Updated Survival Follow-Up for Phase Ib Trial of the Histone Deacetylase Inhibitor Abexinostat With Pazopanib in Patients With Solid Tumor Malignancies
Bibliographic record
Abstract
PURPOSE Histone deacetylase (HDAC) inhibition downregulates hypoxia-inducible factor-1α and modulates multiple metabolomic pathways relevant in cancer. Here we report a potential novel biomarker to predict exceptional responders (>3 years) in patients receiving HDAC and vascular endothelial growth factor (VEGF) inhibition. PATIENTS AND METHODS Patients with solid tumor malignancies were enrolled in this phase Ib trial of abexinostat (4/7 ×21 days) and pazopanib (28/28 days), with a dose expansion in renal cell carcinoma (RCC). Plasma was analyzed for metabolomics and peripheral blood mononuclear cells (PBMCs) for VEGF and HDAC2 expression levels. RESULTS Fifty-one patients were enrolled: n = 36 patients in dose escalation and n = 15 in dose expansion. After the initial report in 2017, six patients had remained on study: four with RCC and one each with medullary thyroid and thymic neuroendocrine carcinoma. One patient with RCC remains on treatment for >11 years after progression on five systemic therapies. Overall, the median duration of therapy measured 5.6 (1-133) months. The median duration of therapy in exceptional responders measured 44.1 (39.8-133+) months. The median overall survival in patients with high PBMC HDAC2 expression versus low HDAC2 was 32.3 versus 9.2 months ( P = .004) for all patients and 43.3 versus 25.1 months for patients with RCC ( P = .09). Exceptional responders had lower kynurenine levels both pre- and post-treatment ( P = .002, P < .001, respectively). HDAC2 and kynurenine expression levels were inversely correlated ( P = .02). CONCLUSION Abexinostat added to pazopanib shows extended tolerability and long-term responses and survival. PBMC HDAC2 levels, the abexinostat target, are relevant predictors of response. In addition, metabolomic assessment points to kynurenine as a predictor for exceptional response to combined VEGF plus HDAC inhibition.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".