Acquired Resistance to Imatinib in Gastrointestinal Stromal Tumor Occurs Through Secondary Gene Mutation
Bibliographic record
Abstract
Most gastrointestinal stromal tumors (GIST) have an activating mutation in either KIT or PDGFRA. Imatinib is a selective tyrosine kinase inhibitor and achieves a partial response or stable disease in about 80% of patients with metastatic GIST. It is now clear that some patients with GIST develop resistance to imatinib during chronic therapy. To identify the mechanism of resistance, we studied 31 patients with GIST who were treated with imatinib and then underwent surgical resection. There were 13 patients who were nonresistant to imatinib, 3 with primary resistance, and 15 with acquired resistance after initial benefit from the drug. There were no secondary mutations in KIT or PDGFRA in the nonresistant or primary resistance groups. In contrast, secondary mutations were found in 7 of 15 (46%) patients with acquired resistance, each of whom had a primary mutation in KIT exon 11. Most secondary mutations were located in KIT exon 17. KIT phosphorylation was heterogeneous and did not correlate with clinical response to imatinib or mutation status. That acquired resistance to imatinib in GIST commonly occurs via secondary gene mutation in the KIT kinase domain has implications for strategies to delay or prevent imatinib resistance and to employ newer targeted therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".