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Record W4412073469 · doi:10.1038/s41698-025-01025-1

Exceptional response to the ATR inhibitor, camonsertib, in a patient with ALT+ metastatic melanoma

2025· article· en· W4412073469 on OpenAlexaff
Natalie Y.L. Ngoi, Ian M. Silverman, Adrienne Johnson, Chenfeng Meng, Joseph D. Schonhoft, Michal Zimmermann, Danielle Ulanet, Hye‐Yeon Kim, Carolina Salguero, Christian Valladolid Brown, Jordi Rodón, Victoria Rimkunas, María Koehler, Timothy A. Yap

Bibliographic record

Venuenpj Precision Oncology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsTelesta Therapeutics (Canada)
FundersNational Cancer InstituteUniversity of Texas MD Anderson Cancer CenterNational Institutes of HealthU.S. Department of Defense
KeywordsMetastatic melanomaMelanomaMedicineDermatologyInternal medicineOncologyCancer research

Abstract

fetched live from OpenAlex

A key hallmark of cancer tumorigenesis is the maintenance of telomere length, which occurs canonically through the reactivation of telomerase. Alternative lengthening of telomeres (ALT) is an atypical, non-canonical telomere maintenance mechanism that uses homologous recombination (HR) to maintain telomere length and is associated with replication stress and defects in genome maintenance. In preclinical models, ALT positivity (ALT+) sensitizes tumor cells to ataxia telangiectasia and Rad3-related (ATR) inhibitors. Camonsertib is a novel potent, and highly selective ATR inhibitor that is synthetic lethal with genomic alterations affecting HR and DNA damage response (DDR). Here we describe a case of confirmed clinical and molecular response to pharmacological ATR inhibition through camonsertib, in a patient with ALT+ metastatic melanoma. To our knowledge, this is the first clinical report of synthetic lethal targeting of a confirmed ALT+ tumor with an ATR inhibitor.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.282
Teacher spread0.272 · 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 designCase report
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

Citations3
Published2025
Admission routes1
Has abstractyes

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