The rapid development of new anticancer drugs that are safe and effective is a common goal shared by basic scientists, clinicians and patients. The current review discusses one such agent, namely niclosamide, which has been used in the clinic for the treatment of intestinal parasite infections. Recent studies repeatedly identified niclosamide as a potential anticancer agent by various high-throughput screening campaigns. Niclosamide not only inhibits the Wnt/β-catenin, mTORC1, STAT3, NF-κB and Notch signaling pathways, but also targets mitochondria in cancer cells to induce cell cycle arrest, growth inhibition and apoptosis. A number of studies have established the anticancer activities of niclosamide in both in vitro and in vivo models. Moreover, the inhibitory effects of niclosamide on cancer stem cells provide further evidence for its consideration as a promising drug for cancer therapy. This article reviews various aspects of niclosamide as they relate to its efficacy against cancer and associated molecular mechanisms.

Abstract The procedure to identify and develop an anti-cancer drug first involves testing drug candidates in cell lines followed by human tumor xenograft models, usually selected based upon the histotype of the cell lines in which the drug showed optimal activity. Many drugs fail at this stage, as activity in cell lines does not often correlate with activity in xenograft models. This is not surprising, as we have previously shown that gene expression in xenograft models does not necessarily correlate with the cell line from which it was derived. In an attempt to improve the success rate of drugs tested in xenograft models, we have developed a fast and cost effective 12-panel human tumor cell line assay that represents the genetic diversity of all our xenograft models and several different cancer histotypes. Affymetrix genomic analysis was performed on 100 human tumor xenograft and cell line models. The genomic profiles obtained underwent Unsupervised Hierarchical Cluster Analysis to group models with similar genetic profiles. This analysis resulted in 12 distinct clusters; a representative cell line was chosen from each cluster. Stocks of each representative cell line were frozen and tested to ensure exponential growth immediately upon thawing, resulting in no waiting time for drug testing. It follows that if a candidate drug shows activity in one or more of these representative cell lines, other cell lines and/or xenograft models in the same cluster can also be tested. As the cell lines and xenograft models within the same cluster will have a similar genetic profile, the chances of success should thus be increased. To test the effectiveness of this approach, we used our database to further develop an internal compound. SRI-20900 had been tested previously in the CCRF-CEM and CAKI-1 xenograft models. The compound showed no activity in CCRF-CEM cells, but excellent activity in CAKI-1 cells. These models were in completely different clusters. So, based on these data, we tested the compound in the SKOV-3 and IGROV-1 xenograft models, as these clustered closely to the CAKI-1 model. The compound showed excellent activity in both SKOV-3 and IGROV-1 models. Although these data provide proof of principle, further work needs to be done by testing targeted compounds in the 12-cell line panel, followed by testing in xenograft models within the same cluster as the cell lines that show optimal activity. In addition, it would follow that a xenograft model within the same cluster as an inactive cell line should also be tested. We hope to start these studies early in 2014. Citation Format: Michael J. Roberts, Tommie A. Gamble, Richard D. May, Murray Stackhouse, Kristy L. Berry, Andrew D. Penman, Robert J. Rooney, Yulia Maxuitenko, Michael S. Koratich. A quick and cost effective 12-cell line panel assay to predict drug activity in human tumor xenograft models. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3730. doi:10.1158/1538-7445.AM2014-3730

Traditionally, drug development has relied upon testing cancer drug candidates in cell lines. Active drugs are then tested in human tumor xenograft models, usually selected based upon the cell lines in which the drug showed activity. The majority of drugs fail at this stage as they do not show activity in the xenograft models chosen. We performed Affymetrix genomic analysis on 42 human tumor xenograft models and the original cell lines from which they were established. The genomic profiles obtained underwent Unsupervised Hierarchical Cluster Analysis to ascertain which cell lines and xenograft models had similar genomic profiles and which did not. The analysis showed that only 24 of 42 human tumor xenograft models clustered side-by-side with the cell line from which they were established. All 6 human leukemia/lymphoma xenograft models clustered very well with the cell lines from which they were established, and they clustered perfectly according to histological class. Five out of six human colon tumor xenograft models clustered well with the cell lines from which they were established and according to histotype. Of the 18 xenograft/cell line pairs that did not cluster side-by-side, 10 pairs remained in the same general cluster, whereas the partners of 8 other pairs were dispersed across different major clusters. Ovarian, breast, melanoma, and pancreatic human tumor xenograft models did not cluster according to histotype. Our data may explain why some drugs that show in vitro activity in some cell lines are not active in other cell lines of the same histological type, and also why some drugs that show activity in vitro then fail in xenograft models. In our laboratory, the PANC-1 cell line is very often chosen as a model of pancreatic cancer. A drug showing activity in the PANC-1 cell line would next be tested in other in vitro models of pancreatic cancer (e.g., MIA PaCa-2, CFPAC-1, and BxPC-3). However, none of these other pancreatic models have a similar genetic profile to PANC-1. Based upon our data, the cell line showing most similarity to the PANC-1 cell line is the breast cancer cell line MDA-MB-231. It is our suggestion that a drug showing activity in the PANC-1 cell line should be tested in other cell lines showing similar genetic profiles, not in cell lines based on histotype. Another example from our analysis is the LOX-IMV1 melanoma cell line. Not only does this cell line not cluster with its corresponding LOX-IMV1 xenograft model, it clusters most closely with the NCI/ADR-RES ovarian cell line. In summary, the genomic profiles of approximately 57% of the tumor xenograft models analyzed closely associate with the cell line from which they were established. Some of the tumor xenograft models show very little similarity to the cell lines from which they were established. Additionally, many of the models (both xenografts and cell lines), do not cluster according to their tissue of origin. Citation Format: Michael J. Roberts, Michael S. Koratich, Murray Stackhouse, Richard D. May, Andrew D. Penman, Tommie A. Gamble, Kristy L. Berry, Joseph F. Murphy, Robert J. Rooney, Yulia Y. Maxuitenko. Tumor target vs. tissue of tumor origin: cluster analysis of genomic profile of 42 human tumor in vitro and in vivo models. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2778. doi:10.1158/1538-7445.AM2013-2778

Tumor growth is not determined solely by the tumor cells but is governed by interactions between tumor cells and host stromal cells, including endothelial cell activation and fibroblastic stroma response. Tumor stroma profoundly influences many steps of tumor progression. In many human cancers, such as breast, prostate, and colon, the stroma comprises the majority of the tumor mass, as a hallmark of the clinical feature called desmoplasia. Numerous studies have showed that tumor-stromal cell interactions play crucial roles in supporting cancer progression and in promoting anticancer drug resistance by alternating gene expression profiles in both tumor and stromal cells through network tumor-stromal interactions in the tumor microenvironments. It has been challenging to obtain separate gene profiles for tumor and stroma with human tumor samples as both tumor and stromal cells share the same genome. In xenograft mouse tumor models, human tumor cells are supported by mouse host stromal microenvironment. Therefore, effects of tumor stromal integrations on gene expression can be profiled separately by taking advantage of this heterogeneous genetic makeup. To understand the underlying biological process of stroma in cancer and select relevant in vitro and in vivo model systems for various targeted anticancer drug discovery and development projects, we selected a panel of 30 commonly used xenograft tumor mouse models that are derived from human tumor cell lines of various cancer histotypes and conducted analyses of differential gene expression in both human cancer cells and mouse host stromal cells before and after their interactions in vivo by using quantitative PCR with mouse and human specific primers. Growing the human tumors as a continuous in vivo passage subcutaneously in immunodeficient mice permits stroma infiltration over a long time. We have examined a number of anticancer target genes involved in different signaling pathways, such as tumor angiogenesis, apoptosis and survival (Akt/mTOR signaling pathway). Our study results have demonstrated that tumor stromal interactions significantly regulate expression levels of various genes important in tumor progression and development of resistance to treatment in both tumor and stromal cells. Tumor animal models play a critical role in translating the bench science to the bedside medical care of cancer patients. Decisions for moving new anticancer agents into costly clinical investigations are mostly based on the preclinical results using xenograft mouse models. The results of this gene profiling approach could provide tools for studying tumor microenvironment and tumor stromal interactions in vivo to advance anticancer drug development. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3114. doi:10.1158/1538-7445.AM2011-3114

The Wnt/β-catenin signaling pathway is important for tumor initiation and progression. The low density lipoprotein receptor-related protein-6 (LRP6) is an essential Wnt co-receptor for Wnt/β-catenin signaling and represents a promising anticancer target. Recently, the antihelminthic drug, niclosamide was found to inhibit Wnt/β-catenin signaling, although the mechanism was not well defined. We found that niclosamide was able to suppress LRP6 expression and phosphorylation, block Wnt3A-induced β-catenin accumulation, and inhibit Wnt/β-catenin signaling in HEK293 cells. Furthermore, the inhibitory effects of niclosamide on LRP6 expression/phosphorylation and Wnt/β-catenin signaling were conformed in human prostate PC-3 and DU145 and breast MDA-MB-231 and T-47D cancer cells. Moreover, we showed that the mechanism by which niclosamide suppressed LRP6 resulted from increased degradation as evident by a shorter half-life. Finally, we demonstrated that niclosamide was able to induce cancer cell apoptosis, and displayed excellent anticancer activity with IC50 values less than 1 µM for prostate PC-3 and DU145 and breast MDA-MB-231 and T-47D cancer cells. The IC50 values are comparable to those shown to suppress the activities of Wnt/β-catenin signaling in prostate and breast cancer cells. Our data indicate that niclosamide is a unique small molecule Wnt/β-catenin signaling inhibitor targeting the Wnt co-receptor LRP6 on the cell surface, and that niclosamide has a potential to be developed a novel chemopreventive or therapeutic agent for human prostate and breast cancer.

The NCI recently performed microarray expression analysis on its 60-panel of human tumor cell lines. This revealed important information, with some cell lines shown to be from a different tissue of origin than originally believed. Traditionally, potential anti-cancer agents have been evaluated in these in vitro models, and then moved into the corresponding in vivo xenograft model(s) based on the in vitro results. It has been shown that drugs which are effective in vitro are not necessarily effective in vivo and vice versa. Systematic microarray analysis of traditional xenograft models in conjunction with their in vitro counterparts has not been performed. The development of a human tumor xenograft in a mouse might be expected to lead to changes in gene expression, and this could account, in some instances, for the disconnect in results observed between in vitro and in vivo models. Our aim was to perform a genetic analysis against the entire human genome using 24 cell lines from 11 differing tissues of origin that were implanted into immune-deficient mice to establish a xenograft model for each. Once the tumors reached approximately 1 cm3 in size, the tumors were removed, cut into approx. 2-3 mm3 fragments, and an in vivo tumor passage was established. Microarray expression in fragments of those xenografted tumors was compared to microarray expression in the cell line from which they were developed. The total mRNA for each sample was split into 3 replicates, and analyzed against the entire human genome using standard Affx WT procedures. The results showed that over 60% (15 of 24) of the xenograft samples clustered with the cell line from which it was developed, whereas approximately 40% (9 of 24) did not, revealing that major changes in gene expression had occurred in 40% of these xenograft samples. Furthermore, when analyzed alone, these particular 24 cell line samples clustered according to their tissue of origin, whereas the tumor fragment samples did not appear to cluster. On the basis of these data we are currently performing the same analysis on an additional 25 tumor fragments and their corresponding matched cell lines to allow for a more accurate, in-depth cluster analysis. These data strongly suggest that although precedent exists to select in vitro models on the basis of their tissue of origin, no such precedent exists for in vivo models. In vivo models should be more carefully selected to ensure that the model chosen is still representative of the tissue to be tested. It follows that a drug candidate effective in a particular in vitro model might be expected to show activity in other in vitro lines from the same tissue of origin. However, a drug candidate effective in a particular in vivo model representative of a tissue of origin should not be expected to show efficacy in other in vivo models representing the same tissue type. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4418. doi:10.1158/1538-7445.AM2011-4418

The total mRNA for each sample of 51 human tumor cell lines representative of 16 different tissues of origin was split into 3 replicates, and analyzed against the entire human genome using standard Affx WT procedures. Approximately 50% of the samples (25 out of the 51 cell lines tested) exhibited low-level clustering related to their tissue of origin. The remaining 50% (26 out of the 51 cell lines tested) did not cluster with other samples of the same tissue of origin. These data reveal the importance of testing potential anticancer agents in multiple models representative of several different tumors of origin, as there is a 50% chance that the model chosen is not actually representative of the intended tissue of origin. This analysis also showed that the pancreatic cancer cell line CFPAC-1 did not cluster with any other cell line tested, revealing the unique genetic profile of this cell line. Interestingly, the reported lung cancer cell lines NCI-H69 and NCI-H82 clustered more closely with leukemic lines than with lung or any other solid tumor. This is particularly interesting as these lines are known to grow/behave more like a suspension culture than a monolayer. The NCI recently published its genetic analysis of their 60-panel, and they revealed that the MDA-MB-435 cell line, traditionally thought to be a breast cancer cell line, more closely resembled a melanoma line; hence, it was re-classified as a melanoma (likely a metastasized melanoma that was taken from the breast site). Our analysis reveals that another traditional breast cancer cell line, UISO-BCA-1, also clusters more closely with the melanomas (including the MDA-MB-435 cell line), suggesting that this cell line also may have been misclassified. Based on these data, we suggest that any potential anticancer agent showing activity in a particular cell line should be tested in other cell lines that cluster with the active line, and not merely in other lines supposedly representative of the same tissue of origin. Furthermore, in early stage testing, it would be more prudent to test several cell lines from different clusters, rather than several cell lines from different tissues of origin. It follows that by testing orphan drugs against several cell lines from each cluster, it would be possible to significantly narrow (and possibly identify), the likely drug target. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3943. doi:10.1158/1538-7445.AM2011-3943