Pathologic and Molecular Heterogeneity in Imatinib-Stable or Imatinib-Responsive Gastrointestinal Stromal Tumors
Bibliographic record
Abstract
PURPOSE: Gastrointestinal stromal tumor (GIST) is the most common sarcoma of the intestinal tract. Nearly all tumors express KIT protein, and most have an activating mutation in either KIT or PDGFRA. Therapy with selective tyrosine kinase inhibitors achieves a partial response or stable disease in approximately 80% of patients with advanced GIST. However, after an initial clinical response, some patients develop imatinib resistance. Our goal was to investigate the spectrum of pathologic response and molecular alterations in a group of GIST patients, clinically defined as having imatinib-stable/imatinib-responsive lesions, who underwent surgical resection. EXPERIMENTAL DESIGN: Forty-three tumor nodules from 28 patients were available for pathologic and molecular analysis, which included genotyping for primary and secondary KIT/PDGFRA-mutations, cell cycle alterations, and biochemical activation status of KIT and downstream targets. The transcriptional changes of a subset of these tumors were compared with a group of imatinib-naive GISTs on a U133A Affymetrix expression platform. RESULTS: The histologic response did not correlate with imatinib therapy duration or with proliferative activity. Second-site KIT mutation was identified in only one tumor nodule. Activation of KIT and downstream targets was consistent in all tumors analyzed. Ultrastructurally, a subset of tumors showed a smooth muscle phenotype, which correlated with overexpression of genes involved in muscle differentiation and function. CONCLUSIONS: The histologic response to imatinib is heterogeneous and does not correlate well with clinical response. Second-site KIT mutations are rare in imatinib-responsive GISTs compared with imatinib-resistant tumors. The gene signature of imatinib-response in GISTs showed alterations of cell cycle control as well as up-regulation of genes involved in muscle differentiation and function.
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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 | 0.001 | 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".