CTL-Tumor Cell Interaction: The Generation of Molecular Probes of Monitoring the HLA-A*0201-HER-2/neu Peptide Complex
Bibliographic record
Abstract
The overall goal of this project is to probe the CTL - tumor cell interaction by generating scFv probes that are able to recognize the HLA-A*0201-HER-2/neu369-377 peptide complex. In the 12 month period covered by this report, I have successfully generated HLA-A*0201-HER-2/neu369-377 complexes, and have isolated two scFv fragment clones that recognize this complex. In addition, I have started to analyze the expression levels of antigen processing machinery (APM) components, HLA class I antigens and beta2m in several breast carcinoma cell lines. This analysis takes advantage of the availability of a wide panel of mAb to these antigens that several investigators in our laboratory, including myself, have developed and characterized. Collectively, the results we have obtained strongly support our future analysis to correlate the expression levels of APM components, HLA class I antigens, beta2m and HER-2/neu with the levels of HLA-A*0201-HER-2/neu369-377 complexes on breast carcinoma cells and lesions. The information derived from these studies is expected to contribute to our knowledge of the variables that influence the levels of HLA class I antigen-TAA derived peptide complex expression on breast carcinoma cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".