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Record W3145769307 · doi:10.1002/cncr.33516

Evaluation of treatment effects in patients with endometrial cancer and <i>POLE</i> mutations: An individual patient data meta‐analysis

2021· review· en· W3145769307 on OpenAlexafffund
Jessica N. McAlpine, Derek S. Chiu, Remi A. Nout, David N. Church, Pascal Schmidt, Stéphanie Lam, Samuel Leung, Stefania Bellone, Adele Wong, Sara Y. Brucker, Cheng‐Han Lee, Blaise Clarke, David G. Huntsman, Marcus Q. Bernardini, Joanne Ngeow, Alessandro D. Santin, Paul J. Goodfellow, Douglas A. Levine, Martin Köbel, Stefan Kommoss, Tjalling Bosse, C. Blake Gilks, Aline Talhouk

Bibliographic record

VenueCancer · 2021
Typereview
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsUniversity of CalgaryCentre for Advancing Health OutcomesPrincess Margaret Cancer CentreBC Cancer AgencySimon Fraser UniversityToronto General HospitalUniversity Health NetworkUniversity of British Columbia
FundersAcademy of Medical SciencesNational Institute for Health and Care ResearchCanadian Institute for Military and Veteran Health ResearchCancer Research UK
KeywordsMedicineMeta-analysisEndometrial cancerOncologyUterine cancerCancerInternal medicinePatient dataGynecology

Abstract

fetched live from OpenAlex

BACKGROUND: Endometrial cancers (ECs) with somatic mutations in DNA polymerase epsilon (POLE) are characterized by unfavorable pathological features, which prompt adjuvant treatment. Paradoxically, women with POLE-mutated EC have outstanding clinical outcomes, and this raises concerns of overtreatment. The authors investigated whether favorable outcomes were independent of treatment. METHODS: A PubMed search for POLE and endometrial was restricted to articles published between March 1, 2012, and March 1, 2018, that provided individual patient data (IPD), adjuvant treatment, and survival. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) reporting guidelines for IPD, the authors used univariate and multivariate one-stage meta-analyses with mixed effects Cox models (random effects for study cohorts) to infer the associations of treatment, traditional prognostic factors, and outcome, which was defined as the time from first diagnosis to any adverse event (progression/recurrence or death from EC). RESULTS: Three hundred fifty-nine women with POLE-mutated EC were identified; 294 (82%) had pathogenic mutations. Worse outcomes were demonstrated in patients with nonpathogenic POLE mutations (hazard ratio, 3.42; 95% confidence interval, 1.47-7.58; log-rank P < .01). Except for stage (P < .01), traditional prognosticators were not associated with progression/recurrence or death from disease. Adverse events were rare (11 progressions/recurrences and 3 disease-specific deaths). Salvage rates in patients who experienced recurrence were high and sustained, with 8 of 11 alive without evidence of disease (range, 5.5-14.2 years). Adjuvant treatment was not associated with outcome. CONCLUSIONS: Clinical outcomes for ECs with pathogenic POLE mutations are not associated with most traditional risk parameters, and patients do not appear to benefit from adjuvant therapy. The observed low rates of recurrence/progression and the high and sustained salvage rates raise the possibility of safely de-escalating treatment for these patients. LAY SUMMARY: Ten percent of all endometrial cancers have mutations in the DNA repair gene DNA polymerase epsilon (POLE). Women who have endometrial cancers with true POLE mutations experience almost no recurrences or deaths from their cancer even when their tumors appear to have very unfavorable characteristics. Additional therapy (radiation and chemotherapy) does not appear to improve outcomes for women with POLE-mutated endometrial cancer, and this supports the move to less therapy and less associated toxicity. Diligent classification of endometrial cancers by molecular features provides valuable information to inform prognosis and to direct treatment/no treatment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.831
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.003
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 head0.233
GPT teacher head0.441
Teacher spread0.208 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations156
Published2021
Admission routes2
Has abstractyes

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