Abstract 5795: Potent and selective degradation of KAT2A and KAT2B induces profound cell state changes and inhibits growth of AML, SCLC and NEPC model systems
Bibliographic record
Abstract
Abstract Phenotypic plasticity, or the ability of cancer cells to switch between cell states, has recently been recognized as a key hallmark of cancer. Cancer cells can hijack normal developmental pathways, preventing terminal differentiation or causing de-differentiation or transdifferentiation to a more proliferative, plastic cell state similar to those found in early development. Using our proprietary AI/ML platform AURIGIN, we created a high-resolution atlas to define the drivers of normal cell states during human development. After mapping primary human tumors to the atlas, we identified the histone acetyltransferase KAT2A as a key driver of tumor cell plasticity in a subset of acute myeloid leukemias (AML), small cell lung cancer (SCLC) and neuroendocrine prostate cancer (NEPC). Targeting KAT2A was genetically validated in MOLM-13 (AML) cells, where knockdown resulted in significant cell growth inhibition and induction of cell state changes consistent with a more mature differentiation phenotype. Similarly, an early tool heterobifunctional degrader of KAT2A and KAT2B (AUR101) resulted in potent growth inhibition and granulocytic differentiation in MOLM-13 cells and in 5/12 primary AML patient samples grown ex vivo. Treatment of NCI-H1048 SCLC cells with AUR101 potently degraded KAT2A/B, induced a significant shift from a dedifferentiated cell state to a more differentiated epithelial cell state and potently inhibited cell growth both in vitro and in vivo. The strong biological validation of KAT2A/B in SCLC and AML tumors, led us to develop AUR1545, a novel sub-nanomolar degrader of KAT2A and KAT2B. AUR1545, with its improved drug like properties, was tested across representative models of AML, SCLC and NEPC. In vitro, treatment with AUR1545 inhibited proliferation and promoted profound cell state changes in MOLM-13 (AML) cells. In NCI-H1048 (SCLC), LASPC-01 (NEPC) cell lines as well as a primary NEPC organoid model, AUR1545 demonstrated potent antiproliferative effects. In vivo, AUR1545 significantly inhibited tumor growth in an NCI-H1048 xenograft model. Taken together, these data genetically and pharmacologically validate KAT2A and KAT2B as key drivers of cell state plasticity in SCLC, NEPC and AML tumors and point to their utility as novel targets in these aggressive, metastatic and drug resistant cancers. Citation Format: Kimberly S. Straley, James Neef, Ileana Antony-Debre, Maulasri Bhatta, Sambad Sharma, Sara Sinicropi-Yao, Joe DeBartolo, Andrew McRiner, Betty Chan, Henry Wilson, Christina S. Lee, Zied Boudhraa, Mohamed El Ezzy, Umar Sharif, Mark Bittinger, Laura Antipov, Thomas G. Graeber, Stephane de Botton, Katharine E. Yen, David S. Millan. Potent and selective degradation of KAT2A and KAT2B induces profound cell state changes and inhibits growth of AML, SCLC and NEPC model systems [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5795.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".