Mutations by Next Generation Sequencing in Stool DNA from Colorectal Carcinoma Patients - A Literature Review and our Experience with this Methodology
Bibliographic record
Abstract
It is well-known that colorectal carcinoma is a disease involving multistep carcinogenesis (hyperplasia-adenoma-carcinoma-metastasizing carcinoma). It is also a disease where therapeutically important driver mutations (especially in the EGFR signaling pathway) have been identified. Since genetic mutations can serve as good diagnostic and predictive markers, their reliable detection in the early stages of the disease and also in the follow-up of treatment efficacy is crucial. There is a fundamental problem encountered with the commonly used formalin-fixed paraffin-embedded (FFPE) specimens from biopsied tumor tissue i.e. it is unlikely that the material for the mutation analysis will be available in either the early stage of the disease or during the treatment period. Therefore recently attempts have been made to identify reliable markers from plasma/serum or from stool specimens. In particular, non-invasive stool specimens have been speculated to represent the situation of ongoing tumorigenesis and thus they can be used to assess treatment efficacy in the follow-upof the patient. The key aims of this paper are firstly, to review the key methodological points when studying genomic alterations in DNA extracted from cells in stool specimens, and secondly, to review results related to biomarker screening and their therapeutic importance. A further aim is to present our new findings by focusing on the issues inherent in Next Generation Sequencing of stool specimens from patients with gastrointestinal tumors. Even though the focus of our paper is human genomic alterations in stool specimens, in our future aspects chapter, we also deal with the bacterial spectrum and its possible interaction with the genomic mutations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 teacher head, 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".