<i>CDK12</i> Loss Promotes Prostate Cancer Development While Exposing Vulnerabilities to Paralog-Based Synthetic Lethality
Bibliographic record
Abstract
SUMMARY Biallelic loss of cyclin-dependent kinase 12 ( CDK12 ) defines a unique molecular subtype of metastatic castration-resistant prostate cancer (mCRPC). It remains unclear, however, whether CDK12 loss per se is sufficient to drive prostate cancer development—either alone, or in the context of other genetic alterations—and whether CDK12 -mutant tumors exhibit sensitivity to specific pharmacotherapies. Here, we demonstrate that tissue-specific Cdk12 ablation is sufficient to induce preneoplastic lesions and robust T cell infiltration in the mouse prostate. Allograft-based CRISPR screening demonstrated that Cdk12 loss is positively associated with Trp53 inactivation but negatively associated with Pten inactivation—akin to what is observed in human mCRPC. Consistent with this, ablation of Cdk12 in prostate organoids with concurrent Trp53 loss promotes their proliferation and ability to form tumors in mice, while Cdk12 knockout in the Pten -null prostate cancer mouse model abrogates tumor growth. Bigenic Cdk12 and Trp53 loss allografts represent a new syngeneic model for the study of androgen receptor (AR)-positive, luminal prostate cancer. Notably, Cdk12/Trp53 loss prostate tumors are sensitive to immune checkpoint blockade. Cdk12 -null organoids (either with or without Trp53 co-ablation) and patient-derived xenografts from tumors with CDK12 inactivation are highly sensitive to inhibition or degradation of its paralog kinase, CDK13. Together, these data identify CDK12 as a bona fide tumor suppressor gene with impact on tumor progression and lends support to paralog-based synthetic lethality as a promising strategy for treating CDK12- mutant mCRPC.
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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) | 0.004 | 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".