Gene expression signatures and response to imatinib mesylate in gastrointestinal stromal tumor
Bibliographic record
Abstract
Despite initial efficacy of imatinib mesylate in most gastrointestinal stromal tumor (GIST) patients, many experience primary/secondary drug resistance. Therefore, clinical management of GIST may benefit from further molecular characterization of tumors before and after imatinib mesylate treatment. As part of a recent phase II trial of neoadjuvant/adjuvant imatinib mesylate treatment for advanced primary and recurrent operable GISTs (Radiation Therapy Oncology Group S0132), gene expression profiling using oligonucleotide microarrays was done on tumor samples obtained before and after imatinib mesylate therapy. Patients were classified according to changes in tumor size after treatment based on computed tomography scan measurements. Gene profiling data were evaluated with Statistical Analysis of Microarrays to identify differentially expressed genes (in pretreatment GIST samples). Based on Statistical Analysis of Microarrays [False Discovery Rate (FDR), 10%], 38 genes were expressed at significantly lower levels in the pretreatment biopsy samples from tumors that significantly responded to 8 to 12 weeks of imatinib mesylate, that is, >25% tumor reduction. Eighteen of these genes encoded Krüppel-associated box (KRAB) domain containing zinc finger (ZNF) transcriptional repressors. Importantly, 10 KRAB-ZNF genes mapped to a single locus on chromosome 19p, and a subset predicted likely response to imatinib mesylate-based therapy in a naïve panel of GIST. Furthermore, we found that modifying expression of genes within this predictive signature can enhance the sensitivity of GIST cells to imatinib mesylate. Using clinical pretreatment biopsy samples from a prospective neoadjuvant phase II trial, we have identified a gene signature that includes KRAB-ZNF 91 subfamily members that may be both predictive of and functionally associated with likely response to short-term imatinib mesylate treatment.
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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) | 0.000 | 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 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".