Breast Cancer Treatment Studies

Repurposing Quetiapine as an Adjuvant Therapeutic Agent for Triple-Negative Breast Cancer

This study evaluates quetiapine as an adjuvant therapy for triple-negative breast cancer, showing it reduces clonogenicity, promotes apoptosis, and impairs DNA repair and migration.

Consensus Through Diversity: A Comprehensive Benchmark of Multi-Omic Approaches for Precision Breast Oncology

The paper benchmarks 22 multi-omic methods on breast cancer data to identify reliable clustering approaches and provides a decision workflow for selecting methods based on study goals and data characteristics.

Elucidation of putative key genes involved in the regulation of triple negative breast cancer development and progression

This study identifies key genes and pathways in triple-negative breast cancer using bioinformatics and experimental validation, finding six hub genes associated with tumor progression.

Topological Analysis of the Human Lymph Node Reticular Network Predicts Outcome in Breast Cancer

The paper analyzes the topology of fibroblastic reticular cell networks in lymph nodes to predict breast cancer outcomes, finding that denser networks correlate with better or worse survival depending on disease stage and treatment.

Integrative Transcriptomic and Machine Learning Analysis of ecDNA-Associated Features for Studying Chemotherapy Resistance in TNBC

This study uses transcriptomic analysis and machine learning to identify ecDNA-associated features linked to chemotherapy resistance in TNBC, finding ecDNA burden and prevalence as key predictors of drug resistance.

Relationship Between Gene Expression and Drug Response in Triple-Negative Breast Cancer: Leveraging Single-Cell RNA Sequencing and Machine Learning to Identify Biomarker Profiles

This study identifies biomarkers for drug response in TNBC using scRNA-seq and machine learning, showing high predictive accuracy and highlighting blood-derived biomarkers.

In Silico Identification of Aminoadipate Semialdehyde Synthase (AASS) as a Novel Prognostic Biomarker in Triple-Negative Breast Cancer

The paper identifies AASS as a novel prognostic biomarker in TNBC using network biology and machine learning, showing its association with favorable prognosis.

Discovery and evaluation of novel biomarkers reveal dasatinib as a potential treatment for a specific subtype of Triple-Negative Breast Cancer

This study identifies BC-markers in TNBC, links them to poor prognosis and dasatinib sensitivity, suggesting TAGLN as a predictive biomarker for targeted therapy.

Hybrid Stacking-Bagging Ensembles for Robust Multi-Omics Breast Cancer Prognosis

This paper introduces a hybrid stacking-bagging ensemble framework for multi-omics breast cancer prognosis, achieving higher accuracy than unimodal models and conventional stacking.

Immune Profiling the Axilla with Fine Needle Aspiration is Feasible to Risk-Stratify Breast Cancer

This paper shows that fine needle aspiration of axillary lymph nodes can capture immune cell profiles that reflect breast cancer metastasis status and help risk-stratify patients.

Mapping lineage and functional diversity in the high-risk human mammary epithelium

The paper profiles proteomic and functional features of mammary epithelial cells in high-risk women, showing how clinical factors influence cellular composition and tumor risk.

Enhanced co-expression of cyclin F and USP7 in luminal A breast cancer correlates with endocrine resistance, high oxidative phosphorylation, and low inflammatory signatures

The paper investigates the USP7-cyclin F axis in HR+/HER2- breast cancer, finding that high co-expression correlates with endocrine resistance, oxidative phosphorylation, and low inflammation.

Exploring the Role of APC in Modulating Chemotherapeutic Response in Triple-Negative Breast Cancer cells

This study investigates how APC depletion affects chemotherapeutic response in TNBC cells, identifying a 43-gene signature linked to chemotherapy tolerance and resilience.

Decoding the Tumor-Suppressive Landscape of SCARA5: A Network-Based Analysis Linking Lipid Metabolism and Immune Regulation in Breast Cancer

The paper identifies SCARA5 as a tumor suppressor in breast cancer, showing its downregulation in aggressive subtypes and its role in lipid metabolism and immune regulation through transcriptomic and spatial analyses.

Spatially distinct cellular and molecular landscapes define prognosis in triple negative breast cancer

This study uses spatial transcriptomics to identify molecular and cellular differences between good and poor prognosis triple-negative breast cancer subtypes, revealing distinct gene signatures that could inform therapy.

Monoallelic expression characterizes a distinct molecular and clinical group of breast tumors

The paper identifies a distinct breast tumor group with monoallelic gene expression linked to poor prognosis and develops a method to detect allelic imbalance.

Identifying Novel Therapeutic Targets for Overcoming TNBC Chemo Resistance Through Comprehensive CRISPR-Cas9 Genome Screening

This study uses CRISPR-Cas9 and RNAseq to identify novel synthetic lethal targets in TNBC cells resistant to cisplatin/doxorubicin, highlighting potential drug targets for combination therapies.

Proteogenomic Characterization of Triple-Negative Apocrine Carcinoma Reveals Molecular Features of Progression and Chemotherapy Response

This study identifies molecular features of triple-negative apocrine carcinoma through proteogenomic analysis, revealing resistance mechanisms and suggesting alternative therapeutic strategies.

PURPL is associated with adverse clinical outcome in Luminal A breast cancer

The paper identifies PURPL as a long non-coding RNA associated with poor clinical outcomes in Luminal A breast cancer through copy number, expression, and survival analyses across multiple cohorts.

Integrative single-cell and spatial mapping of oxidative stress response uncovers GCLC+ mesenchymal tumor cell state linked with favorable outcomes in triple negative breast cancer

The paper maps oxidative stress response in TNBC using single-cell and spatial data, identifying GCLC+ mesenchymal tumor cells linked to better patient outcomes.

A machine learning framework for supervised treatment response prediction from tumor transcriptomics: A large-scale pan-cancer study

This paper develops a machine learning framework, EXPRESSO, to predict treatment response from tumor transcriptomics across multiple cancer types and therapies, showing improved performance over existing methods.

Spatial organization of the tumor-immune microenvironment in ER-positive breast cancer: remodeling during treatment and associations with clinical response

This study examines how the spatial organization of the tumor-immune microenvironment changes during treatment in ER-positive breast cancer and links these changes to clinical response.

Risk factors, prognostic factors, and nomograms for distant metastasis in patients with triple-negative breast cancer: a population-based study

This study identifies risk factors for distant metastasis in triple-negative breast cancer and develops nomograms to predict prognosis and guide treatment.

FOXM1 Expression in Invasive Ductal Breast Carcinoma of No Special Type: Insights from RNA-seq and Immunohistochemical Analysis

This study investigates FOXM1 expression in invasive ductal breast carcinoma of no special type using RNA-seq and IHC, finding it correlates with poor prognosis and tumor progression.

MyGESig: a population-specific gene signature improves survival prediction in Malaysian breast cancer patients

The paper develops a population-specific gene signature, MyGESig, which improves survival prediction in Malaysian breast cancer patients compared to existing models.

Organoid-evaluable clinical biomarkers predict drug responses and guide new breast cancer therapies

The paper develops predictive models using patient-derived breast cancer organoids to identify drug responses and guide new therapies for resistant disease.

An annotated biobank of triple negative breast cancer patient-derived xenografts featuring treatment-naive and longitudinal samples throughout neoadjuvant chemotherapy

The paper establishes a biobank of TNBC patient-derived xenografts with treatment-naive and longitudinal samples to study chemoresistance and metastasis.

Identifying Molecular Determinants and Therapeutic Targets in Luminal B Breast Cancer: A Systems Biology Approach

The paper identifies key driver genes, transcription factors, miRNAs, and pathways in Luminal B breast cancer using in silico analysis of gene expression data.

Invasive lobular carcinoma integrated multi-omics analysis reveals silencing of Arginosuccinate synthase and upregulation of nucleotide biosynthesis in tamoxifen resistance

This study identifies silencing of ASS1 and upregulation of nucleotide biosynthesis as mechanisms of tamoxifen resistance in invasive lobular carcinoma.

Exploring ID4 as a Driver of Aggression and a Therapeutic Target in Triple-Negative Breast Cancer

This study identifies ID4 as a driver of aggressive basal-like breast cancer and shows that its inhibition reduces tumor growth and metastasis, suggesting it as a therapeutic target.