Preclinical Characterization of TTI-281, a Novel, Orally Bioavailable BET Inhibitor: Predictive Biomarker Identification and Combination Treatment Analysis
Bibliographic record
Abstract
Abstract Small molecule inhibitors of the bromodomain and extraterminal domain (BET) proteins are being actively tested in clinical trials for many cancers, but patient selection strategies remain poorly defined. We have previously demonstrated TTI-281 is an efficacious BET inhibitor in multiple hematological malignancies models. Here, we present candidate biomarker genes that predict TTI-281 activity in vitro. Furthermore, we assessed the preclinical activity of TTI-281 in combination with 17 other drugs and found five compounds that improve TTI-281 potency. We first determined the GI50 values for TTI-281 in 19 hematological cell lines. These GI50 values were then correlated with publicly available basal mRNA expression data for 58 candidate genes. Two novel candidates that predict the sensitivity of cancer cells to TTI-281 were identified: BCLAF1 (prediction accuracy: 83.3%) and HAPLN2 (prediction accuracy: 88.9%). Restricting the analysis to leukemia cells and including a third biomarker candidate gene (BCL2L1) improved the prediction accuracy to 100%. Next, 17 drugs that are either approved or in development for the treatment of hematological malignancies were tested in combination with TTI-281. Five drugs were identified that significantly improved the potency (1.5-3.5 fold decrease in GI50) or maximum inhibitory effect (1.7-3.1 fold increase in maximum efficacy) of TTI-281 on multiple leukemia cells: azacitidine, everolimus, idelalisib, nilotinib, and vorinostat. However, the effects of combining TTI-281 with these five drugs were not observed in all hematological tumor cells, thus further studies are required to investigate the molecular mechanisms by which TTI-281 synergizes with other drugs. In summary, the BET inhibitor TTI-281 is a promising anti-tumor agent with novel predictive biomarker candidates, capable of synergizing with azacitidine, everolimus, idelalisib, nilotinib, and vorinostat. Our preclinical data provide a strong rationale for further validating the current predictive biomarkers for TTI-281 in a clinical setting and probing the mechanism by which TTI-281 achieves synergy when combined with other anti-cancer agents. Disclosures Choi: Trillium Therapeutics: Employment. Wang:Trillium Therapeutics: Employment. Wang:Trillium Therapeutics: Employment, Patents & Royalties. Schimmer:Novartis: Honoraria. Petrova:Trillium Therapeutics Inc: Employment, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Slassi:Trillium Therapeutics: Employment, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Uger:Trillium Therapeutics: Employment, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".