Abstract 1837: E7386, a selective inhibitor of the interaction between β-catenin and CREB-binding protein (CBP), enhances antitumor activity in combination with lenvatinib (LEN), and LEN + anti-PD-1 antibody in a preclinical tumor model
Bibliographic record
Abstract
Abstract E7386, a novel orally active CBP/β-catenin modulator, shows antitumor activity in preclinical tumor models with aberrant activation of the Wnt/β-catenin-signaling pathway driven by mutations in genes such as adenomatous polyposis coli (APC). We previously reported that E7386 enhanced the antitumor activity of an anti-PD-1 antibody (Ab) by induction of T cell infiltration into tumors, and E7386 enhanced the antitumor activity of LEN (a multiple kinase inhibitor mainly targeting VEGFRs and FGFRs) by enhancement of antiangiogenic effect in preclinical tumor models. Clinical studies of E7386 in combination with LEN and with pembrolizumab are in progress. Here, we investigated the antitumor activity of E7386 in combination with LEN and with LEN + anti-PD-1 Ab (triple combination) in a mouse renal cell carcinoma RAG model. We also evaluated the mechanism of action underlying the activity of E7386 + LEN and the triple combination. Mice bearing subcutaneous RAG and HepG2 tumors were treated with E7386 at 25 mg/kg (po, qd), LEN at 10 mg/kg (po, qd), and anti-PD-1 Ab at 10 mg/kg (ip, twice weekly) for 4 weeks. Immune-cell population analyses in tumor tissues were performed by flow cytometry. Mouse syngeneic and human xenograft tumor endothelial cells were isolated as CD31-positive cells using magnetic beads and flow cytometry techniques for gene-expression analysis. E7386 + LEN showed enhanced antitumor activity compared with each monotherapy and the triple combination showed enhanced antitumor activity compared with the 2-drug combinations (LEN + anti-PD-1 or LEN + E7386) in the RAG model. Immune-cell population analysis of RAG tumors using flow cytometry revealed that LEN decreased the population of tumor-associated macrophage (TAM) and E7386 + LEN further decreased the population of TAM. E7386 combination decreased the population of immunosuppressive monocytic-myeloid-derived suppressor cells that was increased with LEN monotherapy and with LEN + anti-PD-1 Ab combination. Gene-expression analysis demonstrated that expression levels of Axin2 and Vcan (Wnt/β-catenin target genes) were upregulated in endothelial cells isolated from RAG tumors after LEN treatment. In addition, combination treatment of E7386 + LEN suppressed the upregulation of Axin2 and Vcan with LEN monotherapy in tumor endothelial cells in the HepG2 human hepatoblastoma xenograft model. E7386 suppressed the Wnt signal that was activated with LEN in tumor endothelial cells and E7386 + LEN, and the triple combination also decreased the immunosuppressive myeloid cells that were increased with LEN, and LEN + anti-PD-1 Ab. These activities of E7386 on the tumor microenvironment could provide unique mechanisms of antitumor activity for this triple combination in preclinical tumor models. Citation Format: Yusuke Adachi, Yoichi Ozawa, Mizuki Kimura, Jialing Shen, Masahiko Kume, Ryoga Ishida, Takayuki Kimura, Junji Matsui, Akira Yokoi, Yu Kato, Yasuhiro Funahashi. E7386, a selective inhibitor of the interaction between β-catenin and CREB-binding protein (CBP), enhances antitumor activity in combination with lenvatinib (LEN), and LEN + anti-PD-1 antibody in a preclinical tumor model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1837.
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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 | 0.000 | 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 | 0.001 |
| 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".