Genomic and Molecular Profiling Predicts Response to Temozolomide in Melanoma
Bibliographic record
Abstract
PURPOSE: Despite objective response rates of only approximately 13%, temozolomide remains one of the most effective single chemotherapy agents against metastatic melanoma, second only to dacarbazine, the current standard of care for systemic treatment of melanoma. The goal of this study was to identify molecular and/or genetic markers that correlate with, and could be used to predict, response to temozolomide-based treatment regimens and that reflect the intrinsic properties of a patient's tumor. EXPERIMENTAL DESIGN: Using a panel of 26 human melanoma-derived cell lines, we determined in vitro temozolomide sensitivity, O(6)-methylguanine-DNA methyltransferase (MGMT) activity, MGMT protein expression and promoter methylation status, and mismatch repair proficiency, as well as the expression profile of 38,000 genes using an oligonucleotide-based microarray platform. RESULTS: The results showed a broad spectrum of temozolomide sensitivity across the panel of cell lines, with IC(50) values ranging from 100 micromol/L to 1 mmol/L. There was a significant correlation between measured temozolomide sensitivity and a gene expression signature-derived prediction of temozolomide sensitivity (P < 0.005). Notably, MGMT alone showed a significant correlation with temozolomide sensitivity (MGMT activity, P < 0.0001; MGMT expression, P <or= 0.0001). The promoter methylation status of the MGMT gene, however, was not consistent with MGMT gene expression or temozolomide sensitivity. CONCLUSIONS: These results show that melanoma resistance to temozolomide is conferred predominantly by MGMT activity and suggest that MGMT expression could potentially be a useful tool for predicting the response of melanoma patients to temozolomide therapy.
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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 | 0.003 | 0.002 |
| 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) | 0.000 | 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".