A Genome Wide CRISPR Screen Reveals That HOXA9 Promotes Enzalutamide Resistance in Prostate Cancer
Bibliographic record
Abstract
Androgen receptor inhibitors are commonly used for prostate cancer treatment, but acquired resistance is a significant problem. Codeletion of RB and p53 is common in castration resistant prostate cancers, however they are difficult to target pharmacologically. To comprehensively identify gene loss events that contribute to enzalutamide response, we performed a genome-wide CRISPR knockout screen in LNCaP prostate cancer cells. This revealed novel genes implicated in resistance that are largely unstudied. Gene loss events that confer enzalutamide sensitivity are enriched for GSEA categories related to stem cell and epigenetic regulation. We investigated the myeloid lineage stem cell factor HOXA9 as a candidate gene whose loss promotes sensitivity to enzalutamide. Cancer genomic data reveals that HOXA9 overexpression correlates with poor prognosis and characteristics of advanced prostate cancer. In cell culture, HOXA9 depletion sensitizes cells to enzalutamide, whereas overexpression drives enzalutamide resistance. Combination of the HOXA9 inhibitor DB818 with enzalutamide demonstrates synergy. This demonstrates the utility of our CRISPR screen data in discovering new approaches for treating enzalutamide resistant prostate cancer.
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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".