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Real-world (rw) patient characteristics, treatment (tx) patterns, and clinical outcomes in advanced (adv) and recurrent (rec) endometrial cancer (EC) patients (pts) with known mismatch repair/microsatellite instability (MMR/MSI) status in Canada.

2025· article· en· W4410814346 on OpenAlexafffundabout
Ji-Hyun Jang, Shalak Gunjal, Nikkita Dutta, Ryan Ng, Arushi Sharma, Amyn Sayani, Carly Cooke, Jacob McGee

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsUniversity of OttawaAstraZeneca (Canada)London Health Sciences Centre
FundersAstraZeneca Canada
KeywordsMicrosatellite instabilityMedicineEndometrial cancerDNA mismatch repairCancerInternal medicineGynecologyOncologyMicrosatelliteGeneGenetics

Abstract

fetched live from OpenAlex

e23308 Background: Molecular characterization and treatments for adv/rec EC have evolved in recent years. Given limited biomarker-based data on EC reported in Canada, this rw study examined tx patterns and clinical outcomes in pts with adv/rec EC by MMR/MSI status. Methods: This retrospective study used de-identified patient chart data reported by Canadian physicians. Adult female pts included were diagnosed with adv (Stage III/IV) or rec (Stage I/II with advanced recurrence) EC from 01-01-2017 to 31-12-2022 with ≥2 clinical visits and underwent MMR/MSI testing. Follow-up was until 30-09-2024. Patient characteristics, tx patterns and clinical outcomes were descriptively analyzed for the overall cohort and MMR deficient (dMMR/MSI-H) and MMR proficient (pMMR/MSS) subgroups. Kaplan-Meier analyses estimated overall survival (OS) and progression-free survival (PFS) starting from first-line (1L) tx initiation. Results: 209 pts diagnosed with adv (79.4%) or rec (20.6%) EC were included. The pMMR/MSS and dMMR/MSI-H subgroups comprised of 67.5% and 32.5% of the cohort, respectively. The mean age was 63.6 years (range: 33-86), 54.5% had no comorbidities, 54.5% had BMI ≥30, 71.8% were ECOG performance status 0, and most common histology was endometrioid EC (58.9%). Only 44.0% of pts had MMR testing within 1 month of diagnosis (dx). Besides MMR, most common tested biomarkers were p53 (77.5%), estrogen receptor (73.7%) and progesterone receptor (58.4%). Of the 209 pts, 178 (85.2%) received tx, of which, 120 (67.4%) only received 1L tx. Mean duration of follow-up from 1L initiation was 30.0 months (SD: 20.0). Median time from dx to 1L was 4.1 months (IQR: 2.3-6.1). Pts spent an average of 4.0 months (SD: 4.5) on 1L and 5.8 months (SD: 5.5) on second-line (2L). Most common 1L tx among both subgroups was chemotherapy (CT) (90.2% in pMMR/MSS, 91.1% in dMMR/MSI-H) with carboplatin + paclitaxel as most common regimen. In 2L, most common tx in pMMR/MSS pts after CT (59.1%) was hormone therapy (HT) (15.9%), whereas in dMMR/MSI-H pts it was immunotherapy (IO) (50.0%) then HT (35.7%). Table shows rw survival outcomes. Conclusions: This multi-province study provides the first rw Canadian data in MMR/MSI profiled EC cohort. CT was the most common 1L tx irrespective of MMR/MSI status and in 2L, half of dMMR/MSI-H pts received IO while most pMMR/MSS pts received CT. While MMR/MSI testing is standard in Canada, faster turnaround times at dx may improve access to novel therapies, especially in pMMR/MSS pts who had slightly worse survival outcomes than dMMR/MSI-H pts. EC group n OS PFS 1 year (yr) 2 yr 3 yr 1 yr 2 yr 3 yr Overall pMMR/MSS 122 81.4% 71.5% 66.2% 62.9% 51.1% 45.1% Adv. 105 83.4% 74.3% 68.7% 67.0% 53.9% 47.3% Rec. 17 69.1% 50.4% 50.4% 35.6% 35.6% 35.6% dMMR/MSI-H 56 83.4% 75.3% 73.1% 61.1% 61.1% 58.9%

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 score0.264
Threshold uncertainty score0.531

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.414
Teacher spread0.367 · 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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Citations0
Published2025
Admission routes3
Has abstractyes

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