Abstract 1931: Targeting master neuronal transcription factor BRN2 in neuroendocrine prostate cancer
Bibliographic record
Abstract
Abstract Introduction: Resistance to newly developed androgen receptor pathway inhibitors (ARPIs), such as Enzalutamide (ENZ), rapidly emerges and patients generally die within two years. In particular, a subset of patients who relapse following ARPI therapy exhibit lineage switching whereby tumours shed their dependence on AR signaling and emerge with neuroendocrine features. These tumours, termed treatment induced neuroendocrine prostate cancer (t-NEPC), carry an extremely poor prognosis and, to date, treatment remains decades old cytotoxic chemotherapy which carries a short-lived response at the cost of significant toxicity. Therefore, targeted therapies for this deadly disease are desperately needed. Thus, the need to develop targeted treatments for this devastating disease is of paramount importance. Recently our group identified the neural transcription factor BRN2 as a major clinically relevant driver of NEPC and aggressive tumor growth, both in vitro and in vivo, suggesting targeting BRN2 is a promising strategy to prevent neuroendocrine differentiation or treat NEPC. Methods: Study the effects of BRN2 inhibition using siRNA and CRISPR K/O models. Results: Inhibition of BRN2 by siRNA and by CRISPR/Cas9 knockout drastically reduced cell proliferation in 42DENZR (NEPC) cell lines. This data was re-capitulated in human NEPC NCI-H660 cells. Loss of BRN2 initiated drastic epigenetic changes in NEPC cell lines as well as in G1 arrest through up-regulation of CDKN1A/1B. This was confirmed using our first in field BRN2 inhibitors. Targeting BRN2 also lead to downregulation several known targets in NEPC like EZH2, AURKA, SOX2 and Peg10. Conclusion: No therapies exist for highly lethal NEPC. Hence, the described work aims to verify BRN2 as a central driver of NEPC, and lay the pre-clinical foundation for the integration of targeted therapies into the treatment landscape to improve survival and quality of life for patients suffering from deadly form of prostate cancer. Citation Format: Daksh Thaper, Ravi Munuganti, Shaghayegh Norouzi, Sahil Kumar, Soojin Kim, Kriti Singh, Sepideh Vahid, Jennifer Bishop, Amina Zoubeidi. Targeting master neuronal transcription factor BRN2 in neuroendocrine prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1931.
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".