Invadopodia are proteolytic membrane protrusions formed by highly invasive cancer cells, commonly observed on substrate(s) mimicking extracellular matrix. Although invadopodia are proposed to have roles in cancer invasion and metastasis, direct evidence has not been available. We previously reported that neural Wiskott–Aldrich syndrome protein (N-WASP), a member of WASP family proteins that regulate reorganization of the actin cytoskeleton, is an essential component of invadopodia. Here, we report that N-WASP-mediated invadopodium formation is essential in breast cancer invasion, intravasation and lung metastasis. We established stable cell lines based on MTLn3 rat mammary adenocarcinoma cells that either overexpressed a dominant-negative (DN) N-WASP construct or in which N-WASP expression was silenced by a pSuper N-WASP shRNA. Both the N-WASP shRNA and DN N-WASP cells showed a markedly decreased ability to form invadopodia and degrade extracellular matrix. In addition, formation of invadopodia in primary tumors and collagen I degradation were reduced in the areas of invasion (collagen-rich areas in the invasive edge of the tumor) and in the areas of intravasation (blood-vessel-rich areas). Our results suggest that tumor cells in vivo that have a decreased activity of N-WASP also have a reduced ability to form invadopodia, migrate, invade, intravasate and disseminate to lung compared with tumor cells with parental N-WASP levels.

We report on the development of a new SYBR Green I-based plate assay for analyzing the activities of antimalarial drugs against intraerythrocytic Plasmodium falciparum . This assay is considerably faster, less labor-intensive, and less expensive than conventional radiotracer (e.g., [ ³ H]hypoxanthine and [ ³ H]ethanolamine)-based assays or P. falciparum lactate dehydrogenase activity-based assays. The assay significantly improves the pace at which antimalarial drug discovery efforts may proceed.

We have shown previously that distinct Mena isoforms are expressed in invasive and migratory tumor cells in vivo and that the invasion isoform (Mena^INV) potentiates carcinoma cell metastasis in murine models of breast cancer. However, the specific step of metastatic progression affected by this isoform and the effects on metastasis of the Mena11a isoform, expressed in primary tumor cells, are largely unknown. Here, we provide evidence that elevated Mena^INV increases coordinated streaming motility, and enhances transendothelial migration and intravasation of tumor cells. We demonstrate that promotion of these early stages of metastasis by Mena^INV is dependent on a macrophage–tumor cell paracrine loop. Our studies also show that increased Mena11a expression correlates with decreased expression of colony-stimulating factor 1 and a dramatically decreased ability to participate in paracrine-mediated invasion and intravasation. Our results illustrate the importance of paracrine-mediated cell streaming and intravasation on tumor cell dissemination, and demonstrate that the relative abundance of Mena^INV and Mena11a helps to regulate these key stages of metastatic progression in breast cancer cells.

While it has been established that a number of microenvironment components can affect the likelihood of metastasis, the link between microenvironment and tumor cell phenotypes is poorly understood. Here we have examined microenvironment control over two different tumor cell motility phenotypes required for metastasis. By high-resolution multiphoton microscopy of mammary carcinoma in mice, we detected two phenotypes of motile tumor cells, different in locomotion speed. Only slower tumor cells exhibited protrusions with molecular, morphological, and functional characteristics associated with invadopodia. Each region in the primary tumor exhibited either fast- or slow-locomotion. To understand how the tumor microenvironment controls invadopodium formation and tumor cell locomotion, we systematically analyzed components of the microenvironment previously associated with cell invasion and migration. No single microenvironmental property was able to predict the locations of tumor cell phenotypes in the tumor if used in isolation or combined linearly. To solve this, we utilized the support vector machine (SVM) algorithm to classify phenotypes in a nonlinear fashion. This approach identified conditions that promoted either motility phenotype. We then demonstrated that varying one of the conditions may change tumor cell behavior only in a context-dependent manner. In addition, to establish the link between phenotypes and cell fates, we photoconverted and monitored the fate of tumor cells in different microenvironments, finding that only tumor cells in the invadopodium-rich microenvironments degraded extracellular matrix (ECM) and disseminated. The number of invadopodia positively correlated with degradation, while the inhibiting metalloproteases eliminated degradation and lung metastasis, consistent with a direct link among invadopodia, ECM degradation, and metastasis. We have detected and characterized two phenotypes of motile tumor cells in vivo, which occurred in spatially distinct microenvironments of primary tumors. We show how machine-learning analysis can classify heterogeneous microenvironments in vivo to enable prediction of motility phenotypes and tumor cell fate. The ability to predict the locations of tumor cell behavior leading to metastasis in breast cancer models may lead towards understanding the heterogeneity of response to treatment. A large proportion of cancer deaths are due to metastasis—the spread of cancer from the primary tumor to other parts of the body. Movement of cells may require the formation of protrusions called invadopodia, which degrade extracellular matrix. Although some studies have reported on locomotion in primary tumors, the presence of invadopodia was not tested. Here, we show that single cells from mouse mammary carcinoma can move using a fast- or slow-locomotion mode depending on different levels of cues present in the tumor microenvironment. Using multiphoton microscopy in vivo combined with a machine-learning algorithm we show how manipulation of microenvironmental conditions can induce predictable changes in the number of locomoting cells or switch between the two locomotion modes. We also demonstrate that only the slower moving cells are associated with invadopodia in vivo, and that only tumor cells from regions rich in invadopodia degrade the surrounding extracellular matrix and disseminate. Specific targeting of invadopodia results in inhibition of lung metastasis. This work proposes a systems biology view of how tumor microenvironments regulate tumor progression and presents insight into the heterogeneity of the treatment response. The ability to define and predict conditions under which tumor cells disseminate offers potential therapeutic benefits in regulating tumor progression.