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Detection of colorectal neoplasia associated K-Ras mutations in human urine

2006· article· en· W2597260458 on OpenAlexaff
Dean E. Brenner, Ying‐Hsiu Su, Daniel P. Normolle, Sapna Syngal, Robert S. Bresalier, Norman E. Marcon, John A. Baron, Timothy M. Block

Bibliographic record

VenueJournal of Clinical Oncology · 2006
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsUrineColorectal cancerMolecular biologyUrinary systemMedicineCancerAmpliconDNABiologyPolymerase chain reactionGeneInternal medicineGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.061
GPT teacher head0.439
Teacher spread0.379 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2006
Admission routes1
Has abstractyes

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