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Post hoc analysis of progression-free survival (PFS) and overall survival (OS) by mechanism of mismatch repair (MMR) protein loss in patients with endometrial cancer (EC) treated with dostarlimab plus chemotherapy in the RUBY trial.

2024· article· en· W4399395142 on OpenAlexaff
Mansoor Raza Mirza, Sudarshan Sharma, Henrik Roed, Lisa M. Landrum, Lucy Gilbert, Michael A. Gold, Zoltán Novàk, Mitchell I. Edelson, Mihai Meirovitz, John P. Diaz, Greet Huygh, Joseph Buscema, Bhavana Pothuri, Helen Eshed, Robert L. Coleman, Brian M. Slomovitz, Rumen Kostadinov, Shadi Stevens, Graziana Ronzino, Matthew A. Powell

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineEndometrial cancerProgression-free survivalOncologyChemotherapyOvarian cancerInternal medicineCancerPost-hoc analysisOverall survivalSurvival analysisCancer research

Abstract

fetched live from OpenAlex

5606 Background: MMR deficiency (dMMR) is caused by aberrant expression of MMR proteins that mediate DNA repair. The loss can be explained by epigenetic regulation (epi-dMMR; MLH1 promoter hypermethylation preventing MLH-1 expression) or mutation (mut-dMMR; MMR proteins lost through deleterious mutations) accounting for 70%–75% and 25%–30% of dMMR/microsatellite instability–high (MSI-H) EC, respectively. The GARNET trial showed that mechanism of MMR loss did not influence response of EC to monotherapy with dostarlimab (DOST), an anti-PD-1. No data exist examining OS by mechanism of MMR loss in patients (pts) with EC receiving immunotherapy. Here, we report analyses of PFS and OS in pts with primary advanced or recurrent EC (pA/rEC) in Part 1 of the RUBY trial (NCT03981796) by mechanism of MMR protein loss. Methods: Pts with pA/rEC were randomized 1:1 to receive DOST or placebo (PBO), plus carboplatin-paclitaxel (CP), followed by DOST or PBO monotherapy for up to 3 years. MMR protein status was assessed by immunohistochemistry. MMR loss of function gene mutations were determined by Personalis ImmunoID NeXT whole-exome sequencing assay. MMR protein loss without mutations in MMR genes was a surrogate indicator for epi-dMMR. Post hoc PFS and OS analyses utilized data from the data cut at which each endpoint was met (PFS at Sep 28, 2022; OS at Sep 22, 2023). Results: Part 1 of the RUBY trial included 118 pts with dMMR/MSI-H pA/rEC (DOST+CP = 53; PBO+CP = 65); 39 pts (73.6%) in the DOST+CP arm and 52 pts (80.0%) in the PBO+CP arm had MMR gene mutation data available. Substantial PFS and OS benefits were observed with DOST+CP vs PBO+CP in pts with mut-dMMR or epi-dMMR (Table). No significant differences were seen in PFS or OS in pts with mut-dMMR vs pts with epi-dMMR. Conclusions: DOST+CP led to substantial PFS and OS benefits compared with PBO+CP indMMR/MSI-H pA/rEC, regardless of mechanism of MMR protein loss. A limited number of pts with mut-dMMR treated with DOST+CP were available for analysis; however, the results support that mechanism of MMR loss does not appear to be a significant predictor of clinical benefit for dostarlimab treatment in pts with dMMR pA/rEC in Part 1 of the RUBY trial. Clinical trial information: NCT03981796 . [Table: see text]

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)0.004
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.391
Teacher spread0.352 · 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".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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