Breast Cancer Treatment Studies

This paper evaluates machine learning models to classify breast cancer stages using gene expression data and identifies co-expressed genes associated with stage, though results are limited by small sample size and lack of external validation.

The paper identifies robust and clinically relevant gene signatures in breast cancer by integrating two RNA-seq workflows and validating them across multiple datasets and analyses.

The paper identifies three TNBC subtypes based on spatial transcriptomics and cellular mapping, revealing distinct tumor microenvironment architectures and potential therapeutic targets.

The paper uses unsupervised clustering to classify chemoimmunomodulatory trajectories in breast cancer, identifying two distinct patterns associated with different clinical outcomes.

The paper evaluates multi-omics prognostic models across TCGA-BRCA and METABRIC, finding limited incremental performance and highlighting issues with model transportability and feature stability.

The paper develops an analytical pipeline to identify and target endocrine therapy-insensitive cells in ER+ breast cancer using single-cell RNA sequencing and functional validation in organoid models.

The paper identifies a 6-gene stromal signature, Stroma6, that predicts early relapse in low-risk and chemoresistant breast cancer patients, independent of Oncotype DX results.

This study shows that SU056, a YB1 inhibitor, suppresses TNBC growth and metastasis by targeting the Rb pathway, with enhanced efficacy when combined with CDK4/6 inhibition.

This paper constructs a unified transcriptional landscape of breast cancer using bulk RNA-seq data from multiple cohorts, identifying subtype and microenvironmental programs associated with therapeutic resistance.

The paper adjusts bulk gene correlations for stromal and vascular compartments, showing FOXC1 in Luminal A breast cancer reflects vascular rather than tumour-cell biology.

This study compares immune cell deconvolution and cancer subtype gene signatures in TNBC, finding that immune aggregates better predict outcomes than subtype signatures.

The paper identifies a ten-gene transcriptomic signature from single-cell data that predicts prognosis and therapy response in triple-negative breast cancer.

The paper identifies increased GPCR pathway activity in breast cancer ovarian metastases and links it to migration and F-actin reorganization through CaSR.

The paper identifies ECM proteomic signatures associated with disease-free survival in DCIS and IBC, showing reduced collagen peptides correlate with poorer outcomes.

This study characterizes the E0771 cell line as a triple-negative breast cancer model through molecular and functional analysis, confirming its hormone resistance and basal-like identity.

This study identifies subtype-specific molecular and immune features associated with recurrence risk in breast cancer using multi-omic analysis of 340 tumors and validates predictive models in an external cohort.

This study evaluates existing TNBC prognostic signatures and develops a robust 13-gene model with cross-cohort validation and therapeutic prioritization.

This study identifies adaptive stress response (ASR) signatures in inflammatory breast cancer (IBC) that correlate with tumor emboli survival, dissemination, and therapeutic response.

This study investigates the transcriptomic effects of LKB1 loss in breast cancer using multiomic approaches, revealing subtype-specific gene expression changes and a role in TNBC.

The paper develops a 15-gene prognostic signature and nomogram for TNBC using multi-omic data integration and machine learning, showing strong prognostic separation and external validation.

This study identifies proliferation and stress-adaptation mechanisms in MTUS1-low TNBC and proposes therapeutic strategies based on CRISPR-derived dependency signals.

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.

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.

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.

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

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

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.

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.

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