Randomized phase III study of maintenance selinexor versus placebo in endometrial cancer (ENGOT-EN5/GOG-3055/SIENDO): Impact of subgroup analysis and molecular classification.
Bibliographic record
Abstract
5511 Background: Endometrial cancers (ECs) are stratified into four molecular categories: wild type TP53 with non-specific molecular profile typically with microsatellite stability (NSMP, p53wt/MSS), DNA polymerase ε exonuclease domain-mutated (POLEmut), microsatellite instability high (MSI) and TP53 abnormal (p53abn). These are associated with specific prognoses. Selinexor (SEL) is a specific XPO1 inhibitor that leads to the nuclear retention and activation of tumor suppressor proteins (TSP) including p53. SEL showed improved progression-free survival (PFS) over placebo (PLB) in the stratification adjusted results of the ENGOT-EN5/GOG-3055/SIENDO study (NCT03555422; ESMO 2022). Methods: The SIENDO study is a prospective, multicenter, double-blind, placebo-controlled, phase 3 study of SEL (80 mg once weekly) vs. PLB (2:1 randomization) as maintenance therapy in 263 patients (pts) with advanced or recurrent EC after one line of taxane-platinum therapy with partial or complete remission. TP53 mutations and MSI were assessed by centralized targeted sequencing and local immunohistochemistry. Classification was based on sequencing 648 genes on tumor samples from 172 pts (107 on SEL), assigned first by POLEmut, then MSI, then p53abn or p53wt (NSMP). Preliminary exploratory analyses based on molecular classification were prespecified in the trial. Results: The SIENDO study resulted in a median progression-free survival (PFS) of 5.7 months (SEL) vs. 3.8 months (PLB), with a stratification adjusted (eCRF) hazard ratio (HR) of 0.70 (p =.024; and a stratification non-adjusted (IRT) HR of 0.76 (p=0.063). Among the 172 patients who underwent molecular classification, those on SEL (107 pts) were classified as follows: 37 (35%) NSMP, 2 (2%) POLEmut, 18 (17%) MSI, and 50 (46%) p53abn. A similar distribution was seen in those on PLB (65 pts): 20 (31%) NSMP; 4 (6%) POLEmut; 8 (12%) MSI; 33 (51%) p53abn. Subgroup analysis of pts with TP53wt showed a PFS of 13.7 mo with SEL vs. 3.7 mo with PLB (HR 0.375; 95% CI, 0.210-0.670; nominal p =.0003) and pts with MSS/pMMR disease had a PFS of 6.9 mo with SEL vs. 5.4 with PLB (HR 0.593; 95% CI, 0.388-0.905, nominal p =.007). An analysis of patients with NSMP (p53wt, MSS) showed a substantial difference in PFS for SEL vs. PLB: medians NR and 3.71 months, respectively (HR 0.163; 95% CI, 0.060-0.444; nominal p <.0001). Analyses of the other 3 molecular categories did not show significant differences in PFS between SEL and PLB. Additional biomarker identification studies assessing tumor genetics and epigenetics are ongoing. Conclusions: SEL showed improved PFS over PLB in the SIENDO study based on the stratification adjusted analysis. As an indirect p53 activator, preliminary exploratory subgroup analyses of SEL showed improvement over PLB amongst the patients with TP53wt, MSS, and the NSMP EC comprising approximately 50% of patients with advanced/recurrent EC. Clinical trial information: NCT03555422.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".