Abstract 3299: Harnessing PIKfyve as a therapeutic vulnerability in neuroendocrine prostate cancer
Bibliographic record
Abstract
Abstract Neuroendocrine prostate cancer (NEPC) represents an aggressive subtype characterized by the independence on androgen receptor (AR) signaling and the acquisition of neuroendocrine features. Despite its rarity among prostate cancer cases, NEPC carries a particularly poor prognosis with limited treatment options. Our prior investigation demonstrated the heightened efficacy of targeting PIKfyve with ESK981 in AR- prostate cancer models, prompting further exploration. In this study, we expanded our investigation to assess the efficacy of ESK981 monotherapy across five NEPC models, encompassing one cell line xenograft and four patient-derived xenografts (PDXs). Our findings revealed consistent tumor inhibition and notable regression in select models. Comparative analysis highlighted the superior effectiveness of ESK981 in NEPC compared to AR+ prostate cancer, as evident through percent tumor growth inhibition (%TGI) assessments. Early evaluations post five days of treatment (PD5) unveiled that PIKfyve inhibition induced apoptotic cell death specifically in NEPC models, confirmed through immunoblotting and TUNEL staining in a time-dependent manner. Intriguingly, PD5 TUNEL positivity correlated with endpoint %TGI, suggesting its potential as a predictor for long-term efficacy. To further delineate the role of PIKfyve in NEPC, we successfully established two novel ex vivo NEPC cell lines from these PDXs, overcoming the constraint of limited publicly accessible NEPC cell lines, and confirmed their NEPC characteristics. Employing a doxycycline-inducible PIKfyve knockdown system in these cells demonstrated significant tumor inhibition in xenograft models, albeit with a lower %TGI compared to ESK981 monotherapy. In conclusion, our results illuminate the preferential cytotoxicity of PIKfyve inhibition in NEPC tumors compared to AR+ prostate cancer, advocating PIKfyve as a compelling therapeutic target. This study provides a strong rationale for advancing ESK981 into clinical trials for NEPC patients, underscoring its potential in addressing this challenging malignancy. Citation Format: Yang Zheng, Kyle Garcia Rogers, Arya Gopal Kamat, Sarah Nicole Yee, Rahul Mannan, Xia Jiang, Rowena Kannaiyan, Xuhong Cao, Yuzhuo Wang, Yuanyuan Qiao, Arul M. Chinnaiyan. Harnessing PIKfyve as a therapeutic vulnerability in neuroendocrine prostate cancer [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 3299.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".