Detection of colorectal neoplasia associated K-Ras mutations in human urine
Bibliographic record
Abstract
1005 Background: Since colorectal neoplasia-associated genes have been detected in human blood, we hypothesized that small DNA fragments containing genetic mutations associated with colorectal neoplasias are filtered and excreted in the urine. If so, genes associated with colorectal cancer will be detected in the urine. K-ras mutations are commonly associated with colorectal neoplasia and do not occur in the urinary tract. Methods: K-ras mutation detection: 200 microl of urine was extracted with guanidine thiocyanate and purifed using a Wizard DNA isolation kit. Codon 12 K-ras mutation detection methods–1: restriction enriched PCR, 20 cycles, with primers that amplify both wild type and mutated DNA but with an artificial BstNI site at the 5’ end of the amplified product (>2 K-ras copies per assay); 2: Peptide nucleic acid clamping real time PCR (>15 K-ras copies per assay). Human subjects: Training set = 20 patients with known K-ras mutations in colorectal cancer tissue. Test set = blinded urine samples from colorectal adenocarcinoma (N=48), adenoma (N=31), hyperplastic polyp (N=12) and endoscopically normal colon and rectum (N=60). Results: 1. Human urine contains 150–250 base pair DNA fragments derived from the circulation. These fragments can be readily distinguished from high molecular weight DNA from sloughed urinary tract cells. 2. Training set for K-ras detection (tissue confirmed K-ras mutations): Serum 6/20 (30%), Plasma (11/20 (55%), Urine: 18/20 (90%), (p<0.15 for plasma, p<0.001 for serum). 3. Test set (blinded): a. >2 copies of mutated K-ras genes were detected in: 16/48 (33%) adenocarcinomas; 23/31 (74%) adenomas; 5/12 (42%) hyperplastic polyps, and 19/60 (31%) non-neoplasia controls. b. >15 copies of mutated K-ras genes were detected in: 12/48 (25%) adenocarinomas; 15/31 (48%) adenomas; 3/12 (25%) hyperplastic polyps, and 11/60 (18%) non-neoplasia controls. Conclusions: Small DNA fragments in human urine contain K-Ras mutations identical to those found in colorectal cancer DNA. The sensitivity for detection of K-ras mutations in urine appears equivalent or superior to K-ras mutation detection in feces or serum. DNA mutations from systemic epithelial neoplasias may be detected in filtered urinary DNA fragments and may be useful for early detection of neoplasia. No significant financial relationships to disclose.
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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 | 0.001 | 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 | 0.001 |
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".