Many biological soft tissues are hydrated porous hyperelastic materials, which consist of a complex solid skeleton with fine voids and fluid filling these voids. Mechanical interactions between the solid and the fluid in hydrated porous tissues have been analyzed by finite element methods (FEMs) in which the mixture theory was introduced in various ways. Although most of the tissues are surrounded by deformable membranes that control transmembrane flows, the boundaries of the tissues have been treated as rigid and/or freely permeable in these studies. The purpose of this study was to develop a method for the analysis of hydrated porous hyperelastic tissues surrounded by deformable membranes that control transmembrane flows.For this, we developed a new nonlinear finite element formulation of the mixture theory, where the nodal unknowns were the pore water pressure and solid displacement. This method allows the control of the fluid flow rate across the membrane using Neumann boundary condition. Using the method, we conducted a compression test of the hydrated porous hyperelastic tissue, which was surrounded by a flaccid impermeable membrane, and a part of the top surface of this tissue was pushed by a platen. The simulation results showed a stress relaxation phenomenon, resulting from the interaction between the elastic deformation of the tissue, pore water pressure gradient, and the movement of fluid. The results also showed that the fluid trapped by the impermeable membrane led to the swelling of the tissue around the platen.These facts suggest that our new method can be effectively used for the analysis of a large deformation of hydrated porous hyperelastic material surrounded by a deformable membrane that controls transmembrane flow, and further investigations may allow more realistic analyses of the biological soft tissues, such as brain edema, brain trauma, the flow of blood and lymph in capillaries and pitting edema. Many body parts represent hydrated soft tissues that resembles a water filled sponge, consisting of the complex solid skeleton with multiple fine voids and fluid that fills the voids. Capillaries form a network of blood-filled vessels throughout the body, while cells contain various organelles and intracellular fluid filling the remaining space and the extracellular space is filled with extracellular fluid. In the head, the subarachnoid space (SAS) between the skull and the brain is filled with a trabecular network and cerebrospinal fluid (CSF) –5 Fig. . Most ofFinite element method (FEM) is an effective tool for the investigation and prediction of these phenomena. However, the complex micro-level solid-fluid boundaries in the hydrated soft tissues require microscopically fine meshes, making FEM analysis unrealistic. Therefore, FEMs based on the classical consolidation theory (or more rigorous and versatile mixture theory) have been developed. According to the biphasic theory, which is a mixture theory considering a two-phase mixture of solid and fluid phases, the movement of each microscopic component is treated separately. By considering the averaged interactions between them, the mixture is then macroscopically analyzed. Since hydrated biological tissues generally experience large deformations, Oomens et al.  developeThe aim of the present study was to develop an FEM for the analysis of hydrated biological soft tissues surrounded by deformable membranes which control the transmembrane flows. The transmembrane flow is regarded as the flow velocity component perpendicular to the membrane Fig. . In the Although the finite element formulations of the mixture theory have been developed to be applicable for multi-physics phenomena, such as charged hydrated tissues –19, we fIn the absence of body and inertial forces, the momentum equation of the mixture is ∇x represents the gradient operator with respect to the current configuration and σ is the Cauchy stress tensor for the mixture . For a fully saturated mixture with incompressible constituents, the Cauchy stress is where p is the fluid (pore) pressure, I is the rank-two identity tensor, and σE is the stress induced by solid deformation [σE corresponds to the so-called effective stress described in the classical consolidation theory [where ormation –10. The n theory , 10, 20.α∈{s,f} denotes a quantity related to a particular constituent α (s: solid and f: fluid). The original micro-volume, Ωt as: In the following equations, Jα is the determinant of the deformation gradient tensor, Fα 1) can be∇χ represents the gradient operator with respect to the reference configuration. An integral formulation can be obtained by multiplying Eq. 36) can bnu is the total number of nodes for u in the element, i-th node (see Appendix ui)\documentFext}, {Fint}, {Λext}, and {Λint} represent external force, internal force, external outward flow, and internal outward flow, respectively. Here, the external outward flow {Λext} is the value provided as the Neumann boundary condition representing the characteristics of the membrane which surrounded the mixture. For spatial integration, the Gauss integration can be used. As where the equivalent nodal vectors {Δt. By using the backwards Euler algorithm, t+Δt can be replaced: Since these systems of equations are generally nonlinear, we solved them by using an iterative method, Newton-Raphson, within each time increment t are known and represented as ut and pt, they are adopted as the initial predictors u(0) and p(0) for the iterative scheme to obtain the solution at time t+Δt: where the subscripts indicate time. The algorithm of the iterative method is presented in Fig. k-th predictors uk)( and pk)( are The Δuk)( and Δpk)( and Δpk)( and {Λint}k−1)( and pk−1), an initial solid volume fraction κ=κI (κ=8.0 m4/N-s). As indicated in Eqs. λ=0.0 andext}=0, which has not ever been represented using the FEMs formulated by Suh et al. [πT·N=0. The displacements perpendicular to the surface were confined at the sides and base. We prepared two types of meshes, Mesh A and Mesh B, which consisted of 1200 and 4800 5/4c displacement-pressure elements, respectively. Three different sets of time steps and mesh types used for the numerical examples are presented in Table One of the simplest tests to study the behavior of the hydrated soft tissues is the one-dimensional confined compression test –9, whereh et al.  and LeveTime histories of the reaction forces on the platen and the total volumes of the analyzed tissues are shown in Fig. ϕs increased in the area under the platen due to the outflow of fluid, and decreased under the surface around the platen because of the fluid block and accumulation by the impermeable membrane. The accumulated fluid led to the swelling of the tissue around the platen at the end of the compression period (1 ms). During the hold period, the movement of the fluid relaxed the pore water pressure gradient  displacement-pressure element, which is in the family of MINI elements, is used. This is a tetrahedral first-order element with a linear continuous interpolation. The pressure can be interpolated by 4 nodes located at vertices. For the interpolation of the displacement, a bubble node located at the centroid was added as the 5th node.As the continuity Eq.  involvedri,(i=1 to 4), the volume coordinate of the P can be defined as . By using the volume coordinate, the shape functions of the displacement and the pressure are defined as With the surfaces of the original tetrahedron, a point P in the tetrahedron forms 4 subtetrahedrons. By defining the volume ratios of the subtetrahedrons to the original tetrahedron as


This study aimed to develop a non-invasive method of identifying non-enhancing (necrotic) tissue in Wilms tumour.Volume of necrosis in Wilms tumour is informative of chemotherapy response. Contrast-enhanced T1w imaging pre- and post-gadolinium were included. Fractional T1 enhancement maps were generated from the gadolinium T1w data. Multiple linear regression determined whether fitted parameters from a mono-exponential model (ADC) and bi-exponential model (IVIM – intravoxel incoherent motion)  could predict fractional T1 enhancement in Wilms tumours, using normalised pre-gadolinium T1w (T1wnorm) signal as an additional predictor. Measured and predicted fractional enhancement values were compared using the Bland-Altman plot. An optimum threshold for separating necrotic and viable tissue using fractional T1 enhancement was established using ROC.In this single centre, retrospective study, post-chemotherapy MRI data from 34 Wilms tumour patients were reviewed (March 2012–March 2017). Cases with multiple b value diffusion-weighted imaging (DWI) and TADC and D (diffusion coefficient) provided the strongest predictors of fractional T1 enhancement in tumour tissue (p < 0.001). Using the ADC-T1wnorm model (adjusted R2 = 0.4), little bias  was shown between predicted and measured values of fractional enhancement and analysed via the Bland-Altman plot. The optimal threshold for differentiating viable and necrotic tissue was 33% fractional T1 enhancement .1w imaging predicts enhancement in Wilms tumours and reliably identifies and measures necrotic tissue without gadolinium.Combining ADC and T• Alternative method to identify necrotic tissue in Wilms tumour without using contrast agents but rather using diffusion and T1weighted MRI.• A method is presented to visualise and quantify necrotic tissue in Wilms tumour without contrast.• The proposed method has the potential to reduce costs and burden to Wilms tumour patients who undergo longitudinal follow-up imaging as contrast agents are not used. Wilms tumour is the most common type of paediatric renal tumour, accounting for approximately 90% of all kidney tumours . In Euro1-weighted imaging (T1w), where absent or decreased enhancement may represent necrosis. However, gadolinium requires venous access and raises examination costs. In addition, recent reports have described gadolinium retention in neural and body tissue regardless of renal function; however, currently, there are no known sequelae related to this [Necrosis within Wilms tumour post-chemotherapy is informative of treatment response, particularly when tumour size remains stable. It has been suggested that patients with 100% necrosis post-chemotherapy, when assessed via histological analysis, are associated with relapse-free survival at 5-year follow-up . Thus, q to this . While, 1w images can indicate areas of coagulated blood; thus, we hypothesise combining ADC and pre-gadolinium T1w may enable necrosis in Wilms tumour to be identified and quantified, without the need for exogenous contrast agents.The apparent diffusion coefficient (ADC), derived from diffusion-weighted imaging (DWI), has been shown to be related to cell density; low ADC values correlate with high cell counts in a range of paediatric body tumours . ADC valFurthermore, research has suggested that alternative non-Gaussian diffusion models for DWI, such as intravoxel incoherent motion (IVIM) , provide1w imaging and DWI could estimate the degree of necrosis in Wilms tumour, as opposed to the more traditional method of using gadolinium. We also investigate whether diffusion parameters from IVIM could improve this estimation. Additionally, we aim to establish an upper threshold of enhancement, based on typical values found in necrotic tumour tissue, as defined by manual delineation of necrotic tumour regions on post-gadolinium T1 images by two radiologists. Tissue which shows enhancement below this threshold (using either measured or predicted enhancement data) can be classified as necrotic, allowing the quantification of the volume fraction of necrotic tissue in future Wilms tumour studies.In this study, we hypothesise that the combination of T1w sequences (pre- and post-gadolinium contrast). Cases where the post-chemotherapy size of the tumour did not cover more than two axial slices on diffusion imaging were excluded. Eight patients from our cohort have previously been reported in [Institutional ethical approval was granted and waived the need for consent for this single centre study. A 5-year retrospective review (March 2012–2017) of the radiology imaging system (RIS) was performed for all MRI abdominal studies in children with proven histological diagnosis of Wilms tumour at our institution. Inclusion criteria were children who had completed a full 6-week course of chemotherapy, with MRI sequences that included both DWI and T1w imaging pre- and post-gadolinium. The DWI protocol was as follows: 7 or 8 b values in three orthogonal directions  or 0, 50, 100, 150, 200, 250, 500, 1000 s/mm2 (29 Wilms tumour)); slice thickness, 6 mm; TR/TE, 2600 ms/89 ms; and field of view, 350 mm. Axial T1-weighted imaging was acquired both before and after intravenous administration of gadolinium-based contrast using identical protocols for the pre- and post-gadolinium acquisitions. The full imaging parameters of all clinical sequences used can be found in [All imaging was performed on a 1.5 T Siemens Magnetom Avanto scanner equipped with 40 mT/m gradients. Depending on patient size, one or two body matrix coils were used to obtain full coverage . All patients underwent DWI followed by T1 sequence was started 2 to 4 min after injection of contrast agent. Hyoscine butylbromide (Buscopan) 20 mg/ml (manufactured by Sanofi) was also administered prior to all sequences to prevent peristalsis, dosage 0.02 ml per kg body-weight; however, maximum dosage was based on patient age, 1 month–4 years = 0.25 ml maximum and 4 years–12 years = 0.5 ml maximum.All patients received Dotarem 0.5 mmol/ml (manufactured by Guerbet), dosage 0.2 ml per kg body-weight. The post-contrast TData processing and analysis were performed using in-house routines written in Matlab R2015b . All registrations were performed using NiftyReg  packagesb = 0 (non-diffusion-weighted) images on each axial slice, using all clinically acquired images for guidance. The overlapping areas (between the two radiologists) were defined as the final Wilms tumour ROIs. ROIs which displayed substantial visual mismatch between the two radiologists were reviewed until consensus was achieved. To compare similarity of size between the independently defined ROIs, the intraclass correlation coefficient (ICC) was calculated.Tumour ROIs were independently drawn by two radiologists specialising in paediatric radiology . ROIs were drawn around the perimeters of each Wilms tumour on b values. A bi-exponential model (IVIM9) [Eq. D , D* , and f (volume fraction associated with ‘fast’ diffusion). In each instance, S(b) is the signal at a given b value, and S0 is the signal with no diffusion weighting:Data from each patient was processed twice by two different models of diffusion. A mono-exponential fit Eq.  generateIM9) Eq.  generateD, D*, and f) were calculated in a stepwise fashion. Firstly, a linear fit of ln(S/S0) against b was calculated at high b values (200–1000 s/mm2) to determine the value of D. Following this, D* and f were fit simultaneously using the full b value range (with a fixed D) to improve the stability of the model fitting process. D* had no constraints on upper boundaries, and f was constrained between 0 and 1. This fitting was performed using the Levenberg-Marquardt nonlinear least squares algorithm. All parameter maps were then smoothed with a 2-mm Gaussian kernel.The fitted parameters (1w scans. Post-gadolinium T1w images (post-Gd T1w) were registered to pre-gadolinium T1w images (pre-Gd T1w). All T1w scans were smoothed with a 2-mm Gaussian kernel to counteract registration errors. Voxel-wise fractional enhancement maps were calculated, using fractional enhancement = ((post-Gd T1w – pre-Gd T1w)/(pre-Gd T1w)). For example, a fractional enhancement of 0.50 indicates a 50% increase in signal intensity on the T1w image following gadolinium administration. Fractional enhancement maps were co-registered to DWI space. Additionally, pre-Gd T1w images were normalised to mean pre-Gd T1w signal intensity in a reference tissue in each patient, to produce quantitative, normalised pre-Gd T1w images (T1wnorm). This was achieved by dividing each pre-Gd T1w image by the mean signal intensity in an ROI placed in normal-appearing erector spinae muscles for each patient. These normalised pre-Gd T1w images were also registered to the DWI scans.Fractional enhancement maps were generated from T1wnorm maps. Mean values were calculated for each parameter in every Wilms tumour. The diffusion parameters included ADC and the fitted IVIM parameters . Additionally, the parameter f × D* was investigated. Multiple linear regression was used to calculate the relationship between mean fractional enhancement (dependent variable) and a combination of the mean of a single diffusion parameter and mean T1wnorm (predictor variables). Statistically significant regression coefficients were defined as having a p value < 0.05. Models were then compared based on adjusted R2 values.Wilms tumour ROIs were placed on co-registered fractional enhancement, diffusion, and TUsing the selected regression model, voxel-wise-predicted enhancement maps were generated for each Wilms tumour. Using the Bland-Altman plot, whole tumour values of predicted and measured fractional enhancement were compared, to determine the similarity  and level of bias (mean difference) between the two techniques.We also determined an upper threshold for enhancement in necrotic tissue, which would allow tumours to be separated into viable and necrotic components. Regions within each tumour which confidently represented necrosis were independently delineated by the two radiologists, using all clinically acquired MR sequences for guidance. The overlapping areas between the radiologists were defined as the final necrotic ROIs. These were used as the ‘gold-standard’ to represent the necrotic part of each tumour. The remainder of the Wilms tumour was defined as the viable ROI. Both viable and necrotic ROIs were registered onto corresponding measured fractional enhancement maps. The fractional enhancement value of every necrotic and viable voxel was pooled across the cohort. ROC analysis was used to define a fractional enhancement threshold which best separated viable and necrotic tissue based on AUC (area under curve). Tissue within a Wilms tumour with enhancement below this threshold would be classified as necrotic, and tissue with enhancement above this threshold would be classified as having some degree of viability.A total of 37 Wilms tumours from 34 patients were included as the final cohort. The median age of patients at the time of their MRI scans was 2.6 years . Patient inclusion and exclusion metrics are shown in Fig. After initial delineation of the Wilms tumour ROIs, visual inspection showed that 8/37 (21.6%) had a substantial mismatch between the radiologists; these were re-defined after consensus. The remaining 29 Wilms tumours had a high level of agreement between radiologists with an average overlapping area of 88% . After adjustment of the 8 mismatched ROIs, there was high similarity in the size of the 37 Wilms tumours as defined by the two readers, with an ICC of 0.98 . There also was high similarity in the size of the necrotic ROIs defined by the two readers, with an ICC of 0.83.p < 0.05), as shown in Table All multiple linear regression models used to predict fractional enhancement were statistically significant  = 13.78, p < 0.001, adjusted R2 = 0.42. However, this represented only a very marginal improvement compared to ADC  = 13.2, p < 0.001, adjusted R2 = 0.40). While the other three models all reached significance (p < 0.05); the higher p values and comparatively low adjusted R2 values indicated that they did not describe the data as well. Due to the similarity in performance between the regression models based on D (IVIM) and ADC , and the fact that ADC data are more widely acquired clinically, we chose to focus on the ADC-based model for further analysis.The combination of 1wnorm vs. fractional enhancement. Both ADC (p < 0.001) and T1wnorm (p = 0.001) added significantly to the prediction, with both increased ADC and increased T1wnorm being associated with reduced fractional enhancement. The standard error of the estimate was 0.24.Figure 1wnorm model, predicted enhancement was calculated according to the regression model given by Eq. ‘fitlm’ algorithm in Matlab:2.Using the ADC-TComparisons between fractional enhancement and predicted enhancement maps in three representative patients are illustrated in Fig. . There was a slight bias (9%) in predicted values overestimating the level of enhancement across a wide range of enhancement levels .The level of agreement between fractional enhancement and predicted enhancement is illustrated in the Bland-Altman plot in Fig. 41w imaging, after administration of gadolinium, were classified as necrotic). This threshold provided a sensitivity of 85% and specificity of 90% for identifying the ‘gold-standard’ necrotic tissue, with an AUC of 0.93.ROC analysis provided an optimal threshold to distinguish between viable and necrotic tissue, based on fractional enhancement Fig. a. The upT test revealed a significant difference between fractional enhancement values in the viable  and necrotic  voxels, t(195364) = − 446.96, p < 0.001. The optimum threshold (0.33) which separates necrotic and viable tumour tissue is also highlighted in Fig. Figure xels, t19364 = − 41w images in Wilms tumour. We found good agreement between mean tumour enhancement values calculated using non-gadolinium-based metrics (ADC and T1wnorm) and the level of enhancement measured using gadolinium in the same tumours. Additionally, a threshold of maximum enhancement in necrotic tissue was determined, which separated viable and necrotic tissue in good agreement with manually delineated necrotic tumour regions, as defined by two specialist paediatric radiologists. As such, this threshold could be used to quantify the total fraction of necrotic tissue in Wilms tumours in future studies, using either measured or predicted enhancement values.This study investigated whether necrosis (non-enhancing tissue) could be identified without using gadolinium contrast-enhanced T1w imaging are routinely acquired in Wilms tumour patients, so no additional scan time is needed, aiding the transference to clinical practice.Necrosis within Wilms tumours can indicate chemotherapy response, with high volumes of necrosis representative of ‘good response’ . QuantifThe current gadolinium-based method of identifying non-enhancing tissue has some possible limitations due to potential adverse reactions in patients , and the as-yet-unknown impact of the accumulation of contrast agent in patients undergoing repeated follow-up imaging . Additiob value DWI data allows non-Gaussian diffusion models to be applied, which provide additional fitted parameters. For example, D and f have shown higher accuracy in distinguishing between enhancing and non-enhancing kidney lesions compared to ADC [f and f × D* regression models did not reach high significance. f represents the contribution to the DWI signal due to blood flowing in the randomly orientated capillary network [f × D* represents a surrogate measure of blood flow [ADC is a well-defined diffusion parameter; however, multiple d to ADC . Furtherd to ADC . This st network , and f ×ood flow . Due to D produced the strongest regression model; however, the difference between the predictive power of D and ADC was minimal. Due to the similarity in the performance of these two predictors and the fact that ADC values are routinely acquired in clinical practice , ADC represents the preferred option for predicting fractional enhancement when combined with T1wnorm. This combination is needed as ADC alone cannot account for necrosis via coagulation, and as can be seen (Fig. 1wnorm is added as a predictor.een Fig. , the regb values were used for a small number of our patients; however, previous work has shown high reproducibility between ADC values acquired on different scanners with varying b values [1w and ADC maps. Thirdly, our sample size was fairly small, and a more robust model may be possible with a larger cohort. Furthermore, we did not assess tumour necrosis independently using histological methods. However, it is important to note that for histological analysis of Wilms tumour, only a sub-section of the tumour is sampled, and this may not accurately reflect the total necrosis volume. As such, we preferred in this study to use visual assessment of the entire tumour volume, using all clinically available MRI scans, to ensure the entire tumour volume was assessed.The study had several limitations. Firstly, slightly different b values ; thus, t1w protocol used in this study. Alterations in the delay time between gadolinium administration and T1w imaging may lead to different levels of enhancement on contrast-enhanced T1w scans. However, as our model uses the fractional difference between the pre- and post-contrast T1w signal, provided the T1w protocol remains consistent between these two acquisitions, the influence of variations in the specifics of the T1w acquisition between different institutions should mostly cancel out.An additional limitation may arise from the possible reliance of our model on the specifics of the TFinally, gadolinium is frequently administered for indications broader than necrosis assessment, for example vascular anatomy, and detecting lesions in a variety of organs. We acknowledge that the proposed method cannot entirely replace gadolinium. Despite this, our method would be a suitable alternative for those with severe renal impairment and limit the cumulative gadolinium exposure for patients who have repeated follow-up MRI scans. Gadolinium-free MRI examinations are currently being investigated for paediatric oncology , and theIn conclusion, the proposed model predicts enhancement in Wilms tumour without gadolinium and provides a visual representation of tissue viability and necrosis within tumours. A threshold of maximum enhancement in necrotic regions has also been generated, allowing the percentage of necrotic tissue to be quantified in future Wilms tumour studies, using imaging-based methods.


BRAFV600E mutations and is associated with an immunosuppressive microenvironment; we previously demonstrated that the combination of BRAF inhibitor and checkpoint inhibitor immunotherapy synergistically reduce tumour volume in an immunocompetent mouse model of orthotopic ATC.Patients with anaplastic thyroid cancer (ATC) have an extremely poor prognosis despite aggressive multimodal therapy. ATC has a high prevalence of V600E inhibitor PLX4720 and anti-PD-L1 or anti-PD-1 antibody on survival, and performed immune cell profiling of lymphoid and myeloid-lineage cells during maximal treatment response and tumour regrowth.We again utilised our mouse model of ATC to assess the combination of BRAF+ T cells and NK cells, as well as increases in mostly M1-polarised tumour-associated macrophages (TAM) and decreases in myeloid-derived suppressor-like cells. Regrowth of tumour occurred after 2–3 weeks of ongoing combination therapy, and was most significantly associated with decreased TAMs and a dramatic increase in M2-polarisation.Combination therapy dramatically improved mouse survival. Maximal tumour reduction was associated with increases in the number and cytotoxicity of CD8Combination of PLX4720 and anti-PD-L1/PD-1 antibody dramatically reduced tumour volume, prolonged survival and improved the anti-tumour immune profile in murine ATC. Tumour growth inevitably recurred and demonstrated re-emergence of an immunosuppressive tumour microenvironment. Unfortunately, despite refinement of each component of multimodal therapy, the prognosis remains dismal in stage IVc and recurrent disease; new systemic therapies are urgently needed.5The incidence of thyroid cancer is steadily increasing worldwide.TP53, BRAF, NRAS, the TERT promoter, as well as genes crucial for epigenetic regulation.7 Given the high frequency of BRAFV600E mutations in ATC, selective BRAFV600E inhibition (BRAFi) is an attractive option to improve treatment specificity and minimise toxicity, and has quickly become standard of care in other BRAFV600E-mutant malignancies such as melanoma.8 However, despite initial impressive response rates, the durability of response is limited owing to development of resistance.11 Since reactivation of downstream MEK is a frequent mechanism of resistance, MEK inhibition (MEKi) is a useful adjunct; the combination of BRAFi (dabrafenib) and MEKi (trametinib) demonstrated an overall response rate of 69% in a recent phase II clinical trial which has led to FDA approval of the combination.12ATC has been increasingly recognised to harbour complex genomic changes and instability, including genome copy number alterations, gene fusions, and mutations in 14 Immune cells such as tumour-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), and regulatory T cells (Tregs) have been shown to inhibit anti-tumour T cell function. Our group and others have shown that BRAFV600E-mutant thyroid cancers have increased expression of Programmed Death Ligand 1 , a physiologic “checkpoint” expressed on tumour cells, macrophages, and other cell types. PD-L1 binds to its receptor, PD-1, on effector immune cells, such as T cells, and inhibits proliferation, cytotoxicity, and cytokine production. The development of neutralizing monoclonal antibodies to PD-1/PD-L1  has led to improved patient survival in many cancer types.16 Initial reports of the use of anti-PD-1 in thyroid cancer  show promise, although response rates are variable.18In addition to tumour-intrinsic factors, an immunosuppressive microenvironment has been recognised as a contributor to thyroid cancer growth and metastasis.19 The addition of immunotherapy to agents directed against angiogenesis, proliferation, oncogenic mutations, etc. has recently seen an accelerated progression from bench to clinical trials, and there is great interest in using actionable genetic information from tumour/cell-free DNA to personalise selection of targeted therapies.20 However, given the unique challenge of enroling patients with such a rapidly progressive disease, mouse models have become an invaluable tool to advance our understanding of the biology of ATC, to trial new drugs and combination regimens, and to investigate predictors of response and mechanisms of resistance. As these mechanisms are elucidated, logical combinations will be easier to propose and investigate.23Combinatorial strategies that include agents with multiple mechanisms of action are a potential means to improve efficacy and forestall the development of resistance.BRAFV600E mutations in ATC and its association with higher PD-L1 expression and increased numbers of tumour-infiltrating T cells, the combination of BRAFV600E inhibitors with immunotherapy is of particular interest.25 Our previous study demonstrated that MAPK pathway activity modulates thyroid tumour PD-L1 expression, and that the combination of PLX4720 and anti-PD-L1 antibody in an immunocompetent murine model of orthotopic ATC led to a superior and synergistic anti-tumour response compared to either monotherapy alone; tumour shrinkage was associated with increases in CD8+ T cells and granzyme B staining.24 These preliminary data informed development of an ATC-specific clinical trial to examine the safety and efficacy of atezolizumab with various targeted therapies  or paclitaxel (NCT03181100) based on the mutation status of the tumour.Given the prevalence of BRAFV600E inhibitor and anti-PD-1/PD-L1 combination therapy, and to conduct immune profiling during maximal treatment response and after regrowth of the tumour.In this follow-up study, we sought to assess the effect of combination therapy on survival, and expanded our treatment with anti-PD-1 antibody, which has seen wide clinical use. Since our model is extremely aggressive and regrowth of ATC is essentially inevitable in humans, we extended our experiments to assess for tumour regrowth and to more thoroughly characterise the tumour immune signature of both lymphoid and myeloid lineage cells. We hypothesised that the combination of BRAF inhibition and anti-PD-1/PD-L1 antibody therapy would improve survival,  that tumour regression would be associated with improved immune-mediated anti-tumour cytotoxicity, and that tumour would eventually recur and show re-emergence of an ineffective immune response. To our knowledge, this is the first study to show improved survival with 26 The cell line cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% foetal bovine serum and penicillin/streptomycin and incubated at 37 °C in a 5% CO2 incubator.One murine thyroid cancer cell line, TBP-3743, was used in the study.27 LEAF™ Purified anti-mouse NK1.1  antibody was used for NK cell depletion, as previously described.28The anti-PD-L1 (10F.9G2) and anti-PD-1 (332.8H3) antibodies have been previously characterised; both had less than 2EU endotoxin per mg protein and were injected intraperitoneally (I.P.), as described below.−PE , anti-IA/IE-PerCP/cy5.5 , anti-CD274-PerCP/cy5.5 , anti-NK1.1-PE/cy7 , and anti-CD11b-APC/cy7  antibodies were purchased from BioLegend .For flow cytometry, anti-F4/80-FITC , and anti-Ly6C-PerCP/cy5.5  antibodies were purchased from eBioscience . Anti-Ly6G-PE , anti-CD206b gene expression  were measured using TaqMan Gene Expression Assays (Invitrogen) by real-time reverse transcriptase polymerase chain reaction (RT–PCR) with technical triplicates and two repetitions. Gene expression levels were normalised to GAPDH expression.29RNA isolation was performed using Trizol  and cDNA was synthesised using the Superscript VILO cDNA synthesis kit (Invitrogen), according to the manufacturer’s instructions. PD-L1  and H2-K5 cells from the genetically engineered TBP  3743 BRAFV600E/WT P53−/− murine tumour cell line, as previously described.26 Briefly, mice were anesthetised, the thyroid gland was exposed, and 105 cells were injected into the left lobe using a 27-gauge needle. On post-injection day 7, mice were randomised  received a control diet , the PLX4270 group  received PLX4720-impregnated chow  ad libitum, the anti-PD-1 and anti-PD-L1 groups  received control diet and were injected with anti-PD-L1 or anti-PD-1 antibody at 200 µg/mouse I.P. twice weekly, and the combined treatment group received both PLX4720 chow and either anti-PD-L1 or anti-PD-1 antibody . The mice were sacrificed at 2-weeks, 3-weeks, and 4-weeks post-implantation. Both fresh and formalin-fixed tumours were collected for RNA isolation and IHC analysis, respectively. Tumour volume was calculated using the formula (π/6) × length × width × height.30 The experiment was repeated twice. In order to generate a survival curve, the experiment was repeated using 48 (n = 8 per group) mice and utilizing humane endpoints  rather than timed sacrifice.Seventy-eight 6-week-old female B6129SF1/J mice were implanted with 10Formalin-fixed orthotopic thyroid tumours were cut into 4-μm sections and stained with Haematoxylin and Eosin. IHC analysis was performed for CD8 (Leica Microsystems), Granzyme B (abcam), and F4/80 (eBioscience). The primary antibody was detected using the respective biotin-free secondary antibody. Granzyme B staining was quantified using the mean percentage of total nucleated cells, averaged over five representative high-powered fields.Representative tumours were dissected and enzymatically digested with collagenase (100 μ/ml) and dispase (0.5 mg/ml) at 37 °C for 30–40 min, in preparation for flow cytometric analysis. Cells were washed twice with PBS, and fixed with 2% paraformaldehyde for 10 min on ice. Cells were again washed and then pre-incubated with Trustain fcX™ . Samples were stained with the primary antibodies listed above for 30 min, washed twice, and were resuspended in standard flow cytometric buffer. Cells were analysed using the BD FACSVerse™ flow cytometer . The data were processed using Flowjo software .The Milliplex® MAP kit  was used according to manufacturer specifications. The specific analysed cytokines are listed in Fig. U-test was used to compare independent groups. Two-way factorial analysis of variance (ANOVA) was applied to test the effects of treatments on reduction in tumour volume. Two-tailed values of P < 0.05 were considered significant for all analyses. Kaplan–Meier survival analysis was performed to calculate fractional survival using the Logrank test for each group, compared against the control mice and monotherapy as reference; median survival was reported in days.Statistical analysis was carried out using Microsoft Excel , IBM SPSS Statistics , and GraphPad Prism 7 . All experiments were performed in technical triplicate and repeated twice unless specified otherwise. A fold change in mRNA expression of 2 or more was considered significant. A log power of 2 for fold changes in expression was used to generate box-and-whisker plots based on the median and interquartile range, and the non-parametric Mann–Whitney n = 78). One week after tumour implantation, when tumours were ~100 mm3, mice were randomised to receive either single drug , or combination treatment , whereas dramatic tumour reduction was seen with PLX4720 + anti-PD-1  and PLX4720 + anti-PD-L1 , consistent with our prior work . Control mice survived for a median of 13 ± 0.5 days; anti-PD-1 and anti-PD-L1 groups did not survive for significantly longer. PLX4720-treated mice survived significantly longer than control, , whereas PLX4720 + anti-PD-1  and PLX4720 + anti-PD-L1  groups survived dramatically longer than control, and significantly longer than the PLX4720-treated group (p < 0.001)  Fig. .3. Regrowth during PLX4720 + anti-PD-1/PD-L1 combination therapy appeared between 2 and 3 weeks of treatment; most mice died shortly after 3 weeks of treatment. Tumour volume of PLX4720 + anti-PD-1-treated mice was 186 ± 59 at week 2, and 235 ± 41 at week 3; tumour size of PLX4720 + anti-PD-L1-treated mice was 235 ± 53 at week 2, and 270 ± 109 at week 3 , anti-PD-L1 , PLX4720 + anti-PD-1 , and PLX4720 + anti-PD-L1-treated groups , compared to control (0.38 ± 0.13%). Mice treated with anti-PD-1 alone did not show increased Granzyme B staining, compared to control. FoxP3+ T cells modestly increased with all three monotherapies, but dramatically increased with both combination therapies.Since tumour-infiltrating lymphocytes are the main effectors of immune-cell mediated death, immunohistochemistry was performed for the CD8gs. Fig.  CD8+ staFlow cytometry with anti-NK1.1, an NK cell (and NKT cell) specific antibody, was utilised to assess the tumour NK cell population. Anti-NK1.1 staining was higher in all PLX4720-treated groups, ranging from 57.4 ± 6.6% to 63.0 ± 7.6, roughly twice that of any treatment without PLX4720 Fig. . NK1.1 p1 T cell response. There was no change in IFNγ production in PLX4720-treated mice.To assess a panel of relevant cytokines, a Milliplex® multiplexed assay was performed on tumour lysate after 1 week of treatment, and again after 3 weeks Fig. . The mos33 Treatment with anti-PD-1 and PLX4720 monotherapies for 1 week led to a modest increase in F4/80+ tumour-infiltrating myeloid cells by immunohistochemistry, while treatment with either combination led to a dramatic increase in F4/80+ staining, compared to both control and monotherapy  vs. control, anti-PD-1, and anti-PD-L1-treated groups (15.2 ± 0.9%); more than 98% of these F4/80+CD206− cells were IA/IE+, indicating an M1-polarised phenotype  did not significantly change, compared to control and monotherapy.TAMs are recognised to have a spectrum of dynamic functional polarisation; “M1-polarised” TAMs represent a pro-inflammatory population, while “M2-polarised” are typically anti-inflammatory. TAMs represent the majority of nucleated cells in some ATC tumours.apy Fig. . Combinaype Fig. , a perce+ immature myeloid cells with immunosuppressive activity and are typically organised into early, polymorphonuclear and monocytic subsets. Since MDSCs have been implicated in ATC, we measured tumour MDSC-like cells (phenotypic markers are suggestive but not sufficient to characterise them as MDSCs) by flow cytometry.34 Although polymorphonuclear MDSC-like cells did not change after 1 week of treatment, we observed a significant decrease in monocytic MDSC-like cells in the PLX4720- and combination-treated groups, compared to control , PLX + anti-PD-1 (72.6 ± 9.8%), PLX + anti-PD-L1 (72.7 ± 10.9%) versus control (54.8 ± 7.5%). Analysis of the CD11b+ cells demonstrated a decrease in Ly6G−Ly6C+ (monocytic MDSC-like cells) populations in the PLX4720- (5.9 ± 3.8%), PLX + anti-PD-1- (3.4 ± 3.7%), PLX + anti-PD-L1-treated groups (4.4 ± 3.9%) versus control (17.7 ± 8.3%). No change was seen in the anti-PD-1, and anti-PD-L1-treated groups  were sacrificed after 2 weeks, and another cohort of combination-treated mice  were sacrificed after 3 weeks, just prior to meeting humane criteria for euthanisation.As mentioned earlier, tumour regrowth occurred after 1–2 weeks of continued PLX4720 monotherapy and 2–3 weeks of combination therapy during the survival experiment. To perform repeat immune profiling, a cohort of mice from the PLX4720 and combination-treated groups , and more dramatically in mice treated with combination therapy for 3 weeks . Milliplex® cytokine analysis revealed that IFNγ returned to baseline after 3 weeks, indicating a significant decline in tumour inflammation  cells from >98% to 35.4 ± 6.9% in the PLX4720 + anti-PD-1 group, and to 47.1.0 ± 3.8% in the PLX + anti-PD-L1 group. The M2-polarised (F4/80+CD206+IA/IE−) population dramatically rose from <2% to 54.1 ± 5.7% in the PLX4720 + anti-PD-1 group, and to 25.7 ± 6.1% in the PLX4720 + anti-PD-L1 group. There was no significant change in either polymorphonuclear or monocytic MDSC-like cells after 2 or 3 weeks in any treatment group.After 2 weeks of PLX4720 treatment, F4/80nts Fig. , and theype Fig. . After 335 A recent GWAS showed that BRAFV600E-mutant thyroid cancers tend to have higher levels of T cells (both cytotoxic and regulatory T cells), M2-polarised TAMs, and lower levels of NK cells.36 Bastman et al. have demonstrated a significant CD8+ and Treg infiltrate in BRAFV600-mutant DTC and ATC.14 Wennerberg et al. showed that human ATC cell lines and fresh tumours were enriched in NK cells, although they had poor cytotoxic function due to low NKG2D expression. The profound clinical aggressiveness of ATC is evidence that, despite this lymphoid infiltrate, anti-tumour immunity is ineffective, likely in part due to higher PD-L1 and T cell exhaustion. Since our previous study, several groups have confirmed higher and more diffuse tumour and immune cell PD-L1 staining in a subset of patients with ATC.37 These data are particularly relevant since high PD-1 and PD-L1 expression may correlate with poorer outcomes in ATC, and PD-L1 upregulation has also been shown to be a mechanism of resistance to BRAF inhibition.38It has become increasingly clear that a tumour’s specific genetic configuration can have a profound impact on the tumour microenvironment and anti-tumour immunity, in addition to direct effects on cell growth and proliferation.+ cytotoxic T cells, FoxP3+ Tregs, and NK cells, as well as higher granzyme B staining and IFNγ production, confirming increased cytotoxicity. This improvement parallels findings from early phase clinical trials in melanoma that show encouraging overall response rates with a similar combination, which correlates with improved CD8+ T cell infiltration and IFNγ secretion.39 Interestingly, while the increase in NK cell number was attributable to PLX4720 alone , the dramatic increase in CD8+ T cells and granzyme B staining appeared only with combination therapy. Although FoxP3 staining increased with PLX4720 and combination therapy, it is unclear if this represents a true increase in Tregs since FoxP3 can be transiently expressed by CD4+ and CD8+ T cell populations during activation or rescue from exhaustion.40In the current experiment, we demonstrate that the combination of PLX4720 with anti-PD-1 or anti-PD-L1 leads to a dramatic increase in mouse survival. Maximal tumour shrinkage coincided with a decreased Ki67 proliferative index, increases in tumour CD8+ T cells, NK cells, and loss of granzyme B and IFNγ production, confirming dampened inflammation. While these changes are important, it is now clear that the anti- or pro-tumourigenic nature of the lymphoid infiltrate is not only dependent on the specific type and ratio of various lymphocytes , but also their location within the tumour, and the context of the surrounding cellular  and the cytokine milieu. These observations underscore the importance of thoroughly assessing the immune microenvironment using in vivo models with a full immune cell repertoire.Unfortunately, despite the profound reduction in tumour volume, doubling of survival time, and favourable immune infiltrate during maximal response to treatment, no complete responses were seen and tumour regrowth emerged after 2–3 weeks of combination therapy, quickly leading to mouse death. Tumour regrowth was associated with decreases in CD8h2 T cells, fibroblasts, granulocytes, and tumour cells. While M1-polarised TAMs facilitate inflammation, M2-polarised TAMs hamper adaptive immunity, and facilitate invasion, metastasis, and the epithelial-to-mesenchymal transition, among other mechanisms. TAMs dampen immunity through production of immunosuppressive cytokines (e.g. IL-10 and TGFβ) and prostacyclins, depletion of metabolic substrate, and T cell/NK cell inhibition.41 In thyroid cancer, increased TAM infiltration is found with increasing de-differentiation in PDTC and ATC.42 Although lymphocytes have been the primary target in mechanistic studies of anti-PD pathway efficacy, PD-1 expression has recently been recognised on TAMs, and stimulation leads to inhibited phagocytosis.43 In light of these findings, it is not surprising that the emergence of resistance to combination therapy in our study was marked by a decrease in the number of TAMs with a dramatic increase in the ratio of M2:M1-polarised cells; it is likely that this change, at least in part, is responsible for the reduction in both T and NK cell number and function. Both TAMs and MDSCs have been implicated not only in tumour development, but in resistance to kinase and checkpoint inhibitors in other tumour types.46 Therapies that target TAM recruitment, proliferation and polarisation could be a promising component of future combination therapies.41Indeed, maximal response to combination therapy was also associated with significant changes in the myeloid-lineage compartment, including an increase in predominantly M1-polarised TAMs. TAMs arise from differentiation of circulating MDSCs and monocytes, as well as from recruitment of tissue macrophages. TAM polarisation is heavily influenced by the surrounding immune milieu of B-cells, T44 MDSCs have been detected in the peripheral blood of patients with DTC and ATC, and appear to correlate with disease stage.45 At the time of maximal response to combination therapy, mouse tumours demonstrated a significant decrease in the monocytic MDSC-like cell fraction. Despite this significant decrease, MDSC-like cells remained low during tumour regrowth, suggesting that MDSCs may play a role in response to, but not in resistance to combination therapy.MDSCs are a heterogenous population of circulating immature myeloid cells that arise from dysregulated myelopoiesis in the setting of chronic inflammation, uncontrolled cell growth, trauma, etc. Major subtypes include early (eMDSC), polymorphonuclear (PMN-MDSC), and monocytic (M-MDSC) MDSCs, and although they differ in their phenotype and potency, they share a common ability to suppress effector T cell activity, similar to M2-polarised TAMs.26 It is unclear if the genetically engineered TBP-3743 ATC cell line is comparable to human patients with ATC, especially given the relationship between genetic and epigenetic changes and perturbations in host immunity, but the transgenic model from which TBP-3743 was derived has been shown to recapitulate the dedifferentiated histology, temporal aggressiveness, and gene expression profile of human ATC. Despite this limitation, our model is relentlessly aggressive, which is unfortunately a feature shared by many patients with ATC; down titration of injected cell number has failed to significantly alter the course of disease. Since orthotopic transplantation of TBP-3743 into syngeneic mice is the only validated BrafV600E-mutant immunocompetent model of ATC, we are actively working to develop immunocompetent orthotopic mouse models with alternative ATC cell lines.Our study has several limitations, many of which have been reviewed in our previous manuscripts.39 Although the addition of MEKi to BRAFi has recently been approved by the FDA in human ATC, we chose to focus on the combination of BRAFi with anti-PD-1/PD-L1 given the critical role of MEK signalling in the activation of CD8+ and CD4+ T lymphocytes, and thus the theoretical risk that any gains from MEK inhibition might be abrogated by simultaneous negative effects on T cells. However, triple therapy with BRAFi, MEKi, and immunotherapy is a promising future direction since immunotherapy may counteract the negative immune effects of MEKi.47 Finally, although we identified M2-polarised TAMs and MDSCs using their phenotypic markers, phenotypic assessment is necessary but not sufficient for complete characterisation; demonstration of immunosuppressive function is required to confirm their presence and significance. The use of depletion experiments for each immune cell will confirm the relative importance of each during maximal response and tumour regrowth. We aim to address these limitations in ongoing experiments.Given the focus of this study on establishing the effectiveness and preliminary mechanism of combination therapy, along with the rapid lethality of our animal model, we chose a concurrent treatment algorithm. It is possible that other treatment algorithms, such as sequential therapy, may be better tolerated and more clinically effective, due to capitalisation on immune cell recruitment through MAPK inhibition; this approach is under investigation in melanoma and will be an area of future study.In summary, we demonstrate that the combination of BRAFi and anti-PD pathway immunotherapy dramatically reduces tumour volume and improves survival in an immunocompetent murine model of orthotopic ATC, and that maximal treatment response and regrowth of tumour correlate with significant changes in the tumour immune cell infiltrate. Combination therapy is a promising modality in ATC and other cancers to maximise treatment efficacy, boost response rates, minimise toxicity, and forestall the development of resistance. Multiple clinical trials utilising this and similar treatment combinations are already underway, and it is imperative to rapidly disseminate this preclinical information to clinicians. Additionally, ongoing work to identify predictors and mechanisms of response and resistance is critical to identify optimal candidates for specific therapies, and to further inform the rational addition of “targeted” agents. Immune cell profiling is an auspicious modality to complement mutational assessment in the context of furthering the goal to deliver truly “personalised” medicine.Supplemental Figures


The upward fluid velocities, decreasing from 2.0 to 5.0 cm yr−1 at the center to a negligible value at margin sites, varied with the rate and efficiency of anaerobic methanotrophy, demonstrating the impact of fluid migration on biogeochemical processes and carbon cycling. By extrapolating the velocity pattern, the flux of fluids exported from 13 SMVs into seawater amounted up to 1.3–2.5 × 107 kg yr−1, a quantity accounting for 1.1–28.6% of the smectite-bound water originally stored in the incoming sediments. Our results imply that SMVs could act as a conduit to channel the fluids produced from great depth/temperature into seafloor environments in a subduction system of the western Pacific Ocean.Qualitative and quantitative assessments of fluid cycling are essential to address the role and transport of deeply sourced fluids in subduction systems. In this study, sediment cores distributed across a submarine mud volcano (SMV) offshore southwestern Taiwan were investigated to determine the characteristics of fluids generated through the convergence between the Eurasian and Phillippine Sea Plates. The low dissolved chloride concentration combined with the enrichment of  Such fluid circulation in the deep crustal region also leads to increasing the pore pressure and reactivity of minerals, modifying the rheological property and weakening the rock strength3. The discharge of deeply generated fluids into the bottom ocean further influences the distribution of biological communities and seawater chemistry5. Therefore, a systematic assessment of fluid origins and its intrinsic characteristics in subduction systems would facilitate the implementation of a quantitative framework for fluid cycling and budget.Fluid cycling is of great importance to magma and seismicity generation in subduction systems. The release of fluid from the subducting plate into the overlying wedge decreases the liquidus temperature of lithospheric materials, thereby increasing the degrees of partial melting and facilitating melt extraction for arc magmatism7. In essence, both mechanisms are driven by the increasing pressure and temperature associated with sediment burial and deformation. However, the geochemical signatures inherited from individual mechanisms are drastically different. Porewater excluded by sediment compaction through burial and deformation bears a composition essentially identical to seawater . In contrast, lattice-bound water released by mineral dehydration is salt-free and carries δ18O and δ2H values deviating from its sourced fluid 10. Volatile elements  desorbed from clay minerals are also commonly accompanied with mineral dehydration12. Fluids generated from different mechanisms are, however, susceptible to the mixing during migration, thereby obscuring the pristine signatures that could be used to constrain the source depth, physico-chemical parameters of reactions, and fluid budget. Elucidating the observed characteristics remains challenging and often requires a sampling strategy targeting specific geological features or structures.Sediment compaction and mineral dehydration are the most important mechanisms for fluid generation and circulation in an accretionary prism14. Because the fracture network associated with the plate convergence could be extended to as deep as the lower crust, SMVs are able to efficiently export deeply sourced fluids, sediments, and reducing volatiles to the seafloor, generating morphological domes and depressions, mudflows, and even biological hotspots with colonies depending on leaked gaseous hydrocarbons10. While SMVs provide a window to witness the deep subsurface characteristics, upward migrating hydrocarbon gases produced by thermal maturation or methanogenesis also drive the sulfate-dependent anaerobic oxidation of methane (AOM) at shallow depths10. Such a biological removal mechanism has been considered to be effective in maintaining a low level of methane in seawater15. Overall, the exact characteristics and quantities of deep fluids discharged from SMVs, and their impacts on biogeochemical activities remain poorly constrained in the subduction system of the western Pacific Ocean.As one of the prominent seafloor features, submarine mud volcanoes (SMVs) are commonly distributed in accretionary prisms where rapid sedimentation and tectonic interaction lead to the gravitational instability of unconsolidated shale/mudstone, fluid overpressurization, and formation of dense fracture arrayIn this study, we present a comprehensive geochemical dataset of fluids extracted from sediment cores recovered from sites distributed across an SMV, TY1, in the accretionary prism offshore southwestern Taiwan , is composed of two mud volcanoes  and lower flank site F6-3) sites, significant decreases in sulfate, calcium, and magnesium concentrations coincided with the increase in methane and TA concentrations , concentrations of most ions were close to seawater values. In addition, methane concentrations only increased to at most 50 μM. Both boron and lithium concentrations at site MV12-C decreased with depth Fig. .18O and δ2Η values of porewater are shown in Fig. 18O and δ2Η values of porewater were close to 0‰ at either above 120 or 280 cmbsf, and increased and decreased, respectively, further down core. The data for the deepest sample deviated from the global meteoric water line (GMWL) in a way that δ18O values increased to +6 to +7‰ while δ2Η values decreased to −20 to −12‰ . At the margin sites (sites MV12-C and C-2), δ18O values were close to 0‰ throughout the core, while δ2H values decreased slightly to −1.4‰ at the bottom of the core.Profiles of δ12‰ Fig. . At the 13C-CH4 values increased from −40‰ on the seafloor to −20‰ above the SMTZ. The δ13C-CH4 value was the smallest at the SMTZ (−50 to −45‰) and remained at a constant value below the sulfate reduction zone . The δ13C-DIC (dissolved inorganic carbon) patterns for center and flank sites were similar in a way that the values were the smallest at the SMTZ  and increased towards the seafloor (~0‰) and the bottom of the cores.At the western and southern center sites, δhe SMTZ −0 to −45‰1/C2+ ratios and the δ13C-CH4 values for sites A2-2 and MD4-P3 were between 25 and 90, and between −35.9 and −44.5‰, respectively, falling in the region of thermogenic methane in the plot of C1/C2+ ratio versus δ13C-CH4 value  at the flank sites (sites MV12-A and F6-3) than at the center sites. While the methane fluxes above and beneath the SMTZ decreased with the distance from the center, the ratios (flux beneath SMTZ/flux above SMTZ) were relatively higher at the flank sites than at the center sites. Such a variation was translated into an increase in AOM efficiency from 54% at the center sites to 98% at the lower flank sites  measured at the margin sites and in surface sediments in the region was chosen to represent the regional geothermal gradient. Using this value, the depth producing the observed geochemical signatures was calculated to range between 3.6 and 5.7 kmbsf (kilometer below seafloor).The fluid temperature was calculated to range between 100 and 150 °C. Our estimated temperature is supported by the high concentrations of lithium and boron Fig. , a patteios Fig. . Further36, are excluded because illite and chlorite are dehydrated at a much higher temperature range (>237 °C for illite37 and >650 °C for chlorite38). Kaolinite is also excluded as a result of being less abundant in the northern South China Sea39 and stable over the temperature range estimated in this study (decomposed at >800 °C and 19 GPa40). Since the transformation of smectite to illite proceeds at a temperature higher than 60 °C34 and produces fluids enriched in 18O and depleted in 2H, smectite is considered to be the most plausible mineral candidate to account for the observed geochemical signature. As abundant smectite and mixed layer (1.1‒24.8 wt.% in bulk sediments) have been found in the active margin41 and passive margin off southwestern Taiwan42, and sediments off the Luzon Arc46 , smectite sourced from Taiwan orogeny and Luzon Arc volcanism and later being subducted/accreted as part of the Taiwan accretionary prism is the most likely source.These lines of evidence indicate that a significant fraction of pore fluid is contributed from clay dehydration at high temperatures. Illite and chlorite, which are considerably abundant in sediments offshore southwestern Taiwan47. The fluid-to-mineral ratio for the reaction was calculated based on the mass balance for hydrogen and oxygen isotope compositions47:WO and WH are moles of oxygen and hydrogen, respectively, in porewater; and RO and RH are moles of oxygen and hydrogen, respectively, in target minerals. The superscripts i and f represent the initial and final isotopic compositions, respectively. The subscripts r and pw denote isotopic compositions of rock and porewater, respectively. By rearranging Eqs. . The W/R ratios for oxygen and hydrogen are assumed to be identical (WO/RO = WH/RH). A value of 0‰ was assigned for both the 18Oipw values based on the bottom water measurements. The 51. The 53. The calculations yielded the δ18O and δ2H values varying as a function of equilibrium temperature and W/R ratio  for reactions considering the exchange of oxygen and hydrogen between minerals and pore fluid under a closed-system conditioning Eqs.  and 6)::47. The tio Fig. , the W/R54  was used for the extrapolation. The measurement from IODP 358 yielded a porosity of 17% at the depth of 3.2 kmbsf 54. By applying the exponential reduction function, the porosity at 5.7 kmbsf was calculated to be 5.6% .The amount of needed smectite could be further deduced from these W/R ratios considering the possible volume of porewater and assuming the complete reactivity of all available porewater and smectite. To constrain the possible porosity at the depth estimated for smectite dehydration, the porosity variation for cores collected from the Nankai Trough57:Fa is the flux, Ca is the weight percentage of mineral-bound water in smectite, H is the pre-subducted sediment thickness, ρ is the density of dry sediments, φa is the porosity of sediments from the incoming plate, La is the length of trench, and Va is the subduction rate. The details for calculations were stated in Supplementary Information. The total smectite-bound fluid from the incoming plate was calculated to be 0.7–8.8 × 108 kg yr−1.As subducted-smectite could be the source of freshwater, fluid fluxes in the SMV region of the accretionary wedge were further assessed. The total amount of smectite-bound fluid within the incoming plate was estimated considering the weight of the incoming plate and the content of smectite and porosity in the sediments7 kg yr−1), approximately 80% of this flux could be attributed to the smectite-bound fluid (1–2 × 107 kg yr−1) considering the mixing of seawater and freshwater end components . This quantity of smectite-bound fluid released from the SMVs in the region constituted about 1.1–28.6% of the smectite-bound fluid originally subducted into the trench (0.7–8.8 × 108 kg yr−1).As sediments are subducted, the buried seawater-like pore fluid and produced smectite-bound fluid would be squeezed and lost gradually with the increasing temperature and pressure. As a result, the actual amount of water released into SMVs region would be substantially lower than that from the incoming plate. Of the overall flux of brackish fluid expelled from SMVs offshore Taiwan  would be lost to the frontal prism, leaving a total of 0.56 × 108 kg yr−1 smectite-bound fluids further released into the overlying wedge as the descending sediments reach the region beneath the Kumano basin where mud volcanoes are abundant. If such a fluid budget for the Nankai system is directly applied to the Taiwan subduction system, a total of 0.8–10.4 × 107 kg yr−1 ( = 0.7–8.8 × 108/ (4.17 × 108/ 88%/ 0.56 × 108)) smectite-bound fluids would be released from the descending sediments into the wedge in the Upper Slope. This estimated flux is comparable with the flux of freshwater expelled into seawater through SMVs offshore Taiwan obtained in this study (1–2 × 107 kg yr−1). Whether such an analogy of the fluid budget could be drawn directly remains to be explored even though the plate geometry, heat flux, mineral type and content, convergence rate, and the distance between trench to mud volcanoes are comparable between the Nankai and Taiwan subduction systems , and the AOM efficiency was the lowest (54%) .The impact of the deep fluid on shallow subsurface microbial processes is reflected in geochemical profiles. The decreasing rate of upward fluid flow from the center towards the margin sites Fig.  varied c−1 at the center sites to a negligible level at the margin sites. By extrapolating such a variation pattern for other SMVs offshore Taiwan, the overall flux discharged from SMVs ranged from 1.3 to 2.5 × 107 kg yr−1. This fluid quantity accounts for 1.1–28.6% of the smectite-bound water originally stored in the incoming sediments, implying that SMVs could act as a conduit to channel the fluid produced from great depth/temperature into seafloor environments. The flux variations across the transect of a mud volcano strongly impact the magnitude and efficiency of the biological filtration of methane through methanotrophy.Porewater and gases for cores distributed along a transect of TY1 were systematically analyzed to assess the spatial variation of fluid velocities and biogeochemical activities and further constrain the fluid budget. The co-variation of the isotopic composition and chloride concentration demonstrates that the majority of fluid retrieved from the crater is derived from smectite dehydration at a temperature of 100 to 150 °C or a depth of 3.6 to 5.7 kmbsf. Assuming that the isotopic equilibrium is reached, the water-rock interaction proceeds at a water-rock ratio of more than 0.3. Reactive transport modeling for compounds inert to microbial processes yielded that the velocity of upward migrating fluid varied from 2 to 5 cm yrFour expeditions were conducted in the offshore southwestern Taiwan by R/V Ocean Researcher 5 (legs 1309–2 and 0039) and R/V Ocean Researcher I (legs 1107 and 1118). Piston and gravity cores with lengths ranging from 102 to 720 cm were recovered along a transect from the center to the margin of the TY1 cone structure Fig. . Samplin60 (in cruises OR5-1309-2 and OR5-0039) or 1 N NaOH58 (in cruises OR1-1107 and OR1-1118) as preservatives, sealed with butyl rubber stoppers, crimped with aluminum caps, and stored upside down at room temperature or 4 °C. Pore fluid samples for aqueous geochemistry were obtained through centrifugation and subsequently filtered through 0.22-µm nylon membrane syringe filters. The filtrates were further split into five fractions for the analyses of anion, cation, total alkalinity (TA), carbon isotopic compositions of DIC, and oxygen and hydrogen isotopic composition, and stored at 4 °C. For the cation samples, concentrated nitric acid (70%) was added at a volume ratio of 1:45 to preserve the valence state for elements sensitive to the redox change.Sediments and core top seawater for gas analyses were collected and transferred into serum bottles. These bottles were later filled with saturated NaCl58. Ion concentrations were determined by an ion chromatography  equipped with a Metrosep A Supp 5 column for anions and a Metrosep C 4 column for cations and with the detection limit around 0.1 ppm in weight61. Lithium and boron were analyzed by a sector field inductively coupled plasma mass spectrometry (SF–ICP–MS) for 7Li and 11B at low resolution (M:ΔM ~300). Six standards were prepared from reference material NASS–5  with the uncertainty better than 3%. TA was determined by titrating the sample with 0.02 N HCl while bubbling with nitrogen62. Carbon isotopic compositions of methane and DIC were measured by an isotope ratio mass spectrometry (IRMS) in line with a gas chromatography and a combustion oven. Hydrogen and oxygen isotope compositions of porewater were measured by a LGR Liquid-Water Isotope Analyzer  and a Picarro L2140-i Analyzer (for cruises OR1-1107 and OR1-1118). The obtained isotopic compositions were expressed as the δ notation referenced to standards . Standards are the Vienna Pee Dee Belemnite (VPDB) for carbon isotope, and the Vienna Standard Modern Ocean Water (VSMOW) for hydrogen and oxygen isotopes. The precisions are ±0.3‰ for δ13C values by the IRMS measurements, ±0.2‰ for δ18O values and ±2.0‰ for δ2Η values by the LWIA measurements, and ±0.03‰ for δ18O values and ±0.22‰ for δ2Η values by the Picarro spectroscopic measurements. The water content of cored sediments was calculated based on the weight loss of the sediments after freeze-drying. The porosity was converted from the water content assuming a dry sediment density of 2.7 g cm−3 63.The concentrations of headspace hydrocarbon gases were determined by a gas chromatography  with the precision typically better than 5%64, a simplified one-dimensional reactive transport modeling was applied for porewater geochemistry profiles with a revised code65. The concentration-depth profiles of five dissolved species, including chloride, sodium, potassium, sulfate, and methane, were calculated. This model is formulated by a partial differential equation for solute transport and reactions66. The modeling was first performed on conservative solutes (chloride and sodium) exempted from microbial processes and most abiotic reactions in shallow sediment environments to determine the range of upward advection rates. The sodium and chloride concentrations in the shallow sediments . To generalize the fluid velocities across the entire cone structure, the variations in velocity with distance from the edge of the crater were fitted with the exponential equation . Such a relationship was then applied to calculate the discharge fluxes of other individual SMVs (Supplementary Information, Table 70. More details are described in Supplementary Information.In this calculation, the crater radius of 250 m, the cone diameter of 3600 msupplementary information.supplementary dataset.


The passive leg raising (PLR) maneuver has become standard practice in fluid resuscitation. We aim to investigate the precision and consistency of the PLR for determining fluid responsiveness in critically ill patients and healthy volunteers using bioreactance non-invasive cardiac output monitoring .Fluid responsiveness was defined as a change in stroke volume index (ΔSVI) > 10% with each PLR as determined by NiCOM™. Precision was the variability in ΔSVI after the 3 PLR’s, and determined by range, average deviation and standard deviation. Consistency was the same fluid responsiveness determination of “Yes” (ΔSVI > 10%) or “No” (ΔSVI ≤ 10%) for all 3 PLR’s.This study is prospective, single-center, observational cohort with repeated measures in critically ill patients admitted to the medical intensive care unit and healthy volunteers at a tertiary academic medical center. Three cycles of PLR were performed, each at 20–30 minutes apart. Seventy-five patients and 25 volunteers were enrolled. In patients, the precision was range of 17.2±13.3%, average deviation 6.5±4.0% and standard deviation 9.0±5.2%; and for volunteers, 17.4±10.3%, 6.6±3.8% and 9.0±6.7%, respectively. There was no statistical difference in the precision measurements between patients and volunteers. Forty-nine (65.3%) patients vs. twenty-four (96.0%) volunteers had consistent results, p < 0.01. Among those with consistent results, twenty-four (49.0%) patients and 24 (100%) volunteers were fluid responsive.The precision and consistency of determining ΔSVI with NiCOM™ after PLR may have clinical implication if ΔSVI > 10% is the absolute cutoff to determine fluid responsiveness. Fluid management decisions are a difficult but integral part of daily patient care. With the growing consensus that extremes of fluid balance may be detrimental, an accurate assessment of a patient’s volume status is paramount –4. ScreeFluid responsiveness as an increase in cardiac output (CO) in response to augmentation of preload is fundamentally based on the classical Frank-Starling curve , 6. If pWith less invasive technologies becoming increasingly available, the gold standard of pulmonary artery (PA) catheterization to measure CO has naturally fallen out of favor. As such, the bioreactance-based Non-invasive Cardiac Output Monitor  became popular in recent years given its completely non-invasive platform using four skin sensor pads placed on the patient’s thorax. It measures stroke volume (SV) by using time delays or phase shifts induced by thoracic blood flow during the transmission of electrical current between the sensors –12.accuracy of the PLR for predicting fluid responsiveness has been determined, we were unable to find published data on the precision and consistency of predicting fluid responsiveness in critically ill patients by PLR [At our institution, the NiCOM™ technology has become a primary tool to guide fluid management. We utilize its built-in algorithm for determining fluid responsiveness with the PLR. We a priori accept the NiCOM™ to provide accurate measurements of SV and CO. However, we frequently observed variability in the measured change in stroke volume index (ΔSVI) based on PLR. While the s by PLR . The purThis study is a single-center, prospective observational cohort, with repeated measures in critically ill patients admitted to the medical intensive care unit and healthy volunteers. The study was conducted at an academic tertiary-care medical center from June 2017 to January 2018. Written informed consent was obtained from study participants or their legal representatives. The protocol was approved by the Loma Linda University Institutional Review Board.For the patient cohort, inclusion criteria were: 1) age ≥ 18 years, and 2) suspicion for hypovolemia or indicated for volume expansion due to any one of the following: hypotension , tachycardia (heart rate > 90 beats per min), blood lactate > 2.0 mmol/L, skin mottling, oliguria (urine output < 30 ml/hr), or requiring vasopressor/inotrope support. Patients were excluded if there was an active arrhythmia, bleeding causing hemodynamic instability, intra-abdominal hypertension, pelvic and/or lower extremity trauma/amputation preventing passive leg raising, pulmonary hypertension requiring chronic pulmonary vasodilator therapy, severe valvular heart disease, severe anatomic abnormalities of thoracic aorta, external pacemaker, or renal replacement therapy during the data collection period.Healthy volunteers were enrolled if they were ≥ 18 years of age with self-reported unremarkable medical history, but then excluded if unable to tolerate PLR.To ensure minimal variability, the PLR and data collection were performed only by the authors after they were provided with an in-depth training on the NiCOM™ technology by the manufacturer. Each PLR and associated hemodynamic parameters were performed and recorded as follows. The subject’s head was raised to a semi-recumbent 45° position for 3 minutes with the legs flat in the bed. After a stable hemodynamic signal was achieved on the NiCOM™ system, baseline hemodynamic data was collected. The subject’s head was then placed flat and the legs elevated to 30–45° for 3 minutes with a standardized wedge pillow. The second set of hemodynamic data was collected at the time of PLR “auto-transfusion”. At the end of 3 minutes, changes (Δ) in stroke volume index (SVI) and cardiac index (CI) were displayed on the NiCOM™ monitor, stating whether or not the subject was fluid responsive. The subject was then placed back in pre-PLR position. The above procedure of obtaining baseline hemodynamic values, performing the PLR, and recording ΔSVI and ΔCI was considered one PLR cycle. We performed three PLR cycles, designated as PLR 1, PLR 2, and PLR 3, each separated by a rest period of 20 to 30 minutes .In the patient cohort, if the subject was found to be fluid responsive during the PLR but hemodynamically stable, data collection was continued. Upon completion of the study procedure, the primary team caring for the patient was notified of the results. If the patient was found to be fluid responsive and unstable, or the patient became hemodynamically unstable for any reason during data collection, the study procedure was aborted and the primary team was notified immediately. The study procedure was also aborted if during the data collection the patient required any new hemodynamic intervention, including titration of fluids, vasopressor and/or inotrope; or if the patient did not tolerate the PLR. Data collected from patients with the study procedure aborted were excluded from data analysis.Subject demographic characteristics, hemodynamic parameters, Sepsis-related  Organ Failure Assessment (SOFA) score, laboratory values, admission diagnosis category, and the presence of vasopressor/inotrope and mechanical ventilation were recorded .Fluid responsiveness was defined as ΔSVI > 10% after PLR. Precision was the variability in ΔSVI in response to PLR. We calculated the precision of ΔSVI over the 3 cycles of PLR’s by three methods: 1) range, 2) average deviation, and 3) standard deviation.The ΔSVI after the PLR was calculated by the NiCOM™ proprietary algorithm using the peak value of SVI during the PLR challenge phase divided by the average SVI during the baseline phase. maxx − minxRange = Average Deviation = Standard Deviation = maxx = maximum ΔSVI from 3 cycles of PLR in the same subjectminx = minimum ΔSVI from 3 cycles of PLR in the same subjectix = ΔSVI per PLRPLRn = 3 (number of cycles of PLR in the same subject)Consistency was defined as the same fluid responsiveness determination of “Yes” (ΔSVI > 10%) or “No” (ΔSVI ≤ 10%) for all 3 cycles of PLR’s. For example, a subject was categorized to have consistent results if they were persistently fluid responsive or not fluid responsive for all 3 PLR’s. A subject had inconsistent results if any one of the 3 PLR’s resulted in a fluid responsiveness determination that was different from the other PLR’s.https://www.medcalc.org; 2016).Independent sample t-test, Mann Whitney or proportion chi-square was used to test for subject differences. Repeated measures analysis of variance was used to compare hemodynamic data over the 3 cycles of PLR’s. Cohen’s κ was calculated for agreement in determining fluid responsiveness of “Yes” (ΔSVI > 10%) or “No” (ΔSVI ≤ 10%) between two PLR’s . Pearson’s correlation coefficient (r) was calculated to determine the linear relationship between two PLR measurements of ΔSVI. Data are reported as mean±standard deviation. Categorical data are expressed as proportions. Data were analyzed using MedCalc Statistical Software version 18.2.1  had consistent determination of fluid responsiveness, with all those being fluid responsive (ΔSVI > 10%).Forty-nine (65.3%) patients had consistent determination of fluid responsiveness for all 3 PLR’s . Among taccuracy of the PLR in predicting fluid responsiveness has been studied, our data suggest that the precision can change clinical decisions regarding fluid management when using this technology [We showed that the percent change in SVI measured by the NiCOM™ after a PLR has a precision of approximately ±9% (standard deviation) in both critically ill patients and healthy volunteers. While the chnology . For exaPrevious studies have shown that CO has a variation of 4.4 to 10.1% , 16. HowIn addition to spontaneous variation in CO measurements as explanation for our results, we used a bioreactance-based technology. While a number of studies have shown bioreactance to be a viable technology in determining fluid responsiveness coupled with the PLR, Kupersztych-Hagege et al. showed it to be unreliable when compared to thermodilution in measuring CI, with an error up to 82% and an area under the receiver operating characteristics curve not different from 0.5 for the ability of PLR induced ΔSVI to predict fluid responsiveness . In a vaTM , FloTracTM , NiCOMTM, PA catheter and PiCCOTM , respectively. Thus, we posit that the variability in ΔSVI observed with NiCOMTM in our study will likely be similar with other technologies.Lamia et al. further examined the accuracies of continuous CO measurements by various technologies incorporating arterial pulse contour analysis, bioreactance and thermodilution . They foOur study showed that healthy volunteers were more fluid responsive than critically ill patients, with 96% of the volunteers being consistently fluid responsive with ΔSVI > 10%. Similarly, Miller et al. examined 40 healthy volunteers by performing the PLR followed by 500 mL intravenous bolus of 0.9% normal saline . They shTM against. We also did not confirm fluid responsiveness with administration of a fluid bolus. Recent systematic reviews already concluded that the PLR was the most accurate measure of fluid responsiveness compared to other modalities [We performed an observational study without a reference hemodynamic monitoring technology, such as the PA catheter, to compare the NiCOMdalities , 13. OurWe did not have a priori estimate of the precision of ΔSVI after the PLR in the general population. Therefore, we could not perform a sample size calculation taking into consideration statistical vs. clinical significance. However, based on previous studies examining the accuracy of PLR, our study team arbitrarily agreed to enroll 75 critically ill patients and 25 healthy volunteers .Previous studies examining the variability of CO have taken measurements at intervals of seconds to minutes , 16. We In conclusion, our study showed the precision of determining ΔSVI by the NiCOM™ after the PLR is at ±9% in both critically ill patients and volunteers; however, consistency is significantly higher in volunteers. Although not tested, knowledge of precision and consistency of the PLR in predicting fluid responsiveness may impact clinical decision-making. The ΔSVI cutoff of 10% may need further clarification as 10±9%. With this level of variability, fluid responsiveness could perhaps be redefined as graded levels of responsiveness rather than as a binary prompt when utilizing non-invasive hemodynamic monitoring technologies. For example, ΔSVI < 5% may suggest “not fluid responsive”, ΔSVI 5–15% suggests “possible fluid responsive”, and ΔSVI > 15% “definitely fluid responsive”. Further studies are thus needed to examine how precision of the PLR and such proposed modified definition of fluid responsiveness can be incorporated in fluid resuscitation protocols.S1 File(XLSX)Click here for additional data file.


This is the first report in the literatureanalysing ZIKV in the follicular fluid, cumulus cells, and oocytes.”Indeed, the identification of the virus in follicular fluid has been mentioned for along time (Dear Editor, we read the publication on “Case report of Zika virus during controlledovarian hyperstimulation: results from follicular fluid, cumulus cells and oocytes” witha great interest.


Patient‐derived tumour xenografts and tumour organoids have become important preclinical model systems for cancer research. Both models maintain key features from their parental tumours, such as genetic and phenotypic heterogeneity, which allows them to be used for a wide spectrum of applications. In contrast to patient‐derived xenografts, organoids can be established and expanded with high efficiency from primary patient material. On the other hand, xenografts retain tumour–stroma interactions, which are known to contribute to tumorigenesis. In this review, we discuss recent advances in patient‐derived tumour xenograft and tumour organoid model systems and compare their promises and challenges as preclinical models in cancer research. It was previously demonstrated that orthotopic transplantation can lead to local invasive growth and metastases, similar to those observed in patients , or to which the differentiation was directed (embryonic stem cell‐derived organoids)  xenografts was treated with topotecan, a topoisomerase I inhibitor, combinations of topotecan and the topoisomerase II inhibitor etoposide or alkylating agents ifosfamide or cisplatin at maximum tolerated dose , observed in a breast cancer cohort, within a tumour can be indicative of germline mutations in NTHL1  play an important role in the tumour environment. As such, Öhlund and colleagues showed that a co‐culture of murine pancreatic stellate cells (PSCs) and PDAC tumour organoids recapitulate properties of PDAC desmoplasia. They demonstrate that PSCs differentiate into two distinct subtypes of CAFs with elevated expression of αSMA and secretion of IL‐6 and additional inflammatory mediators, respectively  mouse model can be generated by transplantation of CD34‐positive human hematopoietic stem cells (HSCs) or precursor cells. These cells can be isolated from bone marrow, peripheral blood or umbilical cord blood. CD34‐positive HSC transplantation can be performed alone or in combination with transplantation of human immune tissues, such as thymic tissue  can be differentiated towards several lineages and can be used to model normal development The authors declare that they have no conflict of interest.


The heterogeneity of frictional strength along the megathrust earthquake zone critically controls plate coupling and long-term subduction dynamics. However, the persistence and distribution of high-friction segments through space and time remain poorly constrained. Here, we show that accretion processes, such as tectonic underplating , can be used as a proxy to characterize the long-term frictional zonation of the subduction interface. We carry out numerical thermo-mechanical experiments, which predict a first-order control of tectonic-stress variations on fluid transport in deep fore-arc regions. Accordingly, positive feedback between fluid distribution and effective stress favours the stability of the interface frictional properties at Myr-scale which, in turn, controls the deep accretionary dynamics. We propose that the recognition of thick duplex structures resulting from successive underplating events over tens of Myr, allows for tracking subduction segments exhibiting an increasing frictional behaviour. Our numerical results help ascertain the long-term hydro-mechanical properties and distribution of coupling/decoupling segments of megathrust earthquake zones worldwide where active tectonic underplating is recognized. Based on seismological and geodetic observations, interfaces appear highly segmented with a heterogeneous distribution of coupled and decoupled patches due to pore fluid pressure variations8, but also geological and structural complexities of the lower and upper plates9. However, the persistence of this short-term frictional pattern is difficult to confirm despite crucial implications at various timescales, such as predicting potential ruptures on the interface10 and assessing margin dynamics over thousands to millions of years12.Subduction fluids are intricately linked to tectonic processes13  has been proposed to be triggered by local variations of plate coupling14 and may, to this end, shed light on hydro-mechanical processes and stress distribution along the plate interface. However, the exact connection between interface properties, deformation mechanisms and detachments of tectonic slices remains elusive and requires to deal with pore fluid pressure and effective stress interrelations. In this context, we assess for the first time the relations between long-term fluid flow, stress regime and tectonic underplating events in deep fore-arc regions, using high-resolution, two-dimensional, thermo-mechanical experiments.The accretion of downgoing material at the base of the fore-arc region15, solved in a high-resolution domain . The model takes into account hydration and dehydration processes by implementing compaction and thermodynamically constrained metamorphic reactions16, as well as fluid transport driven by fluid buoyancy and dynamic pressure gradients17. As a result, fluids defined as Lagrangian markers move from high- to low-stress regions, depending on calculated deviatoric stresses  and the overriding continental crust thickness . In our reference model, vperc = 1 cm yr−1, which is of same order of magnitude than estimations for long-term fluid flow in accretionary wedges19. However, these estimations have been obtained from in-situ measurements and numerical modelling on the most frontal part of the accretionary wedge and the uncertainty on the fluid velocity remains high in the deep fore-arc region. Accordingly, additional experiments have been carried out by varying vperc to explore the effect of fluid drainage on plate interface strength and accretion processes , the plate convergence rate  result in significantly different fluid distribution and accretion dynamics  results in a similar evolution as for the cold subduction model during the first ~20 Myr, with the formation of an early duplex by successive underplating events 22. Geophysical imaging of duplex structures has also been reported from several actives subduction zones26 , leading to suggest that tectonic underplating may be ubiquitous along most of active margins worldwide.All our numerical experiments evidence the formation of a more or less developed duplex by accretion of sedimentary and basaltic slices at the base of the fore-arc crust, independently from the age of the oceanic lithosphere, the plate convergence rate and the thickness of the overriding crust Figs  and S5. 28. These include hydrofracture networks into fossilized subduction channels from 10 to >40 km depth30, as well as seismic imaging of a 2–8-km-thick, landward-dipping low-velocity zone (LVZ) with high VP/VS ratio near the mantle wedge corner along several active margins displaying contrasting thermal regimes27 . It is noticeable that fluid release from thermodynamically-constrained metamorphic reactions are activated at shallower depth  in the warm subduction model, resulting in a fluid-oversaturated subduction channel at the base of the fore-arc crust . Independently from the thermal regime, these high fluid pressures must be achieved by undrained, low-permeability conditions at the top of the plate interface35. Several hypotheses to explain this phenomenon have been proposed such as impermeable lithologies36, deformation-controlled grain-size reduction processes37 or mineral precipitation38. According to our numerical experiments, we argue that tectonic-stress variations and associated dynamic pressure gradient on and above the plate interface exert a first-order control on fluid transport  and avoid extensive fluid influx above the interface . Furthermore, additional experiments show that variations in fluid transport properties strongly control the frictional state of the plate interface and, therefore, the efficiency and the depth of accretion processes  along the plate interface have been invoked to explain slow slip events (SSE) updip and downdip the seismogenic zoneI2ELVIS code, which solved the continuity, momentum and heat conservation equations, based on a finite difference scheme and a non-diffusive marker-in-cell technique15. The continuity equation describes the conservation of mass, assuming a visco-elasto-plastic compressible fluid. It is solved on a staggered Eulerian grid and has the form:ρeff is the effective rock density calculated in Eq. . The heat conservation equation is formulated in a Lagrangian form to avoid numerical diffusion of temperature:Cp is the isobaric heat capacity, T the temperature, Hr the radiogenic heat production, Ha the adiabatic heat production, Hs the shear heating and qi the heat flux solved as:k is the thermal conductivity depending pressure, temperature and rock type . This linearly-decreasing pore water content accounts for the kinetics of metamorphic reactions and the natural heterogeneity of rocks that result in distributed fluid release over a temperature range, rather than in discrete pulses61. Bound water release is calculated by free-energy minimization16 as a function of pressure, temperature and rock type11. Resulting free water is then transported as newly-formed Lagrangian markers. Importantly, our models do not take into account the coupling between pore pressure and hydraulic properties  as considered in real two-phase flow systems62. Instead, we make the following assumptions: (i) fluid pressure approaches the solid dynamic pressure and (ii) hydraulic properties remain constant due to the balance between porosity enhancement and destruction17. Accordingly, fluid marker transport is controlled by the Stokes velocity field vi, the fluid buoyancy and the dynamic pressure gradients, such that:vperc the reference percolation velocity  and |ki| = 2 for |ki| > 2. Upward transport is defined by positive values. Once moving, fluid markers may be then consumed by rock markers , depending on their stable water content. Further details on the fluid implementation are provided by refs17.In our experiments, fluids are initially prescribed in the subducting oceanic lithosphere as (i) pore water in sediments and basaltic crust  behaviour such that:η is the effective creep viscosity, n the creep exponent, AD the pre-exponential factor, E the activation energy, V. the activation volume, R the gas constant, C the cohesion, φdry the internal friction angle for dry rocks and λfluid the pore fluid pressure factor. The latter is defined as λfluid = 0 for dry rocks and λfluid = 0.99 for fluid-oversaturated rocks  except at the surface where hydrostatic conditions are assumed . Because no fluid infiltration or alteration is evidenced a few hundreds of meters above fossilized subduction channels63, fluid weakening effects have been deactivated for the upper and lower continental crusts.Details on the calculation of the rheological constitutive equations are available in ref.x and y direction, respectively . Eulerian grid resolution is 2.0 × 1.5 km except at the centre of the domain where it is 0.5 × 0.4 km. Additionally, ~8 millions of randomly distributed markers are initially prescribed for advecting material properties and computing water release, transport and consumption. Convergence rate (defined within the convergence condition region) is 5 cm yr−1. The initial setup is designed with a 30-km-thick overriding continental crust composed of 15 km of felsic upper crust and 15 km of mafic lower crust. The 7.5-km-thick subducting oceanic crust is made up of 0.5 km of pelagic sediments, 2 km of hydrated basaltic crust and 5 km of gabbroic crust . The oceanic crust is initially underthrusted below the continental margin and a 10-km-thick weak zone is prescribed at the interface between the two plates to initiate subduction.In all numerical experiments, the computational domain meases 1500 × 200 km in the x = 0) to 53 Myr (x = 854 km) for the cold subduction setting (model sub53-1). To limit the size of the computational domain, the cooling of the oceanic lithosphere is prescribed as 10 times faster for 0 ≤ x ≤ 200 km. This high cooling zone is located at ~600 km away from the subduction zone, which allows to avoid any thermal or mechanical effect on modelled fore-arc dynamics. In the case of a warm subduction setting (model sub20-1), the maximum cooling age of the oceanic lithosphere is 20 Myr and its cooling is only 2 times faster for 0 ≤ x ≤ 200 km. A geothermal gradient of ~15° km−1 down to 90 km is defined for the continental lithosphere. Below, the asthenospheric geothermal gradient is 0.5° km−1.The thermal structure of the oceanic lithosphere is initially defined by an oceanic geotherm with a cooling age evolving from 10 kyr (y < 10 km) or water. Resulting large viscosity contrast minimizes shear stresses at the top of the lithospheres, which efficiently approximates a free surface64. In our experiments, sedimentation and erosion processes are implemented independently by applying the following equation at the surface65:ysurf is the y coordinate of the surface, vx and vy the horizontal and vertical velocity components of the Stokes velocity field at the surface and verosion and vsedim the global erosion and sedimentation rates, respectively, defined as (i) verosion = 0.3 mm yr−1 and vsedim = 0 mm yr−1 for y < 10 km and (ii) verosion = vsedim = 0 mm yr−1 for y > 10 km. A increased erosion/sedimentation rate of 1 mm yr−1 is applied to regions with steep surface slopes  for smoothing the topographic surface. This is particularly relevant for the offshore fore-arc region where the increased sedimentation rate counterbalances the absence of global sedimentation rate prescribed in our experiments. Newly-formed sedimentary rocks are labelled as terrigenous sediments and display the same properties than the pelagic sediments (Supplementary Table Velocity boundary conditions are free slip for the left, right and upper boundaries, while the lower boundary is open to ensure mass conservation in the computational domain. The top of the lithosphere is calculated as an internal free surface by using a low-viscosity layer simulating air (Movie S1Movie S2Supplementary Information