Abstract A033: Identity fraud: Lineage plasticity as a mechanism of antiandrogen resistance and target for therapy
Bibliographic record
Abstract
Abstract Background: Potent targeting of the androgen receptor (AR) in castration-resistant prostate cancer has altered the archetypal course of the disease, fueling the emergence of aggressive and incurable neuroendocrine prostate cancer (NEPC). Recent evidence suggests that these tumors can arise from non-neuroendocrine cells in response to AR pathway inhibitors (ARPIs), such as enzalutamide (ENZ), an observation consistent with lineage plasticity. What regulates this plasticity that allows cells to shed their dependence on the AR and re-emerge as “AR-indifferent” NEPC? Sequencing studies have uncovered that the evolution toward a NEPC phenotype is aligned with dynamic epigenetic reprogramming, but the molecular basis underlying this phenomenon remains poorly understood. Methods: We developed an in vivo model of acquired ENZ resistance to (a) identify reprogramming factors that facilitate lineage plasticity, and (b) determine how to best capitalize on therapeutic strategies aimed at blocking or reversing lineage transformation. Cell lines derived from ENZ-resistant tumors were profiled by RNA-seq and ChIP-seq, and functionally assessed for stem cell-associated properties. Our findings were validated across NEPC cell lines (NCI-H660), genetically engineered mouse models (PBCre4:Ptenf/f:Rb1f/f), and patient tumors and organoids. CRISPR/Cas9-mediated genomic editing allowed us to assess the effect of knocking out reprogramming factors on therapy-induced neuroendocrine transdifferentiation. Results: AR-indifferent ENZ-resistant tumors were enriched for a Polycomb/EZH2 signature; in particular, we identified EZH2 to be phosphorylated at threonine-350 (pEZH2-T350) by CDK1 in NEPC cell lines, mouse models, and patient tumors. Accordingly, RB1 loss was sufficient to enhance pEZH2-T350, which was required for prostate cancer cells to convert to a metastable stem-like state and, in turn, acquire neuroendocrine features under the pressure of ARPIs both in vitro and in patient-derived xenografts. This therapy-induced NEPC transdifferentation was associated with a marked redistribution of EZH2 and H3K27me3, specifically to a core set of genes governing lineage identity. AR colocalized at the reprogrammed EZH2 binding sites, and was found to be part of the same complex with EZH2. Treating AR-indifferent/NEPC cell lines with clinically relevant EZH2 inhibitors reversed the lineage switch and mitigated ENZ resistance. Conclusions: This research establishes the centrality of epigenetic reprogramming in driving the insurgence of a neuroendocrine phenotype in response to ARPIs, and posits that drugging the epigenome via EZH2 inhibition may reverse or delay lineage transformation to extend the durability of clinically beneficial ARPIs. Citation Format: Alastair Davies, Chiara Bostock, Musaddeque Ahmed, Yen-Yi Lin, Fraser Johnson, Ka Mun Nip, Kirsi Ketola, Jennifer Bishop, Ladan Fazli, David Goodrich, Faraz Hach, Hansen He, Himisha Beltran, Amina Zoubeidi. Identity fraud: Lineage plasticity as a mechanism of antiandrogen resistance and target for therapy [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 A033.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".