High prevalence of APOA1/C3/A4/A5 alterations in luminal breast cancers among young women in East Asia
Bibliographic record
Abstract
In East Asia, the breast cancer incidence rate among women aged <50 years has rapidly increased. Emerging tumors are distinctly characterized by a high prevalence of estrogen receptor (ER)-positive/human epidermal growth factor receptor (HER2)-negative cancer. In the present study, we identified unique genetic alterations in these emerging tumors. We analyzed gene copy number variations (CNVs) in breast tumors from 120 Taiwanese patients, and obtained public datasets of CNV and gene expression (GE). The data regarding CNV and GE were separately compared between East Asian and Western patients, and the overlapping genes identified in the comparisons were explored to identify the gene-gene interaction networks. In the age <50 years/ER + /HER2- subgroup, tumors of East Asian patients exhibited a higher frequency of copy number loss in APOA1/C3/A4/A5, a lipid-metabolizing gene cluster (33 vs. 10%, P < .001) and lower APOA1/C3/A4/A5 expressions than tumors of Western patients. These copy number loss related- and GE-related results were validated in another Taiwanese cohort and in two GE datasets, respectively. The copy number loss was significantly associated with poor survival among Western patients, but not among East Asian patients. Lower APOA1, APOC3, and APOA5 expressions were associated with higher ESTIMATE immune scores, indicating an abundance of tumor-infiltrating immune cells. In conclusion, APOA1/C3/A4/A5 copy number loss was more prevalent in luminal breast tumors among East Asian women aged <50 years, and its immunomodulatory effect on the tumor microenvironment possibly plays various roles in the tumor biology of East Asian patients.
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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".