Application of Oligonucleotide Microarrays to Assess the Biological Effects of Neoadjuvant Imatinib Mesylate Treatment for Localized Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Neoadjuvant administration of antineoplastic therapies is used to rapidly assess the clinical and biological activity of novel systemic treatments. To assess the feasibility of using microarrays to assess molecular end points following targeted treatment in a heterogeneous tumor, we measured global gene expression in localized prostate cancer before and following neoadjuvant treatment with imatinib mesylate. PATIENTS AND METHODS: Patients with intermediate-risk to high-risk prostate cancer were treated for 6 weeks with 200 to 300 mg of oral imatinib mesylate. Frozen tissue was obtained from pretreatment ultrasound-guided biopsies and posttreatment radical prostatectomy specimens. Oligonucleotide microarray analysis following laser capture microdissection (LCM) and RNA amplification was used to assess gene expression changes associated with imatinib mesylate therapy. Immunohistochemistry was used to measure protein expression of MKP1 and CD31 and to assess cellular apoptosis. RESULTS: Of the 11 patients enrolled, high-quality microarray data was obtained from both biopsies (n = 7) and radical prostatectomy specimens (n = 9). Technically introduced intrasample gene expression variability was found to be significantly less than intertumor biological variability. Large gene expression differences were observed, and the gene with the most consistent differential expression (MKP1) was validated by immunohistochemistry. Gene set enrichment analysis suggests that imatinib mesylate therapy results in apoptosis of microvascular endothelial cells, an observation anecdotally supported by immunohistochemistry. CONCLUSIONS: This study shows that high-quality microarray data can be generated using LCM and RNA amplification to discover potential mechanisms of targeted therapy in cancer.
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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 | 0.000 |
| 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".