Further Understanding of High-Grade Serous Ovarian Carcinogenesis: Potential Therapeutic Targets
Bibliographic record
Abstract
Ioannis A Voutsadakis1,2 1Algoma District Cancer Program, Sault Area Hospital, Sault Ste. Marie, Ontario, Canada; 2Section of Internal Medicine, Division of Clinical Sciences, Northern Ontario School of Medicine, Sudbury, Ontario, CanadaCorrespondence: Ioannis A Voutsadakis Algoma District Cancer ProgramSault Area Hospital, 750 Great Northern Road, Sault Ste Marie, ON P6B 0A8, CanadaEmail ivoutsadakis@yahoo.comAbstract: High-grade serous ovarian carcinoma (HGSOC) is the most common type of ovarian cancer and the most lethal gynecologic malignancy due to advanced stage at presentation. Recent years have witnessed progress in the therapy of HGSOC with the introduction of PARP (poly-adenosine diphosphate ribose polymerase) inhibitors and the anti-angiogenic monoclonal antibody bevacizumab to the backbone of chemotherapy or as maintenance therapy after chemotherapy. The improved molecular understanding of ovarian cancer pathogenesis, which has brought these therapies into the clinic, aspires to extend the boundaries of therapies through elucidation of other molecular aspects of ovarian carcinogenesis. This accumulating knowledge has started to be translated to additional targeted therapies that are in various stages of development. These include inhibitors of the function of other proteins involved in homologous recombination deficiency (HRD), such as WEE1 kinase, ATM/ATR kinases and CDK12 inhibitors. Despite disappointing results with immune checkpoint inhibitors monotherapy, harnessing the immune system in HGSOC with combination therapies that promote antigen production and immune cell activation is an avenue being explored. This paper examines arising HGSOC therapies based on molecular understanding of pathogenesis.Keywords: ovarian cancer, serous, genomics, targeted therapies, adavosertib, immunotherapy
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".