Feasibility Study Utilizing NanoString’s Digital Spatial Profiling (DSP) Technology for Characterizing the Immune Microenvironment in Barrett’s Esophagus Formalin-Fixed Paraffin-Embedded Tissues
Bibliographic record
Abstract
Characterization of the Barrett’s esophagus (BE) microenvironment in patients with a known progression status, to determine how it may influence BE progression to esophageal adenocarcinoma (EAC), has been understudied, hindering both the biological understanding of the progression and the development of novel diagnostics and therapies. This study’s aim was to determine if a highly multiplex interrogation of the microenvironment can be performed on endoscopic formalin-fixed, paraffin-embedded (FFPE) samples, utilizing the NanoString GeoMx digital spatial profiling (GeoMx DSP) platform and if it can begin to identify the types of immune cells and pathways that may mediate the progression of BE. We performed a spatial proteomic analysis of 49 proteins expressed in the microenvironment and epithelial cells of FFPE endoscopic biopsies from patients with non-dysplastic BE (NDBE) who later progressed to high-grade dysplasia or EAC (n = 7) or from patients who, after at least 5 years follow-up, did not (n = 8). We then performed an RNA analysis of 1812 cancer-related transcripts on three endoscopic mucosal resections containing regions of BE, dysplasia, and EAC. Profiling with GeoMx DSP showed reasonable quality metrics and detected expected differences between epithelium and stroma. Several proteins were found to have an increased expression within NDBE biopsies from progressors compared to non-progressors, suggesting further studies are warranted.
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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 | 0.000 |
| 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 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".