Frequent occurrence of uniparental disomy in colorectal cancer
Bibliographic record
Abstract
We used SNP arrays to identify and characterize genomic alterations associated with colorectal cancer (CRC). Laser microdissected cancer cells from 15 adenocarinomas were investigated by Affymetrix Mapping 10K SNP arrays. Analysis of the data extracted from the SNP arrays revealed multiple regions with copy number alterations and loss of heterozygosity (LOH). Novel LOH areas were identified at chromosomes 13, 14 and 15. Combined analysis of the LOH and copy number data revealed genomic structures that could not have been identified analyzing either data type alone. Half of the identified LOH regions showed no evidence of a reduced copy number, indicating the presence of uniparental structures. The distribution of these structures was non-random, primarily involving 8q, 13q and 20q. This finding was supported by analysis of an independent set of array-based transcriptional profiles, consisting of 17 normal mucosa and 66 adenocarcinoma samples. The transcriptional analysis revealed an unchanged expression level in areas with intact copy number, including regions with uniparental disomy, and a reduced expression level in the LOH regions representing factual losses (including 5q, 8p and 17p). The analysis also showed that genes in regions with increased copy number (including 7p and 20q) were predominantly upregulated. Further analyses of the SNP data revealed a subset of the identified alterations to be specifically associated with TP53 inactivation (including 8q gain and 17p loss) and lymph node metastasis status (gain of 7q and 13q). Another subset of the identified alterations was shown to represent intratumor heterogeneity. In conclusion, we demonstrate that uniparental disomy is frequent in CRC, and identify genomic alterations associated with TP53 inactivation and lymph node status.
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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 | 0.001 |
| 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 | 0.000 |
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".