Multi‐omics in high‐grade serous ovarian cancer: Biomarkers from genome to the immunome
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) is a lethal gynaecological disease that imposes significant burden on health care and patient quality of life. High-grade serous carcinoma of the ovary (HGSC) is the most prevalent histological type of EOC. A vast majority of HGSC cases are diagnosed at late stages of the disease, limiting the opportunity for clinical intervention and resulting in a 10-year survival rate of <20%. Recent innovations in high-throughput molecular analysis of patient-derived specimens may address these clinical challenges by providing an enhanced understanding of the molecular aetiology of ovarian cancer, in addition to offering several opportunities for rational biomarker and targeted therapy discovery. In this review, we highlight the most significant contributions of omics approaches and how the advent of immunomics can aid in personalized combination chemo-immunotherapy in ovarian cancer treatment. We further provide insights into immunogenomic correlates of pre-treatment tumour immune microenvironment and some of the potential interpretations of immunomic data that require further validation, based on stromal and immune contributions to biomarker signatures. We believe a comprehensive integrative approach via meta-analysis of large ovarian cancer molecular profiling data sets is urgently needed to define robust prognostic and predictive classifiers of disease progression and treatment response. These investigations will inform rationalized biomarker-driven combination chemo-immunotherapy trials for improving response and survival of ovarian cancer patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".