Biology subjects

Beane, J. E.

Publications and source records attributed to Beane, J. E..

4 recordsLinked to original sources

bioRxiv2025

Epithelial miR-149-5p up-regulation is associated with immune evasion in progressive bronchial premalignant lesions

The molecular drivers bronchial premalignant lesion progression to invasive lung squamous cell carcinoma are not well defined. Prior work profiling longitudinally collected bronchial premalignant lesion biopsies by RNA sequencing defined a proliferative subtype, enriched with bronchial dysplasia. We found that a gene co-expression module associated with interferon gamma signaling and antigen processing/presentation was down-regulated in progressive/persistent versus regressive lesions within the proliferative subtype, suggesting a functional impact of these genes on immune evasion. RNA from these same premalignant lesions was profiled by microRNA (miRNA) sequencing and a miRNA-gene network analysis identified hsa-miR-149-5p as a potential regulator of this antigen presentation gene co-expression module associated with lesion progression. hsa-miR-149-5p was found to be predominantly expressed in the epithelium and up-regulated in progressive/persistent versus regressive proliferative lesions while targets of this miRNA, the transcriptional coactivator of MHC-I gene expression, NLRC5, and the genes it regulates were down-regulated. MicroRNA in situ hybridization of hsa-miR-149-5p in tissue from adjacent fixed biopsies showed that hsa-miR-149-5p was increased in areas of bronchial dysplasia in progressive/persistent versus regressive lesions. Imaging mass cytometry showed that NLRC5 protein expression was decreased in progressive/persistent versus regressive lesions within areas of hyperplasia, metaplasia, and dysplasia. Additionally, basal cells with high versus low levels of NLRC5 were found to be in close spatial proximity to CD8 T cells, suggesting that these cells exhibit increased functional MHC-I gene expression in lesions with low hsa-miR-149-5p expression. Collectively, our data suggests a functional role for hsa-miR-149-5p in bronchial premalignant lesions and may serve as a therapeutic target for PML immunomodulation. STATEMENT OF SIGNIFICANCEIntegrative analysis across bronchial premalignant lesions has identified and localized a potential regulator of immune evasion in progressive/persistent lesions that could be a novel therapeutic target.

cancer biology↗

bioRxiv2023

Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival

Multimodal machine learning models are being developed to analyze pathology images and other modalities, such as gene expression, to gain clinical and biological in-sights. However, most frameworks for multimodal data fusion do not fully account for the interactions between different modalities. Here, we present an attention-based fusion architecture that integrates a graph representation of pathology images with gene expression data and concomitantly learns from the fused information to predict patient-specific survival. In our approach, pathology images are represented as undirected graphs, and their embeddings are combined with embeddings of gene expression signatures using an attention mechanism to stratify tumors by patient survival. We show that our framework improves the survival prediction of human non-small cell lung cancers, out-performing existing state-of-the-art approaches that lever-age multimodal data. Our framework can facilitate spatial molecular profiling to identify tumor heterogeneity using pathology images and gene expression data, complementing results obtained from more expensive spatial transcriptomic and proteomic technologies.

bioinformatics↗

bioRxiv2023

Graph perceiver network for lung tumor and premalignant lesion stratification from histopathology

Bronchial premalignant lesions (PMLs) precede the development of invasive lung squamous carcinoma (LUSC), posing a significant challenge in distinguishing those likely to advance to LUSC from those that might regress without intervention. In this context, we present a novel computational approach, the Graph Perceiver Network (GRAPE-Net), leveraging hematoxylin and eosin (H&E) stained whole slide images (WSIs) to stratify endobronchial biopsies of PMLs across a spectrum from normal to tumor lung tissues. GRAPE-Net outperforms existing frameworks in classification accuracy predicting LUSC, lung adenocarcinoma (LUAD), and non-tumor (normal) lung tissue on The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) datasets containing lung resection tissues while efficiently generating pathologist-aligned, class-specific heatmaps. The network was further tested using endobronchial biopsies from two data cohorts, containing normal to carcinoma in situ histology, and it demonstrated a unique capability to differentiate carcinoma in situ lung squamous PMLs based on their progression status to invasive carcinoma. The network may have utility in stratifying PMLs for chemoprevention trials or more aggressive follow-up.

pathology↗

bioRxiv2022

Differential Regulation Analysis Quantifies miRNA Regulatory Roles And Context-Specific Targets

Rewiring of transcriptional regulatory networks has been implicated in many biological and pathological processes. However, most current methods for detecting rewiring events (differential network connectivity) are not optimized for miRNA-mediated gene regulation and fail to systematically examine predicted target genes in study designs with multiple experimental or phenotypic groups. We developed a novel method to address these shortcomings. The method first estimates miRNA-gene expression correlations with Spatial Quantile Normalization to remove the mean-correlation relationship. Then, for each miRNA, genes are ranked by their correlation strength per experimental group. Enrichment patterns of predicted target genes are compared using the Anderson-Darling test and significance levels are estimated via permutation. Finally, context-specific target genes for each miRNA are identified with target prioritization based on the correlation strength between miRNA and predicted target genes within each group. In miR-155 KO RNA-seq data from four mice immune cell types, our method captures the known cell-specific regulatory differences of miR-155, and prioritized targets are involved in functional pathways with cell-type specificity. Moreover, in TCGA BRCA data, our method identified subtype-specific targets that were uniquely altered by miRNA perturbations in cell lines of the same subtype. Our work provides a new approach to characterize miRNA-mediated gene regulatory network rewiring across multiple groups from transcriptomic profiles. The method may offer novel insights into cell-type and cancer subtype-specific miRNA regulatory roles.

bioinformatics↗