Identification of Mammaglobin as a Novel Serum Marker for Breast Cancer
Bibliographic record
Abstract
PURPOSE: Early detection of breast cancer has implications for the management and treatment of patients with this disease. Currently, there exist no highly sensitive and specific serologic biomarkers for detection of breast cancer. Mammaglobin is predicted to be a secreted protein, and expression of this gene seems to be highly specific in breast cancer. The present studies were undertaken to develop the mammaglobin protein as a serum biomarker for detection of breast cancer. EXPERIMENTAL DESIGN: We characterized the mammaglobin protein as a secreted, 14- to 21-kDa species, which is likely post-translationally processed based on its predicted 7-kDa size. Immunostaining for mammaglobin was conducted. An ELISA was developed for the detection of the mammaglobin protein in serum, and levels were compared between women with and without breast cancer. A receiver operating characteristic curve was used to show sensitivity and specificity for cut points on the continuous mammaglobin scale. RESULTS: The protein was detectable by immunostaining in 72% of breast tumors and not in other tumor types. The ELISA was highly sensitive and specific for detection of mammaglobin protein in tissue culture fluids of breast cancer cells and sera of breast cancer patients. The ELISA differentiated healthy women from those with breast cancer with accurate, repeatable results across time and under varying storage conditions. CONCLUSION: Our results indicate that mammaglobin, as measured by the ELISA, holds significant promise for breast cancer screening with the realistic potential to impact management of this disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".