70% inhibition of the chymotrypsin-like (CT-L) activity as early as day 1 of cycle1 at 1 hr post-infusion, with 100% inhibition post-infusion in all patients by the end of cycle 1. Pre-infusion data demonstrate a prolonged effect, with >60% inhibition of CT-L persistent between day 15 of each cycle and day 1 of the next cycle. Trypsin-like (T-L) and caspase-like (C-L) activities increased after the first 1-2 MRZ doses, presumably due to compensatory hyperactivation of these subunits triggered by CT-L inhibition, which was subsequently overcome by repeated MRZ infusion, resulting in 40-60% inhibition of T-L and 10-30% inhibition of C-L evident through cycle 5. Analysis of proteasome enzymatic activity in archival glioma tumor tissue revealed that levels of all three proteasome activities are variable between high grade glioma samples, suggesting the potential for differential sensitivity to proteasome inhibition in glioma patients. Further, there is a linear correlation between CT-L activity (the rate limiting enzyme for proteasomal proteolysis) and C-L activity in these samples, suggesting that a PI such as MRZ with pan-proteasome specificity could potentially exhibit more activity in glioma compared to CT-L specific PIs. The data are currently being expanded to evaluate both proteasome enzymatic activity and subunit mRNA levels, to establish whether these endpoints might serve as a proteasome based biomarker. In conclusion, this study demonstrates that packed whole blood may be suitable as a pharmacodynamic biomarker for proteasome inhibition. This biomarker strategy may be crucial to stratify MRZ responsive patients in glioma. Citation Format: Daniela Bota, Annick Desjardins, Warren Mason, Kaijun Di, Ann P. MacLaren, Nancy Levin, Mohit Trikha. Investigation of pharmacodynamic and predictive biomarkers to define response to proteasome inhibitor marizomib in glioma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3069.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".