Selective Identification of Secreted and Transmembrane Breast Cancer Markers using <i>Escherichia coli</i> Ampicillin Secretion Trap
Bibliographic record
Abstract
Secreted and cell surface proteins play important roles in cancer and are potential drug targets and tumor markers. Here, we describe a large-scale analysis of the genes encoding secreted and cell surface proteins in breast cancer. To identify these genes, we developed a novel signal sequence trap method called Escherichia coli ampicillin secretion trap (CAST). For CAST, we constructed a plasmid in which the signal sequence of beta-lactamase was deleted such that it does not confer ampicillin resistance. Eukaryotic cDNA libraries cloned into pCAST produced tens of thousands of ampicillin-resistant clones, 80% of which contained cDNA fragments encoding secreted and membrane spanning proteins. We identified 2,708 unique sequences from cDNA libraries made from surgical breast cancer specimens. We analyzed the expression of 1,287 of the 2,708 genes and found that 166 were overexpressed in breast cancers relative to normal breast tissues. Eighty-five percent of these genes had not been previously identified as markers of breast cancer. Twenty-three of the 166 genes (14%) were relatively tissue restricted, suggesting use as cancer-specific targets. We also identified several new markers of ovarian cancer. Our results indicate that CAST is a robust, rapid, and low cost method to identify cell surface and secreted proteins and is applicable to a variety of relevant biological questions.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".