Protein Expression of PTEN, Insulin-Like Growth Factor I Receptor (IGF-IR), and Lethal Prostate Cancer: A Prospective Study
Bibliographic record
Abstract
BACKGROUND: Loss of PTEN has been shown to be associated with aggressive behavior of prostate cancer. It is less clear that loss of PTEN also increases the risk of cancer mortality. We investigated the association between PTEN expression and prostate cancer mortality and the potential effect modification by IGF-IR, a direct activator of the phosphoinositide-3-kinase (PI3K) pathway. METHODS: Protein expression in tumor was evaluated using tumor tissues obtained from 805 participants of the Physicians' Health and the Health Professionals Follow-up studies who were diagnosed with prostate cancer and underwent radical prostatectomy. Proportional hazard models were used to assess PTEN expression and its interaction with IGF-IR, in relation to lethal prostate cancer (cancer-specific death or distant metastases). RESULTS: Low PTEN expression was associated with an increased risk of lethal prostate cancer [HR, 1.7; 95% confidence interval (CI), 0.98-3.2; Ptrend = 0.04]. The association was attenuated after adjustment for Gleason grade, tumor stage, and prostate-specific antigen (PSA) at diagnosis. A significant negative interaction between PTEN and IGF-IR was found (Pinteraction = 0.03). Either reduction in PTEN or increase in IGF-IR expression was sufficient to worsen prognosis. Models including PTEN and IGF-IR expression offer additional predicting power to prostate cancer survival, compared to those only including demographic and clinical factors. CONCLUSIONS: Low PTEN protein expression significantly increases the risk of lethal prostate cancer, particularly when the IGF-IR expression remains at normal level. IMPACT: PTEN and IGF-IR expression in tumor are promising candidates for independent prognostic factors to predict lethal prostate cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".