Abstract B052: Immunotherapy for melanoma by adenovirus-mediated full-length antibody, nivolumab
Bibliographic record
Abstract
Abstract Nivolumab, a fully human, IgG4 immune checkpoint inhibitor antibody, binds PD-1 on activated immune cells to disrupt PD-1 interaction with PD-L1 and PD-L2 ligands, thereby attenuating inhibitory signals and augmenting the host antitumour response. In 2014, nivolumab was approved by FDA and has been used to treat various types of cancer such as melanoma, non-small cell lung cancer, renal cell carcinoma, and classic Hodgkin's lymphoma. However, due to the high price of nivolumab and the long cycle of treatment, many patients cannot afford and receive adequate treatment. Here, we generated a new, cheaper form of nivolumab for cancer immunotherapy, by cloning the full-length nivolumab antibody gene into two serotypes of adenovirus vectors, termed as AdHu5-Nivo and AdC68-Nivo. Ad vectors based on human serotype 5 (AdHu5) have been proved in a lot of previous studies to be safe and efficient. Compared with AdHu5, chimpanzee Ads exhibit much lower seroprevalence in human beings, which made them great alternative Ad gene vector. Until now, we have detected a high expression of nivolumab in vitro by Western blot and sandwich ELISA. In vivo studies showed that a single dose of AdHu5-Nivo or AdC68-Nivo can induce sustained nivolumab expression. The biologic activities of the two mAbs are analogous compared with commercial monoclonal antibody. To follow up we will verify their antitumor effects on both cellular level and melanoma transplant animal models. Citation Format: Xuchen Wang. Immunotherapy for melanoma by adenovirus-mediated full-length antibody, nivolumab [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B052.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 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; both teacher heads agree on what is shown here.
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".