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ENGOT-ov60/GOG-3052/RAMP 201: A phase 2 study of VS-6766 (RAF/MEK clamp) alone and in combination with defactinib (FAK inhibitor) in recurrent low-grade serous ovarian cancer (LGSOC).

2022· article· en· W4281671615 on OpenAlexaff
Susana Banerjee, Bradley J. Monk, Els Van Nieuwenhuysen, Kathleen N. Moore, Ana Oaknin, Michel Fabbro, Nicoletta Colombo, David M. O’Malley, Robert L. Coleman, Amit M. Oza, Jonathan A. Pachter, Gloria Patrick, Louis J. Denis, Lorna Leonard, Rachel N. Grisham

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsKRASMedicineMEK inhibitorNeuroblastoma RAS viral oncogene homologOvarian cancerSerous fluidCancerCancer researchSelumetinibInternal medicineMAPK/ERK pathwayKinaseOncologyBiology

Abstract

fetched live from OpenAlex

TPS5615 Background: Low-grade serous ovarian cancer (LGSOC) constitutes up to 10% of all ovarian cancer and has clinical and molecular characteristics distinct from high-grade serous ovarian cancer. Approximately a third of patients (pts) with recurrent LGSOC harbor KRAS mutations (mt) and pts with KRAS wild-type (wt) LGSOC may have mutations in NRAS, BRAF, or other RAS pathway-associated genes. Prior clinical studies with single agent MEK inhibitors have shown response rates of 16-26% in recurrent LGSOC. VS-6766 is a unique small molecule RAF/MEK clamp that inhibits both RAF and MEK activities by trapping them in inactive complexes. This mechanism of blockade has been shown to limit compensatory MEK activation, thereby potentially enhancing efficacy of MEK inhibition. Focal adhesion kinase (FAK) activation is a putative resistance mechanism to RAF and MEK inhibition, and defactinib, a small molecule inhibitor of FAK, has shown synergistic anti-tumor activity with VS-6766 in preclinical models, including organoids from LGSOC pts. Furthermore, FAK inhibition combined with VS-6766 induces tumor regression in a KRAS mt ovarian cancer xenograft model. The combination of VS-6766 and defactinib is currently being evaluated in the ongoing Investigator Sponsored FRAME study (NCT03875820). In this proof-of-concept study, durable objective responses (ORR = 46%; 11/24) have been reported in recurrent LGSOC pts, including pts who have had a prior MEK inhibitor (Banerjee ESMO 2021) and the combination of VS-6766 + defactinib has received FDA Breakthrough Therapy Designation for recurrent LGSOC. These initial preclinical and clinical results support the ongoing phase 2 ENGOT-ov60/GOG-3052 in recurrent LGSOC. Methods: This is an international phase 2, adaptive, multicenter, randomized, open label study designed to evaluate the efficacy and safety of VS-6766 vs VS-6766 in combination with defactinib currently open to enrollment (NCT04625270). The study will be conducted in two parts. Part A will determine the optimal regimen based on confirmed overall response rate (independent radiology review) in KRAS mt and KRAS wt LGSOC. Part B will determine the efficacy of the optimal regimen identified in Part A in KRAS mt and KRAS wt LGSOC. The minimum expected enrollment is 104 pts, 52 pts with KRAS mt and 52 KRAS wt (64 pts in Part A and 40 pts in Part B). Pts will be randomized to receive VS-6766 (4.0 mg orally (PO), twice weekly 3 wks on, 1 wk off) or VS-6766 with defactinib (VS-6766 3.2 mg PO, twice weekly + defactinib 200 mg PO BID 3 wks on, 1 wk off) till progression. Key inclusion criteria include histologically confirmed LGSOC, known KRAS mutation status, prior systemic therapy including platinum for metastatic disease and up to 1 prior line of MEK inhibitor therapy permitted. Part A of this study has completed enrollment and Part B is currently enrolling pts. Clinical trial information: NCT04625270.

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)0.001
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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.003
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 head0.091
GPT teacher head0.457
Teacher spread0.366 · 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 designNon-randomized 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

Citations8
Published2022
Admission routes1
Has abstractyes

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