RESUMEN
Breast cancer in young patients is known to exhibit more aggressive biological behavior and is associated with a less favorable prognosis than the same disease in older patients, owing in part to an increased incidence of brain metastases. The mechanistic explanations behind these findings remain poorly understood. We recently reported that young mice, in comparison to older mice, developed significantly greater brain metastases in four mouse models of triple-negative and luminal B breast cancer. Here we have performed a quantitative mass spectrometry-based proteomic analysis to identify proteins potentially contributing to age-related disparities in the development of breast cancer brain metastases. Using a mouse hematogenous model of brain-tropic triple-negative breast cancer (MDA-MB-231BR), we harvested subpopulations of tumor metastases, the tumor-adjacent metastatic microenvironment, and uninvolved brain tissues via laser microdissection followed by quantitative proteomic analysis using high resolution mass spectrometry to characterize differentially abundant proteins potentially contributing to age-dependent rates of brain metastasis. Pathway analysis revealed significant alterations in signaling pathways, particularly in the metastatic microenvironment, modulating tumorigenesis, metabolic processes, inflammation, and neuronal signaling. Tenascin C (TNC) was significantly elevated in all laser microdissection (LMD) enriched compartments harvested from young mice relative to older hosts, which was validated and confirmed by immunoblot analysis of whole brain lysates. Additional in vitro studies including migration and wound-healing assays demonstrated TNC as a positive regulator of tumor cell migration. These results provide important new insights regarding microenvironmental factors, including TNC, as mechanisms contributing to the increased brain cancer metastatic phenotype observed in young breast cancer patients.
RESUMEN
SUMMARY: Exclusion regions are sections of reference genomes with abnormal pileups of short sequencing reads. Removing reads overlapping them improves biological signal, and these benefits are most pronounced in differential analysis settings. Several labs created exclusion region sets, available primarily through ENCODE and Github. However, the variety of exclusion sets creates uncertainty which sets to use. Furthermore, gap regions (e.g. centromeres, telomeres, short arms) create additional considerations in generating exclusion sets. We generated exclusion sets for the latest human T2T-CHM13 and mouse GRCm39 genomes and systematically assembled and annotated these and other sets in the excluderanges R/Bioconductor data package, also accessible via the BEDbase.org API. The package provides unified access to 82 GenomicRanges objects covering six organisms, multiple genome assemblies, and types of exclusion regions. For human hg38 genome assembly, we recommend hg38.Kundaje.GRCh38_unified_blacklist as the most well-curated and annotated, and sets generated by the Blacklist tool for other organisms. AVAILABILITY AND IMPLEMENTATION: https://bioconductor.org/packages/excluderanges/. Package website: https://dozmorovlab.github.io/excluderanges/.
