Intra-individual Gene Expression Variability of Histologically Normal Breast Tissue

Evaluating Large Language Model (LLM) Performance on Established Breast Classification Systems

Medical researchers are increasingly utilizing advanced LLMs like ChatGPT-4 and Gemini to enhance diagnostic processes in the medical field. This research focuses on their ability to comprehend and apply complex medical classification systems for breast conditions, which can significantly aid plastic surgeons in making informed decisions for diagnosis and treatment, ultimately leading to improved patient outcomes. Fifty clinical scenarios were created to evaluate the classification accuracy of each LLM across five established breast-related classification systems. Scores from 0 to 2 were assigned to LLM responses to denote incorrect, partially correct, or completely correct classifications. Descriptive statistics were employed to compare the performances of ChatGPT-4 and Gemini. Gemini exhibited superior overall performance, achieving 98% accuracy compared to ChatGPT-4's 71%. While both models performed well in the Baker classification for capsular contracture and UTSW classification for gynecomastia, Gemini consistently outperformed ChatGPT-4 in other systems, such as the Fischer Grade Classification for gender-affirming mastectomy, Kajava Classification for ectopic breast tissue, and Regnault Classification for breast ptosis. With further development, integrating LLMs into plastic surgery practice will likely enhance diagnostic support and decision making.

Associations of breast cancer related exposures and gene expression profiles in normal breast tissue-The Norwegian Women and Cancer normal breast tissue study

BACKGROUND

Normal breast tissue is utilized in tissue-based studies of breast carcinogenesis. While gene expression in breast tumor tissue is well explored, our knowledge of transcriptomic signatures in normal breast tissue is still incomplete. The aim of this study was to investigate variability of gene expression in a large sample of normal breast tissue biopsies, according to breast cancer related exposures (obesity, smoking, alcohol, hormone therapy, and parity).

METHODS

We analyzed gene expression profiles from 311 normal breast tissue biopsies from cancer-free, post-menopausal women, using Illumina bead chip arrays. Principal component analysis and K-means clustering was used for initial analysis of the dataset. The association of exposures and covariates with gene expression was determined using linear models for microarrays.

RESULTS

Heterogeneity of the breast tissue and cell composition had the strongest influence on gene expression profiles. After adjusting for cell composition, obesity, smoking, and alcohol showed the highest numbers of associated genes and pathways, whereas hormone therapy and parity were associated with negligible gene expression differences.

CONCLUSION

Our results provide insight into associations between major exposures and gene expression profiles and provide an informative baseline for improved understanding of exposure-related molecular events in normal breast tissue of cancer-free, post-menopausal women.

Immune-and Metabolism-Associated Molecular Classification of Ovarian Cancer

Ovarian cancer (OV) is a complex gynecological disease, and its molecular characteristics are not clear. In this study, the molecular characteristics of OV subtypes based on metabolic genes were explored through the comprehensive analysis of genomic data. A set of transcriptome data of 2752 known metabolic genes was used as a seed for performing non negative matrix factorization (NMF) clustering. Three subtypes of OV (C1, C2 and C3) were found in analysis. The proportion of various immune cells in C1 was higher than that in C2 and C3 subtypes. The expression level of immune checkpoint genes TNFRSF9 in C1 was higher than that of other subtypes. The activation scores of cell cycle, RTK-RAS, Wnt and angiogenesis pathway and ESTIMATE immune scores in C1 group were higher than those in C2 and C3 groups. In the validation set, grade was significantly correlated with OV subtype C1. Functional analysis showed that the extracellular matrix related items in C1 subtype were significantly different from other subtypes. Drug sensitivity analysis showed that C2 subtype was more sensitive to immunotherapy. Survival analysis of differential genes showed that the expression of PXDN and CXCL11 was significantly correlated with survival. The results of tissue microarray immunohistochemistry showed that the expression of PXDN was significantly correlated with tumor size and pathological grade. Based on the genomics of metabolic genes, a new OV typing method was developed, which improved our understanding of the molecular characteristics of human OV.

Immune checkpoint blockade reprograms systemic immune landscape and tumor microenvironment in obesity-associated breast cancer

Immune checkpoint blockade (ICB) has improved outcomes in some cancers. A major limitation of ICB is that most patients fail to respond, which is partly attributable to immunosuppression. Obesity appears to improve immune checkpoint therapies in some cancers, but impacts on breast cancer (BC) remain unknown. In lean and obese mice, tumor progression and immune reprogramming were quantified in BC tumors treated with anti-programmed death-1 (PD-1) or control. Obesity augments tumor incidence and progression. Anti-PD-1 induces regression in lean mice and potently abrogates progression in obese mice. BC primes systemic immunity to be highly responsive to obesity, leading to greater immunosuppression, which may explain greater anti-PD-1 efficacy. Anti-PD-1 significantly reinvigorates antitumor immunity despite persistent obesity. Laminin subunit beta-2 (Lamb2), downregulated by anti-PD-1, significantly predicts patient survival. Lastly, a microbial signature associated with anti-PD-1 efficacy is identified. Thus, anti-PD-1 is highly efficacious in obese mice by reinvigorating durable antitumor immunity. VIDEO ABSTRACT.

Comprehensive analysis of location-specific hub genes related to the pathogenesis of colon cancer

The molecular mechanisms underlying colon cancer lesions at different sites are not entirely clear. Herein, we aimed to explore location-specific gene profiles related to the pathogenesis of colon cancer and to identify their function. The robust rank aggregation (RRA) method was used to integrate colon cancer microarray datasets and screen differentially expressed gene (DEG) profiles between left- and right-sided colon cancers. Then, weighted gene co-expression network analysis (WGCNA) was performed to cluster the DEGs into modules and identify hub genes. The selected hub genes were validated using The Cancer Genome Atlas dataset and clinical tissues. We assessed the association of selected hub genes with the methylation status in immune cells. In total, 905 DEGs were identified by RRA; five gene modules and 18 hub genes were related to the clinical traits of colon cancer by WGCNA. Four hub genes were selected and shown to be associated with colon cancers on different sides and distant metastasis in the validation analysis. The four hub genes showed a low methylation status, and their expression was significantly associated with methylation status. Positive correlations were observed between the four hub genes and tumor purity and among the four types of immune cells. Gene set enrichment analysis revealed that the four hub genes were mainly involved in two cancer-related pathways. In conclusion, this study identified a set of location-specific genes related to the pathogenesis of colon cancer. These four hub genes may act as novel candidate targets for the treatment of colon cancer.

Investigation of the adolescent female breast transcriptome and the impact of obesity

Background

Early life environmental exposures affect breast development and breast cancer risk in adulthood. The breast is particularly vulnerable during puberty when mammary epithelial cells proliferate exponentially. In overweight/obese (OB) women, inflammation increases breast aromatase expression and estrogen synthesis and promotes estrogen-receptor (ER)-positive breast cancer. In contrast, recent epidemiological studies suggest that obesity during childhood decreases future breast cancer risk. Studies on environmental exposures and breast cancer risk have thus far been limited to animal models. Here, we present the first interrogation of the human adolescent breast at the molecular level and investigate how obesity affects the immature breast.

Methods

We performed RNA-seq in 62 breast tissue samples from adolescent girls/young women (ADOL; mean age 17.8 years) who underwent reduction mammoplasty. Thirty-one subjects were non-overweight/obese (NOB; mean BMI 23.4 kg/m²) and 31 were overweight/obese (OB; BMI 32.1 kg/m²). We also compared our data to published mammary transcriptome datasets from women (mean age 39 years) and young adult mice, rats, and macaques.

Results

The ADOL breast transcriptome showed limited (30%) overlap with other species, but 88% overlap with adult women for the 500 most highly expressed genes in each dataset; only 43 genes were shared by all groups. In ADOL, there were 120 differentially expressed genes (DEG) in OB compared with NOB samples (p_adj < 0.05). Based on these DEG, Ingenuity Pathway Analysis (IPA) identified the cytokines CSF1 and IL-10 and the chemokine receptor CCR2 as among the most highly activated upstream regulators, suggesting increased inflammation in the OB breast. Classical ER targets (e.g., PR, AREG) were not differentially expressed, yet IPA identified the ER and PR and growth factors/receptors (VEGF, HGF, HER3) and kinases (AKT1) involved in hormone-independent ER activation as activated upstream regulators in OB breast tissue.

Conclusions

These studies represent the first investigation of the human breast transcriptome during late puberty/young adulthood and demonstrate that obesity is associated with a transcriptional signature of inflammation which may augment estrogen action in the immature breast microenvironment. We anticipate that these studies will prompt more comprehensive cellular and molecular investigations of obesity and its effect on the breast during this critical developmental window.