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The pharmacological bases of the antiangiogenic activity of paclitaxel

In the mid 1990s, researchers began to investigate the antiangiogenic activity of paclitaxel as a possible additional mechanism contributing to its antineoplastic activity in vivo. In the last decade, a number of studies showed that paclitaxel has antiangiogenic activity that could be ascribed to the inhibition of either tubule formation or cell migration, and to an antiproliferative effect towards activated endothelial cells. Furthermore, paclitaxel was shown to downregulate VEGF and Ang-1 expression in tumor cells, and to increase the secretion of TSP-1 in the tumor microenvironment. Moreover, the new pharmaceutical formulations of paclitaxel (such as liposome-encapsulated paclitaxel, ABI-007, and paclitaxel entrapped in emulsifying wax nanoparticles) enhanced the in vivo antiangiogenic activity of the drug. Thus, the preclinical data of paclitaxel may be exploited to implement a novel and rational therapeutic strategy to control tumor progression in patients.

Introduction
Paclitaxel is a cancer chemotherapeutic agent that is commonly used as first line therapy for many common malignancies, including lung, breast, ovarian and head and neck cancer. It also has high antitumor activity against some uncommon malignancies, such as angiosarcoma and Kaposi sarcoma. Paclitaxel is a member of the taxane class of drugs, and it was the first taxane to enter clinical trials and to receive FDA approval; it is a natural product obtained from the North American Pacific yew tree (Taxus brevifolia). Paclitaxel binds to the beta-subunit of polymerized tubulin and inhibits the dissociation rate of the tubulin subunits from the tubule. Thus, micromolar paclitaxel administration to cells results in the formation of microtubule bundles and asters, arresting cells in mitosis [1].
Besides these known pharmacodynamic properties, medical researchers have discovered a new and unexpected additional characteristic of paclitaxel, one that started a second life for this compound and that created new therapeutic approaches using this molecule: that characteristic is its antiangiogenic activity. Although the anti-vascular effects of tubulin-binding agents have been previously reviewed [2][3][4], the present article focuses solely on the pharmacological bases of the antiangiogenic activity of paclitaxel and provides an extensive and updated overview of the field. Table 1 summarizes the published data on the antiangiogenic effects of paclitaxel in different in vitro and in vivo experimental models.
The first ever published evidence about a possible antiangiogenic activity of paclitaxel was a small report some 15 years ago. Dordunoo and colleagues prepared poly(epsilon-caprolactone) (PCL) microspheres containing paclitaxel, and showed this formulation to inhibit angiogenesis in the chick chorioallantoic membrane (CAM) model [5]. A year later, Belotti et al. [6] demonstrated a strong antiangiogenic activity of paclitaxel, suggesting that this property might contribute to its antineoplastic activity in vivo. The aim of that study was to investigate the effect of paclitaxel on endothelial cell functions, and on angiogenesis. In vivo, paclitaxel (20-28 mg/kg i.v.) significantly Table 1 Pharmacological effects of paclitaxel on angiogenesis

Models
Pharmacological effects at cellular level Pharmacological effects at molecular level References In vitro HUVEC Strong anti-proliferative activity [8] HUVEC Inhibition of cell proliferation, motility and invasiveness in a concentration-dependent manner [6] HMVEC-d, HUVEC Selective inhibition of cell proliferation and induction of apoptosis at low concentrations for prolonged periods of time [18] Human leukemia cell lines VEGF downregulation in vitro (even in drug resistant cells) [9] HUVEC Reduction of the capillary network formation in matrigel [20] Rat fat pad endothelial cells Inhibition of migration [21] HUVEC, rat aortic ring explants Inhibition of cell proliferation, migration, and tube formation at one-tenth the concentration needed to achieve a similar effect on tumor cell lines [22] HUVEC, rat aortic ring explants Inhibition of proliferation, differentiation (tube assay) and induction of cell death [23] HUVEC, HMEC-1 Initiation, without completion, of the mitochondrial apoptotic pathway leading to a slowing down of the cell cycle Cytotoxic effects mediated by microtubule network disturbance, G2-M arrest, increase in Bax/Bcl-2 ratio, and mitochondria permeabilization [40] HUVEC, HMEC-1 Increase of interphase microtubule dynamics in vitro [41] HUVEC, HMVEC-L, HMVEC-D

Inhibition of FGF-2-and VEGF-induced cell proliferation and tube formation in matrigel
Increase of the drug cellular uptake in endothelial cells vs. fibroblasts and tumor cells (mechanism unknown) [35] HUVEC Induction of gene and protein expression of TSP-1 at metronomic concentrations [45] Human ovarian cancer cell lines Decrease of survival factors such as Ang-1 and VEGF [47] Rat bone marrow (BM)derived endothelial progenitor cells (EPC) cell line (TR-BME) Inhibition of tube formation and migration of cells at low concentrations [34] HUVEC Inhibition of migration Increase of the levels of acetylated tubulin; increase of forkhead box O3a translocation into the nucleus [44] In vivo Nude mice bearing murine breast cancer VEGF downregulation [9] Cornea assay Inhibition of FGF-2 and VEGF-induced neovascularization [7] Transgenic murine Met-1 breast cancer model Reduced intratumor angiogenesis VEGF downregulation [9] Human oral squamous cell carcinoma, lung tumor Inhibition of tumor angiogenesis Reduced the immunohistochemical expression of CD31, VEGF and VEGF mRNA [11,12] Melanoma spontaneous metastases model

Inhibition of angiogenesis in melanoma tissue lesions
Reduction of VEGF-A expression [25] Rat Walker 256 breast carcinosarcoma cell xenografts A low dose inhibits bone marrow-derived endothelial progenitor cells (EPC) accumulation at the tumor site and decrease the microvessel density [34] inhibited the angiogenic process in a pellet of matrigel containing tumor cell supernatant, that was injected into mice. In vitro, paclitaxel inhibited endothelial cell proliferation, motility, and invasiveness in a concentrationdependent manner [6]. Interestingly, the authors stated that the antiangiogenic activity of paclitaxel was not linked to its cytotoxicity, since inhibition of endothelial cell chemotaxis and invasiveness occurred at drug concentrations which did not affect endothelial cell proliferation [6]. At the same time, a lower dose of paclitaxel (6 mg/kg i.p.) inhibited the bFGF and VEGF-induced neovascularization of the cornea in mice by 45 and 37 %, respectively [7]. Moreover, Iwahana et al. [8] demonstrated a different chemosensitivity of human umbilical vein endothelial cells (HUVEC) and of tumor-derived endothelial cells from rat KMT-17 fibrosarcoma (TEC) when exposed to the same paclitaxel concentrations. Indeed, paclitaxel had strong anti-proliferative activity against HUVECs, but only weakly inhibited the proliferation of TECs, which expressed greater amounts of P-glycoprotein (P-gp), suggesting a drug resistant phenotype of newly formed capillaries [8].
The first evidence of the antiangiogenic activity of paclitaxel via down-regulation of vascular endothelial growth factor (VEGF) in tumors was obtained in a highly-vascularized transgenic murine Met-1 breast cancer model [9]. Paclitaxel was administered intraperitoneally, at noncytotoxic doses of 0-6 mg/kg/day, to nude mice bearing Met-1 breast tumor. Interestingly, the intratumoral angiogenesis, measured by microvessel tortuosity and microvessel density, was significantly reduced by paclitaxel treatment in a dose-dependent manner. Moreover, paclitaxel also suppressed expression of VEGF in Met-1 tumors transplanted in nude mice [9]. Interestingly, paclitaxel monochemotherapy (175 mg/m 2 i.v. every 21 days for three cycles) significantly decreased VEGF plasma levels in metastatic breast cancer patients with partial response or stable disease, whereas no decline was observed in patients with progressive disease [10]. Paclitaxel also showed an inhibitory effect on tumor angiogenesis in a lung tumor xenograft [11], and in transplanted human oral squamous cell carcinoma [12] models, reducing the immunohistochemical expression of CD31 (an endothelial marker), VEGF, and VEGF mRNA.
However, no unanimous consensus was expressed on the antiangiogenic activity of paclitaxel in vivo. A paclitaxel dosage of 6.25 mg/kg s.c., given five times/week, slowed tumor growth in a CWR22R androgen-independent xenograft model of prostate cancer, without any antiangiogenic effects [13]. Similarly, vascular density was not altered by paclitaxel treatment in an orthotopic pancreatic tumor model [14].

The metronomic concept and paclitaxel
Kerbel et al. [15], at the beginning of this century, coined the term ''accidental angiogenesis inhibitors'' to describe a number of drugs that were tested in clinical trials as possible angiogenesis inhibitors, although they were not originally developed to target tumor angiogenesis. This list of ''accidental'' angiogenesis inhibitors included also established agents such as conventional cytotoxic chemotherapeutic drugs. The antivascular side-effects of chemotherapy were found to be most evident by administering such drugs  [5] Matrigel pellet in mice Inhibition of neovascularization [6] Chick chorioallantoic membrane Inhibition of neovascularization at low concentrations [26] Rat mesentery assay A low dose shortened the length of sprouts in VEGF-mediated angiogenesis [27,28] 4T1 metastatic breast cancer Strong antiangiogenic and antilymphangiogenic activities of low doses [30] Rats bearing syngeneic prostate cancer (Dunning AT-1) not expressing TSP-1 Re-induction of TSP-1 expression in tumors [46] HT-29 colon cancer model; 4T1 metastatic breast cancer Upregulation of TSP-1 expression [29,30] Ovarian carcinoma xenograft model Downregulation of VEGF-B, -D and -A; upregulation of Tie-1, Tie-2 and VEGFR-2 [49] Angiogenesis (2013) 16:481-492 483 continuously, or on a frequent (e.g. weekly or even daily) basis, at concentrations well below their maximum tolerated dose (MTD). This continuous low-dose schedule was defined as metronomic chemotherapy [16]. Such a frequent administration in vitro and in vivo of low doses of chemotherapeutic drugs affects the tumor endothelium and inhibits tumor angiogenesis, with minimal concomitant side effects [17][18][19]. In particular, low concentrations of paclitaxel showed only weak effects on in vitro activated endothelial cells using a short-term exposure (24 h) protocol, whereas a striking toxicity toward vascular endothelial cells was observed by a long-term exposure protocol (144 h) that used very low paclitaxel concentrations [18]. Moreover, a potent differential effect was found at the same paclitaxel concentrations if fibroblasts, and drug-sensitive or multidrug-resistant breast cancer cell lines were used. Indeed, a growth inhibitory effect, as well as induction of apoptosis, was observed with IC 50 values in the range of 25-143 pM for paclitaxel only in the endothelial cells, suggesting a selective antiangiogenic therapeutic window for this taxane [18]. At the same time, Hayot et al. [20] demonstrated that paclitaxel, vincristine and vindesine, at non-cytotoxic concentrations, similarly reduced the capillary network formation by HUVEC cells cultured on Matrigel. Furthermore, paclitaxel was also tested in a chemokinetic migration assay using rat fat pad endothelial cells (RFPECs), showing IC 50 values that were approximately 10 -9 M [21]. The different sensitivity of activated endothelial cells to paclitaxel was also confirmed by other studies. Dicker et al. [22] found that paclitaxel inhibited human endothelial cell proliferation, migration, and tube formation (differentiation) at one-tenth of the concentration needed to achieve a similar effect on tumor cell lines ( Fig. 1). Moreover, similar results were obtained on the proliferation, migration, and differentiation, of cultured human umbilical vein endothelial cells and in the capillary sprouting of rat aortic ring explants, demonstrating that endothelial cells are 10-100-fold more sensitive to paclitaxel than are tumor cells [22]. Additionally, it was demonstrated that angiogenesis could be blocked by paclitaxel, and that this effect was primarily due to inhibition of proliferation and differentiation (as measured by Matrigel assay) and by the induction of cell death [23]. The selectivity of paclitaxel inhibition of cell proliferation is also species specific. Indeed, the inhibition of proliferation of human endothelial cells by paclitaxel was observed at ultra low concentrations (0.1-100 pM), whereas 10 4 to 10 5 -fold higher concentrations were necessary to impact human nonendothelial cells, and nonetheless mouse endothelial cells were insensitive to such ultra low (pM) concentrations of paclitaxel [24]. The antiangiogenic activity of paclitaxel at lower doses was also demonstrated in in vivo models of neovascularization, such as the chick embryo chorioallantoic membrane (CAM) [25], where paclitaxel administered at low concentrations (4, 8, and 12 nM) produced a significant dosedependent antiangiogenic activity [26]. Likewise, using the rat mesentery assay, the effect of paclitaxel at a low, atoxic dose, significantly shortened the length of sprouts at the edge of the expanding network in VEGF165-mediated angiogenesis [27]. Moreover, metronomic paclitaxel significantly suppressed VEGF-A-mediated angiogenesis in the rat mesentery to an extent that closely mirrored the significant increase in prostate AT1 tumor necrosis, and the concomitant decrease in tumor growth rate [28]. Paclitaxel metronomic chemotherapy, in colon cancer xenografts, showed stronger antiangiogenic activity than did paclitaxel administered at the maximum tolerable dose (MTD). In contrast, the MTD chemotherapy induced more apoptotic cells than did the metronomic dosage [29]. More interestingly, Jiang et al. [30] investigated the use of low-dose metronomic (LDM) paclitaxel, as a single agent, in a highly metastatic mouse model of 4T1 breast cancers, and compared it with the maximum tolerable dose (MTD) therapy. LDM therapy displayed a stronger anti-tumor activity in suppressing primary and metastatic breast tumors, with less side effects and stronger anti-angiogenic and anti-lymphangiogenic activities, than MTD therapy. On the other hand, MTD therapy showed stronger proapoptotic and anti-proliferative activities in situ. Based on these results, the authors suggested the use of metronomic paclitaxel therapy to treat advanced breast cancer [30]. Metronomic paclitaxel has been successfully applied in preclinical models of metastatic tumors. In one protocol paclitaxel was injected i.p. at 5 mg/kg per day for 3 weeks to treat preclinical spontaneous melanoma metastasis. This protocol resulted in a relative decrease in spontaneous metastases to the lungs, inhibiting angiogenesis in melanoma tissue lesions and reducing the expression of VEGF-A [31]. Moreover, the weekly paclitaxel administration was also effective in inhibiting angiogenesis, tumor growth, and bone marrow hematopoiesis in a lung cancer model [32]. Indeed, the tumor growth rate was reduced by 50 % and the intratumoral microvasculature was inhibited by 70 %, with a concomitant low degree of leukopenia [32]. This important inhibition of tumor neovascularization by paclitaxel was also confirmed in a model of human breast cancer specimens grown in a fibrin clot. Interestingly, paclitaxel significantly reduced the angiogenic sprouting and the formation of new microvessels if compared to vehicle-treated controls [33]. Furthermore, low dose (non-cytotoxic) paclitaxel inhibited bone marrowderived endothelial progenitor cells accumulation at the tumor site in tumor-bearing rats, and caused a decrease in microvessel density [34] (Fig. 1). A possible explanation for preclinical reports describing the endothelial cell's in vitro sensitivity to paclitaxel, the in vivo antiangiogenic effects of the drug, and clinical reports of the relative efficacy of low weekly doses of paclitaxel in patients refractory to standard (every 21 days) doses of this agent came from the study by Merchan et al. [35]. The authors observed that human endothelial cells can accumulate paclitaxel at more than 5 times greater levels than do normal human fibroblasts and several human cancer cell lines. However, the mechanism by which the endothelial cells accumulate paclitaxel remains to be elucidated. Two possibilities have been suggested: (1) the increased transport of the drug into the cells or, (2) the decrease in the efflux of the drug [35].
The effects of paclitaxel on the inflammatory process (that is a characteristic of cancer progression) and in the tumor immune escape process are still quite controversial. The induction of pro-inflammatory genes and proteins by paclitaxel, especially through the lipopolysaccharide (LPS) signaling pathway, have been reviewed by Fitzpatrick and Wheeler [36]. Moreover, Olsen [37] have described that taxanes (including paclitaxel) promote transcription of the cyclooxygenase (COX)-2 gene and the stabilization of the COX-2 messenger RNA. This pharmacodynamic property provided the rationale for the use of COX-2 inhibitors in combination with taxanes, and preclinical studies have shown enhanced anticancer activity from the addition of COX-2 inhibitors to taxane treatment [37]. However, more recent studies suggest that paclitaxel could modulate tissue factor (TF), as is expressed by tumour-associated endothelial and inflammatory cells (as well as by cancer cells themselves), in human mononuclear cells, in HUVECs, and in the metastatic breast carcinoma cell line MDA-MB-231 [38]. Indeed, paclitaxel significantly reduced the strong TF activity expressed by MDA-MB-231, and the TF levels in mononuclear cells and HUVECs even after the induction by LPS, TNF-a and IL-1b. Interestingly, Napoleone et al. [38] also tested whether paclitaxel could influence IL-6 and IL-1b release from the MDA-MB-231, HUVEC and mononuclear cells, since paclitaxel was shown to induce the expression of inflammatory genes in monocytes and in tumour cells. The authors demonstrated that neither the constitutive expression of IL-6 and IL-1b in MDA-MB-231, nor the basal and LPS-induced release from mononuclear cells or from HUVECs, was affected by paclitaxel administration. Recently, another important finding from Chen et al. [39] underlines the importance of the effects of paclitaxel on immunity. These authors showed that the antitumor activity of metronomic low doses of paclitaxel depends on both its capacity to deplete regulatory T cells, and on its inhibition of tumor angiogenesis. Moreover, the combination of metronomic paclitaxel plus an antigen-specific DNA vaccine induced a more potent antigen-specific immune responses and antitumor effects compared to the vaccine alone [39]. In conclusion, the effect of paclitaxel on inflammation and immunity seems to be more complex than previously hypothesized and surely merits further investigations for its relevant role in the antitumor effect of this taxane.

Molecular mechanisms of paclitaxel antiangiogenic activity
The main molecular mechanisms for the antiangiogenic activity of paclitaxel are graphically summarized in Fig. 2 of paclitaxel on human endothelial cell proliferation: a cytostatic effect at low paclitaxel concentrations, and a cytotoxic effect at higher concentrations [40]. The cytotoxic effect involved primarily those signaling networks that are reported to be impaired in tumor cells (i.e., microtubule network disturbance, G 2 -M arrest, increase in the Bax/Bcl-2 ratio, and mitochondria permeabilization) that result in apoptosis. Conversely, the cytostatic effect of paclitaxel involves the inhibition of endothelial cell proliferation without the induction apoptosis and without any structural modification to the microtubule network. In addition, low paclitaxel concentrations just initiate the apoptotic signaling pathway that is stopped upstream of mitochondria permeabilization and it does not lead to endothelial cell death [40]. Moreover, paclitaxel inhibits angiogenesis by an increase in microtubule dynamics in endothelial cells and by the impairment of interphase microtubule functions [41]. Other studies have uncovered additional mechanisms involved in the antiangiogenic effects of taxanes, including the degradation of heat shock protein 90 [42] and the inhibition of Rac1 and of Cdc42 activity [43]. It should be noted that these data have been obtained with docetaxel, but similar or analogous effects have yet to be reported with paclitaxel. Although the two drugs are very similar and belong to the same antineoplastic class of compounds, no data are available to show that such mechanisms could be responsible for the antiangiogenic activity of paclitaxel.
Recently, Bonezzi et al. [44] found that paclitaxel increases the levels of acetylated tubulin in HUVECs. The induction of tubulin hyperacetylation was concomitant with the inhibition of cell motility but not of cell proliferation, which only occurred at concentrations much higher than those that inhibited cell motility. In fact, the overexpression of a tubulin deacetylase such as the NADdependent histone deacetylase sirtuin-2 (SIRT2), increases cell motility and reduces cell response to the anti-motility activity of paclitaxel [44]. In the same article, Bonezzi et al. [44] found another possible target to explain the antimotility activity of paclitaxel: forkhead box O3a (FOXO3a), a member of the family of the forkhead transcription factor FOXO, that negatively regulate cell motility. Indeed, the authors found that paclitaxel caused the translocation to the nucleus of the FOXO3a, which is a pre-requisite for its transcriptional activity. In 2003 we [45] reported that protracted exposure of endothelial cells in vitro (144 h) to low concentrations of several different anticancer agents, including paclitaxel, caused overexpression of thrombospondin-1 (TSP-1), a potent and endothelial-specific inhibitor of angiogenesis. Elevated circulating TSP-1 levels were also detected in the plasma of mice treated with metronomic chemotherapy and, above all, the antiangiogenic and antitumor effects of low-dose continuous cyclophosphamide were absent in TSP-1-null C57BL/6 mice. These results suggested that TSP-1 acts as a mediator of the antiangiogenic effects of low-dose metronomic chemotherapy [45]. These data were later confirmed in other in vivo models. Indeed, systemic low-dose continuous treatment of a rat malignant prostate cancer model (Dunning AT-1) with paclitaxel induced the expression of TSP-1 in the tumor tissue, and inhibited tumor growth [46]. Moreover, metronomic paclitaxel chemotherapy also caused a dramatic increase in the expression of TSP-1 (whereas MTD chemotherapy did not) in a HT-29 colon cancer model [29] and in the highly metastatic 4T1 mouse model of breast cancer [30]. These findings support the hypothesis that the anti-tumour effect of low-dose metronomic paclitaxel is in part mediated by the induction of TSP-1 (Fig. 2).
Other interesting possible mechanisms include the downregulation of survival factors for endothelial cells such as Angiopoietin-1 (Ang-1) and VEGF-A. Ang-1 and angiopoietin-2 (Ang-2) are major ligands for the endothelium-specific tyrosine kinase receptor Tie-2, and they are important regulators of endothelial cell survival [46]. Long-term exposure (168 h) to low concentrations (2 nM) of paclitaxel significantly decreased Ang-1 and VEGF-A gene and protein expression in human ovarian cancer cell lines [47]. The downregulation of VEGF-A after paclitaxel treatment was also observed in tissue samples from 39 patients (diagnosed with invasive carcinoma) that were cultured with paclitaxel for 24 h. The mean VEGF-A levels were significantly lower in the treated samples compared to controls [48]. Moreover, the downregulation of VEGF-B and -D was also reported in response to paclitaxel treatment in ovarian carcinoma xenografts, whereas the expressions of Tie-1 and Tie-2 were significantly upregulated. In the same study, paclitaxel treatment significantly decreased the expression of VEGF-A, and it increased the expression of VEGFR-2 [49] (Fig. 2).

New pharmaceutical formulations of paclitaxel enhance its antiangiogenic activity
In the last decade numerous efforts have focused on removing the use of Cremophor EL to decrease the hypersensitive side effects, or to improve the pharmacokinetic behavior, of paclitaxel. There are a variety of nanoparticle carrier systems being explored for chemotherapeutic drugs, including paclitaxel, such as liposomes, pegylated liposomes, protein nanoparticles, and polymeric nanoparticles [50].
Liposomes represent an established drug delivery system for lipophilic substances, and they act by improving the pharmacokinetics and the therapeutic index of the anticancer drugs [51]. Indeed, liposomal paclitaxel showed improved solubility and similar in vitro cytotoxicity against a variety of tumor cell lines compared to that of the Cremophor EL paclitaxel preparation [52]. However, a major limit in the clinical use of conventional liposome is its rapid clearance by the reticuloendothelial system after systemic administration, and is low bioavailability [53].
In contrast to the conventional liposomes, positively charged cationic liposomes have been developed to be preferentially bound to, and internalized by angiogenic and negatively charged, endothelial cells that are found in tumours and in areas of chronic inflammation [54]. Indeed, Thurston et al. [54] have shown that actively growing angiogenic endothelial cells exhibit a preferential uptake of cationic liposomes by endocytosis.
In 2003, Schmitt-Sody et al. [55] evaluated a novel neovascular targeting therapy, consisting of paclitaxel encapsulated in cationic liposomes, on tumor selectivity and antitumoral efficacy in comparison with the paclitaxel free drug in vivo ( Table 2). The endothelial deposition of paclitaxel in tumor vessels significantly increased during the infusion time (compared to Cremophor EL paclitaxel preparation) and both the subcutaneous tumor growth and the appearance of regional lymph node metastases were significantly delayed in a amelanotic hamster melanoma model [55]. Around the same time, also Kunstfeld et al. [56] demonstrated that paclitaxel encapsulated in cationic liposomes, which have been shown to target blood vessels, prevented melanoma growth and invasive behavior, which overall resulted in an increase in the survival of the tumorbearing mice. Moreover, liposome-encapsulated paclitaxel reduced vessel density at the interface between the tumor and the dermis, and reduced endothelial cell mitosis. In contrast, equimolar concentrations of paclitaxel solubilized in Cremophor EL produced only weak effects on tumor growth and did not reduce the mitotic index of the endothelium in vivo [56]. These data were also confirmed in an animal model of prostate cancer. Thus, paclitaxel encapsulated in cationic liposomes (EndoTAG-1) caused a significant decrease of microvessel density in tumors when compared to paclitaxel alone, confirming the relative suppression of angiogenesis when compared to the conventional treatment [57] (Table 2).
Another promising approach to the synthesis of liposomes with prolonged half-life is the grafting of the liposome with the inert and biocompatible polymer polyethylene glycol (PEG). This modification prevents the recognition of liposome by opsonins and therefore reduces its clearance by cells of the reticuloendothelial system [58]. The pegylated liposome is therefore often referred to as a sterically stabilized liposome (SSL). Huang et al. [59] evaluated the antiangiogenic activity of sterically stabilized liposomes containing paclitaxel (SSL-PTX). SSL-PTX effectively inhibited endothelial cell proliferation and migration in a concentration-dependent manner and its metronomic administration induced marked tumor growth inhibition in the MDA-MB-231 xenograft model via an antiangiogenic mechanism (i.e., reduced microvessel density), when compared to the injection of paclitaxel formulated in Cremophor EL [59].
In order to improve the activity of low concentrations of Cremophor EL-free paclitaxel on endothelial cell proliferation, motility, and tube formation, and to facilitate the delivery to blood vessels, new nanoparticles have been synthesized and tested in vitro and in vivo ( Table 2). Colloidal carriers have been shown to improve tumor therapy by increased drug delivery to tumors resulting directly from the enhanced permeability and retention of the drug. Thus, paclitaxel entrapped in emulsifying wax nanoparticles (PTX NPs) showed enhanced efficacy against colon cancer xenograft models, overcoming paclitaxel resistance and increasing the drug's antiangiogenic effect [60]. Moreover, the development of polymeric nanospheres (NPs), that are able to selectively target the activated vascular endothelium and to deliver co-encapsulated anti-angiogenic agents, has also been studied with paclitaxel [61]. Indeed, NPs loaded with paclitaxel have been successfully tested for their antiangiogenic efficacy in HUVECs in vitro, and in rat aorta rings ex vivo. These effects were more potent than those observed with free paclitaxel [61].
Albumin is a very attractive drug carrier for antineoplastic drugs [62]. It has been demonstrated that albumin helps endothelial transcytosis of protein-bound and unbound plasma constituents, mainly via a cell-surface glycoprotein receptor (gp60). The gp60 protein binds to caveolin-1 a remarkable retardation of tumor growth and appearance of regional lymph node metastases [55] Paclitaxel encapsulated in cationic liposomes In vivo metastatic melanoma model Inhibition of newly blood vessels, prevention of melanoma growth and invasiveness, improvement of mice survival [56] Paclitaxel encapsulated in cationic liposomes (EndoTAG-1)

In vivo prostate tumor xenograft
Decrease of microvessel density [57] Sterically stabilized liposomes containing paclitaxel (SSL-PTX) HUVEC proliferation and migration in vitro; MDA-MB-231 breast cancer xenograft models Inhibition of cell proliferation and migration at low concentrations; decrease of microvascular density of tumors treated with low doses of paclitaxel [59] Paclitaxel entrapped in emulsifying wax nanoparticles (PTX NPs) Colon cancer xenograft models Increase of antiangiogenic effect in the colon cancer xenograft models [60] Polymeric nanospheres loaded with paclitaxel In vitro HUVEC culture and ex vivo rat aortic rings Inhibition of proliferation and inhibition of endothelial sprouts [61] ABI-007, a cremophor EL-free, albumin-bound, 130-nm form of paclitaxel Rat aortic rings, human endothelial cell proliferation and tube formation.

Tumor xenografts
Inhibition of rat aortic microvessel outgrowth, human endothelial cell proliferation, and tube formation.

MCF-7 tumor xenografts Antitumor and antiangiogenic effects in vivo.
Decrease of microvessel density [68] (an intracellular protein) which results in the subsequent formation of transcytotic vesicles. In addition, gp60 also binds to osteonectin. Both caveolin-1 and osteonectin are frequently present in certain neoplasms (such as breast, lung, and prostate cancer), which could account for the accumulation of albumin in some tumors. Such accumulation of albumin could facilitate intratumoral accumulation of albumin-bound drugs [62]. In that respect, albuminbound paclitaxel ABI-007 is a novel, cremophor EL-free, 130-nm particle formulation of paclitaxel [63]. This formulation is obtained by high-pressure homogenization of paclitaxel with serum albumin, resulting in a nanoparticle colloidal suspension [64]. Preclinical studies on human breast cancer xenografts have demonstrated that ABI-007 has a higher penetration into tumor cells, with increased antitumor activity, compared to an equal dose of standard paclitaxel [63]. Interestingly, the antiangiogenic activity of ABI-007 was described in the pivotal study by Ng et al. [65], who employed a therapeutic metronomic approach. Indeed, recent data showed that clinically relevant concentrations of vehicles such as Cremophor EL and polysorbate 80 nullify the antiangiogenic activity of taxanes [66]. These results could hamper the usefulness of paclitaxel metronomic regimens. In order to solve this problem, Ng et al. [65] tested ABI-007 both in in vitro and in in vivo models (Table 2). ABI-007 was found to significantly inhibit rat aortic microvessel outgrowth, human endothelial cell proliferation, and tube formation. Moreover, metronomic ABI-007 significantly suppressed tumor growth in xenograft models with a very low toxicity profile [65].
Recently, hyaluronic acid (HA) conjugates of paclitaxel (HA-PTX) were shown to produce strong anti-tumor effects when dosed metronomically. They also induced anti-angiogenic effects of greater magnitude than those achieved with MTD administration, or with free PTX administration in female nude mice bearing ovarian cancer cells [67]. Moreover, in a taxane-resistant model of ovarian cancer, a significant reduction in tumor weight was noted in the metronomic HA-PTX treated groups, whereas the response of the MTD group was not statistically significant. Metronomic HA-PTX treatment resulted in marked increases in TSP-1, confirming the anti-angiogenic mechanism of action of this novel formulation [67].
Another interesting new paclitaxel formulation was published by Liang et al. [68]. The novel paclitaxel conjugate (PEG-VC-PABC-PTX) was designed and synthesized using p-aminobenzylcarbonyl (PABC), a spacer, and valine-citrulline (VC), a substrate of cathepsin B, to link polyethylene glycol (PEG) and PTX. The PEG-VC-PABC-PTX showed significant antitumor and anti-angiogenic effects in vivo, while the control conjugate was almost ineffective [68].
The development of an oral formulation of paclitaxel is necessary for the clinical development of metronomic chemotherapy. Moes et al. [69] have increased the oral bioavailability of paclitaxel by combining ritonavir with a new oral solid dispersion formulation of paclitaxel (1/11 w/w paclitaxel, 9/11 w/w polyvinylpyrrolidone K30, and 1/11 w/w sodium lauryl sulfate)-which they termed ModraPac001. Currently, the ModraPac001 formulation is being applied in the first clinical trial with oral metronomic chemotherapy of paclitaxel [69].
Among all the new formulations of paclitaxel with demonstrated antiangiogenic characteristics, ABI-007 was the most successful in clinical practice. Indeed, it was initially approved by the US Food and drug Administration in January 2005, and indicated for use with pretreated metastatic breast cancer patients. ABI-007 is currently approved in 42 countries, including the European Union, Canada, India and Japan [64]. Thus, in a relatively short period, there has been an impressive improvement in our understanding of the antiangiogenic mechanism of paclitaxel. Further laboratory and clinical research is necessary to maximize the benefits of the antiangiogenic properties of paclitaxel for the treatment of patients with various cancers. In this context, the knowledge and investigation of metronomic chemotherapy based on the new formulations of paclitaxel, such as ABI-007, HA-PTX or ModraPac001, constitutes today one of the most exciting strategies for improving the clinical control of angiogenesis in primary and metastatic tumors [70]. On the other hand, such treatment strategies should be based on solid and significant results of randomized phase III clinical trials, and not only on their safety profiles or on their impact on the quality of life and on patient preference. Nonetheless, tolerability and compliance will probably become the most important factors in the future, according to the emerging overall importance of quality of life in cancer care [71]. The availability of new drugs for metronomic chemotherapeutic strategies (i.e. ABI-007, HA-PTX and ModraPac001), alone or in association with other targeted molecules, will give the oncologist many different effective treatment options for metastatic patients that are already resistant to standard doses of chemotherapy [70]. Indeed, both preclinical [72] and clinical studies [73,74] have suggested that metronomic chemotherapy could be effective in stabilizing tumors already resistant to the same drug MTD treatments. Moreover, the ability of paclitaxel to inhibit different pathways of the angiogenic process may be a great advantage in the treatment of tumors already resistant to antiangiogenic drugs with a specific target (e.g. bevacizumab or sunitinib).

Conclusion
The aim of this review was to summarize the published data on the molecular and pharmacological bases of the antiangiogenic activity of paclitaxel, and to underline this particular pharmacodynamic characteristic of the drug for possible future clinical development. In fact, paclitaxel (and its novel formulations) both at MTD and at low dose schedules demonstrated significant antiangiogenic activity. This effect could be ascribed both to a cytotoxic or to a cytostatic activity towards activated endothelial cells, or to the inhibition of capillary formation and cell migration. Moreover, paclitaxel has been shown to inhibit the release of VEGF and Ang-1 by tumor cells, and to increase the secretion of TSP-1 to the tumor microenvironment. It is therefore possible that the different antiangiogenic mechanisms of paclitaxel described in this review may explain the antiangiogenic activity of this drug in different human tumors, such as prostate and breast cancer. Indeed, the angiogenic process in these tumors may depend on the balance of different proangiogenic factors and endogenous angiogenic inhibitors. Paclitaxel can definitely affect those different pathways, resulting in an overall clinical activity, and one that is independent from the direct cytotoxic effects of the drug on cancer cells. Thus, based on the available published preclinical data, paclitaxel (and especially its novel formulations) may permit novel and rational therapeutic strategies to control tumor progression in patients.


extfieldsofstudy: Biology Medicine

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Phenome-Wide Association Study (PheWAS) for Detection of Pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network

Using a phenome-wide association study (PheWAS) approach, we comprehensively tested genetic variants for association with phenotypes available for 70,061 study participants in the Population Architecture using Genomics and Epidemiology (PAGE) network. Our aim was to better characterize the genetic architecture of complex traits and identify novel pleiotropic relationships. This PheWAS drew on five population-based studies representing four major racial/ethnic groups (European Americans (EA), African Americans (AA), Hispanics/Mexican-Americans, and Asian/Pacific Islanders) in PAGE, each site with measurements for multiple traits, associated laboratory measures, and intermediate biomarkers. A total of 83 single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) were genotyped across two or more PAGE study sites. Comprehensive tests of association, stratified by race/ethnicity, were performed, encompassing 4,706 phenotypes mapped to 105 phenotype-classes, and association results were compared across study sites. A total of 111 PheWAS results had significant associations for two or more PAGE study sites with consistent direction of effect with a significance threshold of p<0.01 for the same racial/ethnic group, SNP, and phenotype-class. Among results identified for SNPs previously associated with phenotypes such as lipid traits, type 2 diabetes, and body mass index, 52 replicated previously published genotype–phenotype associations, 26 represented phenotypes closely related to previously known genotype–phenotype associations, and 33 represented potentially novel genotype–phenotype associations with pleiotropic effects. The majority of the potentially novel results were for single PheWAS phenotype-classes, for example, for CDKN2A/B rs1333049 (previously associated with type 2 diabetes in EA) a PheWAS association was identified for hemoglobin levels in AA. Of note, however, GALNT2 rs2144300 (previously associated with high-density lipoprotein cholesterol levels in EA) had multiple potentially novel PheWAS associations, with hypertension related phenotypes in AA and with serum calcium levels and coronary artery disease phenotypes in EA. PheWAS identifies associations for hypothesis generation and exploration of the genetic architecture of complex traits.

Introduction
Phenomic approaches are complementary to the more prevalent paradigm of genome-wide association studies (GWAS), which have provided some information about the contribution of genetic variation to a wide range of diseases and phenotypes [1]. While a typical GWAS evaluates the association between the variation of hundreds of thousands, to over a million, genotyped single nucleotide polymorphisms (SNPs) and one or a few phenotypes, a common limitation of GWAS is the focus on a pre-defined and limited phenotypic domain. An alternate approach is that of PheWAS, which utilizes all available phenotypic information and all genetic variants in the estimation of associations between genotype and phenotype [1]. By investigating the association between SNPs and a diverse range of phenotypes, a broader picture of the relationship between genetic variation and networks of phenotypes is possible.
A challenge for PheWAS is the availability of large studies with genotypic data that are also linked to a wide array of high quality phenotypic measurements and traits for study. Biorepositories linked to electronic medical records (EMR) have been an initial resource for PheWAS, but these EMR-based studies are often limited to phenotypes and traits commonly collected for clinical use and may represent sets of limited racial/ethnic diversity [2,3]. While there is no U.S. national, population-based cohort [4], several diverse, population-based studies exist with tens of thousands of samples linked to detailed survey, laboratory, and medical data. These large population-based studies have limitations [5], but collectively [6] they offer an opportunity to perform a PheWAS of unprecedented size and diversity.
To capitalize on the potential for collaborative discovery among some of the large population-based studies of the U.S., the National Human Genome Research Institute (NHGRI) funded the Population Architecture using Genomics and Epidemiology (PAGE) network. PAGE includes eight extensively characterized, large populationbased epidemiologic studies where data were collected across multiple racial/ethnic groups, supported by a coordinating center [7], providing an exceptional opportunity to pursue PheWAS with a large number of SNPs, and thousands of phenotypic measurements including a wide range of common diseases, risk factors, intermediate biomarkers and quantitative traits in diverse populations. Herein, we illustrate the feasibility and utility of the PheWAS approach for large population-based studies and demonstrate that PheWAS provides information on, and exposes the complexity of, the relationship between genetic variation and interrelated and independent phenotypes. We have found PheWAS results that replicate previously identified genotype-phenotype associations with the exact phenotype in previous associations or closely related phenotypes, as well as a series of novel genotype-phenotype associations. This data exploration method exposes a more complete picture of the relationship between genetic variation and phenotypic outcome. PheWAS provides the unbiased, high throughput design achieved by GWAS in the genome and phenotype domains simultaneously. This approach changes the paradigm of phenotypic characterization and allows for exploratory research in both genomics and phenomics.

Results
Data from five PAGE study sites were available for this PheWAS: Epidemiologic Architecture for Genes Linked to Environment (EAGLE) using data from the National Health and Nutrition Examination Surveys (NHANES); the Multiethnic Cohort Study (MEC); the Women's Health Initiative (WHI); and two studies of the Causal Variants Across the Life Course (CALiCo) group: the Cardiovascular Health Study (CHS) and Atherosclerosis Risk in Communities (ARIC). Text S1 provides full information on study design, phenotype measurement, and genotyping for each study. These studies collectively include four major racial/ethnic groups: European Americans (EA), African Americans (AA), Hispanics/Mexican Americans (H), and Asian/ Pacific Islanders (API). All PAGE study sites included both males and females, except for WHI (which includes only women). Table 1 provides an overview of the sample sizes by PAGE study site as well as the number of SNPs and phenotypes available for this PheWAS. Sample size and the number of phenotypes varied across studies, and the sample size for various phenotypes within each study varied dependent on the number of individuals for which a given phenotype was measured. The number of phenotypes available for this PheWAS ranged within studies from 63 (MEC) to 3,363 (WHI). Study sites also had differing numbers of genotyped SNPs, and Table S1 contains the list of all SNPs available for two or more sites in this study, arranged by previously associated phenotypes. The PAGE network has focused on characterization of well-replicated variants across multiple race/ ethnicities, so each study independently genotyped a set of SNPs with previously reported associations with phenotypes such as body mass index, C-reactive protein, and lipid levels.
Tests of association assuming an additive genetic model were performed independently by each PAGE study site for each SNP and each phenotype, stratified by race/ethnicity. The last column of Table 1 presents the total number of comprehensive associations with and without a p-value cutoff of 0.01, showing the proportion of significant results for this many tests of association. The total number of tests of association ranged from .20,000 (MEC) to .1 million (WHI) reflecting the variability in both the number of phenotypes available for study as well as the number of SNPs genotyped by each PAGE study site. As expected, the total number of significant tests of association (p,0.01) represented a fraction of the total number of tests performed.
Results from these tests of association were then compared across study sites to identify overlapping significant associations, as these results most likely represent robust findings. To facilitate determining overlapping significant associations, similar phenotypes that existed across more than one study were binned into 105 distinct phenotype-classes. For some phenotypes, the specific phenotype existed across more than one PAGE study, such as for the phenotype ''Hemoglobin'', where hemoglobin measurements were available for ARIC, CHS, EAGLE, and WHI. Other groups of phenotypes binned within phenotype-classes were within similar phenotypic domains but were not represented in exact same form across studies. Table S2 contains a list of the study level phenotypes, the study from which the phenotype is available, and the phenotype-class for each phenotype that overlapped with another study.
The same or similar phenotypes may or may not have been collected by each PAGE study. Thus, the number of studies that were available for comparison of results across studies varied from one phenotype-class to another phenotype-class. Table 2 presents the number of results where at least two of five independent studies had SNP-phenotype associations with p,0.01 for single phenotype-class and single race/ethnicity group, compared to the total number of SNP-phenotype association tests performed. For example, .8,500 tests of association for the same SNP and same phenotype were available from two PAGE study sites whereas only 906 and 58 tests of association were available from four and five PAGE study sites, respectively. There were 3 results where two or more of the groups had a SNP-phenotype association p,0.01 for a single phenotype class across 5 groups represented.
For this PAGE-wide PheWAS, tests of association were considered significant across PAGE study sites where two or more phenotypes in the same phenotype-class in the same racial/ethnic group passed a significance threshold of p,0.01 with a consistent direction of genetic effect. Based on these criteria, a total of 111 Pubmed ID of study description manuscript for each study. 3 Maximum sample size and Minimum sample size are dependent both on who was genotyped and who had a specific phenotype measured. Not all phenotypic measurements were available for all participants within each study. 4 This is the total number of SNPs available for each study. Table S1 has the list of these SNPs for each study, genotyped across two or more studies. 5 This includes the number of phenotypes transformed and untransformed, as well as categorical phenotypes divided into binary phenotypes, full description in Materials and Methods. 6 Total number of tests of association calculated for each study, in parenthesis is the total number of associations with p,0.01. doi:10.1371/journal.pgen.1003087.t001

Author Summary
In phenome-wide association studies (PheWAS) all potential genetic variants in a dataset are systematically tested for association with all available phenotypes and traits that have been measured in study participants. By investigating the relationship between genetic variation and a diversity of phenotypes, there is the potential for uncovering novel relationships between single nucleotide polymorphisms (SNPs), phenotypes, and networks of interrelated phenotypes. PheWAS also can expose pleiotropy, provide novel mechanistic insights, and foster hypothesis generation. This approach is complementary to genome-wide association studies (GWAS) that test the association between hundreds of thousands, to over a million, single nucleotide polymorphisms and a single phenotype or limited phenotypic domain. The Population Architecture using Genomics and Epidemiology (PAGE) network has measures for a wide array of phenotypes and traits, including prevalent and incident status for clinical conditions and risk factors, as well as clinical parameters and intermediate biomarkers.
We performed tests of association between a series of genome-wide association study (GWAS)-identified SNPs and a comprehensive range of phenotypes from the PAGE network in a high-throughput manner. We replicated a number of previously reported associations, validating the PheWAS approach. We also identified novel genotypephenotype associations possibly representing pleiotropic effects.

Known Associations-Validating the PheWAS Approach
Almost half of the PAGE PheWAS results (52/111; 48%) replicated previously known genotype-phenotype associations. These replicated results serve as positive controls and demonstrate that the high-throughput PheWAS approach is feasible and valid. As an example, low-density lipoprotein cholesterol (LDL-C) has previously been associated with rs4420638 near APOE/APOC1/ C1P1/C2/C4 in European Americans [8,9]. In the PAGE PheWAS, a significant association between the same SNP and LDL-C phenotypes of the ''LDL-C'' phenotype-class in European Americans as reported in the literature [8,9] was observed in two PAGE study sites, with the same direction of effect (b) as well as a third PAGE site with near significant results: ARIC (p = 1.27610 215 , b = 25.75), CHS (p = 7.89610 212 , b = 27.06), and WHI (p = 0.06, b = 24.15). Figure 1 shows the significant PheWAS LDL-C results, as well as other associations considered significant for rs4420638 across PAGE study sites for other phenotype-classes in a similar racial/ethnic group passed a significance threshold of p,0.01 with a consistent direction of genetic effect.

Potentially Novel Associations
PheWAS results were considered novel, if the significant phenotype-class associations varied substantially from the previously reported GWAS and candidate gene studies. Approximately one-third of the PAGE PheWAS results (33/111; 30%) represented novel genotype-phenotype-class associations. Further research will be required to determine the further validity of these exploratory results.
The most statistically significant of the novel phenotype-class associations identified by this PheWAS include multiple associations involving phenotype-classes for hematologic traits in African Americans ( Figure 4). SNPs rs599839 (CELSR2/PSRC1), rs10923931 (NOTCH2), rs2228145 (IL6R), rs2144300 (GALNT2), rs10757278 (CDKN2A,CDKN2B), and rs7901695 (TCF7L2) were each associated with white blood cell count phenotypes among AA (significant p-values ranging 7.96610 23 to 9.99610 215 ). IL6R rs2228145 was also associated with neutrophils and lymphocyte numbers in AA with p-values ranging from 2.44610 24 to 4.66610 210 . These SNPs were previously associated with LDL-C, total cholesterol levels, and coronary artery disease (rs599839)    SE), sample sizes, substudies, number of substudies with results passing our p-value cutoff, the previously associated phenotype for that SNP, and references for the previously associated phenotypes are given. 1 Coded Allele. 2 Coded allele frequency. 3 Associated phenotypes for individual results. 4 Phenotype-class. 5 Race/ethnicity for association, abbreviations: African American (AA), European American (EA), Mexican American/Hispanic (H). 6 P-Values of results that passed p = 0.01 threshold in order of the associated phenotypes. 7 Beta and standard error in order of the associated phenotypes. 8 Sample size in order of the associated phenotypes. 9 Studies with the significant result, in order of the associated phenotypes. 10 Total number of studies with at least one result passing p-value threshold for specific phenotype-class and SNP. 11 Previously reported associated phenotypes for SNP. 12 Pubmed ID's for previously associated phenotypes. doi:10.1371/journal.pgen.1003087.t003  19056105,19106168,19140096,19267250,19280716,19330901,19377912,19387461,19435922,19452524,19542902,19592000,19671870,19833146,19853505,19876004,20043205,20044998

EAGLEIII
Novel associations that met the criteria for PheWAS significance are given here, sorted by the most to least number of PAGE study sites available. Related associations were defined as SNPs significantly associated in this PheWAS with phenotype-classes closely related to phenotypes among known associations. Significance was defined as a test of association with p,0.01 observed in two or more PAGE studies for the same SNP, phenotype class, and race/ ethnicity and consistent direction of effect when relevant. For each, the nearest gene(s), the SNP rs number, coded allele (CA) and frequency (CAF), associated phenotypes, phenotype-class, race/ethnicity, p-values, genetic effect/ beta values (standard error; SE), sample sizes, substudies, number of substudies with results passing our p-value cutoff, the previously associated phenotype for that SNP, and references for the previously associated phenotypes are given. 1 Coded Allele. 2 Coded allele frequency. 3 Associated phenotypes. 4 Phenotype-class. 5 Race/ethnicity for association, abbreviations: African American (AA), European American (EA), Mexican American/Hispanic (H). 6 P-Values of results that passed p = 0.01 threshold in order of the associated phenotypes. 7 Beta and standard error in order of the associated phenotypes. 8 Sample size in order of the associated phenotypes. 9 Studies with the significant result, in order of the associated phenotypes. 10 Total number of studies with at least one result passing p-value threshold for specific phenotype class and SNP. 11 Previously reported associated phenotypes for the SNP. 12 Pubmed ID's for previously associated phenotypes. doi:10.1371/journal.pgen.1003087.t004 [8,[12][13][14]; type 2 diabetes (rs10923931) [16]; C-reactive protein (rs2228145) [17]; coronary heart disease, HDL-C and triglycerides (rs2144300) [13]; MI (rs10757278) [11]; and type 2 diabetes (rs7901695) in EA [18][19][20]. It is likely that the majority of the significant findings for three of the SNPs on chromosome 1 [rs599839 (CELSR2/PSRC1), rs10923931 (NOTCH2), rs2228145 (IL6R)] are not truly novel given that these variants are likely in linkage disequilibrium with the white blood cell count-associated Duffy null allele (DARC rs2814778) [21,22] in African Americans. Of note is GALNT2 rs2144300 (p = 3.32610 26 in WHI and 7.96610 23 in CHS), located outside the 90 Mb region known to be associated with white blood cell counts in African Americans [21] and possibly representing a novel genotype-phenotype association for this trait. Also for chromosome 1, novel associations were identified in African Americans at p,0.01 for the phenotypeclass ''Hemoglobin'' and ANGPTL3 rs1748195, previously associated with triglycerides in European-descent populations [13,19]. Of the remaining hematologic trait associations identified that were not on chromosome 1, rs10757278 near CDKN2A/B on chromosome 9 and TCF7L2 rs7901695 on chromosome 10 were both associated with white blood cell count, neither of which were previously reported in GWAS for this trait [21,22]. For CDKN2A/ B rs1333049, a SNP previously associated with type 2 diabetes, coronary artery disease, and hypertension in European-descent populations [15,23] p,0.01 associations were identified for the phenotype-class of Hemoglobin. Finally, a novel association in European Americans was noted between FADS1 rs174547, a SNP previously associated with LDL-C [13,19], and the phenotypeclass of ''Platelet Count'' at p,0.01.
Aside from hematologic traits, the most significant novel association identified in this PheWAS was identified for phenotypes in the phenotype-class ''Forced Expiratory Volume in 3 Seconds (FEV3)'' and GALNT2 rs2144300 in African Americans (p-values ranging from 8.82610 23 to 4.90610 24 ). GALNT2 rs2144300, previously associated with HDL-C in European Americans and African Americans [13,24], has not previously been associated with lung function or asthma quantitative traits. Interestingly, GALNT2 rs2144300 was also associated with phenotypes in the ''Hypertension'' phenotype-class among African Americans in this PheWAS Specifically the phenotypes were ''High blood pressure ever diagnosed?'' (ARIC, p = 1.61610 23 , b = 0.24) and ''Pills for hypertension ever?'' (WHI, 8.27610 23 , b = 0.15). Indeed, GALNT2 rs2144300 displayed the most suggestion of pleiotropy among all the SNPs tested in this study. In addition to the associations identified in African Americans, rs2144300 was associated with phenotypes in the phenotype-classes ''Serum Calcium'' (p-values ranging from 1.47610 24 to 8.10610 23 ) and ''Artery Treatment'', specifically the phenotypes ''Coronary artery bypass graft (CABG)'' (WHI, p = 2.46610 23 , b = 0.24) and ''Aortic aneurysm repair'' (CHS, 5.49610 23 , b = 0.57) in European Americans. Significant PheWAS associations at p,0.01 for rs2144300 are plotted by phenotype in Figure 5, as well as additional results at p,0.05.

Discussion
The PheWAS results herein present the result of tests of association between a large number of SNPs and an extensive range of phenotypes and traits available within five studies of the PAGE network. For this first PAGE PheWAS analysis we have emphasized associations that replicated across two or more independent PAGE studies for the same phenotype class and same race/ethnicity. Most of the robust findings reported here represent previously known genotype-phenotype relationships, but a tantalizing few also represent potentially novel pleiotropic relationships.
The 33 novel results presented here are intriguing, but it is important to emphasize that these first-pass analyses are considered hypothesis-generating, exploratory, and require additional scrutiny before the findings are further considered for follow-up, unlike the directed a priori hypothesis-testing analyses within PAGE that involve SNPs hypothesized to be associated with specific phenotypes. Further analysis of PheWAS results will be on an individual result basis and will include careful phenotype harmonization for traits and outcomes that cross two or more PAGE studies, as well as considerable investigation of the possible effect of covariates such as age, sex, and environmental exposure(s) on the association between genetic variation and phenotypic outcome.
One of the many challenges for the interpretation of PheWAS results is dissecting the genetic effect observed among correlated phenotypes. In some cases, the relationship is likely attributable to a common biological process with known genetic contribution (e.g., body mass index and waist circumference). In other cases, the networks that exist between intermediary and/or outcome related phenotypes add complexity to interpreting association results. For instance, genetic variation may impact the variation of a single Figure 2. PheWAS associations for rs10757278 near CDKN2A/CDKN2B. SNP rs10757278 was previously associated with myocardial infarction (MI). Associations are plotted clockwise starting at top for the association with the smallest p-value and the length of the line corresponds to -log10(p-value). Lines are labeled with the study-specific phenotype, the PAGE study, racial/ethnic group, and direction of effect (+ or 2). Red lines represent associations at p,0.01, and results with p,0.05 are also plotted in grey to show trends for additional phenotypes. ''LN1'' indicates the phenotype had 1 added to the variable, and then the variable was natural log transformed. The PheWAS phenotypes significantly associated with this SNP varied, from MI (known), to coronary artery disease and MI related phenotypes such as presence or absence of ''percutaneous transluminal coronary angioplasty'', ''angina'', and ''coronary bypass surgery''. doi:10.1371/journal.pgen.1003087.g002 phenotype, but variation in that phenotype could then result in changes in other downstream phenotypes indirectly. Examples of added complexity include obesity leading to impaired immune function [28], and metabolic syndrome where there is a spectrum of risk factors that are all associated with increased risk of cardiovascular disease and type 2 diabetes [25]. As a result, significant associations between a genetic variant and many phenotypes could represent a network or cascade of events. This is a potential interpretation of results found for SNP rs10923931 (NOTCH2) in AA, where type 2 diabetes was the previously reported association for this SNP and the novel result was found for hypertension, and type 2 diabetes and hypertension are often a co-occurrence. Further analysis of individual PheWAS results is necessary to conclusively establish the impact of the relationship between phenotypes on significant SNP-phenotype associations.
With the large number of phenotype-genotype associations calculated, there will be an increase in type 1 error due to multiple testing. A Bonferroni correction could be used within each individual study to choose a cutoff for significance that controls for multiple hypothesis testing. However, this would not take into account the correlations that exist between the phenotypes in these studies that impact the assumption of independence between tests as well as the correlations between the genotypes. For our first PAGE PheWAS analysis, we chose to seek replication of results across studies and required the same direction of effect as one approach to reduce the false discovery rate. Significant results can still be found by chance across more than one study. Multiple challenges arise when attempting to get a metric of the type 1 error rate across multiple studies. First, as with individual studies, correlations between phenotypes and previous associations for the SNPs are still present. Also, there are varying type 1 error rates depending on the number of studies available for seeking replication. Quantification of how many results were found with a p-value cutoff, and without a p-value cutoff, depending on the number of studies where replication could be sought (2, 3, 4, or 5) provides some information about the number of significant results we found, in Table 2. Table 1 has the total number of results with and without p-value cutoff for individual studies. It is important to note that in cases where replication could be sought in more than two studies, there were cases where the result replicated in 3 or more studies, further increasing our confidence in the result.
A potential limitation of this study is the granularity of phenotypes within our phenotype classes. The phenotypes within some phenotype classes are the same or extremely similar, such as white blood cell count measurements across studies. However, the phenotype class ''Artery Treatment'' is broad in terms of the types . This SNP has previously published associations with serum LDL cholesterol levels, total cholesterol, and coronary artery disease. Genotype-phenotype associations are plotted clockwise starting at top for the association with the smallest p-value. The length of the line corresponds to -log10(p-value), the longer the line the more significant the result. The study race/ ethnicity/and phenotype for each tests of association are listed. Red lines represent associations at p,0.01, and results with p,0.05 are also plotted in grey to show trends for additional phenotypes. ''LN1'' indicates the phenotype had 1 added to the variable, and then the variable was natural log transformed. The PheWAS phenotypes significantly associated with this SNP varied, from LDL cholesterol levels (previously published), to lipid levelrelated phenotypes such as ''High cholesterol requiring pills ever''. In the case of coronary artery disease, phenotypes with significant results that were related to coronary artery disease included ''Ever had pain/discomfort in your chest'', and ''Hospitalized for chest pain''. doi:10.1371/journal.pgen.1003087.g003 of phenotypes included, such as presence/absence aortic aneurysm repair and presence/absence of angioplasty of the coronary arteries. For some classes, the replicated results encompass more variation in the phenotypes captured, compared to other results. As a result, significant associations between a genetic variant and all phenotypes in a network may be present. PheWAS is an exploratory and hypothesis generating exercise, thus the choice was made to have a broader match for some groups of phenotypes in order to allow for those phenotypes to be part of the exploration of the data. In addition, misclassification of phenotypes when matching is possible, and thus can limit identification of significant associations across studies. Other potential limitations include sample size/power, study heterogeneity, and the SNPs selected for study. As shown in Table 1, there is much variability across independent PAGE studies. While each PAGE study is sizeable, individual tests of association may be underpowered depending on the availability of the genetic variant, phenotype class, and race/ ethnicity. Tests of association that failed to reach statistical significance may represent underpowered genotype-phenotype relationships and will require larger epidemiologic or clinic-based samples to identify. In regards to the potential impact of heterogeneity, we have some cases where replication existed in only two or three studies out of those where replication could be sought. In some instances this may be due to power, but this also may reflect the heterogeneity between studies, such as how various phenotypes are measured in individual studies and variation in mean age across the different studies. Finally, SNPs were originally selected for this study to replicate known genotype-phenotype associations and to generalize them to diverse populations. A comprehensive set of genome-wide''agnostic'' SNPs may uncover additional pleiotropic or novel genotype-phentoype relationships not tested here.
Despite the the limitations present for this PheWAS, there are multiple strengths within our study. We have had the opportunity to perform a PheWAS of substantial size with an unprecedented diversity of high quality phenotypic measurements and traits, across multiple races/ethnicities. In addition, because of this PheWAS was conducted across multiple independent studies, we were able to identify the most robust genotype-phenotype relationships across studies PheWAS results for blood cell counts and hemoglobin levels. Eleven novel genotype-phenotype-class associations were identified for white blood cell counts and hemoglobin levels collectively. The top track indicates the chromosomal location of each SNP, below that track is a SNP/Phenotype identification track containing the SNP ID, as well as the phenotype, phenotype transformation if present (LN1 = ln(1+variable)), and the race-ethnicity for the test population (AA or EA). The next track is a ''presence/absence'' track, box presence indicates if the SNP was present for ARIC (blue), CHS (red), WHI (orange), or EAGLE (purple). The next tracks are as follows: -log10(p-value), where the each p-value is plotted, the direction of the triangle indicates the direction of effect (triangle pointed up is positive, triangle pointed down is negative), base of the triangle corresponds to the location of the p-value, solid red line is positioned at p-value = 0.01; The next track is magnitude of effect (beta) dotted grey line is positioned at the null; Next are coded allele frequencies (CAF) for each study; Final track is sample size for each test of association. doi:10.1371/journal.pgen.1003087.g004 Figure 5. PheWAS associations for rs2144300 within GALNT2. The previously published associations for this SNP were with triglyceride and HDL cholesterol levels. Genotype-phenotype associations are plotted clockwise starting at top for the association with the smallest p-value. The length of the line corresponds to -log10(p-value), the longer the line the more significant the result. The study race/ethnicity/and phenotype for each tests of association are listed. Red lines represent associations at p,0.01, and results with p,0.05 are also plotted in grey to show trends for additional phenotypes. The novel PheWAS phenotypes significantly associated with this SNP varied, including white blood cell counts, forced vital capacity at three seconds (FEV3), and serum calcium levels. doi:10.1371/journal.pgen.1003087.g005

Conclusion
This initial PheWAS within PAGE has presented challenges in terms of generating high-throughput tests of association across large epidemiologic studies as well as the synthesis of the resulting data and its eventual interpretation. Even with these limitations, this PheWAS demonstrates the utility of investigating the relationship between genetic variation and an extensive range of phenotypes by validating known genotype-phenotype associations as well as identifying novel genotype-phenotype associations, revealing complex phenotypic relationships and perhaps actual pleiotropy. The utility of this hypothesis-generating approach will continue to improve over time as more samples, variants, and phenotypes/traits across diverse populations are available for study in PAGE and other genomic resources. Larger, richer datasets coupled with methods development promise to more fully reveal the complex nature of genetic variation and its relationship with human diseases and traits.

Study Populations
All studies were approved by Institutional Review Boards at their respective sites (details are given in Text S1). The Population Architecture using Genomics and Epidemiology (PAGE) study includes the following epidemiologic collections: Atherosclerosis Risk in Communities (ARIC), Coronary Artery Risk in Young Adults (CARIDA), Cardiovascular Health Study (CHS), the Multiethnic Cohort (MEC), the National Health and Nutrition Examination Surveys (NHANES), Strong Heart Study (SHS), and Women's Health Initiative (WHI). For this PheWAS, data were available from ARIC, CHS, MEC, NHANES III, NHANES 1999-2002, and WHI ( Table 1). The PAGE study design is described in Matise et al [26] and the PAGE PheWAS study design is described in Pendergrass et al [1]. Figure 6. Workflow for phenotype matching, to develop the 105 phenotype classes. A MySQL database was used to filter the data from five studies for any results with p,0.01 to generate lists of the unique phenotypes for each individual PAGE study. The number of phenotypes that passed this significance threshold for each of the four groups was 604 (ARIC), 331 (CHS), 63 (MEC), 324 (EAGLE), 1,342 (WHI). Note that during the binning process, a smaller number of phenotypes are listed in Figure 6 than the total number of phenotypes referred to in the manuscript for the actual associations, in the phenotype matching process we only took into account distinct phenotypes regardless of whether or not they were transformed or untransformed or if they were categorical phenotypes binned into case/control phenotypes. Next, resulting phenotypes were then manually matched up between ARIC, CHS, MEC, EAGLE and WHI using and knowledge about the phenotypes and the known focus of specific PAGE study questions (such as arterial measurements including degree of arterial stenosis). In the last step, phenotypes from all studies, regardless of significance from genotype-phenotype tests of association, were matched to the already-defined phenotype classes using the criteria described above. doi:10.1371/journal.pgen.1003087.g006

SNP Selection and Genotyping
All SNPs considered for genotyping in PAGE were candidate gene or GWAS-identified variants for phenotypes and traits available in the epidemiologic collections accessed by PAGE study sites. Cohorts and surveys were genotyped using either commercially available genotyping arrays (Affymetrix 6.0, Illumina 370CNV BeadChip), and/or custom mid-and low-throughput assays (TaqMan, Sequenom, Illumina GoldenGate or BeadXpress). Quality control was implemented at each PAGE study site independently. Study specific genotyping details are described in Text S1.
In this PheWAS, data were available for SNPs previously associated with HDL-C, LDL-C, and triglycerides [27], body mass index, obesity [28], type 2 diabetes, glucose, insulin [29], and measures of inflammation (C-reactive protein), among other diseases/traits. A total of 83 SNPs overlapped across at least PAGE study sites: ten were specifically selected for body mass index traits replication, three for C-reactive protein, six for coronary/cardiac traits, three for gout/kidney, 41 for lipids, and 20 for type 2 diabetes. Table S1 lists these SNPs, along with references reporting phenotypic associations from the NHGRI GWAS catalog [30] and the open access database of GWAS results of Johnson et al. 2009 [31]. The NHGRI GWAS catalog was most recently accessed in October, 2011. If no references were available from either of those two sources, a PubMed search was performed to retrieve relevant citations.

Statistical Methods
All tests of association were performed independently by each PAGE study site using the following analysis protocol: Linear or logistic regressions were performed for continuous or categorical dependent variables, respectively, assuming an additive genetic model (0, 1, or 2 copies of the coded allele). For variables with multiple categories, binning was used to create new variables of the form ''A versus not A'' for each category, and logistic regression was used to model the new binary variable. Linear regressions were repeated following a y to log (y+1) transformation of the response variable with +1 added to all continuous measurements before transformation to prevent variables recorded as zero from being omitted from analysis. All analyses were stratified by race-ethnicity.
Test of association were calculated for the number of SNPs and phenotypes listed in Table 1 All association results from the tests of association were reported in standardized templates designed by the PAGE coordinating center to facilitate data sharing. All results were then imported into a relational database (MySQL). The database was also used to match previously reported GWAS data with the SNPs analyzed in this study.

Plotting Significant Results
The software PheWAS-View was developed for data visualization of the PheWAS results as well as for plotting ''Sun Plots'' [33]. Synthesis-View [34,35] was also used to present results within this manuscript. Both software packages are freely available software for academic users: http://ritchielab.psu.edu/ritchielab/software, Table 5. Example phenotype-classes and binned subphenotypes within phenotype-classes.

Phenotype Class
Study Sub-phenotype binned within the phenotype-class and can be used with a web interface at: http://visualization. ritchielab.psu.edu/.

Matching Phenotypes
A total of 105 phenotype-classes were developed to manually match related phenotypes across studies. To bin related phenotypes into classes the following steps were used as visualized in Figure 6: First, using a MySQL database, the data from EAGLE, MEC, CHS, ARIC, and WHI were independently filtered for any tests of association results at p,0.01, and then lists of the unique phenotypes for each individual PAGE study were generated. The number of phenotypes that passed this significance threshold for each of the four groups was 604 (ARIC), 331 (CHS), 63 (MEC), 324 (EAGLE), 1,342 (WHI). Resulting phenotypes were then manually matched up between ARIC, CHS, MEC, EAGLE and WHI using knowledge about the phenotypes and the known focus of specific PAGE study survey questions (such as bone fracture questions used primarily for collecting information about osteoporosis). For some phenotypes, the specific phenotype existed clearly across more than one PAGE study, such as for the phenotype ''Hemoglobin'', where hemoglobin measurements were present for ARIC, CHS, EAGLE, and WHI. Other groups of phenotypes that fell within similar phenotypic domains but were not represented in the same form across studies were also collected into phenotype classes. One example is the phenotypes grouped together for the phenotype class of ''Allergy''. EAGLE collected specific quantitative data from allergy skin testing and had survey questions about the presence of allergies in participants. ARIC and MEC did not have skin allergy testing, but did have survey questions about the presence of allergies. Thus these allergy phenotypes were grouped together. Finally, phenotypes from all studies, regardless of significance from genotype-phenotype tests of association, were matched to the already-defined phenotype classes using the criteria described above. A phenotype that matched a phenotype class but was not associated with a SNP at the significance threshold of p,0.01 for a single study would still be included in the phenotype-class list. Using these criteria, a second curator reviewed the resultant phenotypes and phenotype classes for consistency and accuracy. To provide examples of the phenotype-classes, and which subphenotypes were matched with phenotype-classes, we show three phenotype-class examples in Table 5, and Table S2 contains the matched phenotypes across studies within the phenotype-classes for all phenotype-classes used within this study.
It is important to note resources that can be used for further investigation of the phenotypes listed in Table S2, as well as in the results presented in this paper. The following study websites contain additional information about all collected study information, including how those phenotypes were collected: N ARIC http://www.cscc.unc.edu/aric/ N CHS http://www.chs-nhlbi.org/CHSData.htm, https:// biolincc.nhlbi.nih.gov/static/studies/chs/Other_Documents. htm N WHI https://cleo.whi.org/data/Pages/home.aspx N E A G L E h t t p : / / w w w . c d c . g o v / n c h s / n h a n e s /

Criteria for Significance of Association
After creating phenotype-classes, significant PheWAS tests of association for single genotype-phenotype associations across PAGE studies were identified using a database query. Our criteria for considering a PheWAS test of association significant included a threshold of p,0.01 observed in two or more PAGE studies for the same SNP, phenotype class, and race/ethnicity and consistent direction of effect.
A total of 111 PheWAS tests of association met our criteria for significance (Table S3). Significant results were then binned based on class of association: known, related, and novel. In this PheWAS, Known Associations are positive controls and represent previously reported genotype-phenotype associations. Related Associations are SNPs significantly associated in this PheWAS with phenotypes judged to be closely related to phenotypes among Known Associations found here and the literature. Novel Associations are significant PheWAS results where 1) the association does not match a known association and 2) the phenotype for the PheWAS association is not within a similar phenotypic domain as the phenotype of known association.

Ethics Statement
All participating studies were approved by their respective IRBs, and all study participants signed informed consent forms.

Supporting Information
Table S1 The list of all SNPs available for two or more sites in this study, arranged by previously associated phenotypes. (XLSX) Table S2 A list of the study level phenotypes, the study from which the phenotype is available, and the phenotype-class for each phenotype that overlapped with another study. (XLSX) Text S1 Information on study design, phenotype measurement, and genotyping for each study. (DOCX)