Biomedical subjects

Amy Hutchinson

Publications and source records attributed to Amy Hutchinson.

4 recordsLinked to original sources

PubMed2026

Methylation profiling of normal tissue adjacent to breast tumors reveals two distinct groups with divergent tumor microenvironment features.

We previously identified diverse genetic evolutionary patterns in whole-genome sequencing of paired normal tissue adjacent to tumor (NAT) and tumor tissues from Hong Kong breast cancer (HKBC) patients. Here, we investigated whether DNA methylation (DNAm) contributes to NAT heterogeneity and shapes the tumor microenvironment (TME). Genome-wide DNAm profiling was performed on paired NAT and tumor tissues from 188 HKBC patients using the Infinium 850 K array. RNA-seq data were available for 76 NATs and 177 tumors. Cellular composition was inferred using MethylCIBERSORT, CIBERSORTx, and EpiDISH, and histopathologic features were assessed on 115 H&E-stained sections. Unsupervised clustering identified two distinct NAT subtypes with divergent TME characteristics. Cluster 1 (N = 139) showed higher epithelial and fibroblast content and enrichment of estrogen response pathways. Cluster 2 (N = 49) exhibited an immune-metabolic phenotype characterized by increased fat and immune cells, stromal disruption, inflammatory pathway activation, and greater macrophage infiltration. Cluster 2 patients also demonstrated significantly younger epigenetic age estimated using multiple epigenetic clocks. These DNAm-defined NAT subtypes and associated TME features were validated in 97 NAT samples from TCGA breast cancer patients. Overall, our findings identify DNAm-driven NAT heterogeneity with distinct TME landscapes, providing new insights into field cancerization and tumor evolution in breast cancer.

Journal Article↗

PubMed2026

Disruption of CTCF binding by germline non-coding variants in CDKN2B suppress CDKN2A expression and predispose to melanoma.

Some melanoma-prone families linked to the 9p21 locus, harboring the established susceptibility gene CDKN2A, lack pathogenic protein-coding variants. Using whole-exome and targeted sequencing, we identified three rare single-nucleotide variants in two melanoma-prone families and one sporadic melanoma case. Variants map to a conserved CTCF-bound region within the first intron of CDKN2B that physically interacts with CDKN2A. Analysis of UK Biobank showed significant enrichment of variants in this region in melanoma cases. Variants result in diminished CTCF binding in vitro. CTCF ChIP-seq in fibroblasts from the carriers of the largest family demonstrated loss of CTCF binding, accompanied by weakened promoter interactions and allele-specific reduction of CDKN2A p16 transcript expression from the variant haplotype. CRISPR-based perturbation of this region and editing of the large family variant into melanocytes resulted in reduced expression of p14 and p16 CDKN2A transcripts. These findings suggest that non-coding regulatory variants function as high-penetrance susceptibility alleles in melanoma families by altering CDKN2A function.

Journal Article↗

PubMed2025

A prognostic signature for lung adenocarcinoma in people who have never smoked.

Knowledge of tumor cell dynamics can inform prognosis and treatment yet is largely lacking for lung adenocarcinoma in people who have never smoked (NS-LUAD). With RNA-seq data from 684 NS-LUAD and validation in an independent dataset, we identified three subtypes with distinct phenotypic traits and cell compositions. Additional genomic and histological data further characterized the subtypes. 'Steady', marked by low proliferation, high alveolar cell fraction, moderate-to-well differentiation, and fewer driver genes' alterations, is linked to prolonged survival and low immune evasion. 'Proliferative' shows high proliferation markers, TP53 mutations, and gene fusions. 'Chaotic', with high epithelial-to-mesenchymal transition markers, has the worst prognosis even within stage I tumors. Lacking known molecular or histological characteristics, this aggressive subtype is solely identified by transcriptomic data. A 60-gene signature recapitulates the overall classification and strongly predicts survival even within subgroups based on tumor stage or known genomic features, emphasizing its potential for improving NS-LUAD prognostication in clinical settings.

Journal Article↗

PubMed2025

Animal farming and the oral microbiome in the Agricultural Health Study.

BACKGROUND: Raising farm animals imparts various exposures that may shape the human microbiome. The oral microbiome has been increasingly implicated in disease development. Animal farming has also been associated with certain chronic diseases such as cancer; however, underlying biological mechanisms are unclear. We investigated associations between raising farm animals and the oral microbiome in the Agricultural Health Study. METHODS: This analysis included 1,245 participants (865 farmers and 380 spouses) who provided oral wash specimens and information on types and numbers of specific animals raised on their farms within 2 years before sample collection. The oral microbiome was measured by sequencing the V4 region of the 16S ribosomal RNA gene. We evaluated associations of farm animal exposures with alpha and beta diversity metrics (within- and between-sample diversity, respectively), as well as presence and relative abundance of specific bacterial genera. All analyses adjusted for potential confounders (e.g., age, sex, smoking, alcohol consumption). RESULTS: Overall, 63 % of participants raised farm animals, most commonly cattle (46 %) and hogs (20 %). Those who raised a large number of hogs (≥2,000 vs. no hogs) had higher alpha diversity. Conversely, raising sheep/goats and raising larger numbers of poultry were associated with lower alpha diversity. Beta diversity was not significantly different between participants with and without any farm animals. Participants raising any farm animals had higher relative abundance of Porphyromonas and lower relative abundances of Prevotella and Ruminococcaceae UCG-014. Several genera were more likely to be absent with specific animal exposures (e.g., Capnocytophaga for cattle and sheep/goats; Corynebacterium, Dialister, Stomatobaculum, and Solobacterium for sheep/goats and poultry). CONCLUSIONS: This was the largest study of farm animal exposures and the human microbiome to date. Findings suggest that raising specific farm animals may influence the oral microbiome, supporting the need to further investigate the potential role of animal farming in disease etiology.

Microbiota↗