<i>PIK3CA</i> Mutations Correlate with Hormone Receptors, Node Metastasis, and ERBB2, and Are Mutually Exclusive with PTEN Loss in Human Breast Carcinoma
Bibliographic record
Abstract
Deregulation of the phosphatidylinositol 3-kinase (PI3K) pathway either through loss of PTEN or mutation of the catalytic subunit alpha of PI3K (PIK3CA) occurs frequently in human cancer. We identified PIK3CA mutations in 26% of 342 human breast tumor samples and cell lines at about equal frequency in tumor stages I to IV. To investigate the relationship between PTEN and PIK3CA, we generated a cohort of tumors that had lost PTEN expression and compared it with a matched control set that had retained PTEN. A highly significant association between PIK3CA mutations and retention of PTEN protein expression was observed. In addition, PIK3CA mutations were associated with expression of estrogen and progesterone receptors (ER/PR), lymph node metastasis, and ERBB2 overexpression. The fact that PIK3CA mutations and PTEN loss are nearly mutually exclusive implies that deregulated phosphatidylinositol-3,4,5-triphosphate (PIP(3)) is critical for tumorigenesis in a significant fraction of breast cancers and that loss of PIP(3) homeostasis by abrogation of either PIK3CA or PTEN relieves selective pressure for targeting of the other gene. The correlation of PIK3CA mutation to ER/PR-positive tumors and PTEN loss to ER/PR-negative tumors argues for disparate branches of tumor evolution. Furthermore, the association between ERBB2 overexpression and PIK3CA mutation implies that more than one input activating the PI3K/AKT pathway may be required to overcome intact PTEN. Thus, mutation of PIK3CA is frequent, occurs early in carcinoma development, and has prognostic and therapeutic implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".