Risk stratification of gastrointestinal stromal tumors by Nanostring gene expression profiling
Bibliographic record
Abstract
Abstract PurposeThe risk assessment classification schemes for gastrointestinal stromal tumors (GIST) includes tumor site, size, mitotic count and variably tumour rupture. Heterogeneity in high risk GIST poses limitations for current classification schemes. Objective This study aims to demonstrate the clinical utility of risk stratification by gene expression profiling (GEP) using Nanostring technology. MEthodFifty-six GIST cases were analyzed using a 231 gene expression panel. GEP results were correlated with clinical and pathological data. The prognostic performance was assessed in 34 patients with available survival data using ROC curves, Kaplan Meier Survival curves and compared with traditional risk assessment schemes. Results Volcano plot analysis identified seven genes with significantly higher expression (FDR < .05) in high risk than in non-high risk tumors, namely, TYMS, CDC2, TOP2A, CCNA2, E2F1, PCNA, and BIRC5. Together, these transcripts exhibited significantly higher expression in high risk tumors than in intermediate ( P < .001), low ( P < .001), and very low ( P = 0.01) risk tumors. Receiver operating characteristic curve analysis demonstrated area under the curve (AUC) to be 0.861 for the separation of high risk and non-high risk tumors. Kaplan-Meier survival analysis demonstrated improved risk stratification (log-rank test P < .001) compared to the current risk assessment classification ( P = 0.18). Conclusion In addition to current clinical and histology-based risk classification for patients with GIST, gene expression may offer complementary prognostic information.
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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".