433. UNRAVELLING THE MOLECULAR MECHANISMS BEHIND TUMOUR DIFFERENTIATION IN ESOPHAGEAL ADENOCARCINOMA
Bibliographic record
Abstract
Abstract Background Esophageal adenocarcinoma tumors are divided into three grades based on the tumour’s histological differentiation: well, moderate and poor. Poorly differentiated tumours have a worse survival rate than moderate and well tumours. Understanding the molecular programs of this differentiation may lead to the identification of novel therapeutic interventions specific to tumour differentiation. We have utilized laser-capture microdissection to enrich tumour cells followed by gene expression profiling (RNA-seq) to identify gene expression programs and whole genome sequencing for differentiating specific mutations and copy number changes. Collectively, these results will enable us to unravel the molecular drivers of tumour differentiation. Methods Laser capture microdissection was applied to N=127 RNA-seq samples from N=74 patients and N=103 from N=81 patients from a mix of primary tumour biopsies, resections, and metastatic biopsies. Most samples have a matching RNA-seq and WGS sample. We used a standard pipeline to analyze the WGS data and produce somatic mutation, structural variant, and copy number calls. The gene expression data was segregated into two sets a test set consisting of N=74 samples and a test set of N=53 samples. Non-negative matrix factorization was used to identify eleven gene expression programs. Results Our testing RNA-seq cohort consisted of N=74 samples from N=74 patients with N=4 G1, N=26 G2, N=35 G3, and N=9 missing differentiation data. Our initial goal was to unravel the gene expression programs that correlate with tumour differentiation. Our non-negative matrix factorization analysis yielded 11 gene signatures, N=3 programs enriched in glandular gene expression, N=3 enriched in EMT pathways, N=2 with fibroblasts, and N=3 associated with immune / inflammation genes (not shown) (Figure 1). Moreover, the glandular signatures were associated with G1/G2 and the EMT and fibroblast signatures with G3. Moreover, the glandular 2 signature was associated with HER2 amplifications. Conclusion In this work we have begun to unravel the gene expression and genomic changes associated with tumour differentiation. We have found signatures enriched for both G1/G2 and G3 tumours and from these signatures we have observed gene expression heterogeneity within the different tumour differentiation categories. Moreover, the G3 tumours are enriched in fibroblasts despite our laser-capture microdissection. We are currently working on a classification model to predict tumor differentiation from these gene expression programs and are looking to further integrate our whole genome data to find additional genomic drivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".