Multi-modal immune profiling of mucinous ovarian carcinoma: analysis from the Ovarian Tumor Tissue Analysis/Multidisciplinary Ovarian Cancer Outcomes Group consortia
Bibliographic record
Abstract
Abstract Mucinous ovarian carcinoma (MOC) is a rare histotype of ovarian cancer. Women with advanced disease (FIGO stage III/IV) have very poor outcomes, with 5-year survival of <20%. Little is known about the anti-tumor immune response in MOC, limiting potential immunotherapeutic options for patients. Immune data from 3 platforms were analysed. A NanoString mRNA expression panel with 39 immune-related genes (n=60 MOC, n=4198 other ovarian histotypes), a NanoString mRNA plexset with PD-1, PD-L1 (n=241 MOC, n=115 upper and lower gastrointestinal (GI) cancers), and 2 multicolor immunofluorescence (mcIF) panels (n=121 MOC) for CD68, PD-L1, PD-1, CD8 and FOXP3. Data overlapped all platforms for 22 MOC. Immune marker expression by ovarian cancer histotype was measured. Uni- and multivariable Cox Proportional Hazards assessed overall survival (OS) within MOC. Hierarchical clustering looked for subgroups of patients based on co-expression of immune genes. Compared to more common high-grade serous ovarian cancer (HGSC) and GI cancers, MOC were less immunologically active. High IGHM expression was associated with poorer OS (HR 1.31 (95% CI 1.02–1.69), p=0.033). High tumor densities of FOXP3 and PD-1 positive cells conferred poorer OS in a model with age, stage and site; however, these and PD-L1 and CD68 were associated with grade. The 39 gene NanoString and mcIF datasets revealed 4 clusters, roughly translating to immune cold, moderate and hot (groups 3,4). mcIF analysis revealed a subset with PD-L1 expression in the absence of T-regulatory cell infiltrates. This study provides novel insights into the MOC immune landscape. Despite relatively low immune activity overall, immunotherapy could be explored for a subset of patients with MOC. Supported by NSW Ministry of Health and UNSW Sydney, through the NSW Health PhD Scholarship Program 2017-2022; Translational Cancer Research Network top-up scholarship 2021
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".