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Abstract B006: Data-driven discoveries of molecular mechanism and therapeutic vulnerabilities of CDK12 mutant tumors

2024· article· en· W4399504410 on OpenAlexaboutno aff
Lixing Yang

Bibliographic record

VenueMolecular Cancer Therapeutics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyGenome instabilitySomatic cellTranscription (linguistics)GeneticsCancer researchGeneDNA damageCell biologyDNA

Abstract

fetched live from OpenAlex

Abstract Somatic structural variations (SVs) are common in cancer. Although a small fraction of SVs in breast and ovarian cancers can be attributed to homologous recombination deficiency, the underlying molecular mechanisms for the vast majority of somatic SVs remain unclear. Here, we focus on the roles of transcription and DNA replication collisions in genomic instability in cancer. Such collisions are unavoidable in cells since both transcription and replication use the same DNA as template. We hypothesized that transcription replication collisions (TRCs), if not properly repaired, would lead to collapsed replication forks and result in SVs. To this end, we studied somatic SVs in 6193 high-coverage whole-genome sequenced primary and metastatic tumors from three independent pan-cancer cohorts. A total of 13 conserved SV signatures, representing independent molecular mechanisms, were deconvoluted from these cohorts using non-negative matrix factorization approach. We detected replicated-strand bias, the expected footprint of transcription-replication collision, in large tandem duplications (TDs) across multiple cohorts. This bias was only observed in expressed genes, consistent with TRCs depending on transcription activity. Large TDs were abundant in female-specific (breast, ovarian and uterus), upper gastric-intestinal tract, and prostate cancers. They were associated with CDK12 mutations and worse patient survival. CDK12 is a cyclin-dependent kinase, a key regulator of transcription elongation and termination. Deleting or suppressing CDK12 using CRISPR-Cas9 in prostate cell lines increased RNA:DNA hybrids (R-loops), promoted TRCs, and ultimately led to large TDs. Finally, we found that cells lacking CDK12 were sensitive to WEE1, CHK1 and ATR inhibitors. In summary, our data suggest that large TDs in cancer form due to impaired TRC repair and can be used as a biomarker for prognosis and treatment. Citation Format: Lixing Yang. Data-driven discoveries of molecular mechanism and therapeutic vulnerabilities of CDK12 mutant tumors [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 B006.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.021
GPT teacher head0.282
Teacher spread0.261 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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 routes1
Has abstractyes

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