Significance of Loss of Heterozygosity in Predicting Axillary Lymph Node Metastasis of Invasive Ductal Carcinoma of the Breast
Bibliographic record
Abstract
Invasive ductal carcinoma (IDC) of breast metastatic to axillary lymph node (ALN) is a critical factor in determining stage and is a strong predictor of disease prognosis and survival. We studied ALN metastasis using a combined histopathologic/molecular approach to gain insights into the pathobiology implications. Fourteen patients with IDC with positive ALN and 19 with negative ALN were retrieved. Analysis of 17 polymorphic microsatellite repeat markers targeting 1p34-36, 3p24-26, 5q23, 9p21, 10q23, 17p13, 17q12, 17q21, 21q22, and 22q13 was carried out in DNA isolated from primary tumors and metastatic tumors. ALN metastasis correlated with fractional mutation rate of primary and ALN metastatic tumors, primary tumor size, and nuclear grade, and did not correlate with expression of estrogen receptor, progesterone receptor, and Her2/neu. Loss of heterozygosity (LOH) detected at 1p34-36, 3p24-26, 9p21, 10q23, 17p13, 17q12, 21q22, and 22q13 may play an important role in the development and aggressiveness of IDC, and LOHs at 1p34-36, 17p13, and 22q13 may play an important role in metastasis. None of the LOHs were shared by all the tumors, suggesting that IDC develops through various pathways that have unique and personalized patterns of mutational changes, although they share similar morphology. Detection of LOH in IDC is not only useful in studying oncogenesis, but also predicting aggressiveness and ALN metastasis.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 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".