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<i>CCNE1</i> amplification as marker of poor prognosis and novel therapeutic target in advanced breast cancer.

2024· article· en· W4399150704 on OpenAlexaff
Antonio Marra, Pier Selenica, Yingjie Zhu, Anton Safonov, Pedram Razavi, Anne Roulston, María Koehler, Giuseppe Curigliano, Dara S. Ross, Britta Weigelt, Jorge S. Reis‐Filho, Alison M. Schram, Sarat Chandarlapaty, Ezra Y. Rosen

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsTelesta Therapeutics (Canada)
Fundersnot available
KeywordsCyclin E1MedicineInternal medicineGene duplicationBreast cancerCancer researchOncologyCancerBiologyGeneCyclinGeneticsCell cycle

Abstract

fetched live from OpenAlex

1040 Background: Cyclin E1 (CCNE1) overexpression or gene amplification is linked to dismal outcomes in various tumors, including breast cancer (BC). Recent studies suggest that CCNE1 amplification is a potential target for new synthetic lethality-based treatments, including CDK2-selective and PKMYT1 inhibition. This study explores the clinical and genomic characteristics of CCNE1-amplified BCs. Methods: We analyzed genomic and clinical data from consecutive BCs that underwent clinical tumor-normal targeted panel sequencing (MSK-IMPACT) between April 2014 and December 2021. Allele-specific copy number, including CCNE1 amplification, and fraction genome altered (FGA) were calculated by FACETS. Mutual exclusivity and co-occurrence analyses were performed using CoMEt. Benjamini–Hochberg method for multiple testing correction (q) was applied. Real-world progression-free survival (rwPFS) was assessed by the Kaplan Meier method and Cox models, focusing on patients with available pre-treatment samples. Results: Out of 3,753 BCs, 125 (3.3%) had CCNE1 amplification. A higher occurrence of CCNE1 amplification was noted in post-treatment (n=2,368) compared to treatment-naïve tumors (n=1,385; 4% vs 2.4%, p=0.007). CCNE1 amplification was less common in hormone receptor (HR)+/HER2- BCs (2%) compared to HER2+ (7.6%) and triple-negative (7.2%) tumors (p<0.001). BCs with CCNE1 amplification showed higher median FGA, indicating increased genomic instability. In primary BC, TP53 alterations were more frequent in CCNE1-amplified tumors (q<0.001), while CDH1 alterations were mutually exclusive (q<0.001) suggesting that lobular BCs rarely harbor CCNE1 amplification. Similar trends were seen in post-treatment samples. CCNE1 amplification detected at baseline was linked to shorter median rwPFS in HR+/HER2- metastatic BCs treated with CDK4/6 inhibitors plus endocrine therapy (8.8 vs 15.2 months in CCNE1-amplified [n=9] vs CCNE1-non amplified [n=402]; hazard ratio [HR] 2.82, 95% CI 1.38-5.75), and in HER2+ metastatic BCs treated with first-line THP regimen (7.3 vs 20.8 months in CCNE1-amplified [n=5] vs CCNE1-non amplified [n=106]; HR 3.1, 95% CI 1.24-7.87). In a CCNE1-amplified triple-negative cell model (HCC1569), treatment with the PKMYT1 inhibitor RP-6306 significantly reduced tumor growth. Conclusions: CCNE1 amplification in BCs is associated with greater genomic instability and characterizes a group of metastatic BCs with poor response to standard of care therapies. Novel therapies targeting CCNE1-amplified tumors are under evaluation in preclinical and clinical studies.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.410
Teacher spread0.369 · 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 designObservational
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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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