RHAMM regulates MMTV-PyMT-induced lung metastasis by connecting STING-dependent DNA damage sensing to interferon/STAT1 pro-apoptosis signaling.

BACKGROUND: RHAMM is a multifunctional protein that is upregulated in breast tumors, and the presence of strongly RHAMM+ve cancer cell subsets associates with elevated risk of peripheral metastasis. Experimentally, RHAMM impacts cell cycle progression and cell migration. However, the RHAMM functions that contribute to breast cancer metastasis are poorly understood. METHODS: We interrogated the metastatic functions of RHAMM using a loss-of-function approach by crossing the MMTV-PyMT mouse model of breast cancer susceptibility with Rhamm-/- mice. In vitro analyses of known RHAMM functions were performed using primary tumor cell cultures and MMTV-PyMT cell lines. Somatic mutations were identified using a mouse genotyping array. RNA-seq was performed to identify transcriptome changes resulting from Rhamm-loss, and SiRNA and CRISPR/Cas9 gene editing was used to establish cause and effect of survival mechanisms in vitro. RESULTS: Rhamm-loss does not alter initiation or growth of MMTV-PyMT-induced primary tumors but unexpectedly increases lung metastasis. Increased metastatic propensity with Rhamm-loss is not associated with obvious alterations in proliferation, epithelial plasticity, migration, invasion or genomic stability. SNV analyses identify positive selection of Rhamm-/- primary tumor clones that are enriched in lung metastases. Rhamm-/- tumor clones are characterized by an increased ability to survive with ROS-mediated DNA damage, which associates with blunted expression of interferon pathway and target genes, particularly those implicated in DNA damage-resistance. Mechanistic analyses show that ablating RHAMM expression in breast tumor cells by siRNA knockdown or CRISPR-Cas9 gene editing blunts interferon signaling activation by STING agonists and reduces STING agonist-induced apoptosis. The metastasis-specific effect of RHAMM expression-loss is linked to microenvironmental factors unique to tumor-bearing lung tissue, notably high ROS and TGFB levels. These factors promote STING-induced apoptosis of RHAMM+ve tumor cells to a significantly greater extent than RHAMM-ve comparators. As predicted by these results, colony size of Wildtype lung metastases is inversely related to RHAMM expression. CONCLUSION: RHAMM expression-loss blunts STING-IFN signaling, which offers growth advantages under specific microenvironmental conditions of lung tissue. These results provide mechanistic insight into factors controlling clonal survival/expansion of metastatic colonies and has translational potential for RHAMM expression as a marker of sensitivity to interferon therapy.

Genomic copy number variation in Mus musculus.

BACKGROUND: Copy number variation is an important dimension of genetic diversity and has implications in development and disease. As an important model organism, the mouse is a prime candidate for copy number variant (CNV) characterization, but this has yet to be completed for a large sample size. Here we report CNV analysis of publicly available, high-density microarray data files for 351 mouse tail samples, including 290 mice that had not been characterized for CNVs previously. RESULTS: We found 9634 putative autosomal CNVs across the samples affecting 6.87% of the mouse reference genome. We find significant differences in the degree of CNV uniqueness (single sample occurrence) and the nature of CNV-gene overlap between wild-caught mice and classical laboratory strains. CNV-gene overlap was associated with lipid metabolism, pheromone response and olfaction compared to immunity, carbohydrate metabolism and amino-acid metabolism for wild-caught mice and classical laboratory strains, respectively. Using two subspecies of wild-caught Mus musculus, we identified putative CNVs unique to those subspecies and show this diversity is better captured by wild-derived laboratory strains than by the classical laboratory strains. A total of 9 genic copy number variable regions (CNVRs) were selected for experimental confirmation by droplet digital PCR (ddPCR). CONCLUSION: The analysis we present is a comprehensive, genome-wide analysis of CNVs in Mus musculus, which increases the number of known variants in the species and will accelerate the identification of novel variants in future studies.

Biological relevance of CNV calling methods using familial relatedness including monozygotic twins.

BACKGROUND: Studies involving the analysis of structural variation including Copy Number Variation (CNV) have recently exploded in the literature. Furthermore, CNVs have been associated with a number of complex diseases and neurodevelopmental disorders. Common methods for CNV detection use SNP, CNV, or CGH arrays, where the signal intensities of consecutive probes are used to define the number of copies associated with a given genomic region. These practices pose a number of challenges that interfere with the ability of available methods to accurately call CNVs. It has, therefore, become necessary to develop experimental protocols to test the reliability of CNV calling methods from microarray data so that researchers can properly discriminate biologically relevant data from noise. RESULTS: We have developed a workflow for the integration of data from multiple CNV calling algorithms using the same array results. It uses four CNV calling programs: PennCNV (PC), Affymetrix® Genotyping Console™ (AGC), Partek® Genomics Suite™ (PGS) and Golden Helix SVS™ (GH) to analyze CEL files from the Affymetrix® Human SNP 6.0 Array™. To assess the relative suitability of each program, we used individuals of known genetic relationships. We found significant differences in CNV calls obtained by different CNV calling programs. CONCLUSIONS: Although the programs showed variable patterns of CNVs in the same individuals, their distribution in individuals of different degrees of genetic relatedness has allowed us to offer two suggestions. The first involves the use of multiple algorithms for the detection of the largest possible number of CNVs, and the second suggests the use of PennCNV over all other methods when the use of only one software program is desirable.