Infiltrating S100A8+ myeloid cells promote metastatic spread of human breast cancer and predict poor clinical outcome.

The mechanisms by which breast cancer (BrC) can successfully metastasize are complex and not yet fully understood. Our goal was to identify tumor-induced stromal changes that influence metastatic cell behavior, and may serve as better targets for therapy. To identify stromal changes in cancer-bearing tissue, dual-species gene expression analysis was performed for three different metastatic BrC xenograft models. Results were confirmed by immunohistochemistry, flow cytometry, and protein knockdown. These results were validated in human clinical samples at the mRNA and protein level by retrospective analysis of cohorts of human BrC specimens. In pre-clinical models of BrC, systemic recruitment of S100A8+ myeloid cells-including myeloid-derived suppressor cells (MDSCs)-was promoted by tumor-derived factors. Recruitment of S100A8+ myeloid cells was diminished by inhibition of tumor-derived factors or depletion of MDSCs, resulting in fewer metastases and smaller primary tumors. Importantly, these MDSCs retain their ability to suppress T cell proliferation upon co-culture. Secretion of macrophage inhibitory factor (MIF) activated the recruitment of S100A8+ myeloid cells systemically. Inhibition of MIF, or depletion of MDSCs resulted in delayed tumor growth and lower metastatic burden. In human BrC specimens, increased mRNA and protein levels of S100A8+ infiltrating cells are highly associated with poor overall survival and shorter metastasis free survival of BrC patients, respectively. Furthermore, analysis of nine different human gene expression datasets confirms the association of increased levels of S100A8 transcripts with an increased risk of death. Recruitment of S100A8+ myeloid cells to primary tumors and secondary sites in xenograft models of BrC enhances cancer progression independent of their suppressive activity on T cells. In clinical samples, infiltrating S100A8+ cells are associated with poor overall survival. Targeting these molecules or associated pathways in cells of the tumor microenvironment may translate into novel therapeutic interventions and benefit patient outcome.

Utility of redundant combinatorial libraries in distinguishing high and low quality screening hits.

Large one-bead one-compound (OBOC) combinatorial libraries can be constructed relatively easily by solid-phase split and pool synthesis. The use of resins with hydrophilic surfaces, such as TentaGel, allows the beads to be used directly in screens for compounds that bind selectively to labeled proteins, nucleic acids, or other biomolecules. However, we have found that this method, while useful, has a high false positive rate. In other words, beads that are scored as hits often display compounds that prove to be poor ligands for the target of interest when they are resynthesized and carried through validation trials. This results in a significant waste of time and resources in cases where putative hits cannot be validated without resynthesis. Here, we report that this problem can be largely eliminated through the use of redundant OBOC libraries, where more than one bead displaying the same compound is present in the screen. We show that compounds isolated more than once are likely to be high quality ligands for the target of interest, whereas compounds isolated only once have a much higher likelihood of being poor ligands. While the use of redundant libraries does limit the number of unique compounds that can be screened at one time in this format, the overall savings in time, effort, and materials makes this a more efficient route to the isolation of useful ligands for biomolecules.

A new mouse model for the study of human breast cancer metastasis.

Breast cancer is the most common cancer in women, and this prevalence has a major impact on health worldwide. Localized breast cancer has an excellent prognosis, with a 5-year relative survival rate of 85%. However, the survival rate drops to only 23% for women with distant metastases. To date, the study of breast cancer metastasis has been hampered by a lack of reliable metastatic models. Here we describe a novel in vivo model using human breast cancer xenografts in NOD scid gamma (NSG) mice; in this model human breast cancer cells reliably metastasize to distant organs from primary tumors grown within the mammary fat pad. This model enables the study of the entire metastatic process from the proper anatomical site, providing an important new approach to examine the mechanisms underlying breast cancer metastasis. We used this model to identify gene expression changes that occur at metastatic sites relative to the primary mammary fat pad tumor. By comparing multiple metastatic sites and independent cell lines, we have identified several gene expression changes that may be important for tumor growth at distant sites.

Whole genome in vivo RNAi screening identifies the leukemia inhibitory factor receptor as a novel breast tumor suppressor.

Cancer is caused by mutations in oncogenes and tumor suppressor genes, resulting in the deregulation of processes fundamental to the normal behavior of cells. The identification and characterization of oncogenes and tumor suppressors has led to new treatment strategies that have significantly improved cancer outcome. The advent of next generation sequencing has allowed the elucidation of the fine structure of cancer genomes, however, the identification of pathogenic changes is complicated by the inherent genomic instability of cancer cells. Therefore, functional approaches for the identification of novel genes involved in the initiation and development of tumors are critical. Here we report the first whole human genome in vivo RNA interference screen to identify functionally important tumor suppressor genes. Using our novel approach, we identify previously validated tumor suppressor genes including TP53 and MNT, as well as several novel candidate tumor suppressor genes including leukemia inhibitory factor receptor (LIFR). We show that LIFR is a key novel tumor suppressor, whose deregulation may drive the transformation of a significant proportion of human breast cancers. These results demonstrate the power of genome wide in vivo RNAi screens as a method for identifying novel genes regulating tumorigenesis.

Simultaneous analysis of tumor and stromal gene expression profiles from xenograft models.

Identifying the gene expression alterations that occur in both the tumor and stroma is essential to understanding tumor biology. We have developed a dual-species microarray analysis method that allows the dissection of both tumor and stromal gene expression profiles from xenograft models, based on limited interspecies cross-hybridization on Illumina gene expression beadchips. This methodology allows for simultaneous genome-wide analysis of gene expression profiles of both tumor cells and the associated stromal tissue.