Short article: Evaluation of O6-methylguanine-DNA methyltransferase as a predicting factor of response to temozolomide-based chemotherapy in well-differentiated metastatic pancreatic neuroendocrine tumors
Bibliographic record
Abstract
OBJECTIVE: Temozolomide (TMZ) is an alkylating agent frequently used in well-differentiated metastatic pancreatic neuroendocrine tumors (PNETs) with very variable responses. O-methylguanine-DNA methyltransferase (MGMT) is a DNA repair enzyme whose loss of expression has been suggested to be predictive of response to TMZ in various human tumors. We evaluated the predictive value of MGMT status, assessed by immunohistochemistry (IHC) and methylation-specific PCR (MS-PCR), in well-differentiated metastatic PNETs treated by a TMZ-based chemotherapy. PATIENTS AND METHODS: All patients with metastatic PNETs treated with TMZ-based chemotherapy between 2010 and 2016 in two academic centers, for whom the tumor samples were available, were included. Clinical data were collected and the MGMT status of the tumors was analyzed using MS-PCR and IHC. RESULTS: Twenty-two patients (nine men, median age 61 years) were included. The loss of MGMT protein expression detected by IHC was observed in 13 (59%) patients and MGMT promoter hypermethylation was detected by MS-PCR in three (15%) out of 20 interpretable cases. MGMT status did not correlate significantly with the best radiological response according to the Response Evaluation Criteria In Solid Tumors criteria or with progression-free survival. There was no correlation between MGMT protein expression and MGMT gene promoter methylation. CONCLUSION: These results indicate that a deficient MGMT status in PNETs, determined by loss of protein expression in IHC or by the presence of MGMT gene promoter methylation measured by MS-PCR, is not associated with a better response to TMZ-based chemotherapy and cannot be used as a predictive marker to lead treatment decisions.
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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 | 0.001 |
| 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".