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Record W4403239841 · doi:10.1136/ijgc-2024-005919

GOG-3097/ENGOT-ov81/GTG-UK/RAMP 301: a phase 3, randomized trial evaluating avutometinib plus defactinib compared with investigator’s choice of treatment in patients with recurrent low grade serous ovarian cancer

2024· article· en· W4403239841 on OpenAlexaff
Rachel N. Grisham, Bradley J. Monk, Els Van Nieuwenhuysen, Kathleen N. Moore, Michel Fabbro, David M. O’Malley, Ana Oaknin, Premal H. Thaker, Amit M. Oza, Nicoletta Colombo, David M. Gershenson, Carol Aghajanian, Chel Hun Choi, Yeh Chen Lee, Mansoor Raza Mirza, Robert L. Coleman, Lauren Cobb, Philipp Harter, Stephanie Lustgarten, Hagop Youssoufian, Susana Banerjee

Bibliographic record

VenueInternational Journal of Gynecological Cancer · 2024
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Cancer InstituteNational Institutes of HealthVerastem OncologyGOG FoundationAustralia New Zealand Gynaecological Oncology GroupGlaxoSmithKlineAstraZeneca
KeywordsMedicineOvarian cancerSerous fluidRegimenOncologyInternal medicineDiscontinuationKRASTolerabilityClinical trialClinical endpointProgression-free survivalAdverse effectCancerChemotherapyColorectal cancer

Abstract

fetched live from OpenAlex

BACKGROUND: There are no approved treatments specifically for low grade serous ovarian cancer; current standard of care treatment options are limited in efficacy and tolerability. The combination of avutometinib with defactinib has demonstrated efficacy and a consistent safety profile in two clinical trials in recurrent low grade serous ovarian cancer, and a lower discontinuation rate due to adverse events compared with historical rates for standard of care. PRIMARY OBJECTIVE: To compare the progression-free survival of the combination of avutometinib with defactinib versus investigator's choice of treatment in patients with recurrent low grade serous ovarian cancer. STUDY HYPOTHESIS: Combination treatment with avutometinib-defactinib will significantly improve progression-free survival compared with investigator's choice of treatment in patients with recurrent low grade serous ovarian cancer. TRIAL DESIGN: GOG-3097/ENGOT-ov81/GTG-UK/RAMP 301 is a phase 3, randomized, international, open-label study designed to compare avutometinib with defactinib versus investigator's choice of treatment in patients with recurrent low grade serous ovarian cancer who have progressed on a previous platinum-based therapy. On confirmation of disease progression using a blinded independent central review, patients on the investigator's choice of treatment arm may cross over to the avutometinib-defactinib arm. MAJOR INCLUSION/EXCLUSION CRITERIA: Patients must have recurrent low grade serous ovarian cancer (KRAS mutant or wild-type) and have documented progression (radiographic or clinical) or recurrence of low grade serous ovarian cancer after at least one platinum-based chemotherapy regimen. Unlimited additional previous lines of therapy are allowed, including previous MEK/RAF inhibitor. Patients will be excluded if they have co-existing high grade ovarian cancer or had previous treatment with avutometinib, defactinib, or any other FAK inhibitor. PRIMARY ENDPOINT: Progression-free survival according to Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, blinded-independent central review. SAMPLE SIZE: Approximately 270 patients will be randomized in a 1:1 fashion to either the combination avutometinib with defactinib arm (n∼135) or the investigator's choice of treatment arm (n∼135). ESTIMATED DATES FOR COMPLETING ACCRUAL AND PRESENTING RESULTS: The estimated primary completion date of RAMP 301 is 2028, and the estimated study completion date is 2031. TRIAL REGISTRATION: ClinicalTrials.gov NCT06072781.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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)0.0010.000

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 head0.072
GPT teacher head0.410
Teacher spread0.338 · 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 designRandomized trial
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

Citations25
Published2024
Admission routes1
Has abstractyes

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