Two adhesion molecules: New serological diagnostic breast cancer biomarkers
Bibliographic record
Abstract
AACR Annual Meeting-- Apr 12-16, 2008; San Diego, CA 4437 The discovery of new serological breast cancer biomarkers that are suitable for early disease diagnosis may ultimately lead to improved patient management and outcomes. Unfortunately, other than definitive diagnosis by biopsy and histopathology, no diagnostic or screening test is presently suitable for early detection of breast cancer. Newer methods with improved sensitivity and specificity are clearly needed to identify women with early stage breast cancer. We have previously performed a proteomic analysis of the conditioned media of breast cancer cell lines to yield a number of promising candidate markers. In this study, we measured, by immunoassays, human activated leukocyte cell adhesion molecule (ALCAM) and B-cell adhesion molecule (BCAM) concentrations in serum of apparently healthy women and women with breast cancer. The diagnostic sensitivity and specificity of these tests were calculated and compared to those of the classical breast tumor marker CA 15-3. For ALCAM, at 90% specificity, the sensitivity for breast cancer diagnosis (all stages) was 91% whereas for BCAM at 90% specificity, the sensitivity for breast cancer diagnosis (all stages) was 34%. More importantly, at 90% specificity for ALCAM, the sensitivity of the test for breast cancer diagnosis in cancer patients where CA 15-3 is normal (<30 units/mL) was 78%, illustrating that ALCAM can identify a considerable number of patients who will be missed by CA 15-3 testing. We conclude that serum ALCAM and/or BCAM concentrations appear to be new biomarkers for breast cancer and may have value for disease diagnosis. The combined analysis of these novel biomarkers plus CA 15-3 and the development of a multivariate statistical model may lead to a superior clinical tool in comparison to currently used individual markers such as CA 15-3.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".