Accumulating evidence suggests important roles for the receptor tyrosine kinase Axl in cancer progression, invasion, metastasis, drug resistance, and patient mortality, highlighting Axl as an attractive target for therapeutic development. We have generated and characterized a potent and selective small-molecule inhibitor, R428, that blocks the catalytic and procancerous activities of Axl. R428 inhibits Axl with low nanomolar activity and blocked Axl-dependent events, including Akt phosphorylation, breast cancer cell invasion, and proinflammatory cytokine production. Pharmacologic investigations revealed favorable exposure after oral administration such that R428-treated tumors displayed a dose-dependent reduction in expression of the cytokine granulocyte macrophage colony-stimulating factor and the epithelial-mesenchymal transition transcriptional regulator Snail. In support of an earlier study, R428 inhibited angiogenesis in corneal micropocket and tumor models. R428 administration reduced metastatic burden and extended survival in MDA-MB-231 intracardiac and 4T1 orthotopic (median survival, >80 days compared with 52 days; P < 0.05) mouse models of breast cancer metastasis. Additionally, R428 synergized with cisplatin to enhance suppression of liver micrometastasis. Our results show that Axl signaling regulates breast cancer metastasis at multiple levels in tumor cells and tumor stromal cells and that selective Axl blockade confers therapeutic value in prolonging survival of animals bearing metastatic tumors. Cancer Res; 70(4); 1544-54. ©2010 AACR.

BackgroundMyeloid cells are an abundant leukocyte in many types of tumors and contribute to immune evasion. Expression of the enzyme arginase 1 (Arg1) is a defining feature of immunosuppressive myeloid cells and leads to depletion of L-arginine, a nutrient required for T cell and natural killer (NK) cell proliferation. Here we use CB-1158, a potent and orally-bioavailable small-molecule inhibitor of arginase, to investigate the role of Arg1 in regulating anti-tumor immunity.MethodsCB-1158 was tested for the ability to block myeloid cell-mediated inhibition of T cell proliferation in vitro, and for tumor growth inhibition in syngeneic mouse models of cancer as a single agent and in combination with other therapies. Tumors from animals treated with CB-1158 were profiled for changes in immune cell subsets, expression of immune-related genes, and cytokines. Human tumor tissue microarrays were probed for Arg1 expression by immunohistochemistry and immunofluorescence. Cancer patient plasma samples were assessed for Arg1 protein and L-arginine by ELISA and mass spectrometry, respectively.ResultsCB-1158 blocked myeloid cell-mediated suppression of T cell proliferation in vitro and reduced tumor growth in multiple mouse models of cancer, as a single agent and in combination with checkpoint blockade, adoptive T cell therapy, adoptive NK cell therapy, and the chemotherapy agent gemcitabine. Profiling of the tumor microenvironment revealed that CB-1158 increased tumor-infiltrating CD8+ T cells and NK cells, inflammatory cytokines, and expression of interferon-inducible genes. Patient tumor samples from multiple histologies expressed an abundance of tumor-infiltrating Arg1+ myeloid cells. Plasma samples from cancer patients exhibited elevated Arg1 and reduced L-arginine compared to healthy volunteers.ConclusionsThese results demonstrate that Arg1 is a key mediator of immune suppression and that inhibiting Arg1 with CB-1158 shifts the immune landscape toward a pro-inflammatory environment, blunting myeloid cell-mediated immune evasion and reducing tumor growth. Furthermore, our results suggest that arginase blockade by CB-1158 may be an effective therapy in multiple types of cancer and combining CB-1158 with standard-of-care chemotherapy or other immunotherapies may yield improved clinical responses.

Shotgun tandem mass spectrometry-based peptide sequencing using programs such as SEQUEST allows high-throughput identification of peptides, which in turn allows the identification of corresponding proteins. We have applied a machine learning algorithm, called the support vector machine, to discriminate between correctly and incorrectly identified peptides using SEQUEST output. Each peptide was characterized by SEQUEST-calculated features such as delta Cn and Xcorr, measurements such as precursor ion current and mass, and additional calculated parameters such as the fraction of matched MS/MS peaks. The trained SVM classifier performed significantly better than previous cutoff-based methods at separating positive from negative peptides. Positive and negative peptides were more readily distinguished in training set data acquired on a QTOF, compared to an ion trap mass spectrometer. The use of 13 features, including four new parameters, significantly improved the separation between positive and negative peptides. Use of the support vector machine and these additional parameters resulted in a more accurate interpretation of peptide MS/MS spectra and is an important step toward automated interpretation of peptide tandem mass spectrometry data in proteomics.