Association of kallikrein expression in nipple aspirate fluid with breast cancer risk
Bibliographic record
Abstract
Human kallikreins (hK) 2, 3, 6 and 10 are expressed in breast and prostate tissue. hK2 and hK3 (prostate-specific antigen, PSA) are used to screen for prostate cancer. hK6 and hK10 are downregulated in breast cancer compared to normal breast tissue. We demonstrated that levels of PSA in nipple aspirate fluid (NAF) are lower in women with breast cancer than in normal women. We hypothesize that the expression of hK2, 3, 6 and 10 are related and important in detecting breast cancer. The goals of this study are to determine the level of expression of kallikreins in NAF and serum, the association of hK2, 3, 6 and 10 in NAF, and the association of each of the kallikreins with breast cancer. In NAF from 275 women, hK3, 6 and 10 were detectable in >/= 90% and hK2 in 74% of samples analyzed. NAF levels were highest for hK6 and lowest for hK2, regardless of cancer and menopausal status. hK3 was detectable in 15/29 (52%) and hK2 in 0/29 serum samples collected from 6 women. hK2 and hK3 were concentrated in NAF vs. matched serum. The 4 kallikreins were associated with the exception of hK2 with hK6 or hK10. PSA levels were higher in normal pre- than postmenopausal subjects (but not women with breast cancer), whereas levels of hK2, 6 and 10 did not differ by menopausal status. hK2 and PSA were associated with both pre- and postmenopausal breast cancer; hK6 and 10 were not. hK2 and PSA were more associated with pre- than postmenopausal breast cancer. Using logistic regression, PSA and menopausal status provided the best model of breast cancer prediction, with a sensitivity of 91% and specificity of 39%. In conclusion, 4 kallikreins are expressed in NAF. hK2 and PSA, and hK6 and hK10 are highly associated. Higher premenopausal PSA levels suggest the influence of ovarian steroids. PSA shows the most promise in aiding in the early detection of breast cancer.
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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 | 0.001 |
| 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) | 0.001 | 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".