Abstract 4011: Preferential cytotoxicity of ESK981 in neuroendocrine prostate cancer
Bibliographic record
Abstract
Abstract Introduction: Despite advances in the understanding of neuroendocrine prostate cancer (NEPC) development, effective therapeutic options remain limited. We previously reported that ESK981, a phase I-cleared multi-tyrosine kinase inhibitor (MTKI), exhibited tumor growth inhibitory abilities in multiple preclinical castration resistant prostate cancer (CRPC) and androgen-negative models by blocking the PIKfyve activity and disrupting autophagy. The results suggested that AR-negative prostate cancer have better response to ESK981 induced tumor inhibition than AR+ prostate cancer (Qiao et al, Nature Cancer 2022). Methods: In order to examine the tumor growth efficacy of ESK981 in NEPC, we assessed the efficacy of ESK981 monotherapy in six NEPC (LTL-352, LTL-331R, LTL-545, and LTL-610 patient-derived xenografts (PDXs); and NCI-H660 cell line-derived xenograft) in vivo models. PDXs were maintained in CB17 SCID male mice and passaged by subcutaneous implantation. When the tumors reached ~100mm3, mice were randomized and treated with either vehicle (Ora-plus) or 30mg/kg ESK981 administered by oral gavage once a day in a five-day per week schedule. Mouse body weight and tumor volume was monitored throughout the treatment schedule. Subcutaneous tumors were collected post five days of treatment for early assessment and long-term treatment for efficacy evaluation, respectively. H&E staining, TUNEL in situ cell death assay, and western blotting were performed to determine the morphology changes and apoptosis after ESK981 treatment. Single cells were isolated from tumors and stained with Zombie live/dead dye, CD45, CD11b and Ly6G fluorescently conjugated antibodies to assess neutrophil population by flow cytometry. Conclusions: We demonstrated that ESK981 monotherapy is well tolerated in all in vivo models and ESK981 exerted greater cytotoxicity in NEPC than previous evaluated prostate adenocarcinoma by percent tumor growth inhibition. ESK981 also induced dramatic cell deaths in NEPC preclinical models, determined by H&E staining, TUNEL-positive apoptotic tumor cells, and increased protein level of PARP cleavage via western blotting. Moreover, flow cytometry analysis revealed a dramatic increase in the intratumoral neutrophil infiltration after ESK981 treatment in the NEPC preclinical models in a time-dependent manner. NEPC tumors are sensitive to ESK981 monotherapy in vivo, suggesting NEPC patients may be the right population to target with ESK981. Together, ESK981 monotherapy is safe and effective therapy in NEPC preclinical models, and these results will warrant the clinical trial of ESK981 on NEPC patients. Citation Format: Yang Zheng, Kyle J. Garcia-Rogers, Yi Bao, Rahul Mannan, Tongchen He, Sarah N. Yee, Caleb Cheng, Xia Jiang, Isabella E. Taylor, Xuhong Cao, Yuzhuo Wang, Yuanyuan Qiao, Arul M. Chinnaiyan. Preferential cytotoxicity of ESK981 in neuroendocrine prostate cancer. [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 4011.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".