A novel role for Neurog2 in MYCN driven neuroendocrine plasticity of prostate cancer
Bibliographic record
Abstract
Summary Neuroendocrine prostate cancer (NEPC) presents a formidable clinical challenge owing to its aggressive progression and resistance to conventional therapies. A key driver of NEPC is the overexpression of MYCN , a well-established oncogene associated with neuroendocrine tumors. However, efforts to directly inhibit the N-Myc protein encoded by this gene have resulted in limited success, thereby hindering therapeutic advancements. To overcome this obstacle, we conducted unbiased genome-wide screening using isogenic prostate cancer cell lines to identify the synthetic vulnerabilities of MYCN . Among the identified candidates, NEUROG2 emerged as a significant candidate. Neurog2 is a proneural transcription factor (PTF) known for its role in developmental processes and trans-differentiation of adult cells. Our findings demonstrate that Neurog2 depletion does not affect non-malignant cells, but significantly suppresses the growth of MYCN -overexpressing cells and tumors in orthotopic NEPC models. Furthermore, our observations indicate that the Neurog2-mediated regulation of PTFs can facilitate NEPC development. Thus, targeting Neurog2 holds promise as an effective therapeutic strategy for MYCN -overexpressing NEPC.
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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 | 0.001 |
| 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".