Signatures In Breast Cancer Research Articles

Analysis of hydrogen sulfide-related signatures of breast cancer identified 2 distinct subtypes: Implications for individualized therapeutics.

Hydrogen sulfide (H2S) is a vital gasotransmitter involved in breast cancer (BC) pathogenesis. This study aims to employ hydrogen sulfide-related genes (HSRGs) for molecular classification of BC and, accordingly, to establish a robust prognostic risk signature. Transcriptomic, clinical, and mutational data of BC patients were collected from the cancer genome atlas and gene expression omnibus databases. Prognostic relevance was evaluated using Cox regression analysis, while consensus clustering analysis was employed for molecular subtyping. Gene expression profiles, prognosis, immune infiltration patterns, drug sensitivity, and response to immunotherapy were compared between subtypes. Multiple-gene prognostic features were developed and assessed along with a nomogram. The gene expression was validated in clinical samples using quantitative polymerase chain reaction. Among 282 HSRGs, 46 exhibited significant correlations with BC prognosis. Consensus clustering identified 2 distinct molecular subtypes (C1 and C2). C1 displayed significantly improved prognosis compared to C2, accompanied by increased infiltration of B cells, T cells, monocytes, and mast cells but decreased macrophage infiltration. Moreover, C1 demonstrated higher drug sensitivity and immunotherapeutic response relative to C2. Enrichment analysis revealed suppressed immune-related processes and pathways in C2 while cell cycle regulation and chromosomal processes were significantly activated. Additionally, a risk feature comprising 6 differentially expressed genes between subtypes was constructed; this feature performed well in prognostic prediction. Integration of this feature with other clinical parameters (radiotherapy/chemotherapy status, clinical stage, N stage) into a nomogram further enhanced prognostic accuracy. Clinical samples further validated the high expression of ATP13A5, LRTM2, MAFA, and SPDYC and the low expression of CYP4F12 and TNN in BC. Our findings highlight the clinical relevance of HSRGs in BC, providing a basis for precise molecular classification and prognosis evaluation. The developed risk feature and nomogram offer practical tools for guiding personalized treatment strategies in clinical practice.

Characterisation of HER2-Driven Morphometric Signature in Breast Cancer and Prediction of Risk of Recurrence.

Human epidermal growth factor receptor 2-positive (HER2-positive) breast cancer (BC) is a heterogeneous disease. In this study, we hypothesised that the degree of HER2 oncogenic activity, and hence response to anti-HER2 therapy is translated into a morphological signature that can be of prognostic/predictive value. We developed a HER2-driven signature based on a set of morphometric features identified through digital image analysis and visual assessment in a sizable cohort of BC patients. HER2-enriched molecular sub-type (HER2-E) was used for validation, and pathway enrichment analysis was performed to assess HER2 pathway activity in the signature-positive cases. The predictive utility of this signature was evaluated in post-adjuvant HER2-positive BC patients. A total of 57 morphometric features were evaluated; of them, 22 features were significantly associated with HER2 positivity. HER2 IHC score 3+/oestrogen receptor-negative tumours were significantly associated with HER2-related morphometric features compared to other HER2 classes including HER2 IHC 2+ with gene amplification, and they showed the least intra-tumour morphological heterogeneity. Tumours displaying HER2-driven morphometric signature showed the strongest association with PAM50 HER2-E sub-type and were enriched with ERBB signalling pathway compared to signature-negative cases. BC patients with positive HER2 morphometric signature showed prolonged distant metastasis-free survival post-adjuvant anti-HER2 therapy (p = 0.007). The clinico-morphometric prognostic index demonstrated an 87% accuracy in predicting recurrence risk. Our findings underscore the strong prognostic and predictive correlation between HER2 histo-morphometric features and response to targeted anti-HER2 therapy.

Investigating the Prognostic Role of Telomerase-Related Cellular Senescence Gene Signatures in Breast Cancer Using Machine Learning.

Background: Telomeres and cellular senescence are critical biological processes implicated in cancer development and progression, including breast cancer, through their influence on genomic stability and modulation of the tumor microenvironment. Methods: This study integrated bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) data to establish a gene signature associated with telomere maintenance and cellular senescence for prognostic prediction in breast cancer. Telomere-related genes (TEGs) and senescence-associated genes were curated from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A comprehensive machine learning framework incorporating 101 algorithmic combinations across 10 survival modeling approaches, including random survival forests and ridge regression, was employed to develop a robust prognostic model. Results: A set of 19 key telomere- and senescence-related genes was identified as the optimal prognostic signature. The model demonstrated strong predictive accuracy and was successfully validated in multiple independent cohorts. Functional enrichment analyses indicated significant associations with immune responses and aging-related pathways. Single-cell transcriptomic analysis revealed marked cellular heterogeneity, identifying distinct subpopulations (fibroblasts and immune cells) with divergent risk scores and biological pathway activity. Additionally, pseudo-time trajectory analysis and intercellular communication mapping provided insights into the dynamic evolution of the tumor microenvironment. Immunohistochemical (IHC) validation using data from the Human Protein Atlas confirmed differential protein expression between normal and tumor tissues for several of the selected genes, reinforcing their biological relevance and clinical utility. Conclusions: This study presents a novel 19-gene telomere- and senescence-associated signature with strong prognostic value in breast cancer. These findings enhance our understanding of tumor heterogeneity and may inform precision oncology approaches and future therapeutic strategies.

Decoding mutational signatures in breast cancer: Insights from a multi-cohort study.

Decoding mutational signatures in breast cancer: Insights from a multi-cohort study.

A Systematic Review and Meta-Analysis of 16S rRNA and Cancer Microbiome Atlas Datasets to Characterize Microbiota Signatures in Normal Breast, Mastitis, and Breast Cancer.

The breast tissue microbiome has been increasingly recognized as a potential contributor to breast cancer development and progression. However, inconsistencies in microbial composition across studies have hindered the identification of definitive microbial signatures. We conducted a systematic review and meta-analysis of 11 studies using 16S rRNA sequencing to characterize the bacterial microbiome in 1260 fresh breast tissue samples, including normal, mastitis-affected, benign, cancer-adjacent, and cancerous tissues. Studies published until 31 December 2023 were included if they analyzed human breast tissue using Illumina short-read 16S rRNA sequencing with sufficient metadata, while non-human samples, non-breast tissues, non-English articles, and those lacking metadata or using alternative sequencing methods were excluded. We also incorporated microbiome data from The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort to enhance our analyses. Our meta-analysis identified Proteobacteria, Firmicutes, Actinobacteriota, and Bacteroidota as the dominant phyla in breast tissue, with Staphylococcus and Corynebacterium frequently detected across studies. While microbial diversity was similar between cancer and cancer-adjacent tissues, they both exhibited a lower diversity compared to normal and mastitis-affected tissues. Variability in bacterial genera was observed across primer sets and studies, emphasizing the need for standardized methodologies in microbiome research. An analysis of TCGA-BRCA data confirmed the dominance of Staphylococcus and Corynebacterium, which was associated with breast cancer proliferation-related gene expression programs. Notably, high Staphylococcus abundance was associated with a 4.1-fold increased mortality risk. These findings underscore the potential clinical relevance of the breast microbiome in tumor progression and emphasize the importance of methodological consistency. Future studies to establish causal relationships, elucidate underlying mechanisms, and assess microbiome-targeted interventions are warranted.

Characterization of immune landscape and prognostic value of IL-17-related signature in invasive breast cancer.

Recently, interleukin 17 (IL-17) has been found to play a critical role in the development of breast cancer. However, its prognostic significance in invasive breast cancer (IBC) remains unclear. This study aims to determine the role of IL-17-related signatures in IBC to identify novel therapeutic options. IBC data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) were used to identify IL-17-related prognostic genes. A predictive model was developed using TCGA data and validated using METABRIC data. The relationship between IL-17 scores and immune landscape, chemotherapy drug sensitivity [half maximal inhibitory concentration (IC50)], and immune checkpoint gene expression was analyzed. The quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to validate key gene expression in breast tumor and normal tissue samples. The predictive model identified core IL-17-related prognostic genes and successfully estimated the prognosis of IBC patients. The model's validity was confirmed using METABRIC data. Patients with high IL-17 scores had worse overall survival (OS) compared to those with low IL-17 scores. Low IL-17 scores were associated with higher immune checkpoint gene expression and predicted enhanced responses to cytotoxic T-lymphocyte-associated protein 4 (CTLA4) and programmed cell death protein 1 (PD-1) therapies. Patients with low IL-17 scores exhibited a higher abundance of immune microenvironment components. Furthermore, qRT-PCR confirmed the lower expression of OR51E1, NDRG2, RGS2, and TSPAN7 in breast tumors compared to normal tissue. IL-17-related signatures are promising biomarkers for predicting the prognosis of IBC patients. These findings suggest that IL-17-related markers could be used to guide individualized therapeutic strategies, potentially improving outcomes for IBC patients.

Construction of a prognostic signature for breast cancer based on genes involved in unsaturated fatty acid biosynthesis.

The biosynthesis of unsaturated fatty acids (UFAs) has been implicated in the onset and advancement of breast cancer (BC). This study aimed to develop molecular subtypes and prognostic signatures for BC based on UFA-related genes (UFAGs). This study integrates multi-omics and survival data from public databases to elucidate molecular classifications and risk profiles based on UFAGs. Consensus clustering and Lasso Cox regression methodologies are employed for subtype identification and risk signature development, respectively. Immune microenvironment assessment is conducted using CIBERSORT and ESTIMATE algorithms, while drug sensitivity and response to immunotherapy are evaluated via pRRophetic and TIDE methods. Gene set enrichment analysis augments signature characterization, followed by nomogram construction and validation. We successfully identified two distinct BC molecular subtypes with significantly different prognoses utilizing UFAGs correlated with outcomes. A prognostic signature comprising three UFAGs [acetyl-CoA acyltransferase 1 (ACAA1), acyl-CoA thioesterase 2 (ACOT2), and ELOVL fatty acid elongase 2 (ELOVL2)] is developed, stratifying patients into high- and low-risk groups exhibiting divergent outcomes, clinicopathological traits, gene expression patterns, immune infiltration profiles, therapeutic susceptibility, and immunotherapy responses. The signature demonstrates robust prognostic performance in both training and validation cohorts, emerging as an independent predictor alongside age, which is integrated into a nomogram. Decision curve analysis highlights the nomogram's superiority over other factors in prognosis prediction. Calibration plots and receiver operating characteristic curves affirm its excellent performance in BC prognosis assessment. Expression profiles of UFAGs are associated with BC prognosis, enabling the creation of a risk signature with implications for understanding the molecular mechanisms underlying BC progression.

Long non‑coding RNA signatures in breast cancer: Properties as biomarkers?

Breast cancer represents the most common type of cancer in females worldwide. The survival rates for breast cancer patients have been increasing since 1990. However, in 2023 breast cancer is still the second most common cause of malignancy-associated death in women. One decisive reason is the increase of treatment resistance and low therapy response. Therefore, new therapy targets and predictive markers for the response to treatment are needed. The present study analyzed the potential effects triggered by different breast cancer treatments on the transcriptional expression of 12 pre-selected long non-coding (lnc) RNAs and the proliferation markers Cyclin D1 and Ki-67 in six different breast cancer cell lines (BT-474, MDA-MB-231, BT-20, T-47D, SKBR-3 and MCF-7). The results revealed that lncRNA cytoskeleton regulator RNA may be an appropriate biomarker for the response to treatment with both epirubicin and gemcitabine (P<0.001). NF-ĸB interacting lnc RNA may be a marker for therapy response (P<0.001), while HOX transcript antisense RNA overexpression suggested resistance to treatment (P<0.001) with epirubicin. The transcriptional expression of lncRNA BC4 increased during treatment with epirubicin and gemcitabine, which indicated therapy response. Overall, the present data suggested that the aforementioned lncRNAs have a promising potential as biomarkers to detect early therapy response or resistance in and therefore should be analyzed in more detail.

Integrating single-cell RNA-seq and bulk RNA-seq to explore prognostic value and immune landscapes of methionine metabolism-related signature in breast cancer.

Neoadjuvant, endocrine, and targeted therapies have significantly improved the prognosis of breast cancer (BC). However, due to the high heterogeneity of cancer, some patients cannot benefit from existing treatments. Increasing evidence suggests that amino acids and their metabolites can alter the tumor malignant behavior through reshaping tumor microenvironment and regulation of immune cell function. Breast cancer cell lines have been identified as methionine-dependent, and methionine restriction has been proposed as a potential cancer treatment strategy. We integrated transcriptomic and single-cell RNA sequencing (ScRNA-seq) analyses based on The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. Then we applied weighted gene co-expression network analysis (WGCNA) and Cox regression to evaluate methionine metabolism-related genes (MRGs) in BC, constructing and validating a prognostic model for BC patients. Immune landscapes and immunotherapy were further explored. Finally, in vitro experiments were conducted to assess the expression and function of key genes APOC1. In this study, we established and validated a prognostic signature based on eight methionine-related genes to predict overall survival (OS) in BC patients. Patients were further stratified into high-risk and low-risk groups according to prognostic risk score. Further analysis revealed significant differences between two groups in terms of pathway alterations, immune microenvironment characteristics, and immune checkpoint expression. Our study shed light on the relationship between methionine metabolism and immune infiltration in BC. APOC1, a key gene in the prognostic signature, was found to be upregulated in BC and closely associated with immune cell infiltration. Notably, APOC1 was primarily expressed in macrophages. Subsequent in vitro experiments demonstrated that silencing APOC1 reduced the generation of tumor-associated macrophages (TAMs) with an M2 phenotype while significantly decreasing the proliferation, invasion, and migration of MDA-MB-231 and MDA-MB-468 breast cancer cell lines. We established a prognostic risk score consisting of genes associated with methionine metabolism, which helps predict prognosis and response to treatment in BC. The function of APOC1 in regulating macrophage polarization was explored.

Network-based transfer of pan-cancer immunotherapy responses to guide breast cancer prognosis

Breast cancer prognosis is complicated by tumor heterogeneity. Traditional methods focus on cancer-specific gene signatures, but cross-cancer strategies that provide deeper insights into tumor homogeneity are rarely used. Immunotherapy, particularly immune checkpoint inhibitors, results from variable responses across cancers, offering valuable prognostic insights. We introduced a network-based transfer (NBT) of pan-cancer immunotherapy responses to enhance breast cancer prognosis using node embedding and heat diffusion algorithms, identifying gene signatures netNE and netHD. Our results showed that netHD and netNE outperformed seven established breast cancer signatures in prognostic metrics, with netHD excelling. All nine gene signatures were grouped into three clusters, with netHD and netNE enriching the immune-related interferon-gamma pathway. Stratifying TCGA patients into two groups based on netHD revealed significant immunological differences and variations in 20 of 50 cancer hallmarks, emphasizing immune-related markers. This approach leverages pan-cancer insights to enhance breast cancer prognosis, facilitating insight transfer and improving tumor homogeneity understanding. graph of network-based insights translating pan-cancer immunotherapy responses to breast cancer prognosis. This abstract graph illustrates the conceptual framework for transferring immunotherapy response insights from pan-cancer studies to breast cancer prognosis. It highlights the integration of PPI networks to bridge genetic data and clinical phenotypes. The network-based method facilitates the identification of prognostic gene signatures in breast cancer by leveraging immunotherapy response information, providing a novel perspective on tumor homogeneity and its implications for clinical outcomes.

Identification of a Potential PGK1 Inhibitor with the Suppression of Breast Cancer Cells Using Virtual Screening and Molecular Docking.

Breast cancer is the second most common malignancy worldwide and poses a significant threat to women's health. However, the prognostic biomarkers and therapeutic targets of breast cancer are unclear. A prognostic model can help in identifying biomarkers and targets for breast cancer. In this study, a novel prognostic model was developed to optimize treatment, improve clinical prognosis, and screen potential phosphoglycerate kinase 1 (PGK1) inhibitors for breast cancer treatment. Using data from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified in normal individuals and breast cancer patients. The biological functions of the DEGs were examined using bioinformatics analysis. A novel prognostic model was then constructed using the DEGs through LASSO and multivariate Cox regression analyses. The relationship between the prognostic model, survival, and immunity was also evaluated. In addition, virtual screening was conducted based on the risk genes to identify novel small molecule inhibitors of PGK1 from Chemdiv and Targetmol libraries. The effects of the potential inhibitors were confirmed through cell experiments. A total of 230 up- and 325 down-regulated DEGs were identified in HER2, LumA, LumB, and TN breast cancer subtypes. A new prognostic model was constructed using ten risk genes. The analysis from The Cancer Genome Atlas (TCGA) indicated that the prognosis was poorer in the high-risk group compared to the low-risk group. The accuracy of the model was confirmed using the ROC curve. Furthermore, functional enrichment analyses indicated that the DEGs between low- and high-risk groups were linked to the immune response. The risk score was also correlated with tumor immune infiltrates. Moreover, four compounds with the highest score and the lowest affinity energy were identified. Notably, D231-0058 showed better inhibitory activity against breast cancer cells. Ten genes (ACSS2, C2CD2, CXCL9, KRT15, MRPL13, NR3C2, PGK1, PIGR, RBP4, and SORBS1) were identified as prognostic signatures for breast cancer. Additionally, results showed that D231-0058 (2-((((4-(2-methyl-1H-indol-3-yl)-1,3-thiazol-2-yl)carbamoyl)methyl)sulfanyl)acetic acid) may be a novel candidate for treating breast cancer.

Regulatory T cell-associated gene signature correlates with prognostic risk and immune infiltration in patients with breast cancer.

Regulatory T cells (Tregs) play a pivotal role in the development, prognosis, and treatment of breast cancer. This study aimed to develop a Treg-associated gene signature that contributes to predict prognosis and therapy benefits in breast cancer. Treg-associated genes were screened based on single-cell RNA-sequencing (RNA-seq) in TISCH2 database and the bulk RNA-seq in The Cancer Genome Atlas (TCGA) database. Treg-associated gene signature was identified via survival analysis, univariate cox, least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression analyses. Immune status was assessed using single-sample gene set enrichment analysis (ssGSEA) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms. Drug sensitivity was estimated using pRRophetic. Gene set enrichment analysis (GSEA) was conducted to explore the changed pathways. A total of 169 genes were identified as Treg-associated genes, and close interactions existed among these genes. Kaplan-Meier (KM) survival and univariate cox revealed 29 prognostic genes (all P<0.05), and finally a six-gene prognostic signature including TBC1D4, PMAIP1, IFNG, LEF1, MZB1 and EZR was identified by LASSO and multivariable Cox. Based on this signature, patients in high-risk group exhibited a worse survival probability than those in low-risk group in the TCGA training dataset (P<0.001). Additionally, this signature showed a moderate predictive power for 1-, 3- and 5-year survival for breast cancer patients in both training dataset [area under the curve (AUC) =0.705, 0.678 and 0.668, respectively]. Similar predictive power for 1-, 3- and 5-year survival was also observed in validation datasets. Risk scores significantly differed between subgroups divided by clinicopathologic features, especially by molecular subtypes. Patients in high- and low-risk groups showed significant differences on infiltration abundance of multiple types of immune cells (such as, activated B cells/CD8+ T cells/CD4+ T cells), immune and stromal scores (all P<0.05). Moreover, sensitivity to 83 chemotherapeutic drugs such as lapatinib, methotrexate, and gefitinib were significantly differed between the two risk groups (all P<0.001). This is the first to develop a Treg-associated gene signature for breast cancer, which could predict prognosis of patients and help to identify patients who might be benefit from immunotherapy and/or chemotherapy.

Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model.

Breast cancer prognosis remains a significant challenge due to the disease's molecular heterogeneity and complexity. Accurate predictive models are critical for improving patient outcomes and tailoring personalized therapies. We developed a Machine Learning-assisted Telomerase Signature (MLTS) by integrating multi-omics data from nine independent breast cancer datasets. Using multiple machine learning algorithms, we identified six telomerase-related genes significantly associated with patient survival. The predictive performance of MLTS was evaluated against 66 existing breast cancer prognostic models across diverse cohorts. The MLTS demonstrated superior predictive accuracy, stability, and reliability compared to other models. Patients with high MLTS scores exhibited increased tumor mutational burden, chromosomal instability, and poor survival outcomes. Single-cell RNA sequencing analysis further revealed higher MLTS scores in aneuploid tumor cells, suggesting a role in cancer progression. Immune profiling indicated distinct tumor microenvironment characteristics associated with MLTS scores, potentially guiding therapeutic decisions. Our findings highlight the utility of MLTS as a robust prognostic biomarker for breast cancer. The ability of MLTS to predict patient outcomes and its association with key genomic and cellular features underscore its potential as a target for personalized therapy. Future research may focus on integrating MLTS with additional molecular signatures to enhance its clinical application in precision oncology.

Identification of a novel prognostic signature for breast cancer derived from post-translational ubiquitin and ubiquitin-like modification-related genes.

Ubiquitin and ubiquitin-like (UUL) modifications play pleiotropic functions and are subject to fine regulatory mechanisms frequently altered in cancer. However, the comprehensive impact of UUL modification on breast cancer remains unclear. Transcriptomic and clinical data of breast cancer were downloaded from TCGA and GEO databases. Molecular subtyping of breast cancer was conducted using the NMF and CIBERSORT algorithms. Prognostic genes were identified via univariate, lasso and multivariate Cox regression analyses. Clinical pathological features, immune cell infiltration, immune therapeutic response and chemotherapy drug sensitivity were compared between groups using the Wilcoxon test. Survival analysis was performed using the Kaplan-Meier method and log-rank test. A total of 63 UUL modification-related genes were differentially expressed, with 29 up-regulated and 34 down-regulated genes. These genes were used to generate two UUL modification patterns that exhibited significant differences in prognostic features and immune cell infiltration. The UUL modification patterns were associated with 2038 differentially expressed genes that were significantly enriched in nuclear division, chromosome segregation, neuroactive ligand-receptor interaction, cell cycle, and other biological processes. Of these genes, 425 were associated with breast cancer prognosis, which enabled the classification of breast cancer into two clusters with significantly distinct prognoses. We developed a prognostic model, UULscore, which comprised nine genes and showed a significant correlation with partial immune cell infiltration. Furthermore, UULscore demonstrated potential predictive value in breast cancer overall survival prediction, immune therapeutic response, and chemotherapy drug sensitivity. UULscore, stage, radiotherapy, and chemotherapy were identified as independent prognostic factors for breast cancer. Based on these factors, a nomogram model was constructed, which demonstrated exceptional prognostic predictive performance. The present study identified two UUL modification-derived molecular subtypes in breast cancer, and have successfully constructed a risk-scoring model that holds potential value in prognosis, immune infiltration, immune therapeutic response, and chemotherapy sensitivity.

Machine Learning-Based Prognostic Gene Signature for Early Triple Negative Breast Cancer.

This study aimed to develop a machine learning-based approach to identify prognostic gene signatures for early-stage Triple Negative Breast Cancer (TNBC) using next-generation sequencing data from Asian populations. We utilized next-generation sequencing data to analyze gene expression profiles and identify potential biomarkers. Our methodology involved integrating various machine learning techniques, including feature selection and model optimization. We employed logistic regression, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curves to validate the identified gene signatures. We identified a gene signature significantly associated with relapse in TNBC patients. The predictive model demonstrated robustness and accuracy, with an area under the ROC curve (AUROC) of 0.9087, sensitivity of 0.8750, and specificity of 0.9231. The Kaplan-Meier survival analysis revealed a strong association between the gene signature and patient relapse, further validated by logistic regression analysis. This study presents a novel machine learning-based prognostic tool for TNBC, offering significant implications for early detection and personalized treatment. The identified gene signature provides a promising approach for improving the management of TNBC, contributing to the advancement of precision oncology.

Integrated single-cell analysis reveals distinct epigenetic-regulated cancer cell states and a heterogeneity-guided core signature in tamoxifen-resistant breast cancer.

Inter- and intra-tumor heterogeneity is considered a significant factor contributing to the development of endocrine resistance in breast cancer. Recent advances in single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) allow us to explore inter- and intra-tumor heterogeneity at single-cell resolution. However, such integrated single-cell analysis has not yet been demonstrated to characterize the transcriptome and chromatin accessibility in breast cancer endocrine resistance. In this study, we conducted an integrated analysis combining scRNA-seq and scATAC-seq on more than 80,000 breast tissue cells from two normal tissues (NTs), three primary tumors (PTs), and three tamoxifen-treated recurrent tumors (RTs). A variety of cell types among breast tumor tissues were identified, PT- and RT-specific cancer cell states (CSs) were defined, and a heterogeneity-guided core signature (HCS) was derived through such integrated analysis. Functional experiments were performed to validate the oncogenic role of BMP7, a key gene within the core signature. We observed a striking level of cell-to-cell heterogeneity among six tumor tissues and delineated the primary to recurrent tumor progression, underscoring the significance of these single-cell level tumor cell clusters classified from scRNA-seq data. We defined nine CSs, including five PT-specific, three RT-specific, and one PT-RT-shared CSs, and identified distinct open chromatin regions of CSs, as well as a HCS of 137 genes. In addition, we predicted specific transcription factors (TFs) associated with the core signature and novel biological/metabolism pathways that mediate the communications between CSs and the tumor microenvironment (TME). We finally demonstrated that BMP7 plays an oncogenic role in tamoxifen-resistant breast cancer cells through modulating MAPK signaling pathways. Our integrated single-cell analysis provides a comprehensive understanding of the tumor heterogeneity in tamoxifen resistance. We envision this integrated single-cell epigenomic and transcriptomic measure will become a powerful approach to unravel how epigenetic factors and the tumor microenvironment govern the development of tumor heterogeneity and to uncover potential therapeutic targets that circumvent heterogeneity-related failures.

Abstract B005: Investigating distinct methylation signatures characteristic of breast cancer subtypes in residual disease via cell free DNA methylation

Abstract Breast cancer is the most common type of cancer in females and recurrence increases over time, unlike many other cancers. Treatment for recurrent cancer is often the same as the primary with no additional biopsies taken. Current research suggests that subtype switching and tumor character changes frequently occur between primary and recurrent breast cancers. Therefore, it may be beneficial to patients to switch treatment based on these changes. Since physical biopsies are cumbersome and not always feasible, liquid biopsies open a way to monitor tumor changes less invasively and more comprehensively. It has been previously established that cell free DNA (cfDNA) shed into the bloodstream from dying cells can reflect cell type of origin via methylation pattern and rate of death via concentration of cfDNA. In this pilot study, we seek to look at the cfDNA of fifteen late stage pre-/ post-surgical breast cancer patients who also received radiation treatment. We will perform cfDNA extraction on the serum of these patients and both whole genome bisulfite sequencing (WGBS), which chemically converts unmethylated cytosine to uracil/thymine in the DNA and is the current gold-standard of methylation sequencing, and a newer method, enzymatic methylation sequencing (EM-seq), which enzymatically converts (TET2/APOBEC) unmethylated cytosine to uracil/thymine and potentially preserves more of the cfDNA. To validate any signatures found in the cfDNA of the breast cancer patients, we have begun the genomic DNA (gDNA) extraction and WGBS/ EM-seq protocols on a variety of breast cancer cell lines including: MCF10A, MCFDCIS, MCF7, T47D, BT474 MDA MB453, MDA MB436 and MDA MB231 (including in-lab brain, bone, and lung metastatic clones). Bioanalyzer traces are produced from the extracted cfDNA/gDNA and also for the final sequencing libraries. The success of the methylation conversion is evaluated after sequencing data is returned and conversion rates of the cytosine to uracil/thymine are compared to unmethylated DNA control (lambda) and methylated DNA control (pUC19). Once this sequencing data is obtained, we use an in-lab deconvolution algorithm to detect cell types of origin and intend to make the algorithm more robust for cancer cell types as well. We have currently produced breast cancer cell line methylation sequencing libraries and are in the process of producing the libraries for the patient samples. Our current data suggests that there are changes in the cfDNA general fragmentation patterns and cfDNA concentrations between pre-/post- surgery samples. Once our sequencing data is obtained for the patient samples, we will run our deconvolution algorithm. The potential of characterizing breast cancer subtype and progression signatures in cfDNA of late stage pre-/ post-surgical breast cancer patients can have significant impact on patient treatment options. Identifying these breast cancer signatures less invasively and, therefore, more frequently may allow for early and more targeted intervention to improve breast cancer patient outcomes. Citation Format: Amber Alley, Megan McNamara, Sidharth Jain, Anton Wellstein. Investigating distinct methylation signatures characteristic of breast cancer subtypes in residual disease via cell free DNA methylation [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B005.

Machine learning unveils key Redox signatures for enhanced breast Cancer therapy

BackgroundBreast cancer remains a leading cause of mortality among women worldwide, necessitating innovative prognostic models to enhance treatment strategies.MethodsOur study retrospectively enrolled 9,439 breast cancer patients from 12 independent datasets and single-cell data from 12 patients (64,308 cells). Moverover, 30 in-house clinical cohort were collected for validation. We employed a comprehensive approach by combining ten distinct machine learning algorithms across 108 different combinations to scrutinize 88 pre-existing signatures of breast cancer. To affirm the efficacy of our developed model, immunohistochemistry assays were performed. Additionally, we investigated various potential immunotherapeutic and chemotherapeutic interventions.ResultsThis study introduces an Artificial Intelligence-aided Redox Signature (AIARS) as a novel prognostic tool, leveraging machine learning to identify critical redox-related gene signatures in breast cancer. Our results demonstrate that AIARS significantly outperforms existing prognostic models in predicting breast cancer outcomes, offering a robust tool for personalized treatment planning. Validation through immunohistochemistry assays on samples from 30 patients corroborated our results, underscoring the model’s applicability on a wider scale. Furthermore, the analysis revealed that patients with low AIARS expression levels are more responsive to immunotherapy. Conversely, those exhibiting high AIARS were found to be more susceptible to certain chemotherapeutic agents, including vincristine.ConclusionsOur study underscores the importance of redox biology in breast cancer prognosis and introduces a powerful machine learning-based tool, the AIARS, for personalized treatment strategies. By providing a more nuanced understanding of the redox landscape in breast cancer, the AIARS paves the way for the development of redox-targeted therapies, promising to enhance patient outcomes significantly. Future work will focus on clinical validation and exploring the mechanistic roles of identified genes in cancer biology.

Single-cell chemoproteomics identifies metastatic activity signatures in breast cancer.

Protein activity state, rather than protein or mRNA abundance, is a biologically regulated and relevant input to many processes in signaling, differentiation, development, and diseases such as cancer. While there are numerous methods to detect and quantify mRNA and protein abundance in biological samples, there are no general approaches to detect and quantify endogenous protein activity with single-cell resolution. Here, we report the development of a chemoproteomic platform, single-cell activity-dependent proximity ligation, which uses automated, microfluidics-based single-cell capture and nanoliter volume manipulations to convert the interactions of family-wide chemical activity probes with native protein targets into multiplexed, amplifiable oligonucleotide barcodes. We demonstrate accurate, reproducible, and multiplexed quantitation of a six-enzyme (Ag-6) panel with known ties to cancer cell aggressiveness directly in single cells. We further identified increased Ag-6 enzyme activity across breast cancer cell lines of increasing metastatic potential, as well as in primary patient-derived tumor cells and organoids from patients with breast cancer.

Neutrophil-related Signature Characterizes Immune Landscape and Predicts Prognosis of Invasive Breast Cancer.

As a leading prevalent malignancy, breast cancer remains a significant worldwide health issue. Recent research indicates that neutrophils play a crucial role in breast cancer development. The prognostic significance of neutrophil-related genes (NRGs) or the immune landscape of the neutrophil-related signature in invasive breast cancer (IBC) is, nevertheless, unknown. To uncover innovative therapy alternatives, the significance of the neutrophil-related signatures in IBC was evaluated here. Briefly, a prediction model based on neutrophil-related core prognostic genes and The Cancer Genome Atlas data was created (TCGA). The model may assess IBC patients' prognosis. The IBC data from the Gene Expression Omnibus (GEO) confirmed the prognostic accuracy of the model. The overall survival (OS) of patients was worse in the group with a high NRGs score compared to the group with a low NRGs score. In addition, patients with low NRGs scores were considerably more sensitive to vinorelbine, cyclophosphamide, epirubicin, gemcitabine, paclitaxel, 5-fluorouracil, docetaxel, and cisplatin. Patients with low NRGs scores responded better to CTLA-4 and PD-1 treatments. Additionally, the immune microenvironment components were more abundant in patients with low NRGs scores. Moreover, qRT-PCR results confirmed that LEF1 had a higher expression level in tumor samples compared to normal samples, whereas NRG1 and STX11 exhibited lower expression levels in tumor samples than in normal samples. These results suggest that NRGs might be utilized as biomarkers to predict the prognosis of individuals with IBC, thereby paving the way for the creation of customized therapies for IBC.