Reply to treatment of pituitary neoplasms with temozolomide
Bibliographic record
Abstract
We appreciate the comments of Dr. Marucci regarding our paper entitled, “Treatment of Pituitary Neoplasms With Temozolomide: A Review”.1 The 6 patients with carcinomas who responded to treatment were the reported patients in the literature. MGMT immunoexpression was only available in 3 of them, and it was negative.1 The 57% of carcinomas that demonstrated low MGMT immunoexpression were from a retrospective study of paraffin-embedded tissues.2 Dr. Marucci raises an important question, can MGMT immunostaining predict response to temozolomide therapy? At present, this is controversial. Several, but not all, publications conclude that MGMT immunoreactivity is a reliable indicator of therapeutic response to temozolomide in patients with pituitary adenomas. With respect to the use of immunohistochemistry, 2 important problems must be addressed, the method and its interpretation. If tissue fixation, processing, and immunostaining are properly performed, then the immunohistochemical findings are reliable. This has been proven by several authors. Obviously, when methods are suboptimally applied, the results are not acceptable. The dispute is not with the method but with its interpretation. In some tumors, every neoplastic cell is MGMT- immunonegative, whereas in others every cell is immunopositive. The former tumors appear to respond to temozolomide, whereas the latter tumors do not. The challenge is that many pituitary tumors consist of a mixture of MGMT-immunonegative and -immunopositive cells. How should such results be interpreted? Maybe the MGMT-immunonegative cell would respond, whereas the immunopositive cells would be resistant? Can immunonegative cells transform to immunopositive cells or vice versa? Is it possible that temozolomide administration depletes MGMT causing transformation of immunopositive cells to immunonegative cells resulting in a beneficial response to temozolomide? Other genetic and/or epigenetic factors may also exist that could affect temozolomide responsiveness. Lastly, Dr. Marucci alludes to the demonstration of promoter methylation and its greater accuracy in predicting a therapeutic response. In published studies and in our experience, perfect correlation between MGMT staining and methylation status has not been achieved. Clearly, all available methods of assessing likelihood of temozolomide response should be used. We feel strongly that MGMT immunoreactivity is valuable and should be pursued. Obviously, more work is needed to address these issues.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.022 | 0.029 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".