Gastric Adenocarcinomas with CDX2 Induction Show Higher Frequency of TP53 and KMT2B Mutations and MYC Amplifications but Similar Survival Compared with Cancers with No CDX2 Induction
Bibliographic record
Abstract
Background: Gastric cancer is one of the most prevalent gastrointestinal cancers. Mortality is high, and improved treatments are needed. A better understanding of the pathophysiology of the disease and discovery of biomarkers for targeted therapies are paramount for therapeutic progress. CDX2, a transcription factor of hindgut specification, is induced in several gastric cancers, especially with intestinal differentiation, and could be helpful for defining sub-types with particular characteristics. Methods: Gastric cancers with induced CDX2 mRNA expression were identified from the gastric cohort of The Cancer Genome Atlas (TCGA) and were compared with cancers that had no CDX2 mRNA induction. Induced CDX2 mRNA expression was defined as mRNA expression z-score relative to all samples above 0, and non-induced CDX2 mRNA expression was defined as mRNA expression z-score relative to all samples below −1. Results: Patients with gastric cancers with CDX2 mRNA induction were older, had less frequently diffuse histology, and more often had mutations in TP53 and KMT2B and amplifications in MYC. CDX2 induction was correlated with HNF4α induction and was reversely correlated with SOX2. Gastric cancers with CDX2 mRNA induction showed lower PD-L1 expression than cancers with lower CDX2 expression but did not differ in CLDN18 mRNA expression. Progression-free and overall survival of the two groups was also not significantly different. Conclusion: Gastric cancers with CDX2 mRNA induction displayed specific characteristics that differentiate them from cancers with no CDX2 induction and could be of interest for optimizing current and future 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 | 0.000 |
| 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) | 0.002 | 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".