Sunitinib is a potent and clinically approved tyrosine kinase inhibitor that can suppress tumour growth by inhibiting angiogenesis. However, conflicting data exist regarding the effects of this drug on the growth of metastases in preclinical models. Here we use 4T1 and RENCA tumour cells, which both form lung metastases in Balb/c mice, to re-address the effects of sunitinib on the progression of metastatic disease in mice. We show that treatment of mice with sunitinib prior to intravenous injection of tumour cells can promote the seeding and growth of 4T1 lung metastases, but not RENCA lung metastases, showing that this effect is cell line dependent. However, increased metastasis occurred only upon administration of a very high sunitinib dose, but not when lower, clinically relevant doses were used. Mechanistically, high dose sunitinib led to a pericyte depletion effect in the lung vasculature that correlated with increased seeding of metastasis. By administering sunitinib to mice after intravenous injection of tumour cells, we demonstrate that while sunitinib does not inhibit the growth of 4T1 lung tumour nodules, it does block the growth of RENCA lung tumour nodules. This contrasting response was correlated with increased myeloid cell recruitment and persistent vascularisation in 4T1 tumours, whereas RENCA tumours recruited less myeloid cells and were more profoundly devascularised upon sunitinib treatment. Finally, we show that progression of 4T1 tumours in sunitinib treated mice results in increased hypoxia and increased glucose metabolism in these tumours and that this is associated with a poor outcome. Taken together, these data suggest that the effects of sunitinib on tumour progression are dose-dependent and tumour model-dependent. These findings have relevance for understanding how anti-angiogenic agents may influence disease progression when used in the adjuvant or metastatic setting in cancer patients. Sunitinib is an orally available tyrosine kinase inhibitor that potently inhibits vascular endothelial growth factor (VEGF) receptors , platelet derived growth factor (PDGF) receptors (PDGFRα and PDGFRβ) and several other receptor tyrosine kinases, including KIT receptor , 46. ExtDespite these promising results, 20–30 % of mRCC patients show no response to sunitinib and even those that do respond initially will inevitably develop resistance and progress after several months of treatment , 50. ThiImportantly, preclinical studies are revealing mechanisms that allow tumours to exhibit intrinsic or acquired resistance to VEGF-targeted agents. These mechanisms include the stimulation of angiogenesis by alternative pro-angiogenic growth factors, the enhanced recruitment of pericytes or pro-angiogenic myeloid cells or the utilisation of alternative tumour vascularisation mechanisms such as vascular co-option , 38, 53.Contradictory evidence therefore exists regarding the ability of VEGF-targeted agents to control the growth of metastasis. Here we examined the effect of sunitinib on the growth of metastases in mice when it was administered either prior to or following intravenous injection of tumour cells. We identify mouse models of metastasis that show contrasting responses to sunitinib and use these to examine the effects of this drug on tumour growth and outcome.2 at 37 °C. Human umbilical vein endothelial cells (HUVECs) from pooled donors  were cultured in HUVEC-specific medium  in 8 % CO2 at 37 °C. HUVECs were used for experiments at passage 4–8.Female Balb/c mice were obtained from Charles River UK Ltd . Sunitinib malate was obtained from LC laboratories . Tissue culture reagents were obtained from Invitrogen Ltd , except for endothelial cell mitogen that was obtained from Serotech . Unless otherwise stated, all other reagents were obtained from Sigma . The 4T1 murine mammary carcinoma cell line that stably expresses the luciferin gene (4T1-luc) was obtained from Caliper Life Sciences . The RENCA murine renal carcinoma cell line that stably expresses the luciferin gene (RENCA-luc) was prepared as previously described . Tumour Vehicle for sunitinib consisted of 0.5 % w/v carboxymethylcellulose sodium, 1.8 % w/v NaCl, 0.4 % w/v Tween-80, 0.9 % w/v benzyl alcohol dissolved in reverse osmosis deionised water adjusted to pH 6.0. For oral dosing, sunitinib malate powder was added to vehicle and vortexed to create a suspension. This drug suspension was prepared at least 24 h before administration and stored at 4 °C in the dark. Fresh stocks of sunitinib suspension were prepared every week. Oral dosing in mice was performed by administration of 0.2 ml of vehicle or sunitinib suspension by oral gavage.5 cells in 100 μl). Where pre-treatment was employed, mice were administered vehicle or sunitinib at the indicated dose by oral gavage every day for 7 days and cells were injected 24 h after the last dose was administered. Where treatment was administered after intravenous tumour cell injection, mice commenced treatment 24 h after injection with cells. Humane endpoints were used to measure the survival of tumour bearing mice in accordance with UK Home Office guidelines. Specifically, mice were culled when they developed any of the following clinical signs: loss of 20 % body mass, dypsnea, ataxia or seizure. For IVIS imaging, mice were injected intraperitoneally with 75 mg/kg D-luciferin , immediately anaesthetised with isofluorane and then imaged. Imaging was performed using a Lumina II™ IVIS (In Vivo Imaging System) instrument  with quantification of bioluminescence performed using Living Image™ software  according to manufacturers instructions.To examine the effect of sunitinib on the growth of lung metastases, female Balb/c mice at 8–10 weeks of age were injected intravenously with 4T1-luc or RENCA-luc tumour cells .Female Balb/c mice at 8–10 weeks of age were treated with either a single dose of drug, or for seven days with drug  by oral gavage. At the appropriate time point, blood was collected by cardiac puncture under terminal anaesthesia. Blood samples were spun at 250 g for 10 min to isolate the plasma fraction, which was then stored in 100 μl aliquots at −20 °C until analysis. Calibration standards, ranging from 5 nM to 100 μM final sunitinib concentration, were prepared by spiking 100 μl samples of control mouse plasma with sunitinib. Calibration standards and test samples were precipitated with methanol, vortexed and then centrifuged at 21,000For ex vivo quantification of tumour burden, formalin fixed paraffin embedded sections of mouse lung were stained with haematoxylin and eosin (H&E) and then digitally scanned using an automated scanning microscope . Tumour burden in the scanned images was measured using Adobe Photoshop image analysis software . In brief, the marquee tool was used freehand to create regions of interest (ROIs) around areas of tumour in the section. The area of these ROIs was then calculated by the software and added together (t). The total area of the section (T) was measured using the same freehand ROI method. The percentage area of lung section that was occupied by tumour was then calculated according to this formula: (t/T) × 100. Lesion diameters were measured using the ruler tool in Adobe Photoshop.Immunohistochemistry was performed in frozen tissue sections. At sacrifice, lungs were embedded in OCT on dry ice immediately following resection, followed by preparation of frozen sections . For staining, sections were brought to room temperature, fixed in 4 % w/v formalin for 10 min followed by incubation in blocking buffer  for 1 h and then incubation with primary antibodies in blocking buffer overnight at 4 °C. Primary antibodies used were as follows: rat anti-CD31 , rabbit anti-NG2 proteoglycan , FITC-conjugated mouse anti-pimonidazole , Alexa-488 conjugated rat anti-Gr1, phycoerythrin-conjugated rat anti-CD11b , biotin-conjugated rat anti-CD31 (BD Biosciences), rabbit anti-CAIX and rabbit anti-GLUT1 . After 3 × 5 min washes in PBS, sections were incubated with fluorescently conjugated secondary antibodies and/or fluorescently conjugated streptavidin and/or DAPI (Invitrogen Ltd) for 2 h at room temperature. After 3 × 5 min washes in PBS, sections were mounted with a glass coverslip in MOWIOL mounting solution supplemented with antifade .2, (b) the microvessel density per mm2, (c) the % area of tumour that stained positive for pimonidazole, and (d) the number of Gr1/CD11b dual positive cells per mm2. On average, 20–40 tumour lesions were sampled from 6 to 8 mice to generate each data point.Images were captured using a confocal laser scanning microscope (Leica) or the Ariol System automated scanning microscope (Leica). For studies of cell seeding in the lungs, cells were labeled with 10 μM carboxyfluorescein diacetate succinimidyl ester (Invitrogen) as per the manufacturers instructions just prior to intravenous injection. The number of fluorescent cells seeded per lung section was determined by counting using a fluorescence microscope. To determine pericyte coverage of vessels, confocal images of CD31 and NG2 staining were captured and the percentage of CD31 pixels that colocalised with NG2 pixels was calculated using a colocalisation plug-in for ImageJ. To determine microvessel density, the number of CD31 positive vessels present within the viable tissue in each tumour nodule was counted manually. For quantification of pimonidazole staining, mice were injected intraperitoneally with 60 mg/kg pimonidazole hydrochloride in 100 μl of saline at 1 h prior to sacrifice. To measure pimonidazole staining, Adobe Photoshop software was used to draw freehand ROIs around areas of positive staining, followed by automatic calculation of the area by the software. To determine the number of Gr1+/CD11b+ dual positive cells, Adobe Photoshop was used to create multi-channel images and cells labeling for both markers in each nodule were counted manually by an observer that was blinded. Areas were calculated in Adobe Photoshop using a freehand ROI method. These data were then used to calculate (a) the number of tumour cells seeded per mm5 cells in 100 μl). The following day, mice began treatment with vehicle or 60 mg/kg/day sunitinib on a continuous dosing schedule. Mice were imaged on the indicated days after tumour cell injection. On the day of imaging, mice were starved for at least 4 h prior to being anaesthetised and injected via the tail vein with 5–15 Mbq of 18FDG. This was then followed by a 90 min uptake period under continuous isoflurane anaesthesia before PET images were acquired. CT and PET scanning was performed using an Inveon microPET/CT scanner . Isoflurane anaesthesia was continued for the duration of the scans (approximately 6 min for the attenuation CT and 20 min for the PET acquisition). Mice were maintained on a thermostatic heating pad during the entire anaesthesia period so as to maintain core body temperature. Inveon Acquisition Workplace software (Siemens) was used for image acquisition, whilst Cobra software  was used to reconstruct the CT images and Inveon Research Workplace software (Siemens) was used for reconstructing PET images and calculation of Standardized Uptake Values (SUV) according to manufacturers instructions. In order to calculate mean SUV in mouse lungs, Inveon Research Workplace software was used to place a 3-dimensional region of interest (ROI) in the area corresponding to the left lung or the right lung of each mouse, taking care to ensure that the ROIs were not placed in the region of the heart. The radioactivity concentration measured within the ROIs was then normalized to the amount of injected activity and the weight of the mice when calculating the SUV.Female Balb/c mice at 8–10 weeks of age were injected intravenously with 4T1-luc or RENCA-luc tumour cells (2 × 105 cells in 100 ml). The following day, mice began treatment with vehicle or 60 mg/kg/day sunitinib on a continuous dosing schedule. After 14 days (4T1 model) or 19 days (RENCA model), mice were starved for at least 4 h prior to being anaesthetized and injected via the tail vein with 0.5–2 Mbq of 18FDG. This was then followed by a 90 min uptake period under continuous isofluorane anaesthesia, and thermostatic heating, before mice were culled. Lungs, blood and muscle tissue were collected, weighed and then counted immediately in a gamma counter . Counts were corrected to the tissue mass in order to calculate the percentage of injected dose present per gram of tissue.Female Balb/c mice at 8–10 weeks of age were injected intravenously with 4T1-luc or RENCA-luc tumour cells  were plated on 96 well plates at a density of 1,000 cells/well. The next day the medium was changed for M199 plus 10 % fetal calf serum, supplemented with 100 ng/ml VEGF  and with sunitinib at the indicated concentration or vehicle (0.1 % DMSO). 4T1 cells or RENCA cells were plated on 96 well plates at a density of 1,000 cells/well. The next day the medium was changed for M199 plus 10 % fetal calf serum, supplemented with sunitinib at the indicated concentration or vehicle (0.1 % DMSO). After 72 h, cell viability was quantified using the Cell-TitreGlo cell viability reagent  according to the manufacturers instructions. The plates were read in a luminescence plate reader (PerkinElmer).Ethical approval for animal experimentation was granted by: the Institute of Cancer Research Animal Ethics Committee and the Queen Mary University of London Animal Ethics Committee. All procedures were performed in accordance with UK Home Office regulations.t test, except for the analysis of the Kaplan–Maier survival data, which was performed using the Log-rank test. P values below 0.05 were considered to be significant.Statistical analysis of data was performed using Student’s P = 0.0007) and significantly shortened overall survival (P = 0.0009) compared to vehicle treated controls  and 12 days (P = 0.002) after tumour cell injection, tumour burden in the lungs of mice pretreated with 30 or 60 mg/kg/day sunitinib was not statistically different to vehicle treated mice at either time point , continuous therapy with 60 mg/kg/day sunitinib or the 120–60 schedule . In contrast, sunitinib was able to significantly extend survival compared to vehicle controls in RENCA tumour bearing mice . Importantly, sunitinib treatment led to a significant reduction in MVD in both the 4T1 and RENCA lung tumour nodules compared to the corresponding vehicle control tumours  and the hypoxia probe pimonidazole hydrochloride was used to assess tumour hypoxia Fig. a–c. IntrP = 0.001, Fig. Quantification of pimonidazole staining demonstrated that hypoxia was significantly increased in 4T1 tumours in sunitinib treated mice compared to vehicle treated controls (Since sunitinib is a potent inhibitor of PDGF receptor signalling , and sigThe ability of tumours to recruit and retain pro-angiogenic Gr1+CD11b+ myeloid cells has been implicated in both the induction of tumour angiogenesis  and in m18FDG-PET/CT imaging and ex vivo quantification of 18FDG uptake. The presence of multiple tumour nodules in the lungs of the mice prevented us from assessing 18FDG uptake within individual lesions. Instead, tumour glucose uptake was quantified in a semi-quantitative fashion by measuring glucose uptake in the entire lung. In the 4T1 model, the uptake of 18FDG in the lungs of sunitinib treated mice was significantly increased compared to vehicle treated mice at 14 days after injection of tumour cells (Fig. 18FDG was significantly suppressed in the lungs of RENCA tumour bearing mice treated with sunitinib compared to vehicle controls (Fig. 18FDG uptake measured is most likely due to increased glucose metabolism occurring in the 4T1 lung tumour nodules of sunitinib treated mice.Markers associated with increased hypoxia and increased glucose metabolism were both elevated in 4T1 lung tumour nodules in sunitinib treated mice see Fig. . We therlls Fig. a–c. In cols Fig. d–f. Sincols Fig. a–d, the Therapeutic targeting of the VEGF signalling pathway can suppress tumour growth by blocking tumour angiogenesis , 25, 28.First we examined the ability of sunitinib to mediate a “conditioning effect” which promotes the seeding and growth of tumours at the site of metastasis. This was based on a previous study, in which treatment with sunitinib prior to intravenous injection of tumour cells was shown to promote metastasis . In our Whilst this manuscript was under revision, another study  reportedWe also examined the effects of sunitinib when it was administered to mice after the intravenous inoculation of tumour cells. While sunitinib failed to suppress the growth of 4T1 lung metastases in this scenario, the same drug suppressed the growth of RENCA lung metastases. Investigating the underlying mechanisms for this differential response, we found that whilst the vascularisation of RENCA tumours was potently inhibited by sunitinib, 4T1 tumour nodules continued to grow with a well-vascularised viable tumour rim. Functional imaging performed in mRCC patients demonstrates that sunitinib refractory lesions often show signs of extensive central devascularisation, but that the rim of these tumours continues to be well vascularised , 57. TheWe demonstrated that sunitinib fails to prolong overall survival in the 4T1 model when it was administered after intravenous tumour cell injection, indeed we even observed shortening of overall survival in this scenario. This is in agreement with a previous report, which showed that sunitinib administered after intravenous injection of MDA-MB-231 cells can shorten overall survival in mice . HoweverThe experimental models used here have their limitations. In particular, our study was limited to the use of only two murine tumour cell lines and only one angiogenesis inhibitor, so extrapolation to human subjects and other drugs that target the VEGF pathway should be approached with due caution. In addition, our study did not address the ability of sunitinib to promote metastasis from the primary tumour, so no inference can be made regarding the effects of sunitinib on the formation of distant metastasis when it is used in the neoadjuvant setting. Interestingly, recent work suggests that although tyrosine kinase inhibitors can promote metastasis in some mouse models, anti-VEGF monoclonal antibodies do not , 55. In


Ultrasound is being used increasingly to diagnose pathological free fluid accumulation at the bedside. In addition to the detection of peritoneal and pericardial fluid, point-of-care ultrasound allows rapid bedside diagnosis of pleural fluid.In this short report, we describe the sonographic observation of the vertebral or ‘V-line’ to help confirm the presence of pleural fluid in the supine patient. The V-line sign is a result of the fluid acting as an acoustic window to enable visualization of vertebral bodies and posterior thoracic wall, thus confirming the presence of pleural fluid.The V-line is a useful sonographic sign to aid the diagnosis of pleural free fluid. The use of ultrasound for the detection of pleural fluid is reliable when correctly identified. PleuralChest X-ray (CXR), the most commonly used radiographic modality to detect pleural effusion, is able to detect only relatively large effusions. Studies have shown that over 175 mL of fluid is required to cause blunting of the costophrenic angles in an upright CXR. Supine Clinically, the sensitivity and specificity of CXR for pleural fluid diagnosis are relatively poor. In ICU patients with co-existing lung pathology , supine CXRs have a sensitivity of 39% and specificity of 85% for detection of pleural fluid.The significance of pleural fluid to the patient’s presentation is not always clear, and so early detection of pleural fluid is important to guide the decision for drainage, either for diagnostic or therapeutic purposes. The use of ultrasound for the detection of pleural fluid was first described in 1967. Since tThe clinical sensitivity and specificity of point-of-care ultrasound (PoCUS) for the detection of pleural fluid have also been demonstrated. In the thoracic trauma population, PoCUS has consistently been shown to have a sensitivity between 92% to 100% and a specificity nearing 100% for hemothorax-10. In tIn addition to an excellent sensitivity and specificity for detection of pleural fluid, PoCUS drastically reduces the time to clinical diagnosis when compared to CXR. In the trauma population, PoCUS has been shown to provide a diagnosis of hemothorax within 1 min compared to 15 min by CXR,6. When While not yet fully accepted as standard of care by some physicians, lung ultrasound is becoming established as a beneficial tool to diagnose a variety of serious respiratory conditions including pneumothorax, pulmonary edema , and consolidation, as well as pleural effusions-21.Each of the above conditions has a distinct set of sonographic findings related to the underlying pathology. By international convention, many of these findings have been named by their ‘line pattern’. A summaIn this descriptive paper, we describe the V line, a sign that we believe may help the clinician sonographer detect the presence of plural fluid in the supine patient and may also aid the less experienced sonographer to differentiate between the black ‘shadow’ or ‘curtain’ created by normal lung and the anechoic area that represents free pleural fluid. Written informed consent was obtained from the patients for publication of this report and any accompanying images.When scanning a typical patient in a supine position using a low frequency sector probe in the coronal/longitudinal plane, laterally on the torso at the level of the diaphragm Figure , one is Above  to the diaphragm, the vertebral line ends abruptly as aerated lung does not permit transmission of sound waves to the posterior thoracic structures including the vertebral bodies and proximal posterior ribs. Instead, a black acoustic shadow or ‘lung curtain’ is seen, where no posterior structures can be visualized Figure . HoweverThis V-line sign can help to differentiate between fluid and air as the cause of the dark echo-poor area seen above the diaphragm with normal lung and with pleural effusions. The V line should be used in conjunction with previously described sonographic signs of pleural fluid such as, the sinusoid sign, representing the inspiratory movement of lung line toward pleural line; clearer visualization of the diaphragm and abdominal organs during inspiration; less echogenic scatter at the diaphragm; and visualization of lung tissue ‘floating’ in the pleural fluid.The V line is a simple sonographic finding that may assist the clinician with the detection of pleural fluid. We suggest that it will complement other previously described line patterns when performing lung ultrasound for symptomatic breathless patients. It offers a simple memorable sign to assist with training, help confirm the presence of posterior pleural effusions, and differentiate anechoic fluid from the normal black lung curtain. We suggest that a prospective study of the diagnostic characteristics of this sign would be useful.The authors declare that they have no competing interests.GV and PA described this previously unreported sign. PA, JM and OL performed the literature review. PA and JM wrote the initial draft of the paper and acquired the clinical images. All authors read and approved the final manuscript.


To understand the biological nature of ΔT1, we have performed studies in vivo and ex vivo with the allosteric mTOR inhibitor, everolimus.Effective chemotherapy rapidly reduces the spin–lattice relaxation of water protons  to determine levels of the proliferation marker choline and levels of lipid apoptosis markers, prior to and 5 days (endpoint) after daily treatment with vehicle or everolimus (10 mg/kg). At the endpoint, tumours were ablated and an entire section analysed for cellular and necrotic quantification and staining for the proliferation antigen Ki67 and cleaved-caspase-3 as a measure of apoptosis. The number of blood-vessels (BV) was evaluated by CD31 staining. Mice bearing B16/BL6 melanoma tumours were studied by MRI to determine T1 under similar everolimus treatment. At the endpoint, cell bioluminescence of the tumours was measured ex vivo.Mice bearing RIF-1 tumours were studied by magnetic resonance imaging (MRI) to determine TVol and T1 and total choline (Cho) levels, and increased polyunsaturated fatty-acids which are markers of apoptosis. Immunohistochemistry showed that everolimus reduced the %Ki67+ cells but did not affect caspase-3 apoptosis, necrosis, BV-number or cell density. The change in T1 (ΔT1) correlated strongly with the changes in TVol and Cho and %Ki67+. In B16/BL6 tumours, everolimus also decreased T1 and this correlated with cell bioluminescence; another marker of cell viability. Receiver-operating-characteristic curves (ROC) for everolimus on RIF-1 tumours showed that ΔT1 had very high levels of sensitivity and specificity (ROCAUC = 0.84) and this was confirmed for the cytotoxic patupilone in the same tumour model (ROCAUC = 0.97).Everolimus blocked RIF-1 tumour growth and significantly reduced tumour T1 is not a measure of cell density but reflects the decreased number of remaining viable and proliferating tumour cells due to perhaps cell and tissue destruction releasing proteins and/or metals that cause T1 relaxation. ΔT1 is a highly sensitive and specific predictor of response. This MRI method provides the opportunity to stratify a patient population during tumour therapy in the clinic.These studies suggest that ΔT Across several models, the fractional change in tumour T1 (ΔT1) correlated with the percentage of cells positive for the antigen Ki67 (a marker of cycling cells), but not with other markers such as apoptosis, necrosis or blood volume, all of which showed no consistent change with drug-treatment fluoro-glucose (FDG) and 3′-deoxy-3′-[18 F]fluorothymidine (FLT), dynamic contrast-enhanced magnetic resonance imaging for vascular parameters and diffusion-weighted imaging for apoptosis 8 Ffluoro. We have therapy .1 reflects about the tumour biology, we have compared ΔT1 with magnetic resonance spectroscopy (MRS) markers of proliferation and apoptosis in vivo[ex vivo following treatment with the allosteric mTOR inhibitor, everolimus (Afinitor) in two different murine tumour models, RIF-1 and B16/BL6. Everolimus was selected for these studies because although the drug has significant clinical activity in several different types of cancer, there is currently no confirmed molecular marker that can stratify the patient population [1 is a highly sensitive and specific predictor of response to everolimus and also the microtubule stabilizer patupilone. Collectively, these data further suggest that incorporation of T1 measurements in clinical trials should be an important aid to drug development and optimization of existing drugs.To investigate further what ΔTs in vivo, as wellpulation . Using tAll animal experiments were carried-out strictly according to the local Swiss animal welfare regulations. The protocol was approved by the local veterinary authorities . C3H/He female mice (20–25 g) and C57/BL6 mice (20 g) were obtained from Charles River (France) and were acclimatized to local conditions for at least one week prior to experiments. Three experiments were performed in the RIF-1 fibrosarcoma model in C3H/He mice, one experiment was performed in the B16/BL6 melanoma model in C57/BL6 mice. All animal experiments were performed under isoflurane anesthesia, and every effort was made to minimize suffering.Tumour volume (TVol) and animal body-weight (BW) measurements were made at least twice per week including just before treatment (baseline) and the endpoint. TVol was determined using calipers to measure three orthogonal dimensions and applying the formula: l*h*w*π/6.6 in 50 μL phosphate-buffered saline) in the upper thigh of anesthetized C3H/He mice, as previously described [3) for the studies and were divided into two equal groups and treated daily with compound or vehicle. Experiment 1: treatment with everolimus (n = 7) compared to vehicle (n = 7), experiment 2: treatment with everolimus (n = 8) compared to vehicle (n = 8), experiment 3, previously published in [Freshly cultured RIF-1 tumour cells were injected subcutaneously (5 × 10escribed . After 2imus n =  compared4 in 1 μl) into the dorsal pinna of both ears of anesthetized C57/BL6 mice as previously described [Freshly cultured B16/BL6 tumour cells expressing the enzyme luciferase were injected intra-dermally  was obtained as a microemulsion and was freshly diluted in a vehicle of 5% glucose and administered by oral gavage (p.o.) to mice daily in a volume of 10 ml/kg at 10 mg/kg. Patupilone  was dissolved in polyethylene glycol-300 (PEG-300) and then diluted with physiological saline (0.9% w/v NaCl) to obtain a mixture of 30% (v/v) PEG-300 and 70% (v/v) 0.9% saline. Treatment with vehicle  or patupilone  was once weekly using an i.v. bolus of 2–3 sec in the tail vein.1 was measured in all four experiments. MRS was performed in experiment 1 only. Bioluminescence was measured ex vivo in experiment 4 (see below). At the end of everolimus-experiment 1, animals were sacrificed by CO2 inhalation, the tumours ablated and prepared for histology and immunohistochemistry (IHC) as previously described [Mice were divided into different treatment groups so that each group had the same mean TVol, and magnetic resonance (MR) measurements were made before treatment (baseline) i.e. day 0 and at the endpoint. For everolimus, the endpoint was day 5, and for patupilone it was day 7. T2/N2 and placed on an electrically warmed pad for canulation of one lateral tail-vein as previously described [Animals were anaesthetised using 1.5% isoflurane  in a 1:1 mixture of Oescribed . MRI exp1) was measured with an inversion recovery (IR) TrueFISP  of 1.7 ms, and a repetition time (TR) of 3.4 ms. Pixelwise T1 calculation was done using the method described in [1 map and the mean T1 of the central tumour slice was calculated in this ROI. MR image analysis was performed off-line with in-house written software based on IDL 6.0 programming environment .The spin–lattice relaxation of protons  imagingequence, . The basribed in . A regio3 voxel to obtain line widths of <20 Hz. Point resolved spectroscopy (PRESS) experiments at the same voxel position  were performed. One spectrum was acquired with water suppression (400 averages) and one spectrum without water suppression (1 average). The total time for MRS was 10–12 min for each mouse. The water signal (one peak) of the non-suppressed spectrum was used as an internal reference for relative quantification of metabolites using the metabolite to H2O ratio . Peaks in the water-suppressed spectrum were identified by their chemical shifts, so total choline (Cho) was at 3.2 ppm, CH3-lipids at 0.9 ppm, CH2-lipids at 1.3 ppm, creatine at 3.0 ppm, and polyunsaturated fatty-acids (PUFA) at 5.3 ppm and very weakly at 2.8 ppm; however the latter peak was not used for any calculations.Localized shimming with FASTMAP method was performed on a 2.5 mmA tumour slice of 3–4 mm thickness was cut from the largest circumference of the tumour, immersion-fixed in 4% (w/v) phosphate-buffered formalin  at 4°C for 24 hours and processed into paraffin as previously described [2) in both the viable and total (including therefore necrotic) areas. Cleaved caspase-3 positive particles were quantified as positive pixels per total pixels in a semi-automated fashion with the AnalySIS® FIVE software  on six images (346.7 x 260 μm2 each) per section excluding necrotic areas and border zones of necrotic areas. CD31 stained slides were scanned with the Aperio ScanScopeXT slide scanner  and vessels were quantified with the Aperio ImageScope software, using the Microvessel Analysis v1 Algorithm.For quantification, the entire section was scanned using the MiraxScan system . The absolute size of viable, necrotic and complete tissue areas was measured on the full scans using MiraxViewer software . Quantification of Ki67 positive and negative nuclei in the complete viable areas was performed in a fully automated manner with TissueMap software at Definiens AG, Munich, Germany. Results were summarized as the total area, percentage-viable and percentage-necrotic area, total number of nuclei and the cell density , rinsed in the same buffer, and 200 μl triplicates placed in a 96-well plate with 50 μl D-luciferin (1 mg/mL). BioL was measured at an emission wavelength of 560 nm using the imaging chamber of the IVIS™ system  for 1 min at room temperature.Because of the black pigmentation of the C57/BL6 mice, bioluminescence (BioL) could not adequately be measured 1 (ΔT1), were expressed as change between endpoint and baseline divided by value at baseline . The T/C ratio was calculated for all parameters. For parameters measured at one time point only , T/C was calculated as ratio of means of treatment and control mice, respectively. Differences between groups were analysed using a 2-tailed t-test. For the in vivo biomarker analyses which involved longitudinal analyses in the same animals, differences were analysed by a) 2-way repeated measures ANOVA and b) t-test at the endpoint; the latter method is therefore associated with the respective T/C. The different dose groups in experiment 3 were tested with 1-way ANOVA vs. control group. Quantification of the linear-relationship between the various parameters measured in vivo and ex vivo were analysed by Pearson’s correlation to provide the correlation coefficient (r) and the significance (p). Application of the non-parametric Spearman’s correlation did not affect the results except in one case (see Results). To facilitate comparison of PUFA levels which were not always detectable, a 2-sided Fisher’s exact test was also used. For all tests, the level of significance was set at p < 0.05 (two-tailed) where *p < 0.05, **p < 0.01, ***p < 0.001 versus vehicle.Results are presented as mean ± SEM except where stated and all available data are shown. The T/C ratio is commonly used to quantify tumour growth inhibition, where T and C represent the means of the relative tumour volumes  of the treatment and control mice, respectively . Longitu1 as a marker of tumour response to treatment, receiver-operator curves (ROC) were generated [1 at different discrete cut-offs to generate at each ΔT1 value a table of positive and negative predictions for determination of specificity and sensitivity at each value. The plot of 1-specificity versus sensitivity generated the ROC curve and the area under this curve (AUC) was quantified by the trapezoidal method.To determine the sensitivity and specificity of a change in the imaging marker Tenerated ,18 using1 by MRI at baseline gave a mean ± SD of 2301 ± 100 msec  which showed no significant change in vehicle-treated mice, but was reduced in 7/7 mice treated with everolimus providing a small but highly significant mean decrease of 10 ± 2% after 5 days treatment  strongly inhibited tumour growth causing essentially stable disease at the 5 day endpoint with a T/C of 0.05  and saturated (CH2 and CH3) lipids  showed that everolimus significantly increased PUFA (p = 0.007), a parameter that has also been associated with apoptosis [urated CH and CH3 urated CH and CH3 urated CH and CH3 poptosis .2) in the viable  area and the %-necrosis was the same in each group  and this was not affected by everolimus treatment  was highly significantly (p = 0.0032) positively correlated with the change in RIF-1 tumour volume (ΔTVol), see Figure 2O and ΔTVol. Correlation of ΔT1 and ΔCho/H2O reached significance , see Figure 1 showed a significant positive correlation with the %Ki67+ cells ; and of course negative correlation with the %Ki67- cells (graphs not shown). There was no significant relationship between basal T1 value and ΔTVol in keeping with previous observations in many different preclinical models [1 cannot predict response. The final T1 or ΔT1 was unrelated to cell density, or indeed the total number of cells, with flat lines of correlation and wide scatter (results not shown). Thus, ΔT1 was unrelated at the endpoint to cell density or the extracellular space but was related to the remaining number of proliferating cells (and negatively correlated to the number of non-proliferating cells). PUFA levels, which also tended to change with treatment could not be formally correlated since the data was categorical, but a Fisher’s exact test showed a significant association between Cho and PUFA (p = 0.02) i.e. proliferation decreased as apoptosis increased.As previously observed for several different experimental models and drugs, including everolimus , the chas Figure C, a simi1 in those tumours from day 0 to day 5 . Responders to everolimus treatment were defined as showing a maximum change in ΔTVol of +10% (stable disease or regression), so giving 5R and 8NR and providing the ‘gold-standard’. Each of these 13 tumours was then classified as a R or NR by the ΔT1 using different discrete cut-offs ranging from −16.5% to −2.5% to generate at each ΔT1 value a table of positive and negative predictions for determination of specificity and sensitivity at each value, see for example Table Correlations and linear regression provide indications of whether a biomarker could be used to predict response, but a receiver operating characteristic curve (ROC) can be more definitive in terms of the specificity and sensitivity of the marker. To generate such a curve for everolimus, data from experiments 1 and 2 was pooled from RIF-1 tumours Figure A. The le1 in those tumours from day 0 to day 7 (n = 6-8 per dose). Using once more discrete ΔT1 values from −24% to 8.5% a table of positive and negative predictions was generated . T1 measurement failed in one mouse treated with vehicle and in two mice treated with everolimus resulting in n = 8 ΔT1 values in each group. Bioluminescence from these lymph-nodes measured ex vivo was also significantly decreased  of experimental tumours induced by various different types of chemotherapy is strongly correlated with the change in tumour volume and also the immunohistochemical proliferation marker Ki67 [1 (ΔT1). The data presented here on RIF-1 and B16/BL6 tumours confirm these observations for the allosteric mTOR inhibitor everolimus, providing further evidence that ΔT1 reflects the number of remaining proliferating tumour cells following successful chemotherapy. The greater the decrease in T1, the lower the percentage of proliferating cells after therapy. In the previous report, a sample area (10%) of an ex vivo tumour slice was examined histologically, and thus, true cell density and also therefore the overall extracellular space could not be assessed either. In two models, using the cytotoxic patupilone on murine RIF-1 and rat mammary BN472, there were trends for cell-density to decrease by approx. 10% but neither reached significance [We have previously shown that a small but highly significant decrease in the mean spin–lattice relaxation of protons  the amount of water in the extracellular space (but not intracellular) and b) the amount of protein in the water [1[1 decrease that we have always observed following successful chemotherapy with many different agents [1; an effect which has been shown in vitro[RIF-1 cells are sensitive to everolimus with an IC50 ferences ,20), butferences . Daily the water -25. It ihe water , because water [1-25, suggt agents . This su in vitro. However in vitro-29, alth in vitro) and/or  in vitro.+ cells whether expressed as total number or density and there was a proportional increase in the cells negative for Ki67. Everolimus also decreased levels of total choline (Cho) in RIF-1 tumours, which is another marker of viable and proliferating cells, in this case reflecting membrane turnover. Cho tends to be higher in tumour than normal tissue [1, supporting the notion that ΔT1 is a surrogate of the remaining number of proliferating cells in a tumour after therapy even though our histological analysis suggests that it cannot be measuring cell number or density directly. Support for this hypothesis came from the B16/BL6 model treated with everolimus where again the decrease in bioluminescence, which measures viable tumour cell number, was correlated to the ΔT1.Immunohistochemistry (IHC) showed that approx. half of vehicle-treated RIF-1 tumour cells were positive for the nuclear antigen Ki67. Ki67 is considered to be a proliferation marker since it is expressed in all cycling cells  but not therefore in cells in G0, and is a convenient IHC tool in the clinic for assessing tumour growth and response ,32. Givel tissue  and succl tissue ,10 and tl tissue ,35. In t1 in response to successful chemotherapy i.e. characterized by a significant change in TVol in comparison to vehicle-treated tumours. Furthermore, where a tumour was resistant to that particular type of chemotherapy (paclitaxel and patupilone), there was no change in T1[1 is a generic marker of tumour response, because, as discussed above, it reflects overall tumour destruction. But, what is the level of sensitivity and specificity i.e. how useful could such a method be in the clinic? To answer this question, we used receiver-operating-characteristic curves (ROC) to analyse two different models in which mice bearing RIF-1 tumours were treated with either a single dose of everolimus or three different doses of patupilone. In both cases, the ROCs had a large area-under-the-curve (AUC) of 0.84 and 0.97 which is considered of very good to outstanding predictive ability [in vitro using cell lines in which ROC-AUCs of 0.86-0.88 were determined [1 = −8%), then the sensitivity to both drugs would be perfect in this model at 1.0 i.e. providing a negative predictive value of 100%. In other words, after two MRI-scans one could completely eliminate from the study any tumours with a T1 decrease smaller than 8% since these should not benefit from further treatment.It is worth repeating that we have found that six different types of chemotherapy including anti-metabolites, inhibitors of mTOR, microtubules, VEGF-R, PI3K and HSP90  [and unpnge in T1. This su ability . Considetermined ; also ex1 showed a decrease in response to successful chemotherapy in several tumour models using various drugs. Analysis of histological and bioluminescence data from tumours treated with everolimus indicate that ΔT1 is a generic marker of tumour response reflecting overall tumour destruction and the decreased number of proliferating cells.T1 suggests that the method should be tested in the clinic, for example wherever MRI is being used anyway to determine tumour size. Potentially, it provides the opportunity to stratify a patient population after the first cycle of treatment to increase the effective response-rate as well as to save resources by avoiding treatment of patients who are unlikely to respond.The excellent negative predictive value of ΔTAUC: Area under curve; BioL: Bioluminescence; BV: Blood vessel; BW: Body weight; Cho: Total choline; ΔCho/H2O: Change in ratio total choline to unsuppressed water; IR True FISP: Inversion recovery true fast imaging with steady state precession sequence; MoA: Mechanism of action; MRI: Magnetic resonance imaging; MRS: Magnetic resonance spectroscopy; IHC: Immunohistochemistry; PRESS: Point resolved spectroscopy; PUFA: Polyunsaturated fatty-acids; ROC: Receiver operating characteristic curve; ROI: Region of interest; T1: Spin–lattice MR relaxation time; ΔT1: Change in the spin–lattice relaxation time of water protons; ΔTVol: Change in tumour volume; T/C: Treated-value divided by control-value.All the authors are or were (CW) employees of Novartis Pharma AG, Basel, Switzerland. They declare no competing interests.CW carried out the MRI and MRS studies, performed data and statistical analysis, and drafted the manuscript. PA carried out the MRI studies and critically revised the manuscript. MS performed and analysed histological and IHC studies. VR performed IHC studies and prepared figures with IHC data. SF took care of the animal model, the animal treatment, and carried out bioluminescence assessments. PM took care of the study design and coordination, performed data and statistical analysis, and drafted the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/14/88/prepub


Metastasis formation remains an enigmatic process and one of the main questions recently asked is whether metastases are able to generate further metastases. Different models have been proposed to answer this question; however, their clinical significance remains unclear. Therefore a computer model was developed that permits comparison of the different models quantitatively with clinical data and that additionally predicts the outcome of treatment interventions.The computer model is based on discrete events simulation approach. On the basis of a case from an untreated patient with hepatocellular carcinoma and its multiple metastases in the liver, it was evaluated whether metastases are able to metastasise and in particular if late disseminated tumour cells are still capable to form metastases. Additionally, the resection of the primary tumour was simulated. The simulation results were compared with clinical data.The simulation results reveal that the number of metastases varies significantly between scenarios where metastases metastasise and scenarios where they do not. In contrast, the total tumour mass is nearly unaffected by the two different modes of metastasis formation. Furthermore, the results provide evidence that metastasis formation is an early event and that late disseminated tumour cells are still capable of forming metastases. Simulations also allow estimating how the resection of the primary tumour delays the patient's death.The simulation results indicate that for this particular case of a hepatocellular carcinoma late metastases, i.e., metastases from metastases, are irrelevant in terms of total tumour mass. Hence metastases seeded from metastases are clinically irrelevant in our model system. Only the first metastases seeded from the primary tumour contribute significantly to the tumour burden and thus cause the patient's death. Metastases are the main cause of death in cancer patients Two major models of metastatic progression have been developed: the linear and the parallel progression model. In the linear progression model it is assumed that the primary tumour undergoes several rounds of genetic alterations and competitive selection before highly malignant cells are able to disseminate and seed metastases into distant organs. Since these cells are already highly malignant, the newly created metastases are also likely able to spawn further new metastases Although differing in even fundamental aspects of metastasis formation, both of those models raise four fundamental questions: 1) When does the dissemination of malignant cells from the primary tumour starts? 2) Are metastases able to metastasize? 3) Are late disseminated tumour cells capable to form metastases? 4) Does the primary tumour support the outgrowth of metastases via signals?To answer these four questions, mathematical models are highly versatile tools in doing so as their predictions can be examined and verified quantitatively. A great variety of mathematical models have been developed, ranging from very fine-grained ones that consider very detailed biological and molecular or even genetic characteristics of individual tumour cells We therefore wanted to create a computer model which is able to describe the entire aspects of the metastatic cascade. It allows us to model different metastatic characteristics, such as the ability of metastases to metastasize or a variation of the starting point of metastasis. It can furthermore simulate the effects of potential treatment interventions. The computer model is expandable, so that new forms of therapies can be included.Based on a case from an untreated patient with hepatocellular carcinoma (HCC) and multiple metastases in the liver we investigate two fundamental questions, namely whether metastases metastasize in general and secondly if late disseminated tumour cell are capable to form metastases. The results indicate that these late metastases are at least in this one case of a HCC clinically not relevant, if the patient is left untreated.g(x). Different growth functions can be applied for g(x), e. g. linear, exponential, power law or Gompertzian growth. In this work the Gompertzian growth was used since most tumours exhibit this behaviour and it fits to the clinical data in this case.The computer model is based on the mathematical model by Iwata et al. t is given by the function x(t), which is the solution of:g(x), the number of cells at the time t is given by the following function,b is the size of the tumour at its saturated level and a is the growth rate constant.The number of cells in the tumour at time β(x). For this rate Iwata et al. assumed the following form:m is the colonization constant and α is the fractal dimension which describes how well the tumour is supplied with blood. The colonization rate includes only those cells that survive intravasation, the bloodstream and extravasation and are capable of founding new metastases.The primary tumour spreads metastases with the colonization rate g(x) as the primary tumour and are also able to spread metastases with the rate β (x).It is assumed that the metastases growth with the same rate The computer model describes the behaviour of malignant cells in the metastatic progression as presented in detail previously The computer model is developed as a building kit. It provides different building blocks, from which different simulation setups can be assembled. The two most important building blocks are compartments and events. Compartments describe all organs that can contain malignant cells, such as the primary tumour, the blood vessels or the connective tissue of distant organs harbouring metastases. Compartments can be modelled either continuous or discrete . In a coIn a discrete compartment all internal processes are modelled with the help of so called events. An event describes what happens to a single cell in a compartment at a specific time. Events can be cell division, apoptosis, intravasation or the creation of a new metastasis (including extravasation). A discrete compartment can be understood as a bucket, where cells can be put in and deleted from. The growth or decrease of the compartment is modelled by simulating every cell division, apoptosis, intravasation and extravasation of every cell in the compartment. Events are processed in order of the time on which they occur. After processing an event a new event is created which defines what happens next to the cell in the compartment. Every discrete compartment owns a set of possible events that can occur in this specific compartment and every kind of event in this set has a probability with which it occurs. In this way a discrete compartment can be parameterized to describe different settings, like the primary tumour, blood stream or tissues where metastases will develop.Discrete compartments are used to simulate a compartment in detail. Since this detailed simulation is very time consuming, bigger compartments like the primary tumour or the metastases are represented by continuous compartments.This building kit structure of the computer model allows simulation of a larger number of factors as it is possible with the analytical model of Iwata et.al. It allows changing the metastatic behaviour during time course, the simulation of the resection of the primary tumour and the assignment of different growth rates for primary tumour and metastases.The following configuration of compartments and events are used see also :Primary tumour and metastases are modelled as continuous compartments, the growth of both primary tumour and metastases being described by eq. 2. The spread of metastases by the primary tumour and the metastases is described by the colonization function given in eq. 3. In scenarios where metastases are not able to metastasize, a colonisation rate of zero is applied for the metastases. In the scenarios where the ability of late disseminated cells to form a metastasis is tested, the colonisation rate of the corresponding tumours is set to zero as soon as they reach a predefined size. The resection of the primary tumour is simulated by setting the growth rate and colonisation rate to zero at the day of the resection.The blood stream is modelled as a discrete compartment. Intravasation events are created conforming to the colonization rate defined in eq. 3. Processing an intravasation event the number of cells in the bloodstream is increased by one and a new event is created, which describes the behaviour of the new cell within the bloodstream. Since the colonisation rate used only includes those malignant cells that survive in the blood stream and found new metastases, the set of possible events for the blood stream compartment includes only the extravasation event. Each surviving tumour cell in the blood stream extravasates independently into the tissue. Most cells remain in the blood for about 60 minutes but divergences of about +/−20 minutes are possible. As a consequence, the cells don't necessarily leave the blood stream in the same order they entered it. The dwelling time each cell remains in the blood stream is computed following a Gaussian distribution. The values for the mean (60 min) and the standard deviation (20 min) were determined experimentally. Similar times were published by Meng et.al. −12 kg Each scenario was simulated 100 times. After completion of all simulations of a scenario the mean and standard deviation were computed. To compute the total tumour mass of the primary tumour and all metastases a cell mass of 10It is planned to make the software public available in an upcoming publication.The simulation model was compared to clinical data from a case of a patient with hepatocellular carcinoma (HCC) 9 cells at the time of diagnosis and no metastases were detectable. Later during the course of disease several metastases were detected in the liver. Chemotherapy was started 639 days after the first diagnosis. During the time until chemotherapy was commenced several CT scans were performed. The scans were performed at days 0 (day of the first diagnosis), 50, 89, 432, 559 and 632. The progression of the metastases can be monitored in the last three CT images. The number of metastases detected in these CT images was 10 at day 432, 28 at day 559 and 48 at day 632, respectively.The patient used to model cancer progression was diagnosed while still at an early stage. The primary tumour had a size of 1,55×10With the help of the mathematical model Iwata et al. estimated the day of the tumour inception to be 678 days prior the first diagnosis .−1a = 0.00286 day, 10b = 7.3*10, −8 (cell day)−1m = 5.3*10 and α = 0.663The values of the four parameters of the mathematical model were determined by fitting the mathematical curves to the clinical data. The values were: Six different scenarios were examined in the simulations . In the 10 cells). The chosen values are 108, 109 and 1010 cells  were chosen to compare the effects of both extreme cases.In the scenarios C and D the size of the tumour was used as a benchmark for the stage of development of the primary tumour and metastases and therefore for the definition of a late disseminated tumour cell. Three different sizes were chosen with regard to the maximum size of the presented case of a HCC , which is about the minimum size for clinical detection of a primary tumour.In the scenarios AAs an approximate benchmark for comparing the different scenarios in relation to the relevance for the patient, the total tumour mass of the primary tumour and all metastases was used. For HCC different staging systems exist, to predict the prognosis of patients 6 cells the graphs differ noticeably  and D (metastases do not metastasise) show no differences within the range of the clinical data. Differences were only seen late during time course and for very small metastases (<106 cells).The results of the simulations are shown in In contrast to the large differences in the number of metastases, the time reaching a total tumour mass of 1 kg differed only very little. In scenario A it is reached at day 1728 while in scenario B it is reached at day 1740. This accounts for a difference of only 12 days. In contrast the number of metastases varied about 3901 metastases. In the scenarios C and D the day of reaching a total tumour mass of 1 kg was shifted into the future only by 52 days, while the number of metastases varied about 2093 metastases.8 cells, which equals to 0.17129 g. At this size the primary tumour is clinically not detectable, which makes metastasis formation a very early event.As already briefly shown in the mathematical analysis of the patient's data, the computer simulations showed that in this case of a HCC metastasis is an early event. In the computer simulations the first metastasis was seeded at day 496. At this time point the primary tumour had a size of 1.71286*10R and BR were examined. Here the resection of the primary tumour was simulated for two different points in time. Once, two days after the diagnosis and secondly around two months before the first diagnosis, when the primary tumour had reached the smallest detectable size of about 1 g (109 cells). The simulation results of both scenarios are shown in To test the relevance of resection of the primary tumour on metastasis formation, the scenarios AR the metastases were able to metastasize. After the resection of the primary tumour the foundation of new metastases stagnated, seen as plateaus in the graphs AR1 and AR2 of In scenario AR. In this scenario metastases were not able to metastasize. Therefore no new metastases were seeded after the resection of the primary tumour, since only the primary tumour was able to seed metastases. The existing metastases keep on growing but are unable to seed new metastases. After the resection only 8 (resection at day 680) or 4 (resection at day 620) metastases remain in the body of the patient. According to the mathematical model from Iwata et al. (see above), metastases grow with a Gompertzian rate. This implies that they cannot grow indefinitely but stagnate at a specific time point. In this particular case of HCC Iwata assumed a stagnation size of about 7.3*1010 cells. Hence, since only 8 or 4 metastases are left, this stagnation size would lead to the result that the lethal tumour mass of 1 kg was never reached in this scenario. However, this result is unlikely to reflect the clinical situation of the patient  or +20% (8.76*1010 cells) are still compatible with the observed clinical data (data not shown here).Secondly, the value of the stagnation size So, assuming a lower lethal tumour mass and a higher stagnation size of the primary tumour and metastases it may still be possible that the patient dies after the resection of the primary tumour for the scenario that metastases were not able to metastasise.As described in the results section the available clinical data is not detailed enough to decide whether metastases metastasise or whether they do not. Within the range of the clinical data the graphs do not differ .Tumours start to metastasize from a certain minimal size onwards. Therefore the effect whether metastases do metastasize can only be observed, after the first metastasis had enough time to grow and spread itself. Hence, no single value can be attributed for the size of metastases at which the two scenarios A and B can be clearly distinguished.4 cells. Neglecting the standard deviation and only comparing the mean values of both scenarios, the range shifts as follows: day 1110→1–100 cells, day 1237→1–105 cells, day 1310→1–106 cells  or a metastasis (scn. C) reached the size of 106 cells would be necessary. Therefore future studies addressing these questions should include the analysis of smaller metastases.To answer these questions, additional data of the presence of metastases at least smaller than 10The aim of the computer model is to facilitate a better understanding of the processes of metastasis formation. In particular, this computer model allows to analyse different scenarios derived from clinical and experimental observations. Comparing the modelled data with clinical data, it might help to decide which pattern of metastatic spread applies to the clinical data.To be able to do this, it has to be ensured that the computer model works in accordance with the clinical situation. Therefore the simulation data was validated with clinical data from a patient with HCC. The graphs A and B in The capability to simulate different kinds of scenarios is the particular advantage of the computer model used in this paper. It was not only designed for a specific part of the metastatic progression or for a single scenario of the metastatic cascade. On the contrary, the model can be easily adapted to other aspects of the metastatic cascade by changing individual parameters or by changing the growth and colonization functions. This approach would allow modelling the entire metastatic cascade for many different tumour entities. In contrast to analytical models such as described previously The development of the computer model is still at the beginning. The model and the software environment is constantly enhanced and improved but it is already capable to describe a variety of different types of malignancies.The next step is to apply the computer model to other cancer entities and to compare the simulation results with detailed clinical data, in order to unravel the underlying important biological steps in cancer metastasis.Figure S1Simulation results of scenario A with mean and standard deviation. In scenario A primary tumour and metastases are both able to metastasise. The graph shows the cumulative number of metastases in relation to the metastasis size. The thick lines represent the mean for the three days 1110 (green), 1237 (red) and 1310 (blue). The thin black lines above and beneath each thick line display the standard deviation. The circles, squares and triangles represent the clinical data taken from the patient at the days 1110, 1237 and 1310. As can be seen, the clinical data fits well with the simulation results.(TIF)Click here for additional data file.Figure S2Simulation results of scenario B with mean and standard deviation. In scenario B only the primary tumour is able to spread metastases. As in (TIF)Click here for additional data file.Figure S3Comparison of scenario A and B. In this figure the simulation results of the scenarios A and B are displayed in the same graph. The thick lines represent the mean and the dashed lines the corresponding standard deviations. The graph clearly shows that in the range of the clinical data, the two scenarios A and B are nearly identical. Only in the range of the smaller metastases and late during the time course the scenarios can be separated. This is plausible, since tumours have to reach a certain minimal size, before they start metastasizing. Therefore, the effect whether metastases do metastasize can only be observed, after the first metastasis had grown large enough to start spreading metastases of its own. Including the standard deviation it is nearly impossible to clearly separate both scenarios at day 1110. At day 1237 both scenarios can be distinguished only for very small metastases from the size of 1 till 1000 cells. At day 1310 both scenarios can be clearly distinguished in the metastasis size range from 1 till 104 cells. If only the mean values are compared, the metastasis size ranges where both scenarios can be separated change as follows: day 1110→1–100 cells, day 1237→1–105 cells, day 1310→1–106 cells (see ells see .(TIF)Click here for additional data file.Figure S4Simulation results of scenario C with mean and standard deviation. In scenario C both primary tumour and metastases are able to spread metastases. Cells that are disseminated from the primary tumour and from metastases after they reached a size of 109 cells (dashed lines) or 1010 cells (solid lines), respectively, lose their ability to form further metastases. The thick lines represent the mean values, while the thin lines above and beneath each thick line represent the corresponding standard deviation. The clinical data does not fit with the dashed lines, which indicates that cells that are disseminated from tumours larger than 109 cells are still able to form new metastases. In contrast, the clinical data fits well with the solid lines. This finding supports the assumption that cells that are disseminated from tumours larger than 109 cells may lose the ability to form metastases. However, the clinical data is not detailed enough to definitively decide this question. Clinical data from metastases smaller than 107 cells would be necessary to answer this question. The plateaus are caused by the primary tumour that reached the critical size of 109 or 1010 cells, resp. As long as the first metastases spread by the primary do not reach the minimal size to spread metastases of their own, no new metastases are created, which explains the plateau observed. After these metastases start spreading metastases themselves, the plateau dissolves and the number of metastases starts rising again. As soon as the first metastases reach the critical size, too, a second plateau starts to form which can be observed for the dashed lines .(TIF)Click here for additional data file.Figure S5Simulation results of scenario D with mean and standard deviation. In scenario D the metastases are not able to metastasise. Similar to scenario C it is investigated whether cells that are disseminated from the primary tumour and metastases after they reach a size of 109 cells (dashed lines) or 1010 cells (solid lines), respectively, lose their ability to form metastases. The thick lines represent mean values, while thin black lines above and beneath each thick line represent the corresponding standard deviation. As in scenario C the clinical data does not fit with the dashed lines but with the solid lines. In contrast to scenario C the observed plateau remains, since only the primary tumour is able to metastasise. So, as soon as the primary tumour reaches the critical size of 109 or 1010 cell, resp, no new metastases are created.(TIF)Click here for additional data file.Figure S6Simulation results of scenario A and B with higher growth rate for the metastases. The graphs show the simulation results for the scenarios A (metastases are able to metastasise) and B (metastases do not metastasise) with a varied growth rate for the metastases. The solid lines represent the simulation results for the case that primary tumour and metastases grow with the same growth rate of −1a = 0.00286 day. The dashed and dotted lines display the simulations results for the case that metastases grow with a 50% (a = −1a = 0.00429 day) or 100% (a = −1a = 0.00572 day), resp., higher growth rate than the primary tumour. The comparison of the simulation results with the clinical data clearly shows that in this case of a HCC metastases do not grow faster than the primary tumour, but that they in fact grow with the same growth rate as the primary tumour.(TIF)Click here for additional data file.Figure S7Frequency distribution of scenarios A–D. The graphs display the number of metastases belonging to the size ranges applied to the x axis. In scenario A much more new created metastases were present than in scenario B, indicating how much impact metastasising metastases gain on the number of metastases. In scenario B much fewer new metastases were created than in scenario A, since only the primary tumour was able to metastasise. The higher number of metastases for the bigger metastases sizes at the days 1237 and 1310 in scenario B results from the logarithmical division of the metastases size ranges. The graphs C1 and C2 clearly display the decrease of new created metastases after the primary tumour reached the critical size of 109 (C1) or 1010 (C2) cells, respectively, and how the number of new metastases slowly starts rising again after the first metastases started spreading metastases of their own. In graph C1 a second decrease of new created metastases can be observed for the days 1237 and 1310. In contrast to the first decrease, the second decrease occurs less sudden. This happens because at the time the first metastasis reached the critical size of 109 cells already multiple metastases are able to spread metastases. In contrast to scenario C only the primary tumour was able to spread metastases in scenario D. As a result no new metastases were created after the primary tumour reached the critical size of 109 (D1) or 1010 (D2) cells, respectively. This fact can be observed in the graphs D1 and D2. The existing metastases kept on growing, but no new metastases are created. The total number of metastases is the same for the three time points (4 in D1 and 38 in D2).(TIF)Click here for additional data file.


The fluid was verified as interstitial by an isolated fluid:plasma ratio not significantly different from 1.0 for both creatinine and Na+, two substances predominantly present in interstitial fluid. The isolated fluid had a colloid osmotic pressure 79% of that in plasma, suggesting that there was some sieving of proteins at the capillary wall. Using a proteomic approach we detected 769 proteins in the isolated interstitial fluid, sixfold higher than in patient plasma. We conclude that the isolated fluid represents undiluted interstitial fluid and thus a subproteome with high concentration of locally secreted proteins that may be detected in plasma for diagnostic, therapeutic and prognostic monitoring by targeted methods.Major efforts have been invested in the identification of cancer biomarkers in plasma, but the extraordinary dynamic range in protein composition, and the dilution of disease specific proteins make discovery in plasma challenging. Focus is shifting towards using proximal fluids for biomarker discovery, but methods to verify the isolated sample's origin are missing. We therefore aimed to develop a technique to search for potential candidate proteins in the proximal proteome, i.e. in the tumor interstitial fluid, since the biomarkers are likely to be excreted or derive from the tumor microenvironment. Since tumor interstitial fluid is not readily accessible, we applied a centrifugation method developed in experimental animals and asked whether interstitial fluid from human tissue could be isolated, using ovarian carcinoma as a model. Exposure of extirpated tissue to 106  Significant efforts have been invested in the search for biological disease indicators in plasma, for diagnostic and prognostic purposes in vivo, but because of low recovery of macromolecules, fluid isolated with this technique will not reflect the protein composition of native interstitial fluid ex vivo and to apply this fluid as substrate for proteomic analysis Microdialysis is a technique frequently used to access the interstitial space in experimental animals and humans As pointed out in a recent review In the present study we asked whether a method developed for isolation of TIF in experimental animals g.The extirpated human ovarian tumor samples (weight range 0.3–5 grams) used in the present study were from the tumor surface in an area without any apparent necrosis or inflammation, and the tumor fluid was isolated immediately after extirpation by centrifugation for 10 minutes. The tumor fluid yield after centrifugation ranged from 5 to 150 µl/g tissue, and the isolated fluid had a yellow, clear color. Fluid could be isolated from all tumor samples at 106 +. These substances are present predominantly in the extracellular fluid phase and should therefore have similar concentrations in plasma and interstitial fluid In order to address whether the tumor fluid derived from the extracellular space only or had admixture of intracellular fluid, we designed an assay  based on+ (n = 7) .The tumor fluid:plasma ratios were 0.98±0.09 for creatinine (n = 7) and 1.00±0.04 for Na (n = 7) . Dilutio+ measurements we exposed frozen samples to 955 g, a procedure known to cause such release +  approach without prior fractionation. To study proteins that are mainly intracellular, i.e. housekeeping proteins, the protein lists were filtered. First, proteins identified in plasma and proteins assigned to the gene ontology (GO) category cellular component “Extracellular” were excluded. Second, proteins present in a list of housekeeping genes predicted by a Naive Bayes classifier were included Since the isolated tumor fluid obtained from the tumor was undiluted, we were able to measure the COP in interstitial fluid of human ovarian tumors . The COPTo characterize the tumor fluid with respect to protein composition, tumor fluid, plasma and ascites were analyzed by size-exclusion chromatography before and after immunodepletion of the 14 most abundant plasma proteins. Before immunodepletion all samples were dominated by the presence of high abundance plasma proteins with albumin as the most marked peak with retention time of 30.5 minutes , but tumThe tumor fluid was investigated as a starting point for biomarker discovery using a proteomic approach. The proteomic analysis resulted in identification of 769 proteins in the tumor fluid, 124 in patient plasma and 102 in control plasma . ProteinThe protein composition according to GO cellular component may indicate the origin of the proteins found in tumor fluid and patient plasma. For patient plasma 90.3% and 51.6% of the proteins identified were annotated with the GO category “Extracellular” and “Cytoplasm” respectively, whereas tumor fluid had 27.8% of the proteins annotated as “Extracellular” and 83.4% as “Cytoplasm”.In an attempt to investigate whether our method of interstitial fluid isolation has the potential to reveal new biomarker candidates that is not identified with alternative methods, we compared the 769 proteins found in TIF with data from previous in-depth proteomic analysis of ovarian cancer cell line cultures Here we have presented a method to isolate native interstitial fluid from a solid human tumor and verified that the isolated fluid derives from the extracellular fluid phase. Even though the interstitial fluid is an important element of the tissue microenvironment, TIF has been coined as a “misconsidered component of the internal milieu of a solid tumor” 51Cr-EDTA + to assess if the tumor fluid is representative for TIF.The critical question in our work is whether fluid isolated by centrifugation is representative for the interstitial fluid. Previously we have addressed this issue in animal studies by using the extracellular tracer Creatinine originates from degradation of creatine, which mainly occurs in skeletal muscle tissue harboring 98% of the bodily creatine + is in equilibrium between plasma and the interstitial fluid + and creatinine in tumor fluid compared to plasma were not significantly different from 1.0, suggests that the isolated fluid is not diluted by intracellular fluid.Na125I-labeled serum albumin, we have assessed this fraction in fluid isolated by centrifugation from chemically induced mammary carcinomas in rats Obviously, the isolated fluid derives from the entire extracellular fluid phase, and will therefore include some plasma. Using in vivo, which will affect the detection of intracellular proteins, but not affect the creatinine- and Na+-ratios. This is consistent with the finding of a large number of peptides derived from intracellular sources in prenodal lymph + and creatinine to the microenvironment, and thus result in reduced tumor fluid:plasma ratios for these two extracellular markers. Assessing the distribution of proteins in cellular components as defined by GO may indicate that there is a large amount of intracellular proteins present. However, preliminary results from proteomic analysis of interstitial fluid isolated from healthy ovarian tissue (unpublished) gave a GO distribution highly similar to that found in TIF, again supporting our assumption that the observed protein pattern is not a result of tumor necrosis.Having concluded that the tumor fluid is representative for TIF, the finding of intracellular proteins in the same fluid shows that there are some intracellular proteins in TIF. Furthermore, our GO analysis indicates a larger fraction of extracellular proteins in plasma than in tumor fluid. Still, as the tumor fluid originates in the tissue, and plasma is a global extracellular fluid, a higher fraction of cytoplasmic proteins in tumor fluid than in plasma is to be expected. These proteins may derive from cellular catabolism Intracellular contamination has to be considered when interpreting proteomic data originating from frozen samples, e.g. from biobanks, which may be used for targeted proteomics. If, however, frozen tissue is used in a screening phase, this will result in the detection of intracellular non-secretome candidate proteins.Other methods like nipple aspirates and ductal lavage fluid have been proposed for sampling of tumor subproteomes (for review see 3) and eluted in PBS. The supernatant is collected after 1 hour and named TIF per se cannot be used as evidence of intracellular fluid contamination.Another method used for sampling of interstitial fluid from human tumors is tissue elution Access to native, undiluted TIF has additional advantages. Eluate will not as centrifugate reflect absolute concentration in TIF of proteins and signaling substances, of importance for e.g. protein expression and transcapillary fluid balance studies. The determination of COP will only be feasible in undiluted samples, and to our knowledge we are the first to report COP in human tumor tissue, underlining the advantage of tumor fluid isolated by centrifugation. The measured tumor fluid:plasma COP ratio of 0.79 is higher than in human skin As part of the validation of the method we performed a proteomic analysis of the tumor fluid and found a sixfold increase in the number of detected proteins compared to plasma, pointing to TIF as a rich source for tumor specific proteins with biomarker potential. Moreover, a comparison with published in-depth proteomes for ovarian cancer derived from cell cultures and ascites One may argue that we present proteomics data on pooled fluid from few patients and thus question the general validity of the results. Our aim was to present an approach to isolate native TIF, but our data may also be considered as an initial phase of discovery proteomics. In this phase the main goal is to accumulate a library of proteins that may serve as candidates for validation strategies later To summarize, we have presented a centrifugation technique that can be used to isolate undiluted fluid that qualitatively and quantitatively reflects human ovarian carcinoma interstitial fluid. The method can be adapted for application in other solid tumors and be used to address a wide range of questions regarding the tumor microenvironment and tumor biology. Additionally, as suggested here, it can be used for identification of candidate proteins for diagnostic and prognostic use as well as targets for molecular imaging and therapy.The research protocol has been approved by the Norwegian Data Inspectorate (Protocol # 961478-2), Norwegian Social Sciences Data Services (Protocol # 15501) and the local ethical committee (Protocol ID REKIII nr. 052.01). All samples were collected after obtaining the patients' written informed consent. The work conformed to the standards set by the latest revision of the Declaration of Helsinki.g for 10 minutes, and fluid without erythrocytes were collected and stored −80°C until further processing. Fluid that was isolated from fresh tissue is referred to as tumor fluid. As a positive control for admixture of intracellular fluid, a piece of the tumor sample was frozen without centrifugation, and subsequently thawed and centrifuged at 955 g for 10 minutes, referred to as frozen tumor fluid. Ascites fluid was centrifuged to remove the cellular fraction, and cell free ascites fluid was extracted. The fluid samples were stored at −80°C until further processing. The sample validation strategy is outlined in Samples from extirpated human ovarian tumors were collected from the Department of Obstetrics and Gynecology at Haukeland University Hospital. The samples were taken from the tumor surface in an area without any apparent necrosis or inflammation, and ascites fluid was collected in patients presenting with fluid in the abdominal cavity. Blood samples were collected from the patients 1–2 days before surgery and EDTA was used as the coagulation agent. Tumor fluid was isolated immediately after extirpation by centrifugation as described earlier g for 10 minutes to remove precipitated proteins, 98 µl of supernatant was pipetted off, and 50 µl supernatant was injected in the HPLC system with 20 mM sodium acetate buffer (pH 4.68). At this pH creatinine, with a pKa of 5.02, is positively charged and has some retention on two strong cation exchange (SCX) columns. A Bio-Monolith SO3 column  in series with a Resource S column  were used for separation of creatinine from TCA and other contaminants. Further separation was done with a Proswift SCX-1S Analytical column  and by changing the buffer to 10 mM phosphate buffer (pH 7.01) in the third column the creatinine molecule is rendered neutral, reducing the retention in the column and focusing it, thus producing a sharp and well-defined peak with absorbance at UV 234 nm.The concentration of creatinine was measured in individual samples (n = 7) by HPLC on an Agilent HPLC system, using the principle presented by Ambrose et al. 2HPO4 + 50 mM NaH2PO4) to a final concentration of 50 and 90 U/ml for creatininase and creatinase, respectively. Ten µl of sample was incubated at 25°C overnight with 5 µl of each enzyme added. The following day the sample was deproteinated by adding 80 µl of 5% TCA and processed further as described above.The specificity of the method was tested by analyzing plasma and tumor fluid before and after enzyme hydrolysis by creatinase  and creatininase  as suggested by Paroni et al. + was measured in individual samples (n = 7) by an AAnalyst 200 flame spectrometer . The instrument was calibrated using solutions with Na+-concentrations of 10, 20, 50, 100 and 200 mM that were further diluted 1∶1000 in 0.65% HNO3. Five µl of sample was diluted in 10 ml 0.65% HNO3 and the concentration was measured in triplicate. All sample measurements were in the linear area of the calibration curve, between 10 and 100 mM.The concentration of NaCOP was determined in tumor fluid and plasma for individual samples (n = 6) in a colloid osmometer using a membrane with cutoff 30 kDa and equipped with a transducer as described in detail in an earlier publication For proteomic analysis equal volumes of tumor fluid from three tumor samples and the respective plasma samples were pooled and an overview of the patient samples used is given in The flow-through fraction from the immunodepletion was denatured by adding 0.48 g/ml urea and 13 µl/ml neat acetic acid and the samples were injected onto a macroporous Reversed-Phase (mRP) C18 Column  and fractionated, following recommendations by the manufacturer.www.chem.agilent.com; publication no. USHUPO3), desalted by C-18 spin columns  and analyzed by liquid chromatography coupled with tandem MS (LC-MS/MS) with an Agilent 1100 LC/MSD Trap XCT Plus system with a HPLC-Chip/MS interface. The HPLC-Chip contained a 0.075×43 mm ZORBAX 300SB C18 5- µm column and an integrated 9 mm 160 nL enrichment column packed with the same material . In addition, tumor fluid and frozen tumor fluid from two patients were digested by trypsin and analyzed by LC-MS/MS in triplicate without any prior separation. The enzyme-to-protein ratio used in the digestion protocol was 1∶35. For two-dimensional LC-MS/MS, each mRP fraction was separated further offline on a SCX column  before MS analysis. Spectrum Mill MS proteomics workbench software  was used to process the MS spectra and identifications were accepted with at least two unique peptides and with a score of more than 13. Proteincenter  was used for processing the MS results, and Graphpad Prism was used for statistical analysis . Results are presented as mean ± SEM.Fraction collection was started at retention time 4 minutes. Twenty-three mRP-fractions of 1.8 ml were collected, evaporated (Eppendorf Concentrator 5301) and subsequently diluted in 100 mM ammonium bicarbonate, denatured and digested by trypsin in accordance with a protocol provided by Agilent Technologies (Pooled samples of control plasma (n = 5), ascites (n = 9) and tumor fluid (n = 6) were analyzed by size exclusion chromatography before and after immunoaffinity depletion on a TSKgel SW2000  and a TSKgel SW3000  coupled in series. Samples that were not immunodepleted were diluted 1∶100 in mobile phase buffer . Immundepleted samples were not diluted, and 20 µL of all samples were injected on the HPLC system for separation.Table S1Intracellular proteins. Number of spectra identified for albumin and 15 selected intracellular proteins.(PDF)Click here for additional data file.Table S2Information on identified proteins. Summary of identified proteins, with detailed information, in tumor fluid, patient plasma and control plasma with a Spectrum Mill score higher than 13 and at least two peptides.(XLS)Click here for additional data file.


Several studies have suggested a role for blood coagulation proteins in tumour progression. Herein, we discuss (1) the activation of the blood clotting cascade in the tumour microenvironment and its impact on primary tumour growth; (2) the intravascular activation of blood coagulation and its impact on tumour metastasis and cancer-associated thrombosis; and (3) antitumour therapies that target blood-coagulation-associated proteins. Expression levels of the clotting initiator protein TF (tissue factor) have been correlated with tumour cell aggressiveness. Simultaneous TF expression and PS (phosphatidylserine) exposure by tumour cells promote the extravascular activation of blood coagulation. The generation of blood coagulation enzymes in the tumour microenvironment may trigger the activation of PARs (protease-activated receptors). In particular, PAR1 and PAR2 have been associated with many aspects of tumour biology. The procoagulant activity of circulating tumour cells favours metastasis, whereas the release of TF-bearing MVs (microvesicles) into the circulation has been correlated with cancer-associated thrombosis. Given the role of coagulation proteins in tumour progression, it has been proposed that they could be targets for the development of new antitumour therapies. In the 1860s, the French physician Armand Trousseau reported the occurrence of ‘mysterious’ thrombotic disorders in cancer patients and concluded that spontaneous blood coagulation events are frequent in these individuals because of a ‘special crisis in their blood’ [in vitro coagulation tests are found in more than 90% of patients with cancer, irrespective of their thrombotic status [Since the publication of ‘Clinique Médicale de l’Hôtel-Dieu de Paris’ by Trousseau in 1865 ,3. In fac status .Various authors have demonstrated a significant correlation between the incidence of thromboembolic events and a worse prognosis of neoplastic disease, supporting the idea that the activation of the blood coagulation system contributes to tumour aggressiveness and vice versa. Sorensen et al.  noted thCorroborating clinical data indicate a close association between tumour progression and the development of a thrombophilic profile. Diverse cellular and molecular evidence linking cancer with a hypercoagulable state has been described. Histopathological analyses demonstrate the presence of fibrin deposition and platelet aggregates in and around different tumours, indicating local activation of coagulation . In addiThese findings can be partly explained by the inflammatory response associated with neoplasia, by a change in protein metabolism and/or by venous stasis. However, various studies have demonstrated the importance of the participation of specific procoagulant properties of tumour cells, including the expression of TF (tissue factor), the central trigger of the coagulation cascade; the surface exposure of the phospholipid PS (phosphatidylserine), which provides a negatively charged surface required for the assembly of some catalytic active coagulation complexes; and the shedding of circulating procoagulant MVs (microvesicles). In the present review, we will discuss the different mechanisms of blood coagulation activation in cancer and their role in tumour progression.in vitro procoagulant activity and cell aggressiveness of different tumour cell lines [The blood coagulation cascade is initiated upon the binding of FVIIa (activated factor VII), a plasma protein, to TF, a 47-kDa transmembrane protein that is constitutively expressed on the surface of subendothelial cells and some extravascular tissues . This bill lines –18. Morell lines .ras, EGFRvIII  and HER-2 , as well as the loss of the tumour suppressor genes p53 and PTEN (phosphatase and tensin homologue) [TF overexpression in malignant tumour cells seems to be directly related to oncogenic events such as the presence of the mutant oncogenes K-mologue) –22. Furtmologue)  showed tTumour cells exhibit higher levels of the phospholipid PS on their membrane outer surface  , supportin vitro studies have shown that a strong correlation exists between TF expression and VEGF  production [In cancer patients, TF expression in different neoplasias has been correlated with tumour grade, increased vascular density and worse prognosis ,35. Addioduction . In factoduction . Of the oduction –39. Thesoduction .In vitro and in vivo studies have demonstrated, for example, that the intracellular signalling promoted by thrombin, through the proteolysis of PAR1 in tumour and endothelial cells, elicits a proangiogenic process that is associated with VEGF production and signalling, as well as the secretion of MMPs  [PARs are usually overexpressed in various human cancer types, and many studies have shown that a strong correlation exists between their expression and aggressive behaviour of tumour cells –45. The oteases) –49.Most of the pro-tumoural functions of TF concerning angiogenesis and primary growth have been correlated with the intracellular signalling triggered by TF binding to FVIIa and FVIIa/FXa-mediated proteolysis of PAR2 –56. Rema−/− mice, as well as in double-deficient mice, further linking TF and PAR2 signalling in the spontaneous development of invasive breast cancer [There is evidence that PAR2 responses are strongly correlated with intracellular signalling mediated by the TF cytoplasmic domain . Indeed,t cancer . Consistt cancer .in vivo tumour growth in the human breast cancer cell line MCF-7 [In addition to PAR2, several pro-tumoural responses might be evoked by PAR1 ,64. Remane MCF-7 . Yin et ne MCF-7  have demne MCF-7 ,68.Altogether, these lines of evidence indicate that the presence of TF and blood coagulation enzymes in the tumour microenvironment plays an important role in neoplastic progression, particularly through activation of PAR1 and PAR2 receptors.in vitro and in vivo [Under specific conditions, TF can be detected in the plasma circulation at abnormally elevated concentrations, where it is found mainly incorporated into tumour-derived MVs. This condition allows for the formation of the TF/FVIIa complex and subsequent intravascular activation of blood coagulation reactions, which are correlated with thrombosis occurrence. MVs are regarded as vesicular structures that are generated from the outward blebbing of the plasma membrane of various cell types, including normal and malignant cells . This ph in vivo ,73, angi in vivo ,75.Various studies have demonstrated the presence of TF on the surface of tumour MVs, likely supporting the formation of the TF/FVIIa complex ,75–77. Iin vivo [in vivo procoagulant activity of MVs, such as the strong correlation between elevated numbers of TF-positive, rather than PS-positive, MVs in human plasma and the increased risk of developing thromboembolic complications, as observed in various studies [Several studies suggest that circulating MVs are key players in cancer-associated thrombosis . The prein vivo ,84,85, ain vivo –89. Othe studies –93. Conv studies ,95.in vivo [in vivo, through adhesion-dependent, bidirectional signalling in neutrophils and platelets [In addition to procoagulant MVs, other mechanisms have been proposed to explain the occurrence of cancer-associated thrombotic events. Mucins are highly glycosylated proteins that become aberrantly glycosylated in carcinomas and then are inappropriately secreted into the bloodstream. Carcinoma mucins have long been implicated in thrombus occurrence in vivo ,97. Muciin vivo . Selectiin vivo . More relatelets ,100.In a murine model of chronic myelogenous leukaemia, Demers et al. observed that cancer predisposes the release of chromatin into blood through the generation of NETs (neutrophil extracellular traps). Furthermore, mammary and lung carcinoma tumour-bearing mice showed an increase in peripheral blood neutrophils sensitized toward NET formation, as well as spontaneous thrombosis associated with NET generation .As mentioned previously, studies employing cultured cells as well as patient specimens have shown that a strong correlation exists between TF expression and aggressive tumour behaviour ,103. TF in vivo tumour cell survival, suggesting a crucial role for coagulation in the establishment of a premetastatic niche [In particular, TF procoagulant activity, which is mediated by its extracellular domain, has been correlated with the metastatic potential of tumour cells. This activity is essential for the establishment of cancer cells in sites distal to the primary tumour, as demonstrated in melanoma, breast cancer and fibrosarcoma models ,57,109. ic niche . This efin vivo is observed upon the co-expression of PAR1 and TF on tumour cells. In fact, several adhesion molecules, cytokines, growth factors and proteases have been identified as downstream targets of PAR1 and have been shown to modulate tumour cell metastasis [Activation of PAR1 by thrombin on tumour cells has also been linked to the facilitation of tumour metastasis . Notablytastasis .in vitro assays employing cell lines demonstrated that PAR2 enhances migratory and invasive properties, which may be relevant in terms of the acquisition of a pro-metastatic phenotype in vivo [In contrast to PAR1 inhibition, blocking PAR2 signalling in tumour cells has a minor impact on tumour metastasis. In this context, a non-anticoagulant monoclonal antibody that blocks TF-mediated signalling through PAR2 exhibited no effect on tumour cell dissemination in a breast cancer model . On the  in vivo ,119.In addition to its procoagulant role, the exposure of PS on cellular membranes and membrane-derived MVs can stimulate several anti-inflammatory responses that are involved in malignant processes. In this context, our group demonstrated that tumour-derived MVs also favour the establishment of melanoma metastasis in a PS-dependent manner, possibly by down-regulating the host's inflammatory and/or anti-tumoural immune responses . NeverthAncylostoma caninum anticoagulant peptide) [Given the proposed role of blood coagulation proteins in tumour progression, it has been hypothesized that targeting specific blood clotting proteins or PARs could serve as an adjuvant therapy in cancer. It has been well appreciated that antibodies that block the procoagulant function of TF attenuate metastasis . Furtherpeptide) . It is nAncylostoma caninum [Because TF is suggested to play an important role in tumour biology, it has been postulated that the specific TF inhibitors attenuate cancer progression. Our group has extensively worked with ixolaris, a tick 140-amino acid salivary protein containing ten cysteines and two Kunitz-like domains. Ixolaris binds to FXa or FX as scaffolds for the inhibition of the TF/FVIIa complex, in which the FVIIa catalytic site is inactivated, as previously demonstrated by the inhibition of macromolecular  substrates . Ixolari caninum . It disp caninum . NAPc2 h caninum . Also, a caninum . Finally caninum , demonstIt must be emphasized that a great challenge in proposing the use of anticoagulant molecules as adjuvants for the treatment of cancer is the increased tendency of patients with cancer for bleeding complications ,136. In A substantial amount of data implicates TF in cancer-associated hypercoagulable states. In addition, TF-dependent activation of coagulation proteases and PARs seem to be crucial for tumour growth, angiogenesis and metastasis. Thus, therapeutic strategies targeting some effector molecules of blood coagulation activation could attenuate their deleterious effects in cancer, certainly affecting the morbidity and survival of cancer patients. Nevertheless, additional studies are needed to identify mechanistic insights into the role of blood coagulation activation and coagulation-dependent signalling pathways in cancer biology, as well as recognize potential blood coagulation targets to inhibit cancer progression without compromising haemostasis.