An experimental strategy for quantitative analysis of the humoral immune response to prostate cancer antigens using natural protein microarrays
Bibliographic record
Abstract
The identification of human tumor antigens has potential utility in the diagnosis and treatment of cancers. We demonstrate here a complete strategy to profile immunoreactivity and identify tumor antigens from proteins derived from tumor cell lines. Microarrays of proteins produced from 2-D LC fractionation of prostate tumor cell-line lysates were used to profile immunoreactivity in the sera of prostate cancer patients and control subjects. Cancer-associated immunoreactivity to distinct groups of chromatography fractions was present in about 50% of the patients, with greater immunoreactivity present in patients with non-organ-confined cancer than in patients with organ-confined cancer. We grouped the immunoreactive fractions by similarities in elution order and patterns of immunoreactivity to guide and interpret the MS analysis of selected fractions, which was used to identify the proteins that may be responsible for the immunoreactivity. As a complementary method to further characterize and validate the immunoreactivity of the proteins identified by mass spectrometry, we demonstrate the use of focused microarrays of recombinant proteins. Disease-associated immunoreactivity was confirmed for one of the identified proteins, human Kallikrein 11. These results demonstrate a practical approach to screening, identifying, and validating immunoreactive proteins that could be applied to diverse studies on humoral immune responses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".