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Bone destruction induced by breast cancer metastasis causes severe complications, including death, in breast cancer patients. Communication between cancer cells and skeletal cells in metastatic bone microenvironments is a principal element that drives tumor progression and osteolysis. Tumor-derived factors play fundamental roles in this form of communication. To identify soluble factors released from cancer cells in bone metastasis, we established a highly bone-metastatic subline of MDA-MB-231 breast cancer cells. This subline (mtMDA) showed a markedly elevated ability to secrete S100A4 protein, which directly stimulated osteoclast formation via surface receptor RAGE. Recombinant S100A4 stimulated osteoclastogenesis in vitro and bone loss in vivo. Conditioned medium from mtMDA cells in which S100A4 was knocked down had a reduced ability to stimulate osteoclasts. Furthermore, the S100A4 knockdown cells elicited less bone destruction in mice than the control knockdown cells. In addition, administration of an anti-S100A4 monoclonal antibody (mAb) that we developed attenuated the stimulation of osteoclastogenesis and bone loss by mtMDA in mice. Taken together, our results suggest that S100A4 released from breast cancer cells is an important player in the osteolysis caused by breast cancer bone metastasis.

Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant genetic variation (synthetic association). Gene-wise variant burden (GVB) is a gene-level measure of deleteriousness, reflecting the cumulative effects of deleterious coding variants, predicted in silico. To test potential associations between noncoding and coding pharmacogenetic variants, we computed a drug-level GVB for 5099 drugs from DrugBank for 2504 genomes of the 1000 Genomes Project and evaluated the correlation between the long-known noncoding variant-drug associations in PharmGKB, with functionally relevant rare and common coding variants aggregated into GVBs. We obtained the area under the receiver operating characteristics curve (AUC) by comparing the drug-level GVB ranks against the corresponding pharmacogenetic variants-drug associations in PharmGKB. We obtained high overall AUCs (0.710 &plusmn, 0.022&ndash, 0.734 &plusmn, 0.018) for six different methods (i.e., SIFT, MutationTaster, Polyphen-2 HVAR, Polyphen-2 HDIV, phyloP, and GERP++), and further improved the ethnicity-specific validations (0.759 &plusmn, 0.066&ndash, 0.791 &plusmn, 0.078). These results suggest that a significant portion of the long-known noncoding variant-drug associations can be explained as synthetic associations with rare and common coding variants burden of the corresponding pharmacogenes.