The idiotype (Id) cascade in mice elicited the production of anti‐R24 Id and anti‐anti‐Id monoclonal antibodies with antitumor and protective activity against human melanoma
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
Gangliosides have been considered as potential targets for immunotherapy because they are overexpressed on the surface of melanoma cells. However, immunization with purified gangliosides results in a very poor immune response, usually mediated by IgM antibodies. To overcome this limitation, we immunized mice with R24, a monoclonal antibody (mAb) that recognizes the most tumor-restricted ganglioside (GD3); our goal was to obtain anti-idiotype (Id) antibodies bearing the internal image of GD3. Animals produced anti-Id and anti-anti-Id antibodies. Both anti-Id and anti-anti-Id antibodies were able to inhibit mAb R24 binding to GD3. In addition, the anti-anti-Id antibodies were shown to recognize GD3 directly. Anti-Id and anti-anti-Id mAb were then selected from two fusion experiments for evaluation. The most interesting finding emerged from the characterization of the anti-anti-Id mAb 5.G8. It was shown to recognize two different GD3-expressing human melanoma cell lines in vitro and to mediate tumor cell cytotoxicity by complement activation and antibody-dependent cellular cytotoxicity. The biological activity of the anti-anti-Id mAb was also tested in a mouse tumor model, in which it was shown to be a powerful growth inhibitor of melanoma cells. Thus, activity of the anti-anti-Id mAb 5.G8 matched that of the prototypic anti-GD3 mAb R24 both in vitro and in vivo. Altogether, our results indicate that the idiotype approach might produce high affinity, specific and very efficient antitumor immune responses.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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