Abstract 350: AUTX-703, a novel and potent KAT2A and KAT2B protein degrader, induces differentiation and provides a potential therapeutic opportunity in subtypes of prostate cancer
Bibliographic record
Abstract
Abstract Introduction: Phenotypic plasticity, or the ability of cancer cells to switch between cell states, is a hallmark of cancer. Cancer cells can hijack normal developmental pathways and push cells to a more proliferative, plastic cell state similar to those found in early development. To identify therapeutic targets driving tumor plasticity, we utilized our proprietary AI-ML platform to create a high resolution atlas of normal human development. Tumors were mapped to the atlas to identify the genes driving their oncogenic cell state. Our platform identified KAT2A, a lysine acetyltransferase, as a key driver in neuroendocrine prostate cancer (NEPC). Additionally in castration resistant prostate cancer (CRPC), elevated expression of KAT2A has been reported in patients with high grade disease or biochemical recurrence and is associated with poor clinical survival. Literature has demonstrated that shKAT2A can modify resistance to anti-androgens and suggests that KAT2A can play a role in the localization of androgen receptor (AR). Our studies investigate the opportunity for targeting KAT2A in treatment refractory prostate cancer (NEPC and CRPC). Methods: To pharmacologically target KAT2A, and its paralog KAT2B, we developed AUTX-703, a novel, potent and orally bioavailable heterobifunctional protein degrader. The effects of AUTX-703 on cellular differentiation and growth inhibition were evaluated in NEPC cell lines and patient derived organoids models. The effects of AUTX-703 on AR localization and signaling were also evaluated in cell lines resistant to anti-androgens. Results: Treatment of the NEPC cell line, LASCPC01 and two NEPC organoid models with AUTX-703 degraded KAT2A and KAT2B, induced differentiation toward an epithelial cell state and inhibited growth, in vitro. In AR positive treatment refractory prostate adenocarcinoma models, AUTX-703 alone or in combination with anti-androgens reduced cell growth, decreased levels of nuclear AR, and decreased levels of AR and Myc signaling. Unlike anti-androgen treatment which shifted cells toward a more stem-like state, treatment with AUTX-703 alone or in combination with anti-androgens shifted cells towards a more differentiated epithelial cell state. Conclusion: Our studies validate KAT2A and KAT2B as key drivers of tumor cell state plasticity. Degradation of KAT2A and KAT2B with AUTX-703 induces a more differentiated cell state and inhibits growth in NEPC and CRPC models. These data support progression of AUTX-703 into clinical development for NEPC and targeting KAT2A and KAT2B in the treatment of anti-androgen resistant prostate cancer. Citation Format: Sara L. Sinicropi-Yao, Joe DeBartolo, Umar Sharif, Zied Boudhraa, Mohamed El Ezzy, Sambad Sharma, Maulasri Bhatta, Christina Lee, Betty Chan, Henry Wilson, James Neef, Mark Bittenger, Laura Antipov, Thomas G. Graeber, David S. Millan, Katharine E. Yen, Kimberly S. Straley. AUTX-703, a novel and potent KAT2A and KAT2B protein degrader, induces differentiation and provides a potential therapeutic opportunity in subtypes of prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 350.
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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) | 0.000 | 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) | 0.002 | 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".