Abstract A004: Broad and synergistic anti-tumor effects of small-molecule inhibitors of hypoxia inducible factors when paired with immune checkpoint blockade
Bibliographic record
Abstract
Abstract Motivation: Hypoxia inducible factors (HIFs) are critical transcriptional regulators that play fundamental roles in adaptive responses to low oxygen, both in normal and disease conditions. Dysregulated activation of HIFs has been implicated in a variety of cancers due to their role in mediating a broad range of key processes including angiogenesis, glycolysis, cell survival, cancer stem cell specification, and metastasis. Methods: In this study, we utilized virtual and cell-based high-throughput screening approaches to identify compounds that disrupt the binding of HIF-1α and HIF-2α to HIF-1β, leading to the suppression of HIF target gene expression. Results: In syngeneic animal models, we observed significant tumor regression when HIF inhibitors are combined with immunotherapy agents in multiple tumor models. The combined treatment enhanced the infiltration of natural killer (NK) cells into the tumor microenvironment. Conclusion: This study highlights the anti-tumor effects of HIF inhibitors and the immune response resulting in sustained tumor regression. The promising outcomes warrant further exploration of these inhibitors as potential cancer treatment agents. Citation Format: Shaima Salman, Tina Y. Huang, Yousang Hwang, Anmol Kumar, Dominic Dordai, Alexander D. MacKerell, Gregg L. Semenza. Broad and synergistic anti-tumor effects of small-molecule inhibitors of hypoxia inducible factors when paired with immune checkpoint blockade [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A004.
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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 | 0.000 |
| 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".