Tumor cell migration screen identifies SRPK1 as breast cancer metastasis determinant.

Tumor cell migration is a key process for cancer cell dissemination and metastasis that is controlled by signal-mediated cytoskeletal and cell matrix adhesion remodeling. Using a phagokinetic track assay with migratory H1299 cells, we performed an siRNA screen of almost 1,500 genes encoding kinases/phosphatases and adhesome- and migration-related proteins to identify genes that affect tumor cell migration speed and persistence. Thirty candidate genes that altered cell migration were validated in live tumor cell migration assays. Eight were associated with metastasis-free survival in breast cancer patients, with integrin ß3-binding protein (ITGB3BP), MAP3K8, NIMA-related kinase (NEK2), and SHC-transforming protein 1 (SHC1) being the most predictive. Examination of genes that modulate migration indicated that SRPK1, encoding the splicing factor kinase SRSF protein kinase 1, is relevant to breast cancer outcomes, as it was highly expressed in basal breast cancer. Furthermore, high SRPK1 expression correlated with poor breast cancer disease outcome and preferential metastasis to the lungs and brain. In 2 independent murine models of breast tumor metastasis, stable shRNA-based SRPK1 knockdown suppressed metastasis to distant organs, including lung, liver, and spleen, and inhibited focal adhesion reorganization. Our study provides comprehensive information on the molecular determinants of tumor cell migration and suggests that SRPK1 has potential as a drug target for limiting breast cancer metastasis.

Ferritin heavy chain in triple negative breast cancer: a favorable prognostic marker that relates to a cluster of differentiation 8 positive (CD8+) effector T-cell response.

Ferritin heavy chain (FTH1) is a 21-kDa subunit of the ferritin complex, known for its role in iron metabolism, and which has recently been identified as a favorable prognostic protein for triple negative breast cancer (TNBC) patients. Currently, it is not well understood how FTH1 contributes to an anti-tumor response. Here, we explored whether expression and cellular compartmentalization of FTH1 correlates to an effective immune response in TNBC patients. Analysis of the tumor tissue transcriptome, complemented with in silico pathway analysis, revealed that FTH1 was an integral part of an immunomodulatory network of cytokine signaling, adaptive immunity, and cell death. These findings were confirmed using mass spectrometry (MS)-derived proteomic data, and immunohistochemical staining of tissue microarrays. We observed that FTH1 is localized in both the cytoplasm and/or nucleus of cancer cells. However, high cytoplasmic (c) FTH1 was associated with favorable prognosis (Log-rank p = 0.001), whereas nuclear (n) FTH1 staining was associated with adverse prognosis (Log-rank p = 0.019). cFTH1 staining significantly correlated with total FTH1 expression in TNBC tissue samples, as measured by MS analysis (Rs = 0.473, p = 0.0007), but nFTH1 staining did not (Rs = 0.197, p = 0.1801). Notably, IFN γ-producing CD8+ effector T cells, but not CD4+ T cells, were preferentially enriched in tumors with high expression of cFTH1 (p = 0.02). Collectively, our data provide evidence toward new immune regulatory properties of FTH1 in TNBC, which may facilitate development of novel therapeutic targets.

CD49f-based selection of circulating tumor cells (CTCs) improves detection across breast cancer subtypes.

Circulating tumor cells (CTCs) can be enumerated using CellSearch, but not all breast cancer subtypes, specifically those with epithelial-mesenchymal transition (EMT) characteristics, sufficiently express the enrichment (EpCAM) and selection (CK8/18/19) markers used in this method. While CD146 can detect EpCAM-negative CTCs, we here evaluated the value of various cytokeratins and CD49f to detect CK8/18/19-negative CTCs. The tested cytokeratins provided no substantial benefit, but adding CD49f to CK8/18/19 as a selection marker resulted in improved recovery of normal-like cell lines. Combined staining of CK8/18/19 and CD49f after CD146/EpCAM enrichment is likely to further improve CTC detection in breast cancer.

Detection of circulating tumor cells in breast cancer may improve through enrichment with anti-CD146.

Most assays to detect circulating tumor cells (CTCs) rely on EpCAM expression on tumor cells. Recently, our group reported that in contrast to other molecular breast cancer subtypes, "normal-like" cell lines lack EpCAM expression and are thus missed when CTCs are captured with EpCAM-based technology [J Natl Cancer Inst 101(1):61-66, 2009]. Here, the use of CD146 is introduced to detect EpCAM-negative CTCs, thereby improving CTC detection. CD146 and EpCAM expression were assessed in our panel of 41 breast cancer cell lines. Cells from 14 cell lines, 9 of which normal-like, were spiked into healthy donor blood. Using CellSearch technology, 7.5 ml whole blood was enriched for CTCs by adding ferrofluids loaded with antibodies against EpCAM and/or CD146 followed by staining for Cytokeratin and DAPI. Hematopoietic cells and circulating endothelial cells (CECs) were counterstained with CD45 and CD34, respectively. A similar approach was applied for blood samples of 20 advanced breast cancer patients. Eight of 9 normal-like breast cancer cell lines lacked EpCAM expression but did express CD146. Five of these 8 could be adequately recovered by anti-CD146 ferrofluids. Of 20 advanced breast cancer patients whose CTCs were enumerated with anti-EpCAM and anti-CD146 ferrofluids, 9 had CD146+ CTCs. Cells from breast cancer cell lines that lack EpCAM expression frequently express CD146 and can be recovered by anti-CD146 ferrofluids. CD146+ CTCs are present in the peripheral blood of breast cancer patients with advanced disease. Combined use of anti-CD146 and anti-EpCAM is likely to improve CTC detection in breast cancer patients.

Identification of a putative protein profile associated with tamoxifen therapy resistance in breast cancer.

Tamoxifen resistance is a major cause of death in patients with recurrent breast cancer. Current clinical factors can correctly predict therapy response in only half of the treated patients. Identification of proteins that are associated with tamoxifen resistance is a first step toward better response prediction and tailored treatment of patients. In the present study we intended to identify putative protein biomarkers indicative of tamoxifen therapy resistance in breast cancer using nano-LC coupled with FTICR MS. Comparative proteome analysis was performed on approximately 5,500 pooled tumor cells (corresponding to approximately 550 ng of protein lysate/analysis) obtained through laser capture microdissection (LCM) from two independently processed data sets (n = 24 and n = 27) containing both tamoxifen therapy-sensitive and therapy-resistant tumors. Peptides and proteins were identified by matching mass and elution time of newly acquired LC-MS features to information in previously generated accurate mass and time tag reference databases. A total of 17,263 unique peptides were identified that corresponded to 2,556 non-redundant proteins identified with > or = 2 peptides. 1,713 overlapping proteins between the two data sets were used for further analysis. Comparative proteome analysis revealed 100 putatively differentially abundant proteins between tamoxifen-sensitive and tamoxifen-resistant tumors. The presence and relative abundance for 47 differentially abundant proteins were verified by targeted nano-LC-MS/MS in a selection of unpooled, non-microdissected discovery set tumor tissue extracts. ENPP1, EIF3E, and GNB4 were significantly associated with progression-free survival upon tamoxifen treatment for recurrent disease. Differential abundance of our top discriminating protein, extracellular matrix metalloproteinase inducer, was validated by tissue microarray in an independent patient cohort (n = 156). Extracellular matrix metalloproteinase inducer levels were higher in therapy-resistant tumors and significantly associated with an earlier tumor progression following first line tamoxifen treatment (hazard ratio, 1.87; 95% confidence interval, 1.25-2.80; p = 0.002). In summary, comparative proteomics performed on laser capture microdissection-derived breast tumor cells using nano-LC-FTICR MS technology revealed a set of putative biomarkers associated with tamoxifen therapy resistance in recurrent breast cancer.