GEO series
Breast cancer-subtype and infiltrating macrophage signatures predict chemotherapy-response and survival
GSE260693
Homo sapiens
Expression profiling by high throughput sequencing
73 samples
2024/03/20
GPL24676
Summary
Triple-Negative Breast Cancer (TNBC) is a heterogeneous collection of cancers where personalized treatment is difficult and chemotherapy and immunotherapy combinations are the main treatment options. Many attempts to tackle patient heterogeneity have focused on defining cancer-intrinsic subtypes based on differential tumor mRNA-expression across patient cohorts. While these multi-gene diagnostics have shown success in hormone receptor- positive cancers (e.g. OncotypeDX), no TNBC classifiers have shown clinical utility in predicting patient survival or treatment response. We hypothesize that TNBC-infiltrating immune-cells both affect mRNA-based classification and contribute to treatment response variability. To evaluate this hypothesis, we benchmarked the performance of common TNBC-classification (TNBC-type) and infiltrating immune (CIBERSORT) algorithms on the same underlying datasets. Encouragingly, we found that – as with OncotypeDx– highly proliferative TNBC-subtypes (BL1) show the strongest evidence of response to cytotoxic chemotherapies. Interestingly, this cancer-proliferative signature (BL1) is strongly correlated with enrichment in tumor-infiltrating lymphocyte signatures (TIL) which show superior prognostic and predictive power. In addition, Tumor Associated Macrophage (TAM) signatures show independent predictive and prognostic power for both patient survival and response to anthracycline- and taxane-based chemotherapies. These gene signature-based correlations were validated in a new independent cohort of 67 TNBC-patients treated with neoadjuvant chemotherapy. Overall, these results argue for the independent contributions of both cancer-intrinsic and -extrinsic factors in predicting treatment response in the neoadjuvant setting.
Download
NCBI GEO page ↗
{# Names what the click gives you. "Open in finder" meant nothing to a
visitor who arrived from a search engine and has never seen the tool. #}
Find more
human RNA-seq datasets →
Similar datasets
- GSE328275 Single-cell RNA sequencing of CD45+ immune cells across primary tumor, sentinel tumor-draining lymph node, and axillary lymph node in treatment-naive triple-negative breast cancer 28 samples
- GSE341753 Cohesin loading at regulatory elements shapes 3D genome folding during erythropoiesis [RNA-Seq] 12 samples
- GSE319969 Spatial and Bulk Transcriptomic Profiling Defines the Molecular Evolution of Cutaneous Squamous Cell Carcinoma and Reveals Stage-Specific Biomarkers of Clinical Relevance [RNA-Seq] 24 samples
- GSE313035 METIMMOX: Colorectal Cancer METastasis - Shaping Anti-tumor IMMunity by OXaliplatin 67 samples
- GSE339456 Integrated bulk and spatial transcriptomic analysis identifies progression-associated molecular signatures in biopsy-proven hypertensive nephropathy [RNA-seq] 35 samples
- GSE342462 Integrated transcriptomic and bioelectrical profiling of stem-like cellular states in a colorectal cancer using SdFFF and UHF-DEP 12 samples
- GSE330029 Temporal changes in metabolism guide oligodendrocyte precursor cell dynamics in aging and multiple sclerosis [BulkRNAseq] 108 samples
- GSE341139 A conserved HAND2-BMP5-SMAD1/5/9 axis drives hepatic stellate cell activation and extracellular matrix overproduction in multiple fibrotic etiologies 10 samples
Share this dataset
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.