Drug transport and its uptake by tumour cells are strongly dependent on tumour properties, which vary in different types of solid tumours. By simulating the key physical and biochemical processes, a numerical study has been carried out to investigate the transport of anti-cancer drugs in 3-D tumour models of different sizes. The therapeutic efficacy for each tumour is evaluated by using a pharmacodynamics model based on the predicted intracellular drug concentration. Simulation results demonstrate that interstitial fluid pressure and interstitial fluid loss vary non-linearly with tumour size. Transvascular drug exchange, driven by the concentration gradient of unbound drug between blood and interstitial fluid, is more efficient in small tumours, owing to the low spatial-mean interstitial fluid pressure and dense microvasculature. However, this has a detrimental effect on therapeutic efficacy over longer periods as a result of enhanced reverse diffusion of drug to the blood circulation after the cessation of drug infusion, causing more rapid loss of drug in small tumours. A variety of therapeutic agents are routinely delivered by intravenous administration in clinical cancer treatments. The transport of therapeutic agents is determined by physicochemical properties of the drug and biologic properties of the tumour, including molecular structure of the drug, microvasculature density of the tumour and interstitial fluid pressure . The bioet al [et al [et al [2 from vasculature to extracellular space in these idealized models was examined in their subsequent work by assuming the same tissue properties for all tumours [Given the multiple processes involved in drug delivery and interactions between drugs and intratumoural environment, mathematical modelling has become an important tool to understand the limiting factors in effective delivery of anticancer drugs to solid tumours. A 1-D computational framework was developed by Baxter and Jain –9 to stuet al  to invesl [et al  to addrel [et al  in ideal tumours . HoweverIn the present study, the effect of tumour size on drug transport and its uptake by tumour cells are determined by means of 3-D computational modelling applied to realistic tumour geometries reconstructed from magnetic resonance images (MRI). The computational model incorporates the key physical and biochemical processes involved in drug transport from tumour vasculature to tumour interstitial space and across tumour cells. Tumours are treated as porous media and the vasculature density in each model is dependent on tumour size. Using the predicted intracellular drug concentration, anticancer efficacy is evaluated based on the percentage of viable tumour cells obtained by directly solving the pharmacodynamics equation corresponding to continuous infusion of doxorubicin.In order to examine the interactions among multiple drug transport steps, tumour properties and drug properties, the current modelling platform consists of descriptions of interstitial fluid flow, convection and diffusion of drug in tumour interstitial space, transport of drug across cell membrane and a pharmacodynamics model. Tumour interstitium is modelled as a porous medium, with tumour vasculature being treated as a source term in the governing equations, without considering its geometric structure. The main assumptions are as follows: (1) the interstitial fluid is incompressible and Newtonian with a constant density and viscosity; (2) homogeneous transport properties in tumour; (3) uniform distribution of blood vessels and tumour cells in tumour tissue, with all cells being identical and stationary; (4) tumour growth is negligible within the simulation timeframe, so that all the physiological parameters and tumour geometry are independent of time.The mathematical models consist of the mass and momentum conservation equations for interstitial fluid flow, mass transfer equations for the free and bound drug, as well as equations describing the intracellular drug concentration and pharmacodynamics. Numerical solutions are obtained by solving the interstitial fluid flow equations first to provide the basic biomechanical environment for drug transport. This is followed by solution of the mass transfer equations for drug transport, which is described schematically in kas and kds respectively, drug exchange between blood and extracellular space, and influx/efflux of drugs from extracellular space to tumour cells. The rate of cell killing is governed by a pharmacodynamics model based on the predicted intracellular concentration of anticancer drugs. Detailed descriptions of the mathematical models have been reported elsewhere [The dynamics process in drug delivery includes drug association/disassociation with proteins at the rate lsewhere , a briefFive tumour models were reconstructed from images acquired from prostate cancer patients using a 3.0-Tesla MR scanner . Multislice anatomical images of the prostate were acquired in three orthogonal planes with echo-planer (EP) sequence, with each image comprising 256 by 256 pixels. Other imaging parameters are given in A sample MR image used for geometric reconstruction of the tumour models is shown in The reconstructed models shown in All the geometric and transport parameters are fixed within the simulation timeframe. Baseline values for model parameters are summarized in et al measured S/V in normal tissues and found this to be 70 cm-1 [S/V in breast tumours of different volumes and at different growth stages by using morphometric methods. Based on their data [S/V and tumour size can be obtained as shown in S/V value for each tumour model is derived according to its own size. Tumour volume and the corresponding S/V for each tumour model are summarized in This ratio reflects the microvasculature density. Pappenheimer  70 cm-1 , which w 70 cm-1  in their 70 cm-1  measuredeir data , the rel2 [Owing to the known toxicity of doxorubicin, the lifetime dose a patient can receive is approximately 550 mg per unit body surface area. For a patient of 70 kg body weight, a standard dose of 50 mg/m2  is choset is time, Dd is the dose of total dose of administrated doxorubicin and Td is the infusion duration. A1, A2 and A3 are compartment parameters and α1, α2, α3 are compartment clearance rates.Doxorubicin concentration in blood plasma is modelled as an exponential decaying function of time. The form of equations depends on the infusion mode. For continuous infusion, a tri-exponential decay function is adopted based on the plasma pharmacokinetics of doxorubicin , 18.{Cvet al. [Cfp) and bound (Cbp) doxorubicin concentrations in plasma are assumed to be 25% and 75%, respectively.Free doxorubicin in plasma can easily bind with proteins, such as albumin. Greene et al.  found th−5 and 1×10−8, respectively.The mathematical models described above are coded in C programming language and implemented via user defined functions in ANSYS-Fluent, which is a finite volume based computational fluid dynamics (CFD) code . The momentum and drug transport equations are discretised using the second order UPWIND scheme, and the SIMPLEC algorithm is employed for pressure-velocity coupling. The Gauss-Seidel smoothing method is used to update values at nodal points after each iteration step. Convergence is controlled by setting residual tolerances of the momentum equation and the drug transport equations to be 1×10In order to generate initial conditions for the transient simulation, the interstitial fluid flow equations are firstly solved to obtain a steady-state solution for the entire computational domain. The obtained pressure and velocity values are then used to initialise the simulation of drug transport and cellular uptake. The second order implicit backward Euler scheme is used for temporal discretisation, and a fixed time step size of 10 seconds is chosen. This time step is deemed sufficiently fine based on a time step sensitivity test. The initial doxorubicin concentrations are assumed to be zero in both tumour and the surrounding normal tissue.There are two boundary surfaces in this model: an internal boundary between the tumour tissue and normal tissue, and the outer surface of the normal tissue. At the internal boundary, conditions of continuity in terms of interstitial pressure and fluid flux are applied. At the outer surface, a constant relative pressure of 0 Pa and zero flux of drug are assumed.2 [Numerical simulations have been carried out for 2-hour continuous infusion of 50 mg/m2  doxorubi2 . The obtBecause of abnormalities in tumour vasculature and the absence of lymphatic drainage, tumour interstitial fluid pressure (IFP) has been found to be higher than that in normal tissues –9, 20. CThe predicted spatial mean IFP and spatial mean transvascular flux per tumour volume in each tumour are shown in Fv) is calculated as the product of hydraulic conductivity of vessel wall, microvascular density as well as the difference between effective vascular pressure and IFP. Fv is higher in small tumour. This is because: (1) the driving force for transvascular flow is high in small tumour owing to the low value of spatial mean IFP, and (2) higher S/V in small tumour further improves this exchange. However, referring to the spatial distribution of IFP in The transvascular flux per tumour volume from blood to interstitium  in the tumour interior. This rather uniform IFP dictates that pressure-induced interstitial drug migration is weak, and convective transport in the interstitium only occurs within a thin layer at the tumour periphery, where a steep pressure gradient exists.Interstitial fluid pressure (IFP) in tumour and its surrounding normal tissue plays an important role in determining not only the convective drug exchange between microvessels and the interstitial space driven by transvascular pressure gradient, but also drug convection in extracellular space. Computational simulation results show that tumour IFP is uniform except at the boundary in all models regardless of their sizes. This is because IFP equilibrates with the effective vascular pressure, which is governed by α, which is defined asR is the equivalent radius of the tumour, Kv is the hydraulic conductivity of the microvascular wall, and K is the hydraulic conductivity of the interstitium, which is given by K = κ/μ  the microvasular density and (2) the concentration gradient across the vessel wall. The former is responsible for the higher tranvascular exchange rate observed in small tumours , 21, 22.et al  reportedIt has also been reported that tumour IFP approximately equals to the surrounding pressure for very small tumours with an equivalent radius of less than 0.2 mm . ConvectS/V (blood vessel surface area to tumour volume ratio) and its tumour size dependence are derived from data reported on breast tumour, due to the lack of such information on prostate tumour. Thirdly, uniform transport properties and uniform tumour cell density [vivo [The mathematical model employed here has a number of limitations. Firstly, the tumour microvascular network is treated as a distributed source term in the governing equations instead of being modelled explicitly. Hence, the influence of geometric features of tumour vasculature on drug delivery is not included. Secondly, values for  density , 24–34 aty [vivo , and thety [vivo –38. Chanty [vivo . BecauseThe transport of doxorubicin in five tumours with different sizes and microvasculature densities has been studied under direct continuous infusion. Computational simulation results demonstrate nonlinear relationships between spatial-mean interstitial fluid pressure and tumour volume, as well as between transvascular flux per tumour volume and tumour volume. During drug infusion, transvascular drug exchange depends mainly on diffusion, driven by the concentration gradient of unbound drug between blood and interstitial fluid. As a consequence, transvascular transport is more efficient in small tumours, owing to the low spatial-mean interstitial fluid pressure and dense microvasculature. However, anticancer effectiveness appears to be compromised in small tumours as a result of enhanced reverse diffusion of drug to the blood circulation after the cessation of drug infusion. Future computational studies should aim to incorporate tumour-specific properties in order to understand the quantitative effect of tumour size on treatment outcomes for different tumour types.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


The prognostic effect of tumour budding was retrospectively analysed in a cohort of 173 patients with resected pancreatic ductal adenocarcinomas (PDACs) of the prospective clinical multicentre CONKO-001 trial.2 field of view and additionally in 10 high-power fields (HPF) (HPF = 0.238 mm2).Haematoxylin and eosin (H&E)-stained whole tissue slides were evaluated. In two independent approaches, the mean number of tumour buds was analysed according to the consensus criteria in colorectal cancer, in one 0.785 mmp < 0.001) but not with distant or lymph node metastasis. Regardless of the quantification approach, an increased number of tumour buds was significantly associated with reduced disease-free survival (DFS) and overall survival (OS) (10 HPF approach DFS: HR = 1.056 (95% CI 1.022–1.092), p = 0.001; OS: HR = 1.052 (95% CI 1.018–1.087), p = 0.002; consensus method DFS: HR = 1.037 (95% CI 1.017–1.058), p < 0.001; OS: HR = 1.040 (95% CI 1.019–1.061), p < 0.001). Recently published cut-offs for tumour budding in colorectal cancer were prognostic in PDAC as well.Tumour budding was significantly associated with a higher tumour grade (Tumour budding is prognostic in the CONKO-001 clinical cohort of patients. Further standardisation and validation in additional clinical cohorts are necessary. The clinical outcome of pancreatic ductal adenocarcinoma (PDAC) is determined by the invasive spread of tumour cells in the adjacent tissue, as well as into regional lymph node. Therefore, a morphological analysis of different patterns of tumour invasion might lead to a better prognostic classification of PDAC.2 EMT describes a process of cell-plasticity, in which cells loose epithelial and gain mesenchymal characteristics. This process promotes organ formation during embryogenesis and plays an important role during wound healing.3 In cancer, aberrant activation of EMT pathways promote tumour invasion, progression and metastasis formation. We studied the prognostic effect of tumour buds in a cohort of 173 PDACs from the clinical multicentre CONKO-001 trial. CONKO-001 is a prospective phase III trial, investigating the role of adjuvant gemcitabine in pancreatic cancer patients.4 It disposes prospectively collected clinical data and a follow-up of more than 5 years, as well as two randomised groups of patients: one treated for 6 months postoperatively with gemcitabine compared to one that was only observed.Tumour budding is best described as a certain type of invasive growth pattern, often but not exclusively present at the invasion front, that is observed at histological examination of carcinomas. It is defined as isolated tumour cells or small non-glandular tumour cell cluster and commonly believed to represent epithelial–mesenchymal transition (EMT) of tumours.5 in one 0.785 mm2 field of view with highest tumour budding and additionally in 10 high-power fields (HPF) (HPF = 0.238 mm2). For both quantification methods, we examined the prognostic value of tumour budding and assessed its correlation to clinicopathological features of PDAC. Finally, we evaluated if tumour budding is associated with the presence of distant or lymph node metastasis formation in PDAC, and compared the number of tumour buds of metastasised and non-metastasised tumours of CONKO-001 with 38 PDACs with synchronous resected liver metastases.In this translational investigation, we used H&E-stained slides of formalin-fixed paraffin-embedded tumour specimen of the CONKO-001 trial, to evaluate the hypothesis that in PDAC high numbers of tumour buds are linked to a worse prognosis. Therefore, in two independent approaches, the mean number of tumour buds were analysed, according to the recently published consensus criteria for colorectal cancer of the International Tumour Budding Consensus Conference (ITBCC),2 d1, 8, 15, q29) was continued for 6 months and compared to observation only. Median disease-free survival (DFS) ,4 overall survival (OS)  and 5-year survival (20.7% vs. 10.4%)6 were significantly improved by gemcitabine. The study was approved by the institutional review committee .Baseline data and patient’s characteristics: Between July 1998 and December 2004, a total of 368 patients were recruited with resected pancreatic cancer (R0 and R1 resection). In an outpatient setting, adjuvant treatment with gemcitabine  tissue samples of CONKO-001 patients could be collected retrospectively. In most cases, only one tumour bearing tissue block was available. Staging of tumours was accomplished using UICC TNM 7. Although only 49.7% of tumour samples of the CONKO-001 trial were collected, the data regarding clinical and histopathological features of the subset with available tumour tissue  clusters of up to four tumour cells.To demonstrate the reproducibility of the tumour bud count, ten cases of PDAC were retrieved from the archives of the Institute of Pathology, Charité—Universitätsmedizin Berlin. From all cases, three slides from different tumour areas were chosen and five images at 400× magnification of representative areas of the tumour were taken from each slide, resulting in 150 images each with 952 × 768 pixels. The number of tumour buds on the images were independently counted by two observers, one an experienced pathologist (P.L.) and the other a non-pathologist trained to detect tumour buds (A.J.). The intra-class correlation coefficient between both observers was 0.904 (95% CI 0.729–0.959) for the total number of tumour buds per case.Subsequently, counting of tumour buds was accomplished by an experienced pathologist (P.L.) blinded to clinical outcome using an Olympus BX40 microscope .5 for reporting tumour budding in colorectal cancer. Therefore, tumour buds were counted in one field of view  at the “hotspot” of budding and the number of tumour buds per 0.785 mm2 was determined using a normalisation factor, as published.5 Budding was grouped according to ITBCC into Bd 1 (0–4 buds), Bd 2 (5–9 buds) and Bd 3 (10 or more buds). Using this method, 173 PDACs of the CONKO-001 study could be analysed. (2) Additionally, in areas of maximal tumour budding, detected at scanning magnification, the number of tumour buds was counted in 10 high-power fields . Since we, like others,9 frequently observed tumour buds within the central parts of the tumour and because of difficulties in defining the edge of tumours, tumour budding was not subclassified into peritumoural and intra-tumoural. Using this method, 162 tumours were evaluable; 11 tumours had to be excluded, since less than 10 HPF could be analysed.Two different approaches to quantify the number of tumour buds were used Fig. . (1) We DFS was defined as time from study entry to local or distant disease relapse or death, whichever came first. OS was defined as time from study entry to death due to any cause.To investigate the prognostic impact of tumour buds for both quantification approaches, Cox regression analyses without a cut-off, using continuous data and proportional hazards regressions, were performed. Proportional hazards regression was calculated for multivariable and univariable survival models. The potential relevant clinical variables tumour budding, treatment arm, age, sex, pT stage, pN stage, grading and resection margin status were subsequently included in a multivariable proportional hazards model.5 with only slight modifications. Pearsons correlation method was used to correlate tumour budding groups and clinicopathological parameters. Student's T-test was used for statistical analysis of the differences in the mean of tumour buds between groups with and without metastasis of the CONKO-001 cohort and the 38 PDACs with synchronous liver metastasis. In general, p values were calculated 2-sided and considered as significant when <0.05.The Kaplan–Meier method with log-rank tests was used for univariable survival analyses. Cut-offs were as published for colorectal cancer,10The Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) criteria were followed for reporting this study.5 all 173 tumours were evaluable. The mean number of tumour buds was 8.0, the median 5.8 (range 0–35). Eighty-three tumours had 0–4 buds (Bd 1) per 0.785 mm2, 33 tumours had 5–9 buds (Bd 2) per 0.785 mm2 and 57 tumours could be assigned to Bd 3 with more than 9 buds per 0.785 mm2. When Bd 1 and 2 were subsumed in the low budding group, this group comprised 116 patients, the low budding group (Bd 3) 57 patients; 162 tumours could be analysed with the 10 HPF approach. The mean number of tumour buds per HPF was 5, the median 3.1 (range 0–23). Representative cases with high and low numbers of tumour buds are shown in Fig. With the quantification approach, recently published by the ITBCC,p < 0.001) between the budding groups and tumour grade.We assessed the correlation of the clinicopathological parameters, patients age (<65 and ≥65 years), sex , Karnofsky Performance Status Scale Score (≤80 and >80), pT classification (pT1/2 and pT3/4), nodal status (pN0 and pN1), resection margins (R0 and R1), and grading (G1/2 and G3), with the budding groups (low budding and high budding) (Table p < 0.001).When analysing the mean number of tumour buds, determined with the 10 HPF method, high-grade tumours had significantly more tumour buds than low-grade tumours. G1/2 tumours had a mean number of 2.74 tumour buds per HPF and G3 tumours 7.99 tumour buds per HPF , p < 0.001). When stratifying into treatment arms, this effect was present in patients treated with adjuvant gemcitabine (DFS: HR = 1.042 (95% CI 1.015–1.069), p = 0.002; OS: HR = 1.040 (95% CI 1.014–1.067), p = 0.002) and in the untreated control group (DFS: HR = 1.041 (95% CI 1.006–1.077), p = 0.02; OS: HR = 1.038 (95% CI 1.003–1.075), p = 0.033).We first investigated the prognostic impact of tumour buds by performing a Cox regression analysis without a cut-off, using continuous data Table . All 173N = 83), Bd 2 (N = 33) and Bd 3 (N = 57). Since there was no significant difference between Bd 1 and 2 , we subsumed Bd 1 and 2 in the low budding group, while Bd 3 was referred to as high budding group. Low budding tumours were significantly associated with a longer DFS and OS in the overall study population. Median DFS/OS were 13.1/26.7 months for low budding tumours and 6.4/12.3 months for high budding tumours (p < 0.001/ = 0.001)  Fig. .Fig. 3a p = 0.003/0.004). In the observation group, patients of the low budding group had a median DFS/OS of 8.3/21.5 months compared to 4.9/14.9 months for patients of the high budding group (p = 0.027/0.082)  Fig. .p = 0.003), budding groups (HR = 2.222 (95% CI 1.460–3.383), p < 0.001), treatment arm (HR = 1.669 (95% CI 1.207–2.308), p = 0.002) and pN stage (HR = 1.633 (95% CI 1.100–2.422), p = 0.015) retained their prognostic status on DFS. The pT stage (HR = 2.402 (95% CI 1.355–4.259), p = 0.003), budding groups (HR = 1.935 (95% CI 1.294–2.893), p = 0.001), pN stage (HR = 1.612 (95% CI 1.084–2.398), p = 0.018) and grading (HR = 1.589 (95% CI 1.111–2.273), p = 0.011) retained their prognostic status on OS.Additionally a multivariable survival analysis, including budding group, treatment arm, age, sex, pT stage, pN stage, grading and resection margin status Table . Only pTp = 0.001; OS: HR = 1.052 (95% CI 1.018–1.087), p = 0.002). When stratifying into treatment arms, this effect was only detectable in patients treated with adjuvant gemcitabine (DFS: HR = 1.071 (95% CI 1.021–1.123), p = 0.005; OS: HR = 1.069 (95% CI 1.019–1.121), p = 0.006) but not in the untreated control group (DFS: HR = 1.038 (95% CI 0.990–1.088), p = 0.12; OS: HR = 1.038 (95% CI 0.991–1.087), p = 0.112). The test for interaction was not significant (p = 0.474).We next investigated the prognostic impact of tumour buds by performing a Cox regression analysis without a cut-off, using continuous data of 162 tumours analysed with the 10 HPF approach (Table p = 0.262).Tumour budding is regarded as a morphologic detectable sign of EMT and EMT is seen as a crucial step in the development of metastasis. We therefore asked whether a higher number of tumour buds is associated with the development of distant metastasis in PDAC of the CONKO-001 trial. However, there was no correlation between the budding group and the presence of distant metastases and no significant difference between the number of tumour buds per 10 HPF in PDACs with and without distant metastasis (mean number 4.93 (95% CI 3.88–5.99) and 3.68 (95% CI 1.75–5.61), respectively; p = 0.672) or without  distant metastasis of the CONKO-001 cohort.We additionally analysed for the first time the number of tumour buds in a set of 38 resected PDACs with resected and histologically proven synchronous liver metastasis. The mean number of tumour buds was 4.24 (95% CI 2.72–5.76). Again there was no significant difference in the number of tumour buds between the cases with synchronous liver metastasis and the cases with . In addition, we demonstrated that PDAC could be stratified into prognostic subgroups by using the proposed budding groups with only slight modification. We, like others,9 found a highly significant positive correlation between the number of tumour buds and tumour grading. In our study, a higher number of tumour buds was significantly correlated with poorly differentiated tumours. This might at least in part be due to some overlapping between tumour grade and tumour budding. We did not subclassify into peritumoural and intra-tumoural budding because of difficulties in defining the edge of the tumours and the frequent observation of intra-tumoural buds in PDAC.9 Grading of PDAC according to the WHO26 includes the semi-quantitative assessment of mucin production, mitoses, nuclear atypia and glandular differentiation.27 Poorly differentiated tumours contain poorly formed glands and can show individual infiltrating cells and solid areas. This might at least in part explain our here-found association between grading and tumour budding. However, not all poorly differentiated tumours show high budding, and in multivariate analysis, tumour budding was an independent prognostic factor. Together with previously published data,25 our results link tumour budding to a worse prognosis in PDAC and underline the aggressive nature of budding-positive tumours.Using H&E-stained sections from FFPE tissue from patients of the prospective CONKO-001 trial, we could demonstrate that the presence of tumour buds are associated with highly significant differences on patient outcome. Shortly, two different approaches to quantify tumour buds were used. We used the quantification approach that has recently been included in a standardised evidence-based consensus scoring system for tumour budding in colorectal cancer.Both quantification methods are able to discriminate prognostic subgroups in patients adjuvantly treated with gemcitabine. However, when directly comparing both quantification methods, the consensus method might be the method of choice for daily practice for several reasons. It is the same quantification approach as already known from colorectal carcinoma and as only one field of view has to be counted, it is not as time consuming as the 10 HPF approach. Moreover, this method was also able to discriminate prognostic subgroups in patients with PDAC in the observation arm of CONKO 001.29 However, in our investigations in PDAC, we found no significant difference in the number of tumour buds between cases from the CONKO-001 trial with metachronous distant metastasis and no distant metastasis. We additionally determined from the mean number of tumour buds in 10 HPF from 38 PDACs with resected and histologically proven synchronous liver metastasis. We compared the mean number of tumour buds to PDAC of the CONKO-001 trial with and without distant metastasis and found no significant differences between the groups. We suppose that this might be due to different reasons: the fact that tumour budding is more correct to be regarded a “partial” EMT,30 the quite complex orchestrated mechanisms of metastasis induction and the aggressive nature of PDAC with a comparatively short survival. In PDAC, immunohistochemical studies detected a reduced, yet not abolished, E-cadherin expression and nuclear beta catenin expression in tumour buds.23 In other studies nuclear beta catenin expression could not be demonstrated in PDAC, indicating that the EMT-inducing activation of the canonical Wnt pathway is not a prerequisite for tumour budding in PDAC.31 Although tumour buds appear disconnected from the main tumour mass in two-dimensional (2D) histological slides, studies on 3D reconstructions and serial section showed that tumour buds are actually connected to the main tumour mass.32 These findings imply that there is no complete transition to a mesenchymal phenotype, therefore tumour budding is discussed to be “partial” EMT. After the induction of EMT, metastatic cancer cells migrate away from the original tumour, and spread to distant sites.33 There a mesenchymal–epithelial transformation (MET) is required for successful development of metastasis. According to the “seed and soil” hypothesis by Paget,34 the microenvironment of the distant site is crucial for the development of distant metastasis. Taken together, EMT is only the first step of the highly complex process of metastasis formation, and tumour budding might not completely reflect EMT. This might at least partially explain why in our study the presence of tumour buds in PDAC did not correlate with metastasis formation.Tumour budding is discussed to represent the morphological correlate of EMT. The loss of epithelial characteristics and gain of mesenchymal characteristics reduces cell adhesion and enhances cell motility. Therefore, EMT is seen as a first step towards metastasis formation. Indeed, tumour budding has been linked to the presence of distant metastases in colorectal cancer.35 Although the use of cytokeratin stains has been described to be useful in identifying tumour buds, we used H&E-stained slides for several reasons. First, the quantification of tumour buds on H&E-stained slides can be easily implemented in the daily routine of surgical pathologists. Second, we and others9 have demonstrated a good reproducibility in recognising tumour buds in H&E-stained sections of PDAC. Third, the quantification of tumour buds in H&E-stained slides is consensus in colorectal cancer.5Our study has limitations. For our here-presented analysis, 183 tissue samples of the initial 354 CONKO-001 patients were available and due to low quality of tissue fixation or limited tumour in the paraffin blocks, only 173 samples were suitable for analysis of tumour buds. Although only 173 (47%) tumour samples of the CONKO-001 trial were retrospectively collected and eligible for further analysis, the data regarding clinical and histopathological features of the subset with available tumour tissue are comparable with the overall intention to treat the population. Another limitation may be the fact that in most cases only one tumour-bearing tissue block was available. We used H&E-stained slides to determine the number of tumour buds, which—when compared to enumeration with the use of cytokeratin stains—could underestimate the real number of tumour buds.To summarise, we show data on the impact of tumour buds in resected PDACs using two different quantification methods. We could demonstrate a positive correlation of tumour budding and tumour grade. Further, high numbers of tumour buds were associated with a significantly worse survival in resected PDACs. In contrast to previous published data in colorectal cancer, we could not demonstrate that tumour budding in PDAC is associated with metastasis formation. Analysis of tumour buds in PDAC could be easily implemented in routine diagnostic processes, but needs further standardisation and verification by prospective studies.Supplementary Table 1


Crotalus scutulatus) attempting to capture Merriam’s kangaroo rats (Dipodomys merriami). Strikes occurred from a range of distances (4.6 to 20.6 cm), and rattlesnake performance was highly variable. Missed capture attempts resulted from both rapid escape maneuvers and poor strike accuracy. Maximum velocity and acceleration of some rattlesnake strikes fell within the range of reported laboratory values, but some far exceeded most observations. Thus, quantifying rapid predator-prey interactions in the wild will propel our understanding of animal performance.Predation plays a central role in the lives of most organisms. Predators must find and subdue prey to survive and reproduce, whereas prey must avoid predators to do the same. The resultant antagonistic coevolution often leads to extreme adaptations in both parties. Few examples capture the imagination like a rapid strike from a venomous snake. However, almost nothing is known about strike performance of viperid snakes under natural conditions. We obtained high-speed (500 fps) three-dimensional video in the field (at night using infrared lights) of Mohave rattlesnakes ( Everything is eaten by something. Thus, predation is a fundamental force in ecology and evolution, structuring both mechanics of movement within an organism and the web of interacting organisms within an ecosystem1345678Predation can be a relatively rare event; it is also unpredictable and often extremely fast. Many predators attempt to maximize their chance of success by ambushing prey with high-speed pursuit or attack behaviors10111214151618Crotalus scutulatus) and Merriam’s kangaroo rat (Dipodomys merriami). Mohave rattlesnakes specialize on kangaroo rats, incorporating them as the bulk of their diet32C. scutulatus and evasive performance of D. merriami? We predict that performance of rattlesnakes will be higher in nature compared to laboratory conditions due to the potential for lower stress and natural prey behavior. We predict high acceleration from kangaroo rats in order to evade rapid strikes. (2) What factors determine the success of a strike or an escape? We predict that the evasive maneuvers from kangaroo rats will define the outcome of the interactions . In these two cases, the time between initiating the escape response and completely clearing the trajectory of the snake was 24 ms and 30 ms. The basis for successful strikes was also variable. For example, one successful strike occurred when the kangaroo rat was stationary and facing away from the snake, likely precluding the use of vision for detecting the snake. However, another successful attempt involved the kangaroo rat facing the snake in a stationary position. Although the kangaroo rat did begin to initiate an escape response, the strike was initiated 4.8 cm from the kangaroo rat and the entire event (from beginning of snake movement until contact) lasted 78 ms. Finally, one hit occurred while the kangaroo rat was in mid air  and the rattlesnake captured it before it could land and escape.We recorded four successful prey capture attempts and four missed attempts, with average snake body temperatures of 23.1 ± 1.5 °C during the strikes. One missed strike could not be analyzed quantitatively, and so was retained only for general observations. Snake performance was variable and, in several cases, very high. There was a wide range of strike speeds and accelerations , and theThis study is the first to quantify the biomechanics of natural predator-prey interactions between rattlesnakes and their prey. In fact, studies that quantify the dynamics of prey capture by vertebrates in the wild are rare, often tracking the predator via accelerometers or animal-borne cameras  and non-venomous ratsnakes (Pantherophis obsoletus), and found that strike performance was not different between themC. scutulatus) have the ability to greatly exceed the defensive strike speeds and accelerations observed in laboratory studies: The maximum velocities achieved during successful (4.2 ms−1) and unsuccessful (4.8 ms−1) strikes were 19% and 36% faster, respectively, than the maximum values observed by ref. −2, 30% greater than the maximum observed by ref. C. atrox, and observed maximum acceleration (878 ms−2) and velocity (5.5 ms−1) values during predatory strikes that exceeded those of our studyC. atrox strikes in nature, so it is unclear whether the laboratory trials represent comparable values to snakes in nature. Regardless, we argue that strikes of snakes in nature must be quantified before any definitive conclusion concerning relative performance is made. There are a number of reasons to expect higher (and more variable) performance in nature, including lack of stress, elevated motivation, natural light levels and temperature, and natural prey behavior.A recent study examined defensive strikes from both vipers (Zapus trinotatus)D. merriami in our study were larger and reached velocities that were almost 50% greater  consume other species of kangaroo rat (e.g. D. deserti)We propose that the rattlesnake-kangaroo rat system is a model system for studying the dynamics of high-power predator-prey interactions, given that they can be observed (with some effort) under completely natural conditions. This system could be used to test a number of important questions about predation success and prey escape responses in nature. Given the extreme performance on the part of the snakes and kangaroo rats, it is very likely that elastic energy storage is important for both species in circumventing the limits of neuromuscular function. Future studies should address this possibility. Finally other species of rattlesnake  were caught near Rodeo, New Mexico during the summer of 2015. Three were female and three were male. Snakes were anesthetized and implanted with a small, temperature-sensitive radiotransmitter as in ref. −1 were oriented towards the snake in order to capture three-dimensional motions. Following a strike attempt, a large calibration object with known dimensions encompassing the volume of the predator-prey interaction was placed in the field of view in order to obtain 3D coordinates. The fields of view were illuminated using multiple infrared flood lights (CMVision LED Infrared Illuminators), providing illumination for cameras to visualize the snake and kangaroo rat under natural ambient light conditions. Both successful and unsuccessful strikes were recorded.Six Video files were converted from MOV to AVI and digitized in MATLAB 2013 using DLTDV5How to cite this article: Higham, T. E. et al. Rattlesnakes are extremely fast and variable when striking at kangaroo rats in nature: Three-dimensional high-speed kinematics at night. Sci. Rep.7, 40412; doi: 10.1038/srep40412 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Sphingosine-1-phosphate (S1P) is a pleiotropic bioactive lipid mediator that regulates many physiological and pathological processes. It has been suggested that S1P gradient with high concentrations in the blood and lymphatic fluid and low concentrations in the peripheral tissue plays important roles in immune cell trafficking and potentially cancer progression. However, only a few reports have assessed S1P levels in the lymphatic fluid due to lack of an established easy-to-use method. Here, we report a simple technique for collection of lymphatic fluid to determine S1P.Lymphatic fluid was collected directly with a catheter needle  or was absorbed onto filter paper after incision of cisterna chyli (new method) in murine models. Blood, lymphatic fluid and mesenteric lymph nodes were corrected from wild type and sphingosine kinase 2 (SphK2) knockout mice to determine S1P levels by mass spectrometry.The volume of lymphatic fluid collected by the new method was at least three times greater than those collected by the classical method. S1P concentrations in lymphatic fluid are lower than in blood and higher than in lymph nodes. Interestingly, S1P levels in lymphatic fluid from SphK2 knockout mice were significantly higher than those in wild type, suggesting an important role of SphK2 and/or SphK1 to regulate S1P levels in lymphatic fluid.In agreement with the previous theory, our results confirm “S1P gradient” among blood, lymphatic fluid and peripheral lymphatic tissues. Convenient methods for collection and measurement of sphingolipids in lymphatic fluid are expected to provide new insights on functions of sphingolipids. The HPLC grade solvents were obtained from VWR .2.2All animal studies were conducted in the Animal Research Core Facility at VCU School of Medicine in accordance with institutional guidelines. We utilized SphK2 knockout mice as models since they are well characterized, and because we have previously found that SphK2 knockout mice demonstrate compensatory higher expression of SphK1 in the tissues 2.3To increase the lymphatic flow in the abdomen, 200 μl of mineral oil was administered by gavage to mice 2 h prior to euthanasia. At that time, the abdomen was opened under anesthesia. Lymphatic flow was confirmed by the white lucent color in the intestinal trunk and the cisterna chyli under stereomicroscopy . The int2.4Whole blood was collected from inferior vena cava (IVC), and 40 μl immediately transferred into 500 μl of methanol in a glass tube for measurement of sphingolipids. The remaining collected blood from the IVC was left undisturbed at room temperature, allowing the blood components to coagulate. Serum was then collected by centrifuging at 2,600 x g for 10 min in a refrigerated centrifuge.2.5Fluid absorbed onto filter paper was placed in 13 × 100 mm borosilicate tubes with a Teflon-lined cap . Methanol (1 ml) and chloroform (0.5 ml) were added along with the internal standard cocktail. The contents were dispersed by sonication at room temperature for 30 s and incubated at 48 °C overnight. KOH (1 M) in methanol (75 μl) was added and, after brief sonication, samples were incubated in a shaking water bath for 2 h at 37 °C to hydrolyze glycerophospholipids. The extract was brought to neutral pH with 6 μl of glacial acetic acid, centrifuged using a table-top centrifuge, and the supernatant was removed and dried in a Speed Vac. The dried residue was reconstituted in 0.5 ml of the starting mobile phase solvent, briefly sonicated and then centrifuged for 5 min in a tabletop centrifuge before transfer of the clear supernatant to the autoinjector vials.2.6For LC-ESI-MS/MS analyses, a Shimadzu LC-20 AD binary pump system coupled to a SIL-20AC autoinjector and DGU20A3 degasser coupled to an ABI 4000 quadrupole/linear ion trap (QTrap)  operating in a triple quadrupole mode was used. Q1 and Q3 were set to pass molecularly distinctive precursor and product ions (or a scan across multiple m/z in Q1 or Q3), using N2 to collisionally induce dissociations in Q2 (which was offset from Q1 by 30–120 eV).3OH/H2O/HCOOH, 58/41/1, v/v/v, with 5 mM ammonium formate) and 5% mobile phase B1 , and after sample injection , the A1/B1 ratio was maintained at 95/5 for 2.25 min, followed by a linear gradient to 100% B1 over 1.5 min, which was held at 100% B1 for 5.5 min, followed by a 0.5 min gradient return to 95/5 A1/B1. The column was re-equilibrated with 95:5 A1/B1 for 0.5 min before the next run.Sphingolipids were separated by reverse phase LC using a Supelco 2.1(i.d.)x50 mm Ascentis C18 column  and a binary solvent system at a flow rate of 0.5 ml/min. Prior to injections, the column was equilibrated for 0.5 min with a solvent mixture of 95% mobile phase A1  with a filter paper absorption method after opening the cisterna chyli by incision with the bevel of a needle after adequate exposure of the lymphatic vessel (new method) . The papS1P levels were measured by mass spectrometry in the lymphatic fluid collected either by direct aspiration or by paper absorption A. The vo3.2Next, we tested the theory of S1P gradient among serum, lymphatic fluid, and peripheral tissue such as MLN utilizing our new method to measure S1P in the lymphatic fluid. Levels of Sph, DHSph, S1P, and DHS1P in whole blood, serum, lymph, and MLN from WT mice were determined by LC-ESI-MS/MS . S1P and3.3The relative contributions from each of the SphK isoforms to lymphatic fluid and tissue levels of S1P have not been fully elucidated. To this end, we next examined the effects of deletion SphK2. In agreement with previous reports , 28, S1P4High levels of S1P in blood and lymphatic fluid are critical for maintenance of tone and integrity of the vascular endothelium. The S1P gradient between high levels in the circulation and the low levels in tissues due to the presence of S1P degrading activity from phosphatases and S1P lyase is important for immune cell trafficking , 32. In The most commonly described technique for lymphatic fluid collection is by cannulation of lymphatic vessels , 33, 34.SphK1 which is localized mainly in the cytosol and translocated to the plasma membrane upon stimulation by cytokines and growth factors, is important for export of S1P and regulation of its extracellular levels , 35. In 5We determined the levels of S1P in lymphatic fluid, which is lower than blood and higher than lymph nodes. In agreement with the previous theory, our results confirm the “S1P gradient” among blood, lymphatic fluid and the peripheral lymphatic tissues. Convenient methods for collection and measurement of sphingolipids in lymphatic fluid are expected to provide new insights on functions of sphingolipids.Masayuki Nagahashi: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.Akimitsu Yamada, Tomoyoshi Aoyagi: Performed the experiments.Jeremy Allegood: Performed the experiments; Contributed reagents, materials, analysis tools or data.Toshifumi Wakai: Analyzed and interpreted the data.Sarah Spiegel: Analyzed and interpreted the data; Wrote the paper.Kazuaki Takabe: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.This work was supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research Grant Number 15H05676 and 15K15471 for Masayuki Nafahashi, and 15H04927 and 16K15610 for Toshifumi Wakai. Masayuki Nagahashi is also supported by the Uehara Memorial Foundation, Nakayama Cancer Research Institute, Takeda Science Foundation, and Tsukada Medical Foundation. Kazuaki Takabe is supported by NIH/NCI grant R01CA160688 and Susan G. Komen Investigator Initiated Research Grant IIR12222224.The authors declare no conflict of interest.No additional information is available for this paper.


As only a minor portion of the information present in histological sections is accessible by eye, recognition and quantification of complex patterns and relationships among constituents relies on digital image analysis. In this study, our working hypothesis was that, with the application of digital image analysis technology, visually unquantifiable breast cancer microarchitectural features can be rigorously assessed and tested as prognostic parameters for invasive breast carcinoma of no special type.Digital image analysis was performed using public domain software (ImageJ) on tissue microarrays from a cohort of 696 patients, and validated with a commercial platform (Visiopharm). Quantified features included elements defining tumour microarchitecture, with emphasis on the extent of tumour-stroma interface. The differential prognostic impact of tumour nest microarchitecture in the four immunohistochemical surrogates for molecular classification was analysed. Prognostic parameters included axillary lymph node status, breast cancer-specific survival, and time to distant metastasis. Associations of each feature with prognostic parameters were assessed using logistic regression and Cox proportional models adjusting for age at diagnosis, grade, and tumour size.p = 0.003 for a 1-SD increase in nest number, OR = 0.75, p = 0.006 for mean nest area). Nest number was also associated with survival (hazard ratio (HR) = 1.15, p = 0.027), but total nest perimeter was the parameter most significantly associated with survival in luminal tumours . In the relatively small cohort of triple-negative tumours, mean circularity showed association with time to distant metastasis  and survival .An arrangement in numerous small nests was associated with axillary lymph node involvement. The association was stronger in luminal tumours (odds ratio (OR) = 1.39, We propose that tumour arrangement in few large nests indicates a decreased metastatic potential. By contrast, organisation in numerous small nests provides the tumour with increased metastatic potential to regional lymph nodes. An outstretched pattern in small nests bestows tumours with a tendency for decreased breast cancer-specific survival. Although further validation studies are required before the argument for routine quantification of microarchitectural features is established, our approach is consistent with the demand for cost-effective methods for triaging breast cancer patients that are more likely to benefit from chemotherapy. Breast cancer is the most common cancer in the UK, with a lifetime risk around 1 in 8 women , breast Pathological assessment is the gold standard for surgical and oncological treatment decision making, as tumour morphology remains the strongest predictor of clinical outcome and the financially and practically preferred option , 4. In vAs only a minor portion of the vast amount of information present in histological sections is accessible by eye, recognition and quantification of complex patterns and relationships among constituents relies on computer-aided quantitative digital image analysis (DIA). This approach has the potential to go beyond automation and standardisation of established morphological parameters –13. In hThe working hypothesis for our study was that, with the application of DIA technology, previously unquantifiable breast cancer microarchitectural features can be rigorously assessed in detail and tested as prognostic parameters for invasive carcinomas NST. Special attention was given to conceivable differences in the four subgroups deriving from expression of oestrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2). Quantified features included elements defining the extent of tumour-stroma interface, the arena of tumour-stroma interactions implicated as determinants of cancer progression. In addition, features reflecting the remarkably variable microgeometry of invasive carcinomas NST were analysed. The biological rationale for pursuing these features was based on studies in experimental models implicating the microarchitectural arrangement of breast cancer cells as an indicator of their transcriptomic profile  and a prTo test the hypothesis, DIA was initially performed using the public domain software ImageJ –18 on tiA cohort of 957 adult breast cancer patients with clinical, histopathological, and outcome data was collated in Nottingham between 1987 and 1998. TMAs were assembled using archival diagnostic formalin-fixed paraffin embedded tumour blocks from patients in the cohort as previously described . In the TMA sections were stained with cytokeratin 7/8 immunohistochemistry to highlight tumour cells and counterstained with haematoxylin as described previously .Digitised images of the slides were acquired using the high-resolution digital scanner . Slides were scanned at × 20 magnification and images were saved in the NDPI format. For the initial analysis, virtual slides were opened in the NDP.view 2 software (Hamamatsu) and individual tissue spots were exported as JPEG (8-bit) files at × 100 magnification (1272 × 944 pixels). Cut-out spots and spots with < 50% remaining tissue were excluded from the image analysis. For the validation stage, TMA whole slides were opened in the VIS software package (Visiopharm) and de-arrayed into spots using the Tissuearray module.2) set between 30 and infinity. A cut-off of 30 was taken as this is slightly greater than the area of a single cell since our aim was to include all tumour cell aggregates (“nests”) ranging between single cell and large groups that involved the major part of the TMA spot area. Any particles with a size < 30 were discarded as background (set to 255). Internal object holes  were filled and thus included as forming a part of any particle in which they were found. Features were extracted by measuring the pixel numbers for any particles in the final image. Features extracted included number of particles present in the image (taken to be the number of tumour nests), combined particle surface area in total pixels , mean particle size in pixels (taken to be the average tumour nest size), combined perimeter in pixels (the number of pixels forming the one pixel thick border of any particle) for all particles , mean particle circularity (4π(area/perimeter2))—an ImageJ in-built function which is stated to equate to particle smoothness (taken to be the average nest perimeter smoothness)—and mean particle roundness—also an ImageJ function which is a ratio of longest diameter to shortest diameter in pixels of any particle (taken to be the average roundness of a tumour nest). Data points were recorded in pixels and exported to Microsoft Excel alongside clinical outcome data.The initial image analysis was carried out using the Fiji package of ImageJ (NIHR public domain). Individual tissue spot JPEG files were imported into ImageJ and the image was split into RBG channels. The image segmentation of tumour from non-tumour was carried out on the blue colour space; the red and green channels were discarded. The segmentation to identify objects of interest was carried out by adjusting the greyscale data to a binary image with foreground pixels of interest set to black  and background pixels set to white (maximum 8-bit intensity of 255). The pixel intensity threshold for classifying all pixels as either foreground or background was defined automatically using the ImageJ histogram thresholding feature. TMA spots with weak or no immunohistochemical staining (therefore not segmented successfully with thresholding) were excluded from the study at this point. Objects of interest (particles) were identified as any co-localised foreground pixels (i.e. adjacent pixels with no intervening background) with a particle size  increase, where appropriate, and as categorical to assess the shape of the associations, grouped in tertiles such that there was a sufficient number of observations in each group. For analysis of associations between tumour microarchitectural features with BCSS and TTDM, only cases that did not receive chemotherapy were selected. For BCSS, individuals were censored at last follow-up. For TTDM, individuals were censored at the latest date known to be alive and free of distant metastasis. Analyses were repeated in subgroups defined by ER and HER2 status and in subgroups defined by tumour grade. Features were used both as categorical and as numeric variables, log transformed where appropriate. Analyses were performed using R .Tumour microarchitectural features quantified by ImageJ and by Visiopharm were compared by inspecting scatterplots and correlation coefficients.+/HER2–, 9% ER+/HER2+, 8% ER–/HER2+ and 11% were ER–/HER2–. The mean age at diagnosis was 53.5 years and the mean tumour size was 20.3 mm. From the 667 patients with available information on the axillary lymph node status at the time of operation, 286 patients (43%) had at least one involved lymph node and 381 had clear axilla. During the postoperative follow-up period, 242 died from breast cancer, 319 were alive, and 106 died from unrelated or unknown causes. From the 664 patients with available information, 258 patients developed distant metastasis whereas 406 did not.Data were successfully extracted from the TMAs of 696 patients as described in the p = 0.035). It was a very strong predictor for breast cancer-specific death (adjusted hazard ratio (HR) per unit increase 1.69, p = 1.9 × 10–6) and distant metastasis  in individuals who did not receive chemotherapy.In the complete cohort, tumour grade was associated with lymph node status adjusting for age and tumour size (adjusted odds ratio (OR) per unit increase 1.28, p = 2.9 × 10–9). It was also strongly associated with breast cancer-specific death  and distant metastasis  in individuals who did not receive chemotherapy.In the complete cohort, tumour size was significantly associated with lymph node status adjusting for age and tumour grade . In the ER+/HER2– subgroup, the association was stronger compared to the whole cohort . The association in the ER+/HER2– subgroup was significant in grade 2 tumours . In grade 3 tumours there was no significant association. The other three subgroups derived from ER/HER2 combinations showed no significant associations between number of nests and nodal status.Multivariable logistic regression analysis in the complete cohort showed an association of number of nests with lymph node status adjusting for grade and tumour size. Higher number of nests was associated with positive lymph node status  1.02 to 1.42; p = 0.06). Higher mean nest area was associated with negative lymph node status in the ER+/HER2– subgroup (OR per 1 SD higher log(mean area): 0.75, 95% CI 0.61 to 0.92; p = 0.006), and the association was even stronger in the grade 2 ER+/HER2– subgroup : 0.59, 95% CI 0.42 to 0.84; p = 0.003; Figs. Multivariable logistic regression analysis in the complete cohort showed that mean nest area was borderline significantly associated with lymph node status (OR per 1 SD higher log(mean area): 0.85, 95% CI 0.72 to 1.01; p = 0.06). Higher mean nest perimeter was associated with negative lymph node status in the ER+/HER2– subgroup (OR per 1 SD higher log(mean perimeter): 0.77, 95% CI 0.63 to 0.94; p = 0.01), and the association was even stronger in the grade 2 ER+/HER2– subgroup (OR 1 SD higher log(mean perimeter): 0.60, 95% CI 0.43 to 0.85; p = 0.003; Fig. Like mean nest area, mean nest perimeter was borderline significantly associated with lymph node status in the complete cohort (OR per 1 SD higher log(mean perimeter): 0.85, 95% CI 0.72 to 1.01; p = 0.033). This correlation was even stronger in grade 2 tumours .Multivariable logistic regression analysis in the whole cohort showed an association between mean nest roundness and lymph node status adjusting for grade and tumour size. Higher nest roundness was associated with positive lymph node status  and stronger associated in grade 2 tumours .Mean circularity was borderline significantly associated with lymph node status in the whole cohort . The association was significant only in the ER+/HER2– subgroup , and in this subset tumour size was not significantly associated with BCSS , but the size of this subset was relatively small (n = 108).Analysis of the whole cohort using Cox proportional hazard models adjusted for grade and tumour size showed an association between number of nests and BCSS. A higher number of nests was associated with a lower survival , the number of nests showed a certain trend  in this relatively small subset (n = 69). HER2+ tumours did not show analogous trend .In the triple-negative subgroup (ERMean nest area and mean nest perimeter were not associated with BCSS.p = 0.003). This association was only seen in the ER+/HER2– subset  = 1.26, 95% CI 1.07 to 1.49; p = 0.005; Figs. p = 0.003) and size .Analysis of the whole cohort of patients who did not receive chemotherapy using Cox proportional hazard models adjusted for grade and tumour size showed an association between total nest perimeter and BCSS. Higher total nest perimeter was associated with a lower survival probability  = 1.22, 95% CI 1.07 to 1.39; –/HER2–), mean circularity was associated with BCSS. Higher nest circularity was associated with a lower survival , whereas grade only reached a borderline association (p = 0.048) and tumour size showed no prognostic correlation (p = 0.67) with survival in this subset.In the triple-negative subgroup .Using Cox proportional hazards models adjusted for grade and tumour size, there was a significant association between number of nests and TTDM in grade 1 tumours , whereas grade and tumour size were only borderline significantly associated  with TTDM in this subset.Similar to BCSS, triple-negative cancers showed a trend for cases with higher number of nests to present with higher risk of distant metastasis  developed by Beck et al. which implements a data-driven approach with minimal user direction . The fea+/HER2– tumours, a somewhat superfluous group of invasive ductal carcinomas. It finally relates to our aspiration for identifying features with prognostic value in triple-negative cancers, a subgroup lacking reliable prognostic markers. To the extent of our knowledge, our study is also the first to deal with the association between tumour nest microarchitecture and axillary lymph node involvement.We believe that our current study is the first to address the differential prognostic impact of tumour nest geometry in the four subgroups that derive from combinations of ER and HER2 expression. The rationale behind this approach was based on the evidence that ER and HER2 expression profiles endow tumours with different biological properties. It also reflects our aim to exploit microarchitectural features as potential dividers of grade 2 ERMetastasis is a complex process comprising multiple interactions that enable tumour cells to migrate through the stromal microenvironment and gain access to the lymphovascular compartment. The two main types of cell migration described in vitro are single-cell migration/multicellular streaming, in which the cytoskeleton of each cell acts independently for traction force generation, and collective cell migration and invasion, in which the junctions between the cells are retained . Several+/HER2– tumours mean nest area, but not total nest perimeter, affects predisposition for lymph node involvement, although it is not prognostic for BCSS. On the other hand, total nest perimeter rather than mean nest area affects BCSS but not lymph node status. This disparity is quite intriguing. For a given nest size, a longer perimeter implies a more outspread arrangement, with more tumour cells intimately contacting the tumour stroma. For this reason, total tumour nest perimeter can be considered as a more explicit marker of the extent of tumour-stroma interface and cross-talk. Our data indicate that this is the most highly significant tumour microarchitectural predictor of survival, consistent with the previously proposed role of the tumour-stroma interface in the pathogenesis of breast cancer metastasis, and its promise as a therapeutic target [We found that in ERc target . As note– and HER2+ tumours might rely to a varying degree on their extent of tumour-stroma interface.The potency of stroma in affecting the phenotype and physiological behaviour of malignant cells is highlighted by findings such as upregulation of epithelial-mesenchymal transition-associated proteins such as N-cadherin  and SnaiWe certainly take note of the prognostic associations between mean nest roundness/circularity and lymph node involvement. Perhaps even more interestingly, we notice the association between mean nest circularity and TTDM/BCSS in triple-negative tumours, a subgroup of breast cancers where prognostic markers are needed for subclassification and oncological practice support . However+ and triple-negative cases. While certain associations and trends were observed in the latter, larger numbers of cases are necessary for definite conclusions to be drawn. A further limitation is the use of a relatively old cohort of patients, some of whom were treated according to former guidelines. However, the benefit of a long follow-up is obviously an invaluable merit. A lesser weakness in our study was the variability in the intensity of immunostaining. Although the vast majority of included cases showed strong and uniform pattern of cytokeratin 7/8 expression, there was certainly a requirement for all cores to be visually evaluated by a pathologist before and after the image analysis. Assessment by a pathologist was also necessary to exclude occasional normal ducts and ducts involved by ductal carcinoma in situ (DCIS).Our paper has certain limitations that should be addressed in subsequent studies. Analysis was carried out in TMAs, which only partly disclose the tumour entireness. This limitation is potentially more significant for mixed tumours. As indicated above, studies in whole tissue sections have the potential to reveal intratumoural heterogeneity that might be crucial in refining the prognostic significance of certain microarchitectural features. The clinical implications of breast cancer heterogeneity assessment with a digital methodology is elegantly demonstrated in studies on the prognostic significance of the spatial distribution of tumour infiltrating lymphocytes , 36. AnoTaking into consideration the findings and data from experimental platforms, the evidence suggests that tumour arrangement in few large nests indicates a decreased metastatic potential, possibly related to low motility. On the other hand, arrangement in numerous small nests or as single cells provides the tumour with increased metastatic potential to regional lymph nodes, which is theoretically related to increased availability of and response to motility factors. An outstretched pattern with extensive tumour-stroma interface bestows tumours with a tendency for distant metastasis and deriving increased likelihood of breast cancer-specific death. This is conceptually related to co-occurrence of additional factors, with TGFβ being a prime candidate. We are currently in the process of correlating our histomorphometric data with expression profiles of selected tumour motility-associated proteins and other microenvironmental components including tumour-infiltrating lymphocytes.The accordance of our findings with previous studies together with our demonstration of highly concordant data between the public domain and the commercial software provides strong evidence for the validity and genuine prognostic significance of tumour microarchitectural feature analysis. Further validation studies, especially on whole tissue sections where the impact of tumour heterogeneity and the potential significance of hotspots could be suitably addressed, are required before the argument for routine application of microarchitectural feature quantification along grading is firmly established. We strongly believe that this digital image analysis approach deserves consideration, especially in view of the high demand for cost-effective methods for triaging breast cancer patients that are more likely to benefit from chemotherapy.Histological sections provide an ideal platform where cancer can be studied within its native milieu, a clear advantage over other methodologies, especially in view of the cross-talk between malignant cells and their microenvironment. We hope that this paper has the potential to raise attention to the fact that, although histological sections do not enjoy the advantage of in-vitro systems in actively experimenting with the impact of specific parameters in tumour progression, they have the capacity to reveal spatial inter-relationships within multiparametric real-life settings, as in tumours of individual patients. It is exactly in the elucidation of such complex systems where digital image analysis can play a key role, not merely facilitating analysis but, in fact, bringing to light correlations inaccessible to perception and poorly denoted by semi-quantitative approaches.


The continued application of our longitudinal cellular and molecular imaging tools will yield a better understanding of the mechanism(s) by which this physiological inhibition (CTR) and/or enhancement (CTE) occurs.The mechanisms that influence metastatic growth rates are poorly understood. One mechanism of interest known as concomitant tumour resistance (CTR) can be defined as the inhibition of metastasis by existing tumour mass. Conversely, the presence of a primary tumour has also been shown to increase metastatic outgrowth, termed concomitant tumour enhancement (CTE). The majority of studies evaluating CTR/CTE in preclinical models have relied on endpoint histological evaluation of tumour burden. The goal of this research was to use conventional magnetic resonance imaging (MRI), cellular MRI, and bioluminescence imaging to study the impact of a primary tumour on the development of brain metastases in a syngeneic mouse model. Here, we report that the presence of a 4T1 primary tumour significantly enhances total brain tumour burden in Balb/C mice. Using  One of the most common, as well as most fatal sites of metastatic growth for breast cancer patients is the brain, with the incidence of brain metastasis increasing and prognosis remaining poor2. Improved knowledge regarding how quickly cancer cells disseminate from the primary tumour and the rate of secondary metastases development, as well as the mechanisms that control proliferation rate, are key to developing new therapies to prevent or halt metastatic growth and prevent cancer mortality.Breast cancer is the second leading cause of cancer related deaths in North America with the majority of deaths due to metastasis, the dissemination of cancer cells from the primary tumour to other parts of the body4. The relevance of CTR has been shown by numerous observations of the removal of a primary tumour being followed by an abrupt acceleration of residual metastatic disease. CTR has been observed in both breast cancer patients5 and animal models of breast cancer6. It has also been observed in patients with other solid tumour types8. Conversely, it has been shown that the presence of a primary tumour can likewise increase metastatic outgrowth, a phenomenon coined concomitant tumor enhancement (CTE). However, in the clinic, very few examples of CTE have been reported with most of them being related to suspected regressions of hepatic and/ or pulmonary metastases following nephrectomy for renal cell carcinoma12. While imaging has been used to describe CTR/CTE effects in patients13, the majority of studies evaluating CTR/CTE in preclinical models have relied on endpoint histological evaluation of tumour burden15. The application of cellular and molecular imaging tools will allow the non-invasive and longitudinal visualization of metastatic progression to study CTR/CTE effects in vivo, which will ultimately yield better evaluation of putative molecular mechanism(s) by which this physiological inhibition and/or enhancement occurs. In turn, this may lead to new anti-metastatic therapeutics aimed at these mechanisms and their non-invasive evaluation.The mechanisms that influence metastatic growth rates are poorly understood. One mechanism of interest is concomitant tumour resistance (CTR), which describes the ability of the primary tumour to restrict the growth of distant metastases16. Cellular MRI involves pre-labeling cultured cancer cells with superparamagnetic iron oxide nanoparticles prior to transplantation into mice. This labeling allows transplanted cells to be tracked over time with iron-sensitive MRI techniques. Single cell imaging of cancer cells arresting in the brain at the time of injection and of non-dividing, iron-retaining cancer cells over time is achievable with this technique17. However, limitations of iron oxide based cellular MRI are that there is limited ability to differentiate between dead and viable cells, that it is possible for iron particles to be transferred to bystander cells such as macrophages upon cell death, and that the iron particles are diluted during cell division leading to loss of cell detection in proliferative cells. These limitations can be overcome using reporter gene based cell tracking tools, since a stably expressed reporter gene is passed on to daughter cells and will not be expressed in bystander cells. By engineering cells to express a luciferase reporter, bioluminescence imaging (BLI) can provide a direct readout of cell viability in dividing cell populations. Overall, as recently described18, combining our highly sensitive cellular MRI tools and BLI yields complementary information on the fate of metastatic cancer cells in preclinical models. As others have pointed to a role of the immune system in CTR/CTE and differences in primary tumour effects have been seen across models of the same cancer type8, the purpose of this study was to apply our multimodality imaging tools to study whether CTR/CTE effects are present in the syngeneic 4T1 immune competent mouse model of breast cancer metastasis.Previously, we applied cellular MRI techniques to study the impact of a primary tumour on metastatic outgrowth in an immune deficient mouse model of experimental breast cancer metastasis to the brain and demonstrated clear CTR effects in the brainLuciola Italica luciferase (Red-FLuc) and green fluorescent protein (GFP) using a commercial lentiviral vector . Cells were transduced and FACS sorted based on GFP expression using a FACSAria III flow cytometric cell sorter (BD Biosciences). The resultant 4T1BR5-Red-FLuc/GFP cells were maintained in DMEM containing 10% FBS and 1% antibiotics, at 37 °C and 5% CO2. For iron labeling, 2 × 106 4T1BR5-Red-FLuc/GFP cells were plated, and 24 hours later were incubated with 25 μg/mL of micron-sized superparamagnetic iron oxide (MPIO) beads for an additional 24 hours . Cells were washed three times with Hanks balanced salt solution (HBSS), collected and thoroughly washed three more times with HBSS to wash off residual unincorporated MPIO before in vitro evaluation or injection into animals.The 4T1-BR5 cells were received from Dr. Patricia Steeg’s lab and engineered to stably co-express red-shifted in vitro results are from three independent experiments with three replicates of each condition. To evaluate the relationship between cell number and BLI signal, 1 × 103, 5 × 103, 1 × 104, 1.5 × 105, and 5 × 105 4T1BR5-Red-FLuc/GFP cells were seeded in each well of 24-well plates. Twenty-four hours later, 5 μL of D-luciferin  was added to the growth medium and BLI images were collected 5 minutes later using a hybrid optical/X-ray scanner . BLI signal was measured with region-of-interest (ROI) analysis using LivingImage Software (Perkin Elmer). An ROI was drawn around each well to measure the average radiance (photons/second/mm2/steradian), and the mean average radiance across replicates was determined for each independent experiment.All 5 4T1BR5-Red-FLuc/GFP cells were seeded in each well of a 24-well plate. Twenty-four hours later, 25 μg/mL of MPIO was added for half of the wells. After an additional twenty-four-hour incubation period, the media for all wells was replaced with fresh media without iron, 5 μL of D-luciferin (30 mg/mL) was added for BLI, and BLI signal per well was analyzed as above.To evaluate if MPIO labeling influenced BLI signal, 1.25 × 103 4T1BR5-Red-FLuc/GFP or naïve 4T1BR5 cells were seeded in each well of 96 well plates and cells were evaluated 24 and 48 hours later. Twenty microliters of MTT solution was added to each well and absorbance at 450 nm was measured using a microplate spectrophotometer . Cell labeling efficiency was assessed by Perl’s Prussian blue (PPB) staining, as previously described19.Vybrant MTT proliferation assays were used to evaluate the effects of genetic engineering on cell proliferation. 6.25 × 102(length), for approximating the volume (mm3) of an ellipsoid21. MFP tumours grew for either seven days  or 14 days (Large MFP) prior to all mice receiving an intracardiac injection of 2 × 104 MPIO-labeled 4T1BR5-Red-FLuc/GFP cells in 0.1 mL of HBSS. Injections were performed under image guidance using a Vevo 2100 ultrasound system .Figure In Vivo Imaging System, PerkinElmer). Mice were anesthetized with isoflurane (2% in 100% oxygen) using a nose cone attached to an activated carbon charcoal filter for passive scavenging. Anesthetized mice received a 150 μL intraperitoneal injection of D-luciferin (30 mg/mL) and BLI images were captured for up to 35 minutes. On day 0, approximately one hour following intracardiac injection, whole body BLI was used to screen mice for successful intracardiac injection on day 0 and to measure brain BLI signal. Only mice with brain BLI signal proceeded to MRI .BLI was performed on days 0, 7 and 14 for all mice after intracardiac injection using a hybrid optical/X-ray scanner (IVIS Lumina XRMS 23. Mice were anesthetized with isoflurane (2% in 100% oxygen) and images were obtained using a 3D balanced steady state free precession (bSSFP) imaging sequence [Fast Imaging Employing Steady State Acquisition (FIESTA) on the GE system], which has been previously optimized for iron detection24. Small MFP and control mice were imaged on days 0, 7, and 14 and large MFP and control mice on days 0 and 14 after intracardiac injection. The scan parameters for day 0 were: repetition time (TR) = 8 ms; echo time (TE) = 4 ms; bandwidth (BW) = 41.7 kHz; flip angle (FA) = 35 degrees; averages (NEX) = 2; phase cycles = 4; matrix = 150 × 150; field-of-view (FOV) = 1.5; resolution: 100 × 100 × 200 μm; and scan time = 19.25 minutes. For days 7 and 14, a longer scan time was required for tumour detection using the following imaging parameters: TR = 10 ms; TE = 5 ms; BW = 12.5 kHz; FA = 35 degrees; NEX = 2; phase cycles = 8; matrix = 150 × 150; FOV = 1.5; resolution = 100 × 100 × 200 μm; scan time = 36.76 minutes. In addition, large MFP mice and corresponding control mice (n = 16), a 3D bSSFP imaging sequence was used to obtain high resolution MR images of the whole mouse body on day 9 using a solenoidal whole-body radiofrequency coil. Sequence parameters were as follows: TR = 6.3 ms; TE = 3.1 ms; BW = 31 kHz; FA = 35 degrees; NEX = 2; phase cycles = 8; matrix = 300 × 150; FOV = 60 × 30 mm; resolution = 200 × 200 × 200 μm3; and scan time = 22 minutes. One large MFP mouse and one control mouse also had whole body MR scans at endpoint (day 14) to allow us to match MR and BLI detectable metastases to whole-mouse cryo-fluorescence images as described below.MRI was performed on a 3 T GE clinical MR scanner  using custom-built gradient and solenoidal mouse brain radiofrequency coils2/sr) was measured, and the peak value over the 35-minute imaging session was used for each mouse at each time point. Whole body BLI signal was measured the same way as listed above with the ROI drawn around the whole mouse body. MRI images were analyzed using OsiriX software . For days 0 and 7 images, total brain signal void number, representing iron labeled cancer cells, was determined by manually counting voids in every MR slice. For day 14 images, brain metastases were manually traced in every bSSFP image slice for each mouse and 3D tumour volumes were reconstructed using the OsiriX volume algorithm.Brain BLI signal was measured with region-of-interest (ROI) analysis using LivingImage Software (Perkin Elmer). An ROI was drawn around the brain, the average radiance . Block-face images were collected with an in-plane resolution of 10.5 × 10.5 μm2. Brightfield and fluorescent images were acquired, stitched together and visualized using proprietary software .After endpoint MR imaging (day 14), one large MFP and one control mouse were sacrificed by isoflurane overdose and then flash frozen in OCT freezing medium by liquid nitrogen immersion. The entire mouse was sectioned and optically imaged every 50-μm using a cryo-fluorescence imager  were sacrificed by isoflurane overdose and perfused with 4% paraformaldehyde via the left ventricle. Mouse brains were removed and cryopreserved in ascending concentrations of sucrose  for 24 hours each. Brains were immersed in optimal cutting temperature (OCT) compound, oriented in a sectioning plane parallel to that of MRI, and frozen using liquid nitrogen. Contiguous 10-μm frozen sections were collected and select sections were stained with hematoxylin and eosin (H&E). Stained sections were imaged using an Invitrogen EVOS FL Auto Cell Imaging System and histological images were manually matched to MR slices using anatomical landmarks such as the ventricles and MR-visible tumours. Fluorescence images of 4T1BR5-Red-FLuc/GFP cancer cells were also collected on the same microscope and matched to MR slices. Spleens were collected and weighed. Contiguous 10-μm paraffin embedded sections were collected, select sections were stained with H&E, and imaged using a Zeiss 510 laser scanning confocal microscope.in vitro data was not normally distributed and thus, a non-parametric test (Mann-Whitney) was performed on proliferation experiments. Pearson’s rank correlation was used to determine a relationship between cell number and BLI signal. Student’s two-tailed unpaired t test was used to compare the other in vitro experiments as well as between animal groups. A nominal p-value less than 0.05 was considered statistically significant.A power analysis was performed using G*Power software to determine the appropriate sample size for this study. All statistics were calculated using GraphPad Prism 4. A Shapiro-Wilk normality test found that some of our All data generated or analyzed during this study are included in this published article (and its Supplementary Information files).7 ± 1.37 × 106 p/s/mm2/sr) compared to those that were not labeled (2.48 × 107 ± 1.80 × 106 p/s/mm2/sr)  Suppl. . Further4 ± 8.5 × 103 p/s/mm2/sr) and without (4.7 × 104 ± 7.8 × 103 p/s/mm2/sr) a small primary MFP tumour, nor with (2.9 × 104 ± 6.8 × 103 p/s/mm2/sr) or without (3.3 × 104 ± 8.7 × 103 p/s/mm2/sr) a large primary MFP tumour  shown in Fig. our Fig. .3) and 14 . Similarly, we found a significant difference in Large MFP tumour volume between days 0 (308.4 ± 9.14 mm3) and 14 (817.9 ± 15.12 mm3).All MFP tumors were manually assessed with caliper measurements throughout the study. We found a significant difference in Small MFP tumour volume between days 0  to 7 (1.5 × 104 ± 3.6 × 103 p/s/mm2/sr) and for small MFP mice from day 0 (4.4 × 105 ± 8.5 × 103) to day 7  had significantly higher total brain tumour volume than control mice (0.72 ± 0.17 mm3)  had significantly increased total brain tumour volume than control mice (1.65 ± 0.44 mm3)  Fig. . Similarm3) Fig. . We alsom3) Fig. .Figure 35 ± 5.1 × 104 p/s/mm2/sr) and without (4.3 × 104 ± 3.5 × 105 p/s/mm2/sr) a small MFP tumour  and without (1.1 × 104 ± 4.7 × 103 p/s/mm2/sr) a large MFP tumour  and without (4.5 × 106 ± 2.5 × 106 p/s/mm2/sr) a primary MFP tumour  and without (1.2 × 105 ± 7.9 × 104 p/s/mm2/sr) a large MFP tumour . Secondly, this effect was amplified when the primary tumour was larger at the time of secondary injection of experimental metastatic cells. To our knowledge, this is the first report of multimodality imaging being used to study the impact of a primary tumour on metastatic outgrowth in the 4T1 model.Clinical and experimental evidence suggests that a primary tumour can both accelerate (CTE) or retard (CTR) the growth of distant metastases16. For this study, MRI revealed fewer brain metastases developed in mice with than without a primary tumour. Interestingly, using cellular MRI, we also found that significantly more signal voids (representing non-dividing cancer cells) persisted in endpoint images of mice that had a primary tumour compared to mice that did not. This highlighted that a primary tumour can in part maintain cancer cell dormancy and mitigate overt metastases formation, a potential new mechanism for CTR. We could not assess endpoint signal void number in the current study due to the very low number of cells arresting and remaining in the brain at endpoint. In future studies, we could shorten the experimental timeline to enable a greater number of cells injected into the heart, which in turn could result in more voids persisting at endpoint.In our previous studies in immune compromised mice (nu/nu) we found the presence of a human primary breast tumour (MDA-MB-231) significantly inhibited the growth of MDA-MB-231BR brain metastases et al., found that immune competent mice are first protected and then made more susceptible to the growth of lung metastases by the presence of a progressively growing primary tumour7. Similarly, Vaage et al., showed that immunity decreases around day 20 after implanting tumour cells into mice and that this phenomenon was associated with an excess of tumour antigen, and that after removal of the primary tumour immunity is quickly restored25. This may provide support as to why we found an amplified CTE effect in our large MFP mice. This cohort of mice had 14 days of primary tumour growth  before secondary injection, followed by 14 days of metastatic growth. This theoretical 20-day period would fall fairly early (6 days) into the period of metastatic growth, leaving these mice vulnerable to enhanced metastatic growth due to decreased immunity. Whether the immune system is playing a significant role in the CTE effects seen in this study could be determined by moving our 4T1/4T1BR5 model from BALB/c mice into an immune compromised mouse , expecting either a CTR effect or a lesser CTE effect than seen in the present study. Similarly, we may see a more significant CTR effect by studying the MDA-MB-231/MDA-MB231BR model into more immune deficient mice than used before (nu/nu) such as nod-scid-gamma (NSG) mice, or evaluating whether a CTE effect might appear in humanized mice.Our results between the two models suggests the immune system may play a role in mediating CTR or CTE effects, as also suggested previously by others. Janik et al., found splenomegaly in tumour-bearing animals was associated with tri-lineage extramedullary hematopoiesis as well as a reduction in white pulp in their mouse model of CTR14. Other groups have also seen splenomegaly in tumour-bearing animals, specifically in the 4T1-model. Thus, differences in the ratio of red vs. white pulp in splenic tissue needs to be studied further to determine whether this is an effect of prolonged 4T1 tumor growth or an effect of CTE.An immunological component to the CTE effects seen is also suggested from our findings in the spleens from our animals. We found splenomegaly in tumour-bearing mice however, this finding was associated with a reduction in red pulp and not differences in white pulp. Kirstein et al., previously demonstrated secondary tumour growth can be either stimulated or inhibited depending on the ratio between the mass of the primary tumour relative to that of the secondary tumour implant. They found that high ratios tended to cause inhibition of secondary tumour growth while low ratios induced a stimulation effect8. For the present study, we injected our secondary cancer cell line at an early time point (7 days post MFP injection) when the primary MFP tumour is relatively small. We did this in an effort to best match the size of our MDA-MB-231 model at the time of secondary injection. However, despite similar primary tumour size at the time of secondary injection, these two models produced opposite effects (CTE/CTR). This may be due to differences in cell line aggressiveness; 4T1 metastases can be detected as early as 7 days whereas the less aggressive MDA-MB-231 model may take between 2–3 weeks before metastases start to form in the brain. This is significantly more time for the MDA-MB-231 primary tumour to grow (increasing the ratio between primary and secondary) to produce a CTR effect on secondary tumour growth. In the 4T1 model, as metastases quickly develop, the ratio between primary and secondary tumours becomes smaller over time. In the current study, we also injected a second cohort of animals at a late time point (14 days post MFP injection) when the primary tumour is relatively large to try to increase the ratio between primary and secondary tumour (producing a CTR effect). However, we found that increasing the size of the primary tumour at the time of secondary injection significantly amplified our CTE effect.Past studies have shown that the size of the primary tumour plays a key role in whether a CTE or CTR effect is observed. For instance, Bruzzo 26. Our multimodality imaging tools allow us to noninvasively monitor both the number (MRI) and viability (BLI) of arrested single cancer cells in the brain, allowing us to for the first time study the effect of a primary tumour on cancer cell arrest as well as cancer cell clearance. In the present study, we found there was not a significant difference in viable cancer cell arrest in the brain between mice with a primary MFP tumour and control mice. We also found that there was not a significant difference in cancer cell clearance from the brain, suggesting the presence of a primary MFP tumour does not influence the number of viable, iron labeled cancer cells that arrest in the brain at day 0, nor does it affect the clearance of these cells, indicating that in this model the increased tumour growth we detected in mice with primary tumors at endpoint is not due to effects at the early stages of the metastatic cascade. Another important step is the intravasation of cells from the primary tumour. We will explore this in the future by evaluating the effects of removal of the primary tumour on spontaneous metastasis formation.An important step in the metastatic cascade is the initial arrest of cancer cells. When cancer cells enter the circulation, they can travel through the bloodstream and arrest in the capillary beds of distant sites throughout the body and extrasvasate into the interstitial space where they can begin to form new tumours. For breast cancer patients, metastases are most commonly found in the brain, bone, lung and liver28. Although surprising, it should be noted that MRI and BLI are measuring two different tumour characteristics; MRI is measuring tumour volume, which is affected by many things including the number of tumour or stromal cells present and the presence of edema, amongst other things, whereas BLI is providing a measure of the viability of the engineered cancer cells over time. Moreover, BLI is not without its caveats, particularly when studying metastatic disease. First, BLI signal is depth dependent. Small shallow tumours will appear brighter or equivalent in signal to larger deep tumours. Hence, BLI should be used for monitoring relative tumour viability over time rather than considering BLI signal at any one-time point to represent a direct measure of absolute tumour burden. Second, necrosis may be present within some of the metastases. While our MRI tumour burden may be composed of both live and dead tumour tissue, BLI signal is representative of viable cancer cells only. Third, any areas of hypoxia in a tumour may demonstrate lower BLI signal due to the need for oxygen in the luciferase/luciferin reaction. Despite these caveats, our previous work has shown that the information provided by both imaging modalities over time makes them very complementary technologies for studying cancer metastasis18.BLI was performed at endpoint on all mice to measure viable tumour burden in both the brain as well as whole body. Although our MR analysis shows a strong CTE effect in the brain of both the small and large MFP mice, we only found significant differences in brain bioluminescence signal between mice with a large primary MFP tumour and control mice. Previous studies have also shown disagreement in tumour volume and BLI measurements at endpoint29. For example, Tao et al., shows that by week 6 after mammary fat pad injection of 1 × 106 4T1 cells, only 1 of 6 mice had BLI-detectable metastases in the brain. This is more than three times the number of cells that we are injecting for the current study and thus, we do not expect spontaneous metastases to form in our 4-week time line. In this study, we also show significant differences in endpoint tumour burden in the brain with BLI measures. If a large portion of the MRI tumour burden was from spontaneous metastases, we predict we would not have seen the differences we found in our BLI measurements.To account for the possibility of spontaneous metastases developing from our naïve primary cell line and contributing to the main findings in this study, our metastatic cell line was engineered to express GFP. As a result, we can confirm that any GFP-positive metastases came from our intracardiac injection of metastatic cells, whereas GFP-negative metastases are likely spontaneous metastases. In our endpoint histology, we did not find any GFP-negative metastases , suggesting that all brain metastases detected with MRI at endpoint came from our metastatic cell line and not the primary tumour. In addition, we performed a study where we injected 300,000 4T1-FLuc-GFP cells into the mammary fat pad of immune competent mice. We then monitored primary tumour growth over 28 days with BLI. In this 4-week period we were not able to detect distant metastases in the brain or any other organ. Since BLI is more sensitive than MRI, we can be fairly confident that there are also not any MR-detectable metastases at this time point. This has also been shown by other groups studying spontaneous metastasis of 4T1 cells with BLIin vivo BLI and MRI we could determine that these differences in endpoint tumour burden were not related to differences in the initial arrest or clearance of viable cells in the brain, which suggests that the presence of a primary tumour can increase the proliferative growth of brain metastases in this syngeneic 4T1 mouse model. Future work will utilize our imaging tools to explore the role of the immune system in promoting or preventing metastatic growth. Understanding the different immunological and/or molecular mechanisms of stimulation (CTE) versus inhibition (CTR) will be extremely valuable in finding new therapeutic options for breast cancer patients.In this work, we applied cellular and molecular imaging tools to evaluate the effect of a primary breast tumour and its size on the growth of brain metastases in the immune competent 4T1 mouse model. We found total brain tumour burden was significantly greater in mice with a primary MFP tumour  compared to those without. We also found that mice with a large MFP tumour had significantly more BLI signal in the brain at endpoint compared to control mice. Interestingly, using Supplementary Information