3-Phosphoinositide–Dependent Kinase 1 Potentiates Upstream Lesions on the Phosphatidylinositol 3-Kinase Pathway in Breast Carcinoma
Bibliographic record
Abstract
Lesions of ERBB2, PTEN, and PIK3CA activate the phosphatidylinositol 3-kinase (PI3K) pathway during cancer development by increasing levels of phosphatidylinositol-3,4,5-triphosphate (PIP(3)). 3-Phosphoinositide-dependent kinase 1 (PDK1) is the first node of the PI3K signal output and is required for activation of AKT. PIP(3) recruits PDK1 and AKT to the cell membrane through interactions with their pleckstrin homology domains, allowing PDK1 to activate AKT by phosphorylating it at residue threonine-308. We show that total PDK1 protein and mRNA were overexpressed in a majority of human breast cancers and that 21% of tumors had five or more copies of the gene encoding PDK1, PDPK1. We found that increased PDPK1 copy number was associated with upstream pathway lesions (ERBB2 amplification, PTEN loss, or PIK3CA mutation), as well as patient survival. Examination of an independent set of breast cancers and tumor cell lines derived from multiple forms of human cancers also found increased PDK1 protein levels associated with such upstream pathway lesions. In human mammary cells, PDK1 enhanced the ability of upstream lesions to signal to AKT, stimulate cell growth and migration, and rendered cells more resistant to PDK1 and PI3K inhibition. After orthotopic transplantation, PDK1 overexpression was not oncogenic but dramatically enhanced the ability of ERBB2 to form tumors. Our studies argue that PDK1 overexpression and increased PDPK1 copy number are common occurrences in cancer that potentiate the oncogenic effect of upstream lesions on the PI3K pathway. Therefore, we conclude that alteration of PDK1 is a critical component of oncogenic PI3K signaling in breast cancer.
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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 | 0.000 | 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) | 0.001 | 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".