Abstract 6853: Spatial proteomic and transcriptomic characterization of the inflammatory landscape of formalin fixed paraffin embedded Barrett’s esophagus tissues
Bibliographic record
Abstract
Abstract Esophageal adenocarcinoma (EAC), a significant cause of morbidity and mortality, develops from its metaplastic precursor lesion termed Barrett’s esophagus (BE). BE progresses to EAC within an inflammation rich environment, however a detailed understanding of the types of inflammatory cells present, how that population changes over time or diagnosis, and the related immunomodulatory pathways that may mediate BEs progression is still understudied. Traditional immunohistochemistry is often inadequate to provide detailed information about a broad number of cells, local cellular proportions, and cellular heterogeneity; while bulk or single cell RNA sequencing lose the spatial context and relationships between the stroma and epithelium. Utilizing modern multiplex profiling technologies such as Nanostring’s GeoMx digital Profiler (DSP) aid in identifying both cellular composition and distribution of cells within the epithelium and stroma. Therefore, our aim of this study was to determine the suitability of the DSP platform for analyzing formalin fixed paraffin embedded (FFPE) endoscopic samples of BE and determine any differences between non-dysplastic BE (NDBE) samples from patients who eventually progressed (progressors) compared to those who did not (non-progressors), with the goal to gain deeper insights about cellular composition and spatial distribution of immune cells that may dictate progression of BE. Our secondary goal was to investigate the relationship of these cells to the BE epithelial cells through understanding the immune signaling and regulatory pathways. Our pilot study utilizing a 49-plex antibody panel on 7 NDBE samples from progressors and 8 NDBE samples from non-progressors showed good quality metrics and deciphered an immune rich microenvironment in BE with several immune markers including CD66b (neutrophils), CD14 (monocytes), CD68 (macrophages) and CD56 (NK cells) being overexpressed in the stroma of progressors compared with non-progressors, corrected p value less than 0.05. The myeloid activation marker CD80 was overexpressed in the BE epithelial cells of progressors. To determine how spatial transcriptomic profiling performed in FFPE endoscopic samples, 1,812 transcripts within 48 regions of interest of four BE, HGD, and EAC samples was interrogated. Only 36 (2%) probes fell below the limit of detection quality threshold. Thus, our pilot results show that robust proteomic and transcriptomic profiling can be obtained from endoscopic BE biopsies using this system. Based on these results, we systematically interrogated the immune cell makeup and distribution as well as their related inflammatory and immunoregulatory pathways in 40 NDBE biopsies (20 progressors and 20 with stable non-progressing disease) within discrete compartments (stroma and epithelium) through spatial whole transcriptome profiling. Citation Format: Qurat ul Ain, Nicola Frei, Amir M. Khoshiwal, Pim Stougie, Robert Odze, Sophie Camilleri-Broet, Lorenzo Ferri, Lucas C. Duits, Jacques Bergman, Matthew D. Stachler. Spatial proteomic and transcriptomic characterization of the inflammatory landscape of formalin fixed paraffin embedded Barrett’s esophagus tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6853.
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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 | 0.001 | 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 | 0.001 |
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".