Abstract 1830: E7386, a selective inhibitor of the interaction between β-catenin and CREB-binding protein (CBP), in combination with lenvatinib (LEN), exerts antitumor activity in preclinical tumor models with prior immune checkpoint inhibitor (ICI)-based combination treatment
Bibliographic record
Abstract
Abstract Introduction: Combination anti-CTLA-4 antibody (Ab) + anti-PD-1 Ab is approved in multiple cancer types and has improved clinical outcomes. In addition, atezolizumab + bevacizumab combination therapy is approved for unresectable hepatocellular carcinoma. Given the rapid changes in the therapeutic landscape, it is important to investigate effective treatments for people with prior ICI-based combination therapy. E7386 modulates Wnt/β-catenin signaling, and a phase 1 study in combination with LEN (a receptor tyrosine kinase inhibitor that mainly targets both VEGFR and FGFR), in people with solid tumors (NCT04008797) is ongoing. In this study, we examined the antitumor activity of the combination of E7386 + LEN, with either prior anti-CTLA-4 Ab + anti-PD-1 Ab or anti-PD-1 Ab + aflibercept (a soluble decoy receptor against VEGF), in the mouse lung tumor KLN 205 model. Methods: Antitumor activity of E7386 (25 or 50 mg/kg), LEN (3 mg/kg), and the combination, was evaluated in the KLN 205 model (orally, once daily x 14 days), with or without prior treatments of either anti-CTLA-4 Ab (0.2 mg/head, weekly x 3, intraperitoneally [ip]) + anti-PD-1 Ab (0.2 mg/head, weekly x 3, ip)─ie, CTLA-4 + PD-1; or aflibercept (5 mg/kg) + anti-PD-1 Ab (0.2 mg/head, twice a week x 2, ip)─ie, AFL + PD-1. Mice with relatively rapid-growing tumors and prior treatments were given E7386, LEN, and their combination. RNA-seq analyses were conducted by using tumor tissues collected from mice one day after the prior treatment period or without treatment to analyze gene-expression alterations with prior treatments. Results: In the mouse model without prior treatments, LEN had clear antitumor activity. E7386 had limited antitumor activity as a monotreatment. E7386 + LEN had superior antitumor activity vs LEN in a dose-dependent manner. In addition, E7386 + LEN had superior antitumor activity to each single treatment despite either prior CTLA-4 + PD-1 or AFL + PD-1. E7386 + LEN with prior CTLA-4 + PD-1 had equivalent antitumor activity to LEN without prior treatments. Conversely, E7386 + LEN with prior AFL + PD-1 had strong antitumor activity, including marked tumor regression, compared to both LEN alone and E7386 + LEN without prior treatments. RNA-seq analyses showed that intratumoral Axin2, a target gene in the canonical Wnt-signaling pathway, was upregulated with prior AFL + PD-1—suggesting a potential activation of the Wnt-signaling pathway. Conclusion: E7386 + LEN combination shows antitumor activity in the mouse KLN 205 lung tumor model when there is prior ICI-based (CTLA-4 + PD-1 or AFL + PD-1) combination treatment. Further, E7386 + LEN shows enhanced antitumor activity with prior AFL + PD-1; possibly due to activation of Wnt-signaling pathway during prior treatments. Citation Format: Satoshi Kawano, Yusuke Adachi, Megumi Kuronishi, Kohta Toshimitsu, Kotaro Kodama, Takayuki Kimura, Masahiko Kume, Jialing Shen, Saori Watanabe Miyano, Akira Yokoi, Junji Matsui, Yu Kato, Yasuhiro Funahashi. E7386, a selective inhibitor of the interaction between β-catenin and CREB-binding protein (CBP), in combination with lenvatinib (LEN), exerts antitumor activity in preclinical tumor models with prior immune checkpoint inhibitor (ICI)-based combination treatment [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 1830.
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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) | 0.001 | 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) | 0.005 | 0.001 |
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".