Establishment of a neuroendocrine prostate cancer model driven by the RNA splicing factor SRRM4
Bibliographic record
Abstract
// Yinan Li 1 , Ruiqi Chen 1 , Mary Bowden 1 , Fan Mo 1 , Yen-Yi Lin 1 , Martin Gleave 1 , Colin Collins 1 and Xuesen Dong 1 1 Department of Urologic Sciences, Vancouver Prostate Centre, The University of British Columbia, Vancouver, Canada Correspondence to: Xuesen Dong, email: // Keywords : neuroendocrine prostate cancer, cell/tumor models, SRRM4, RNA splicing Received : June 30, 2017 Accepted : July 06, 2017 Published : August 03, 2017 Abstract Neuroendocrine prostate cancer (NEPC) is becoming more prevalent as more potent androgen receptor (AR) pathway inhibitors are applied to patients with metastatic tumors. However, there are limited cell and xenograft models currently available, hindering the investigation of signal pathways involved in regulating NEPC progression and the design of high throughput screening assays for inhibitors to treat NEPC patients. Here, we report an NEPC model, LnNE, that is derived from prostate adenocarcinoma cells and has global similarity in transcription and RNA splicing to tumors from NEPC patients. LnNE xenografts are castrate-resistant and highly aggressive. Its tumor take is ~3-5 weeks and tumor doubling time is ~2-3 weeks. LnNE expresses multiple neuroendocrine markers, preserves AR expression, but is PSA negative. Its neuroendocrine phenotype cannot be reversed by androgen treatment. LnNE cells grow as multi-cellular spheroids under 2-dimensional culture conditions similar to the NEPC cell line NCI-H660, but have higher proliferation rate and are easier to be transfected. LnNE cells can also adapt to 3-dimensional culture conditions in a 96-plate format, allowing high throughput screening assays. In summary, the LnNE model is useful to study the mechanisms of NEPC progression and to discover potential therapies for 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".