Emerging peptide therapeutics for the treatment of ovarian cancer
Bibliographic record
Abstract
INTRODUCTION: The discovery of therapeutic proteomic targets has resulted in remarkable advances in oncology. Identification of functional and hallmark peptides in ovarian cancer can be leveraged for diagnostic and therapeutic targeting. These targets are expressed in different tumor cell locations, making them excellent candidates for theranostic imaging, precision therapeutics, and immunotherapy. The ideal target is homogeneously overexpressed in malignant cells with no expression in healthy cells, thereby avoiding off-tumor bystander toxicity. Several peptides are currently undergoing extensive evaluation for the development of vaccines, antibody-drug conjugates, monoclonal antibodies, radioimmunoconjugates, and cell therapy. AREAS COVERED: This review focuses on the significance of peptides as promising targets in ovarian cancer. English peer-reviewed articles and abstracts were searched in MEDLINE, PubMed, Embase, and major conference databases. EXPERT OPINION: Peptides and proteins expressed in tumor cells are an exciting area of research with great potential and may significantly influence precision therapeutics and immunotherapeutic strategies. Accurate utilization of peptide expression as a predictive biomarker has the potential to greatly enhance treatment precision. The ability to measure receptor expression paves the way for its use as a predictive biomarker for therapeutic targeting and requires critical validation of sensitivity and specificity for each indication to guide therapy.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 0.002 |
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".