PMEL is a predictive biomarker for mTORC1 inhibitor treatment of renal angiomyolipoma in tuberous sclerosis complex patients
Bibliographic record
Abstract
Background: We aimed to demonstrate the function of premelanosome protein (PMEL) as a biomarker to predict the effectiveness of mammalian target of rapamycin complex 1 (mTORC1) inhibitor treatment in renal angiomyolipomas (RAMLs) in tuberous sclerosis complex (TSC) patients. Methods: 95 whole blood samples from 49 patients diagnosed with TSC-RAMLs were collected. PMEL, N4BP2, and PCSK1N expression in the plasma samples were tested by quantitative sandwich ELISA. The target tumor volume assessed by maximum cross-sectional area (CSAmax) in CT scans. Correlation analysis was used to determine the relationship between PMEL expression and target tumors, as well as the tumor reduction rate. Results: The tumor size of TSC-RAMLs positivity correlated with PMEL expression (r = 0.30, p = 0.036) and PCSK1N expression (r = 0.23, p = 0.027), but had no significant relationship with N4BP2 (r = 0.06, p = 0.89). The positive correlation between TSC-RAML tumor volume and PMEL expression still existed in TSC patients before (r = 0.30, p = 0.026) and after mTORC1 inhibitor treatment (r = 0.41, p = 0.0017), but the correlation between tumor volume and PCSK1N expression no longer existed. Further analysis found that PMEL expression negatively correlated with the reduction rate of TSC-RAMLs after mTORC1 inhibitor treatment (r = -0.50, p = 0.0022), both after 3 months (r = -0.47, p = 0.048) and 6 months of treatment (r = -0.52, p = 0.028). Conclusion: PMEL expression positively correlated with the tumor size of TSC-RAMLs, and inversely with the reduction rate of TSC-RAMLs after mTORC1 inhibitor treatment, which may suggest that PMEL may serve as a predictive biomarker for the efficacy of mTORC1 inhibitor treatment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".