Genomic Landscape of ctDNA and Real-World Outcomes in Advanced Endometrial Cancer
Bibliographic record
Abstract
PURPOSE: ctDNA is a novel technique extensively studied in solid tumors, although not currently well defined in endometrial cancer. EXPERIMENTAL DESIGN: A de-identified retrospective analysis of 1,988 patients with advanced/recurrent endometrial cancer was performed. In addition, an analysis of a real-world evidence cohort was completed (n = 1,266). Patients underwent ctDNA testing using Guardant360 during routine clinical care. The objective was to describe and assess molecular landscape using ctDNA. RESULTS: Among 1,988 ctDNA samples, at least one somatic alteration was detected in 91.6% (n = 1,821). Most frequently altered genes were TP53 (64%), PIK3CA (29%), PTEN (25%), ARID1A (20%), and KRAS (14%). Overall, 18.5% had amplifications, with the majority identified in CCNE1 (40.9%), PIK3CA (22%), and EGFR (19.3%). From the real-world evidence cohort, those with TP53 mutations had a worse overall survival (OS) versus those without TP53 mutations (P = 0.02) and those with TP53 comutations had an inferior OS in comparison with TP53-mutated only (P = 0.016). Amongst these, patients with a PIK3CA comutation (P = 0.012) and CCNE1 amplification (P = 0.01) had an inferior OS compared with those with only TP53 mutations. Fifty-seven patients with newly diagnosed endometrial cancer had at least two serial ctDNA samples showing evolution in detected variants compared with baseline samples, with TP53 being the most frequent change. CONCLUSIONS: This study is one of the largest cohorts of ctDNA currently reported in endometrial cancer. The presence of TP53 mutation and other comutations detected by ctDNA have a negative effect on outcomes. This report suggests that ctDNA analysis is feasible and could become a useful biomarker for endometrial cancer.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".