Results from Ion AmpiSeq Cancer Panel in 200 cases of gastric and gastroesophageal junction cancer.
Bibliographic record
Abstract
39 Background: We used the Ion AmpiSeq Cancer panel which contains multiplex PCR primers covering 739 potential cancer- related mutations in 46 genes to profile 200 gastric and gastroesophageal cancers. Methods: The assay requires only 10ng of genomic DNA isolated from formalin-fixed paraffin-embedded (FFPE) archival tissue blocks. Samples were retrieved from biopsies from either the primary or metastatic lesion. All samples generated usable DNA and the resultant amplicons were sequenced on the Ion Torrent PGM platform to achieve in depth coverage of potential mutations. Results: Mutations in the tumour suppressor gene TP53 (39.5%) are most commonly identified in our cohort which is consistent with known mutation profiling of gastric carcinoma. We have also identified mutations in MET (8%), PIK3CA (7.5%), KRAS (4%), BRAF (4%) in addition to other known oncogenes. Lastly, We identified two cases with hotspot mutations in IDH1 (R132H and R132C), hotspot mutation traditionally associated with glioma and acute leukemia, which suggest novel role(s) of this mutant protein in the pathogenic progression of this cancer. Conclusions: These findings might predict response to targeted therapeutic agents or have prognostic implications. This study highlights the potential of focused profiling of cancer related genes using the Ion Torrent platform and its advantage of utilizing small amount of DNA from archival pathology specimens.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".