A multi‐ethnic analysis of immune‐related gene expression signatures in patients with ovarian clear cell carcinoma
Bibliographic record
Abstract
Little is known about the immune environment of ovarian clear cell carcinoma (OCCC) and its impact on various ethnic backgrounds. The aim of this OCCC immune-related gene expression signatures (irGES) study was to address the interaction between tumour and immune environment of ethnically-diverse Asian and Caucasian populations and to identify relevant molecular subsets of biological and clinical importance. Our study included 264 women from three different countries (Singapore, Japan, and the UK) and identified four novel immune subtypes (PD1-high, CTLA4-high, antigen-presentation, and pro-angiogenic subtype) with differentially expressed pathways, and gene ontologies using the NanoString nCounter PanCancer Immune Profiling Panel. The PD1-high and CTLA4-high subtypes demonstrated significantly higher PD1, PDL1, and CTLA4 expression, and were associated with poorer clinical outcomes. Mismatch repair (MMR) protein expression, assessed by immunohistochemistry, revealed that about 5% of OCCCs had deficient MMR expression. The prevalence was similar across the three countries and appeared to cluster in the CTLA4-high subtype. Our results suggest that OCCC from women of Asian and Caucasian descent shares significant clinical and molecular similarities. To our knowledge, our study is the first study to include both Asian and Caucasian women with OCCC and helps to shine light on the impact of ethnic differences on the immune microenvironment of OCCC. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".