Kallikrein 6 as a biomarker for the detection of metastatic cancer cells in blood of ovarian cancer patients
Bibliographic record
Abstract
Proc Amer Assoc Cancer Res, Volume 45, 2004 1058 Kallikreins are a family of secreted serine proteases that have been recently discovered and seem to associate with cancer. The genetic locus of these proteases lies at 19q13.3-13.4. Human Kallikrein 6 (hK6) is a member of this protein family and it is encoded by KLK6 gene. We have previously found that hK6 is elevated in the serum of ovarian cancer patients and over-expression of hK6 tended to be greater at later stages of the disease. These findings suggest a potential role of serum hK6 concentration as a biomarker for ovarian cancer. This present study aims to utilize KLK6 gene transcripts for the detection of disseminated ovarian cancer cells in blood. Previous studies on ovarian, prostate and breast cancer patients, using molecular markers, have detected circulating metastatic cancer cells. In some cases, the methods show high sensitivity and detect metastatic cells at a ratio of around 1 metastatic cell to106 cells not expressing the marker gene. We first identified a cancer cell line, PC3(AR6), that strongly expresses KLK6 and another cell line, ES2, that does not express KLK6. To determine the detection limit of our method we diluted KLK6 transcripts (KLK6-pBluescript plasmid) up to 1 transcript per reaction. Using KLK6 as a marker and Polymerase Chain Reaction (PCR), we were able to detect around 10 transcripts per reaction. We also diluted the PC3(AR6) cells up to 1 cell and using Reverse Transcription PCR (RT-PCR) we were able to detect less than 1 PC3(AR6) cell per reaction. We then mixed PC3(AR6) cells with ES2 cells in ratios up to 1:106. Using RT-PCR, we were able to detect less than 1 PC3(AR6) cell per reaction. We can conclude that we have developed a highly sensitive method for monitoring KLK6 mRNA and cells expressing this gene. In the future we will examine if this technology can detect circulating ovarian cancer cells for the purpose of disease diagnosis and prognosis.
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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 | 0.002 |
| 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 | 0.001 |
| 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".