siRNA Library Screening Identifies a Druggable Immune-Signature Driving Esophageal Adenocarcinoma Cell Growth
Bibliographic record
Abstract
BACKGROUND & AIMS: Effective therapeutic approaches are urgently required to tackle the alarmingly poor survival outcomes in esophageal adenocarcinoma (EAC) patients. EAC originates from within the intestinal-type metaplasia, Barrett's esophagus, a condition arising on a background of gastroesophageal reflux disease and associated inflammation. METHODS: This study used a druggable genome small interfering RNA (siRNA) screening library of 6022 siRNAs in conjunction with bioinformatics platforms, genomic studies of EAC tissues, somatic variation data of EAC from The Cancer Genome Atlas data of EAC, and pathologic and functional studies to define novel EAC-associated, and targetable, immune factors. RESULTS: By using a druggable genome library we defined genes that sustain EAC cell growth, which included an unexpected immunologic signature. Integrating Cancer Genome Atlas data with druggable siRNA targets showed a striking concordance and an EAC-specific gene amplification event associated with 7 druggable targets co-encoded at Chr6p21.1. Over-representation of immune pathway-associated genes supporting EAC cell growth included leukemia inhibitory factor, complement component 1, q subcomponent A chain (C1QA), and triggering receptor expressed on myeloid cells 2 (TREM2), which were validated further as targets sharing downstream signaling pathways through genomic and pathologic studies. Finally, targeting the triggering receptor expressed on myeloid cells 2-, C1q-, and leukemia inhibitory factor-activated signaling pathways (TYROBP-spleen tyrosine kinase and JAK-STAT3) with spleen tyrosine kinase and Janus-activated kinase inhibitor fostamatinib R788 triggered EAC cell death, growth arrest, and reduced tumor burden in NOD scid gamma mice. CONCLUSIONS: These data highlight a subset of genes co-identified through siRNA targeting and genomic studies of expression and somatic variation, specifically highlighting the contribution that immune-related factors play in support of EAC development and suggesting their suitability as targets in the treatment of EAC.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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) | 0.001 | 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) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".