328. MOLECULAR SUBTYPING OF ESOPHAGEAL ADENOCARCINOMA BY NON-NEGATIVE MATRIX FACTORIZATION OF LASER CAPTURE MICRODISSECTED RNA-SEQ SAMPLES
Bibliographic record
Abstract
Abstract Previous attempts to construct prognostic molecular subtypes for esophageal adenocarcinoma (EAC) using gene expression profiling has met with little success when compared to adenocarcinomas from other disease sites. We hypothesized that this is in part due to the low tumour cellularity obtained from EAC biopsy and resection specimens, which obscures the tumour-intrinsic gene expression signals due to normal cell contamination. We systematically collected biopsies from pre-treatment, post-induction, and metastatic EAC tumors. Laser capture microdissection (LCM) was utilized to enrich for tumour cells for whole transcriptome sequencing (RNA-seq) (N = 54). We utilized non-negative matrix factorization (NMF) with 7 components to identify common gene expression programs across our samples. After applying NMF to our samples, we identified 4 tumour-intrinsic components and 3 tumour-extrinsic components (including immune infiltrate and normal esophagus). These components validated on TCGA EAC and esophageal squamous cell carcinoma RNA-seq, single cell RNA-seq from EAC organoids, and the cancer cell line encyclopedia. Moreover, we observed that these tumour-intrinsic gene expression programs were preserved in EAC samples metastatic to other organ sites such as liver and lymph nodes. We observed that most tumours expressed more than one tumour component and that approximately 25% of our samples had low tumour signals despite LCM enrichment. We used LCM to enrich for tumour signal in EAC gene expression profiling data and identified 4 tumour-intrinsic gene expression programs that validate across multiple datasets. We have currently conducting survival analysis on our data and TCGA data to further validate the prognostic potential of our tumour signatures.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".