Combined genomic and molecular analysis defines prognostic markers of relapse in stage IA-IC1 clear cell ovarian carcinoma
Bibliographic record
Abstract
OBJECTIVE: Clear cell ovarian carcinoma (CCOC) is generally associated with a favourable prognosis, however up to 30 % of low-stage cases relapse within five years. The benefit of adjuvant chemotherapy in early-stage disease (FIGO IA-IC1) remains uncertain. This study aimed to identify molecular and immune markers associated with relapse in a well-characterized CCOC cohort. METHODS: We analyzed 85 CCOC cases identified through the Edinburgh Ovarian Cancer Database. Targeted DNA sequencing assessed genomic alterations, while CD3 and CD8 immunohistochemistry evaluated tumour immune infiltration. Tumours were stratified by stage (IA-IC1, IC2-II, III-IV), and progression-free survival (PFS) analysis included stage, age, genomic features, and immune markers. RESULTS: Common genomic alterations included ARID1A (49 %) and PIK3CA (42 %) mutations, PIK3-AKT pathway perturbations (60 %), and mismatch repair-related mutational signatures (25.9 %). Genomic features were not significantly associated with tumour stage; however, low-stage tumours (IA-IC1) were enriched in CD3+ (40 % cases) and CD8+ (29 % cases) tumour-infiltrating lymphocytes (TILs) compared to higher-stage tumours (IC2-II: 13 %/7 %; III-IV: 9 %/0 %). In univariate analysis, low CD3+ TIL levels were significantly associated with reduced PFS (HR = 4.4, P = 0.042), and ARID1A wild-type status was linked to poorer PFS in low-stage tumours (HR = 7.2, P = 0.088). Notably, the combination of ARID1A wild-type status and CD3+ TIL depletion identified a high-risk subgroup with increased relapse risk (HR = 11.7, P = 0.051). CONCLUSIONS: The combination of ARID1A wild-type status and low CD3+ TIL levels suggests higher relapse risk in low-stage CCOC. These findings warrant further investigation into targeted and immune-based therapies for high-risk early-stage patients.
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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 | 0.000 | 0.000 |
| 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".