Abstract A029: Targeting neural transcription factor BRN2 in neuroendocrine prostate tumors
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 tumors shed their dependence on AR signaling and emerge with neuroendocrine features. These tumors, 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. 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 that targeting BRN2 is a promising strategy to prevent neuroendocrine differentiation or treat NEPC. Methods: Using the integrated power of computational drug discovery platform and biologic testing we identified first-in-field inhibitors for BRN2. Results: One of the most potent inhibitors identified, Cpd 18, binds to BRN2 using a Drug Affinity Responsive Target Stability (DARTS) assay, decreases the expression of the neuroendocrine genes SOX2, NCAM1, CGA, NSE, PEG10, and N-Myc in BRN2Hi NCI-H660 cells with similar effect in 42DENZR cells. Moreover, Cpd18 displays pronounced antiproliferative activity in NCI-H660 and 42DENZR without any effect on BRN2 low/neg 16DCRPC and LNCaP cells. Combination treatment of Cpd 18 with ENZ prevents the trans-differentiation of 16DCRPC cells into NEPC-like morphology and downregulates the expression of NEPC markers. These outcomes confirmed that the BRN2 is a novel drug target that can help address the problem of ENZ-resistant form of NEPC. Conclusion: No therapies exist for highly lethal NEPC. Hence, the described work will identify first-in-field inhibitors for BRN2, a central driver of NEPC, and lay the preclinical foundation for the integration of such drugs into the treatment landscape to improve survival and quality of life for patients suffering from deadly form of prostate cancer. Citation Format: Ravi Shashi Nayana Munuganti, Daksh Thaper, Amina Zoubeidi. Targeting neural transcription factor BRN2 in neuroendocrine prostate tumors [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr A029.
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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 | 0.000 |
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 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".