Abstract 1224: A patient derived xenograft (PDX) platform for development of next generation KIT kinase inhibitors in imatinib-resistant gastrointestinal stromal tumors (GIST)
Bibliographic record
Abstract
Abstract Despite best efforts in the treatment of gastrointestinal stromal tumors (GIST), patients continue to face a poor prognosis. That said, introduction of imatinib into clinical practice has vastly improved outcomes for KIT positive patients. However, as with many kinases, resistance has developed into a clinical dilemma, rendering the drug ineffective. A number of resistance mutations have previously been identified including the well known D816 activating mutation. To overcome this challenge, we have established a set of GIST patient derived xenograft models to recapitulate the original patient tumor, including genetics of resistance. In this work, we show four primary GIST samples which were selected based on mutational and clinical profile. PDX models were developed in immunocompromised mice and were further characterized by immunohistochemistry, additional sequencing and pharmacological efficacy. We then evaluated the efficacy of chemotherapeutic agents in these models. We describe imatinib resistance mutations, and demonstrate in vivo efficacy of dasatinib over imatinib in the resistant GIST PDX model. Take together, this data validates these GIST PDX models as a novel platform for the evaluation of new drug candidates to better delay and circumvent resistance now found in the clinic. Citation Format: Chelsea Mullins, Jill Ricono, Patrick Carson, Gaston Habets, Rafe Shellooe, Hoa Nguyen, Thomas Broudy, Cyrus Mirsaidi, Praveen Nair. A patient derived xenograft (PDX) platform for development of next generation KIT kinase inhibitors in imatinib-resistant gastrointestinal stromal tumors (GIST). [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1224. doi:10.1158/1538-7445.AM2014-1224
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".