Alterations in Glycopeptides Associated with Herceptin Treatment of Human Breast Carcinoma MCF-7 and T-Lymphoblastoid Cells
Bibliographic record
Abstract
The therapeutic humanized monoclonal antibody IgG1 known as Herceptin® has shown remarkable antitumor effects. Although this type of therapy has increased the cancer-free survival of patients, not all tumors respond to this treatment and cancers often develop resistance to the antibody. Despite the fact that Herceptin function has been extensively studied, the precise mechanism underlying its antitumor activity still remains incompletely defined. We previously demonstrated on human breast MCF-7 carcinoma and T-lymphoblastoid CEM cells that monoclonal antibody in combination with Lipoplex consisting of Lipofectamine mixed with plasmid DNA showed a more profound effect on cancer cell viability than antibody alone. The analyses of N-glycans isolated from cancer cells showed dramatic differences in profiles when cells were exposed to Herceptin. Moreover, the investigation of glycosylated peptides from the same cancer cell models after treatment revealed further alterations in the post-translational modifications. Tandem mass spectra obtained from the samples treated confirmed the presence of a series of glycopeptides bearing characteristic oligosaccharides as described in IgG1. However some of them differed by mass differences that corresponded to peptide backbones not described previously and more of them were detected from Herceptin treated samples than from cells transfected with Heceptin/Lipoplex. The results indicate that the presence of Lipoplex prevents antibody transformation and elongates its proper function. The better understanding of the multipart changes described in the glycoconjugates could provide new insights into the mechanism by which antibody induces regression in cancers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 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".