Fine allelotyping of <i>Erbb2</i>‐induced mammary tumors in mice reveals multiple discontinuous candidate regions of tumor‐suppressor loci
Bibliographic record
Abstract
Loss of heterozygosity (LOH) at human chromosome bands 1p32-36 and 10q23-26 is frequent in various human tumors, including breast cancers, and is thought to reflect the loss of tumor-suppressor genes (TSGs). To map such genes, high-resolution LOH analysis was performed on 93 Erbb2-induced mammary tumors from (BALB/c x C57BL/6) F1 MMTV/Erbb2 transgenic mice. A panel of 24 microsatellite markers specific to the region of mouse chr4, homologous to human 1p31-36, and 16 markers specific to the mouse chr19 region, homologous to human 10q23-26 were used. In addition, lower-density mapping was performed on the remaining portion of mouse chr4 [homologous to human 9p13, 9p21-24, 9q21-22, 9q31-34 (12 markers)] and chr19 [homologous to 9q21, 9p24, 11q12-13 (9 markers)]. Several distinct, discrete, and discontinuous LOH regions flanked by areas of heterozygosity were identified, 22 on chr4 and 14 on chr19. Among these, 13 were mapped in the region of homology with human 1p31-36 (between D4Mit153 and D4Mit254) and 9 in the region of homology with human 10q23-26 (between D19Mit46 and D19Mit6). Although several LOH loci span a large interval, many are relatively short (1-4 Mb), and a few span an interval of <1 Mb. This allelotyping represents the highest density of LOH loci yet mapped in these chromosomal regions. The presence of numerous LOH regions in alternation with regions of heterozygosity, consistent with mitotic recombination as a mechanism for generating such a mosaic pattern, suggests the presence of several TSGs in these regions and should facilitate their identification.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".