Breast Cancer Treatment Studies

Explainable Machine Learning Reveals the Role of the Breast Tumor Microenvironment in Neoadjuvant Chemotherapy Outcome

The paper uses scRNA-seq and explainable machine learning to analyze how tumor microenvironment cell types affect neoadjuvant chemotherapy outcomes in breast cancer.

Updated Biomarkers for TNBC in African vs. Caucasian American Women

The paper identifies 23 race-specific prognostic biomarkers for triple-negative breast cancer (TNBC) in African American and Caucasian American women using publicly available data.

Building a genetic and epigenetic predictive model of breast cancer intrinsic subtypes using large-scale data and hierarchical structure learning

The paper develops UGES, a genetic and epigenetic classifier for breast cancer subtypes using hierarchical structure learning, achieving higher AUC and improved clinical stratification.

Machine learning to construct an mRPS risk prognostic model for directing immunotherapy in patients with breast cancer based on the mCAF signature genes

The paper was withdrawn due to inadequate informed consent procedures and is not intended for citation.

Predicting Distant Recurrences in Invasive Breast Carcinoma Patients Using Clinicopathological Data: A cross-institutional and AI-based study

This study uses clinicopathological data and AI models to predict distant recurrences in invasive breast carcinoma patients, showing cross-institutional validity with AUC 0.75-1.0.

Demystifying the role of prognostic biomarkers in breast cancer through integrated transcriptome and pathway enrichment analyses

The paper identifies five hub genes associated with poor prognosis in breast cancer through transcriptome and pathway analyses of TCGA data.

Bioinformatic-based identification of 3 immune-related genes for prognostic in breast invasive carcinoma

This study identifies three immune-related genes (CXCL2, CXCL9, RBP7) as prognostic biomarkers for breast invasive carcinoma using bioinformatics analysis of public datasets.

Proteomic Dynamics of Breast Cancers Identifies Potential Therapeutic Protein Targets

This study identifies a 24-protein panel distinguishing TNBC from other breast cancers and shows proteomics improves drug sensitivity predictions.

ENDORSE: a prognostic model for endocrine therapy response in advanced estrogen-receptor positive breast cancers

The paper develops a prognostic model called ENDORSE to predict endocrine therapy response in advanced ER+ breast cancers using transcriptomic data and survival outcomes.

Machine learning enables accurate prediction of breast cancer five-year survival using somatic genomic variants

This study uses machine learning to predict breast cancer five-year survival using somatic genomic variants and phenotype data, achieving an AUC of 0.70 in an external dataset.

A comprehensive study of mRNA and long noncoding RNAs in Indian Breast cancer patients using transcriptomics approach

This study identifies subtype-specific mRNA and lncRNA expression patterns in Indian breast cancer patients and finds a gene set that segregates pre and postmenopausal women.

Tumor cell intrinsic and extrinsic features predicts prognosis in estrogen receptor positive breast cancer

This study identifies genomic and immunological features that predict prognosis in ER+ breast cancer, showing improved performance over Oncotype DX.

Deep learning based on multi-omics integration identifies potential therapeutic targets in breast cancer

The paper develops a deep learning model integrating multi-omics data to stratify breast cancer patients by prognosis and identify potential therapeutic targets.

Circulating proteins reveal prior use of menopausal hormonal therapy and increased risk of breast cancer

The paper identifies a proteomic signature associated with menopausal hormonal therapy use and increased breast cancer risk using a data-driven analysis of plasma proteins.

Lift the Veil of Breast Cancers Using Four or Fewer Critical Genes

This paper identifies four genes with 100% accuracy in classifying breast cancer subtypes using a competing linear factor analysis method, showing robust and precise results across multiple studies.

Gene-level germline contributions to clinical risk of recurrence scores in Black and White breast cancer patients

The paper investigates race-specific germline genetic associations with breast cancer recurrence scores using GReX and finds differential associations between germline variants and CRS in Black and White patients.

Transcriptomics based prediction of metastasis in TNBC patients: Challenges in cross-platforms validation

This paper identifies 15 gene signatures for predicting lymph node metastasis in TNBC using TCGA data, but finds poor cross-platform validation and develops a webserver for prediction.

Exploring the metastatic role of the inhibitor of apoptosis BIRC6 in Breast Cancer

The paper investigates BIRC6's role in breast cancer metastasis using genomic and transcriptomic analyses of TCGA data, finding it correlates with poor prognosis and metastatic pathways.

Survival Outcomes are Associated with Genomic Instability in Luminal Breast Cancers

The paper examines the association between genomic instability, measured by copy number alterations, and survival outcomes in luminal breast cancers, finding that higher CNA burden correlates with poorer survival in luminal A grade 3 tumors.

Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches

The paper develops a machine learning predictor for TNBC subtyping using a limited number of probes, achieving better performance with gradient boosting.

Identification of Key Genes Potentially Related to Triple Receptor Negative Breast Cancer by Microarray Analysis

This study identifies potential biomarkers for triple receptor negative breast cancer using microarray analysis and bioinformatics tools.

Stagewise identification of significantly mutated gene clusters in major molecular classes of breast invasive carcinoma

The paper identifies significantly mutated gene clusters in major molecular classes of breast invasive carcinoma using RNA-sequencing data and validates findings through experimental methods.

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

This study identifies essential genes in breast cancer using OncoOmics approaches, revealing 144 genes with 33% being cancer hallmarks and 14% significant across multiple methods.

Immune gene expression profiling reveals heterogeneity in luminal breast tumors

The paper identifies three immune subtypes in luminal breast cancer through immune gene expression profiling and links them to distinct genomic and clinical features.

HspA1B Is a Prognostic Biomarker and Correlated With Immune Infiltrates in different subtypes of Breast Cancers

This study identifies HspA1B as a prognostic biomarker in breast cancer and shows its correlation with immune infiltrates across multiple cancer types.

Claudin-low-like mouse mammary tumors show distinct transcriptomic patterns uncoupled from genomic drivers

The paper identifies claudin-low-like mouse mammary tumors with transcriptomic similarities to human claudin-low breast cancer, but no specific genomic drivers distinguish them.

Evaluating cell lines as models for metastatic cancer through integrative analysis of open genomic data

This study evaluates the genomic similarity between breast cancer cell lines and metastatic samples, finding significant differences and highlighting organoids as better models.

The prognostic effects of somatic mutations in ER-positive breast cancer

The paper identifies somatic mutations with prognostic effects in ER+ breast cancer using genomic data from multiple cohorts.

Reparameterization of PAM50 expression identifies novel breast tumor dimensions and leads to discovery of a breast cancer susceptibility locus at 12q15

The paper reparameterizes PAM50 gene expression into tumor dimensions and identifies a breast cancer susceptibility locus at 12q15 through genetic analysis of high-risk pedigrees.

Methylation-To-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, And Pathologic Complete Response in Multiple Cohorts

The paper develops and validates M2EFM, a methylation-to-expression model, to predict breast cancer outcomes across multiple cohorts with good accuracy and clinical utility.