N-Glycomic Changes in Human Breast Carcinoma MCF-7 and T-Lymphoblastoid Cells After Treatment with Herceptin and Herceptin/Lipoplex
Bibliographic record
Abstract
The humanized monoclonal antibody IgG1 in combination with chemotherapy has been demonstrated to enhance survival benefit in cancer treatment. Despite positive outcomes, some cancer cells develop multidrug resistance. Numerous mechanisms in cancers can be involved in the process of treatment therapy and most of them are not still well understood. To address how the carbohydrate moieties of cells are affected during treatment, the glycan profiles from the two most common cancer cell lines - human breast MCF-7 carcinoma and T-lymphoblastoid CEM cells - were studied here and compared with profiles after treatment with Herceptin alone or in combination with Lipofectamine mixed with plasmid DNA to form Lipoplex. N-Glycans were released from total cells by digestion with PNGaseF and analyzed by matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS). In summary, both original cell lines showed a dominant occurrence of high-mannose glycans. After treatment, these structures were suppressed and biantennary core-fucosylated glycans originating from IgG1 were the major carbohydrate products identified in cells. The high incidence of additional fucosylated or nonfucosylated galactosylated oligosaccharides, which were not detected in original cells or Herceptin, varied with conditions and time of exposure of cells to the antibody. The results presented in this study provide strong evidence for a role of glycosylation during antibody treatment.
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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".