Potential Monoclonal Antibody Therapy for the Treatment of Ovarian Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although ovarian cancer is the fifth most common cancer among women, it causes more death than any other type of female reproductive cancer Besides difficulties in early detection, limited options for the treatment of ovarian cancer at late stages have been the major cause of high mortality rate About 76% of women with ovarian cancer survive 1 year after diagnosis, but only about 45% will live 5 years after diagnosis Therefore, it may be desirable to look for alternative means of treating this type of cancer rather than the conventional ones including chemo-or radiotherapy. During the last two decades, target-oriented antibody-based anti-cancer drugs have become the main stream choices for cancer treatments in humans. Although the efficacy of cancer treatments varies greatly with individual cases, overall improvements of patients' care and survival are significant, when compared to those of the conventional ones. Besides those approved by the FDA of the United States of America for the clinical treatments of cancer, numerous antibody-drug candidates are still at various stages of clinical trials and pending for the final approval by the FDA Generally speaking, the majority of antibody-based anti-cancer drugs are target-oriented and the adverse side effects upon infusion of the antibody drugs are relatively mild as compared to those of the traditional ones. Therefore, selections of suitable targets against the tumor cells have become an essential step for the long term antibody drug development. In general, the ideal tumor target for the antibody drugs can be selected based on its accessibility, high abundance and surface homogeneity. Moreover, it should not be highly expressed on normal cells or tissues, especially the vital organs in humans Recently, two monoclonal antibodies were identified and selected based on these criteria for ovarian cancer. One is RP215 which recognizes a carbohydrate-associated epitope found preferentially in cancer cell-expressed immunoglobulin superfamily proteins, designated in general as CA215. The other is GHR106 which was shown to react with the extracellular domain of human GnRH receptor. Both CA215 and GnRH receptor are widely expressed among cancer cells of different tissue origins, especially those of the human ovary with positive rates ranging from 60-80% Ovarian Cancer -Basic Science Perspective 386 2010a). The binding of either of these two monoclonal antibodies was found to inhibit the growth of ovarian cancer cells in vitro and in vivo through studies of induced apoptosis and complement-dependent cytotoxicity. Therefore, additional preclinical studies were performed to elucidate the mechanisms of action of these monoclonal antibodies as anticancer drugs for the treatment of ovarian cancer. These studies should represent our efforts to demonstrate the potential use of these monoclonal antibodies as the anti-ovarian cancer drugs in the future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it