Large-Scale Allelotype of Pancreaticobiliary Carcinoma Provides Quantitative Estimates of Genome-Wide Allelic Loss
Bibliographic record
Abstract
Studies of the allelotype of human cancers have provided valuable insights into those chromosomes targeted for genetic inactivation during tumorigenesis. We present the comprehensive allelotype of 82 xenografted pancreatic or biliary cancers using 386 microsatellite markers and spanning the entire genome at an average coverage of 10 cM. Allelic losses were nonrandomly distributed across the genome and most prevalent for chromosome arms 9p, 17p, and 18q (>60%), sites of the known tumor suppressor genes CDKN2A, TP53, and MADH4. Moderate rates of loss (at any one locus) were noted for chromosome arms 3p, 6q, 8p, 17q, 18p, 21q, and 22q (40-60%). A mapping of individual loci of allelic loss revealed 11 "hot spots" of loss of heterozygosity (>30%) in addition to loci near known tumor suppressor genes, corresponding to 3p, 4q, 5q, 6q, 8p, 12q, 14q, 21q, 22q, and the X chromosome. The average genomic fractional allelic loss was 15.3% of all tested markers for the 82 xenografted cancers, with allelic loss affecting as little as 1.5% to as much as 32.1% of tested loci, a remarkable 20-fold range. We determined the chromosome location (in cM) of each of the 386 markers used based on mapping data available from the National Center for Biotechnology Information, and we provide the first distance-based estimates of chromosome material lost in a human epithelial cancer. Specifically, we found that the cumulative size of allelic losses ranged from 58 to 1160 cM, with an average loss of 561.32 cM/tumor. We compared the genomic fractional allelic loss of each xenografted cancer with known clinicopathological features for each patient and found a significant correlation with smoking status (P < 0.01). These findings offer new loci for investigation of the genetic alterations common to pancreaticobiliary cancers and aid the understanding of mechanisms of allelic loss in human carcinogenesis.
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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 | 0.001 | 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 | 0.000 |
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".