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AI-assisted discovery of an ethnicity-influenced driver of cell transformation in esophageal and gastroesophageal junction adenocarcinomas
Pradipta Ghosh, … , Kit Curtius, Debashis Sahoo
Pradipta Ghosh, … , Kit Curtius, Debashis Sahoo
Published September 22, 2022
Citation Information: JCI Insight. 2022;7(18):e161334. https://doi.org/10.1172/jci.insight.161334.
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Research Article Gastroenterology Immunology

AI-assisted discovery of an ethnicity-influenced driver of cell transformation in esophageal and gastroesophageal junction adenocarcinomas

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Abstract

Although Barrett’s metaplasia of the esophagus (BE) is the only known precursor lesion to esophageal adenocarcinomas (EACs), drivers of cellular transformation in BE remain incompletely understood. We use an artificial intelligence–guided network approach to study EAC initiation and progression. Key predictions are subsequently validated in a human organoid model, in patient-derived biopsy specimens of BE, a case-control study of genomics of BE progression, and in a cross-sectional study of 113 patients with BE and EACs. Our model classified healthy esophagus from BE and BE from EACs in several publicly available gene expression data sets (n = 932 samples). The model confirmed that all EACs must originate from BE and pinpointed a CXCL8/IL8↔neutrophil immune microenvironment as a driver of cellular transformation in EACs and gastroesophageal junction adenocarcinomas. This driver is prominent in White individuals but is notably absent in African Americans (AAs). Network-derived gene signatures, independent signatures of neutrophil processes, CXCL8/IL8 expression, and an absolute neutrophil count (ANC) are associated with risk of progression. SNPs associated with changes in ANC by ethnicity (e.g., benign ethnic neutropenia [BEN]) modify that risk. Findings define a racially influenced immunological basis for cell transformation and suggest that BEN in AAs may be a deterrent to BE→EAC progression.

Authors

Pradipta Ghosh, Vinicius J. Campos, Daniella T. Vo, Caitlin Guccione, Vanae Goheen-Holland, Courtney Tindle, Guilherme S. Mazzini, Yudou He, Ludmil B. Alexandrov, Scott M. Lippman, Richard R. Gurski, Soumita Das, Rena Yadlapati, Kit Curtius, Debashis Sahoo

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Figure 2

Generation and validation of Boolean network map of BE.

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Generation and validation of Boolean network map of BE.
(A) Schematics o...
(A) Schematics outline the workflow (steps 1–4) and training data sets used to create a Boolean map of NE to BE transition using BoNE(4). (B) Graph showing invariant patterns of gene expression changes during NE→BE progression. Gene clusters identified by machine learning are indicated in bold. (C) Gene clusters in B were refined by filtering through a second data set (GSE39491). The resultant signature involves progressive downregulation of SPINK7 cluster with a concomitant upregulation of SLC44A4 cluster. (D) Bar plots show sample classification accuracy across diverse data sets, with corresponding ROC-AUC values. The sample numbers for healthy (H) and BE analyzed in each data set are annotated on the left margin. (E and F) Summary (E) of a published SPT6-depleted organoid model of BE. Hypergeometric statistical analyses (F) show significant overlaps in both up- and downregulated genes between gene signatures identified in the BE maps in B and C and differentially expressed genes in the SPT6-depleted organoid model of BE (5). (G) Esophageal biopsy specimens from men with (Eso from BE) or without (Eso from non-BE) BE were analyzed for SPT6 and TP63 expression by IHC. Red and black arrowheads point to crypts staining positive. Interrupted circles highlight crypts with little or no expression. Fields representative from 3 participants are shown; boxed regions above are magnified below. Scale bar: 100 μm. Violin plots display the percentage of cells positive for staining in regions of interest in G, as determined by the ImageJ plug-in, IHC profiler. P values were determined by 2-tailed Mann-Whitney test. DEG, differentially expressed gene.

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