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Abstract PR010: <i>CDK12</i> loss promotes prostate cancer development while exposing vulnerabilities to paralog-based synthetic lethality

2024· article· en· W4399505170 on OpenAlexaffabout
Jean C. Tien, Yu Chang, Yuping Zhang, Jonathan Chou, Yunhui Cheng, Xiaoju Wang, Jianzhang Yang, Rahul Mannan, Palak Shah, Xiao Ming Wang, Abbey Todd, Sanjana Eyunni, Caleb Cheng, Xuhong Cao, Yi Bao, James Neiswender, Rachel Brough, Stephen J. Pettitt, Estefanía Labanca, Yuzhuo Wang, Marcin Cieślik, Christopher J. Lord, Ke Ding, Arul M. Chinnaiyan

Bibliographic record

VenueMolecular Cancer Therapeutics · 2024
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsPTENProstate cancerSynthetic lethalityCancer researchProstateBiologyCancerContext (archaeology)MedicineDNA repairPI3K/AKT/mTOR pathwaySignal transductionCell biologyGeneticsGene

Abstract

fetched live from OpenAlex

Abstract 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 Cdk12knockout 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 fidetumor suppressor gene with impact on tumor progression and lends support to paralog-based synthetic lethality as a promising strategy for treating CDK12-mutant mCRPC. Citation Format: Jean C. Tien, Yu Chang, Yuping Zhang, Jonathan Chou, Yunhui Cheng, Xiaoju Wang, Jianzhang Yang, Rahul Mannan, Palak Shah, Xiao-Ming Wang, Abbey Todd, Sanjana Eyunni, Caleb Cheng, Xuhong Cao, Yi Bao, James Neiswender, Rachel Brough, Stephen Pettitt, Estefania Labanca, Yuzhuo Wang, Marcin Cieslik, Christopher J. Lord, Ke Ding, Arul M. Chinnaiyan. CDK12 loss promotes prostate cancer development while exposing vulnerabilities to paralog-based synthetic lethality [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr PR010.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.049
GPT teacher head0.346
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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