Assessment of the interstitial fluid pressure of tumors by dynamic contrast-enhanced magnetic resonance imaging with contrast agents of different molecular weights

Predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning

Liposome-based anticancer agents take advantage of the increased vascular permeability and transvascular pressure gradients for selective accumulation in tumors, a phenomenon known as the enhanced permeability and retention(EPR) effect. The EPR effect has motivated the clinical use of nano-therapeutics, with mixed results on treatment outcome. High interstitial fluid pressure (IFP) has been shown to limit liposome drug delivery to central tumour regions. Furthermore, high IFP is an independent prognostic biomarker for treatment efficacy in radiation therapy and chemotherapy for some solid cancers. Therefore, accurately measuring spatial liposome accumulation and IFP distribution within a solid tumour is crucial for optimal treatment planning. In this paper, we develop a model capable of predicting voxel-by-voxel intratumoral liposome accumulation and IFP using pre and post administration imaging. Our approach is based on physics informed machine learning, a novel technique combining machine learning and partial differential equations. through application to a set of mouse data and a set of synthetically-generated tumours, we show that our approach accurately predicts the spatial liposome accumulation and IFP for an individual tumour while relying on minimal information. This is an important result with applications for forecasting tumour progression and designing treatment.

Drug delivery through nanoparticles in solid tumors: a mechanistic understanding

Aim: In this study, the main goal was to apply a multi-scale computational model in evaluating nano-sized drug-delivery systems, following extracellular drug release, into solid tumors in order to predict treatment efficacy. Methods: The impact of several parameters related to tumor (size, shape, vessel-wall pore size, and necrotic core size) and therapeutic agents (size of nanoparticles, binding affinity of drug, drug release rate from nanoparticles) are examined in detail. Results: This study illustrates that achieving a higher treatment efficacy requires smaller nanoparticles (NPs) or a low binding affinity and drug release rate. Long-term analysis finds that a slow release rate in extracellular space does not always improve treatment efficacy compared with a rapid release rate; NP size as well as binding affinity of drug are also highly influential. Conclusions: The presented methodology can be used as a step forward towards optimization of patient-specific nanomedicine plans.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Monoclonal Antibody Tumor Disposition

The prediction of monoclonal antibody (mAb) disposition within solid tumors for individual patients is difficult due to inter-patient variability in tumor physiology. Improved a priori prediction of mAb pharmacokinetics in tumors may facilitate the development of patient-specific dosing protocols and facilitate improved selection of patients for treatment with anti-cancer mAb. Here, we report the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), with tumor penetration of the contrast agent gadobutrol used as a surrogate, to improve physiologically based pharmacokinetic model (PBPK) predictions of cetuximab pharmacokinetics in epidermal growth factor receptor (EGFR) positive xenografts. In the initial investigations, mice bearing Panc-1, NCI-N87, and LS174T xenografts underwent DCE-MRI imaging with the contrast agent gadobutrol, followed by intravenous dosing of an 125Iodine-labeled, non-binding mAb (8C2). Tumor concentrations of 8C2 were determined following the euthanasia of mice (3 h-6 days after 8C2 dosing). Potential predictor relationships between DCE-MRI kinetic parameters and 8C2 PBPK parameters were evaluated through covariate modeling. The addition of the DCE-MRI parameter Ktrans alone or Ktrans in combination with the DCE-MRI parameter Vp on the PBPK parameters for tumor blood flow (QTU) and tumor vasculature permeability (σTUV) led to the most significant improvement in the characterization of 8C2 pharmacokinetics in individual tumors. To test the utility of the DCE-MRI covariates on a priori prediction of the disposition of mAb with high-affinity tumor binding, a second group of tumor-bearing mice underwent DCE-MRI imaging with gadobutrol, followed by the administration of 125Iodine-labeled cetuximab (a high-affinity anti-EGFR mAb). The MRI-PBPK covariate relationships, which were established with the untargeted antibody 8C2, were implemented into the PBPK model with considerations for EGFR expression and cetuximab-EGFR interaction to predict the disposition of cetuximab in individual tumors (a priori). The incorporation of the Ktrans MRI parameter as a covariate on the PBPK parameters QTU and σTUV decreased the PBPK model prediction error for cetuximab tumor pharmacokinetics from 223.71 to 65.02%. DCE-MRI may be a useful clinical tool in improving the prediction of antibody pharmacokinetics in solid tumors. Further studies are warranted to evaluate the utility of the DCE-MRI approach to additional mAbs and additional drug modalities.

Computational modeling of PET tracer distribution in solid tumors integrating microvasculature

BACKGROUND

We present computational modeling of positron emission tomography radiotracer uptake with consideration of blood flow and interstitial fluid flow, performing spatiotemporally-coupled modeling of uptake and integrating the microvasculature. In our mathematical modeling, the uptake of fluorodeoxyglucose F-18 (FDG) was simulated based on the Convection-Diffusion-Reaction equation given its high accuracy and reliability in modeling of transport phenomena. In the proposed model, blood flow and interstitial flow are solved simultaneously to calculate interstitial pressure and velocity distribution inside cancer and normal tissues. As a result, the spatiotemporal distribution of the FDG tracer is calculated based on velocity and pressure distributions in both kinds of tissues.

RESULTS

Interstitial pressure has maximum value in the tumor region compared to surrounding tissue. In addition, interstitial fluid velocity is extremely low in the entire computational domain indicating that convection can be neglected without effecting results noticeably. Furthermore, our results illustrate that the total concentration of FDG in the tumor region is an order of magnitude larger than in surrounding normal tissue, due to lack of functional lymphatic drainage system and also highly-permeable microvessels in tumors. The magnitude of the free tracer and metabolized (phosphorylated) radiotracer concentrations followed very different trends over the entire time period, regardless of tissue type (tumor vs. normal).

CONCLUSION

Our spatiotemporally-coupled modeling provides helpful tools towards improved understanding and quantification of in vivo preclinical and clinical studies.

Evaluation of solid tumor response to sequential treatment cycles via a new computational hybrid approach

The development of an in silico approach that evaluates and identifies appropriate treatment protocols for individuals could help grow personalized treatment and increase cancer patient lifespans. With this motivation, the present study introduces a novel approach for sequential treatment cycles based on simultaneously examining drug delivery, tumor growth, and chemotherapy efficacy. This model incorporates the physical conditions of tumor geometry, including tumor, capillary network, and normal tissue assuming real circumstances, as well as the intravascular and interstitial fluid flow, drug concentration, chemotherapy efficacy, and tumor recurrence. Three treatment approaches-maximum tolerated dose (MTD), metronomic chemotherapy (MC), and chemo-switching (CS)-as well as different chemotherapy schedules are investigated on a real tumor geometry extracted from image. Additionally, a sensitivity analysis of effective parameters of drug is carried out to evaluate the potential of using different other drugs in cancer treatment. The main findings are: (i) CS, MC, and MTD have the best performance in reducing tumor cells, respectively; (ii) multiple doses raise the efficacy of drugs that have slower clearance, higher diffusivity, and lower to medium binding affinities; (iii) the suggested approach to eradicating tumors is to reduce their cells to a predetermined rate through chemotherapy and then apply adjunct therapy.

Drug delivery to solid tumors with heterogeneous microvascular networks: Novel insights from image-based numerical modeling

The present study examines chemotherapy by incorporating multi-scale mathematical modeling to predict drug delivery and its effects. This approach leads to a more-realistic physiological tumor model than is possible with previous approaches, as it obtains the capillary network geometry from an image, and also considers the tumor's necrotic core, drug binding, and cellular uptake. Modeling of the fluid flow and drug transport is then performed in the extracellular matrix. The results demonstrate a 10% drop in the fraction of killed cancer cells 69% rather than the 79% reported earlier for a tumor of similar geometry a more-accurate value. This study examines how tumor-related parameters including the necrotic core size and tumor size, and also drug-related parameters drug dosage, binding affinity of drug, and drug degradation can affect the delivery of the drug to solid tumors. Results indicate that concentration of drug are high in the tumor, low in normal tissue, and remarkably low in the necrotic core. Results also offer a treatment of tumors with smaller necrotic core. Tumor size, which implies the tumor progression, has a considerable impact on treatment outcomes, so to be more effective, treatment should be applied at a specific size of tumor. It is demonstrated that binding affinity of drugs to cell-surface receptors and drug dosage have significant impact on treatment efficacy, so they should be regulated based on a balanced quantification between maximum treatment efficacy and minimum side effects. On the other hand, considering the effects of drug degradation in the model has not significant effect on treatment efficacy. The findings of the present study provide insight into the mechanism of drug delivery to solid tumors based on analyzing the effective parameters and modeling how their behavior in the tumor microenvironment affects treatment efficacy.

Rapid stromal remodeling by short-term VEGFR2 inhibition increases chemotherapy delivery in esophagogastric adenocarcinoma

Anti-angiogenic agents combined with chemotherapy is an important strategy for the treatment of solid tumors. However, survival benefit is limited, urging the improvement of combination therapies. We aimed to clarify the effects of vascular endothelial growth factor receptor 2 (VEGFR2) targeting on hemodynamic function and penetration of drugs in esophagogastric adenocarcinoma (EAC). Patient-derived xenograft (PDX) models of EAC were subjected to long-term and short-term treatment with anti-VEGFR2 therapy followed by chemotherapy injection or multi-agent dynamic contrast-enhanced (DCE-) MRI and vascular casting. Long-term anti-VEGFR2-treated tumors showed a relatively lower flow and vessel density resulting in reduced chemotherapy uptake. On the contrary, short-term VEGFR2 targeting resulted in relatively higher flow, rapid vasodilation, and improved chemotherapy delivery. Assessment of the extracellular matrix (ECM) revealed that short-term anti-angiogenic treatment drastically remodels the tumor stroma by inducing nitric oxide synthesis and hyaluronan degradation, thereby dilating the vasculature and improving intratumoral chemotherapy delivery. These previously unrecognized beneficial effects could not be maintained by long-term VEGFR2 inhibition. As the identified mechanisms are targetable, they offer direct options to enhance the treatment efficacy of anti-angiogenic therapy combined with chemotherapy in EAC patients.

Ultra-early changes in vascular parameters from dynamic contrast enhanced MRI of breast cancer xenografts following systemic therapy with doxorubicin and liver X receptor agonist

Background

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) may be used to depict tumour vascular structure and for therapy response assessment in various tumour sites. The purpose of the current work is to examine whether ultra-early changes in tumour physiology following cytotoxic treatment with doxorubicin and liver X receptor (LXR) agonist GW3965 are detectable by DCE-MRI.

Methods

36 female, athymic nude foxn1nu mice with bilaterally implanted breast cancer xenografts (17 with ER-positive HBCx34, 19 with triple-negative HBCx39) were randomised in the following treatment groups; control, GW3965 (40 mg/kg p.o.), doxorubicin (8 mg/kg i.v.) and a combination therapy of GW3965 and doxorubicin. DCE-MRI (3D FLASH on a 7 T preclinical scanner) was performed at baseline and one and six days after onset of treatment. Wash-in (30 s p.i.) and wash-out (300 s p.i.) enhancement were quantified from dynamic uptake curves, before voxel-by-voxel fitting to the pharmacokinetic Tofts model and generation of maps for the resulting parameters K^trans, ν_e and ν_B. Treatment effect was evaluated by univariate repeated measures mixed-effects maximum likelihood regression models applied to median tumour data.

Results

We found no effects of any treatment 24 h post treatment. After 6 days, doxorubicin given as both mono- and combination therapy gave significant increases of ~ 30% in wash-in enhancement (p < 0.011) and K^trans (p < 0.017), and 40–50% in ν_B (p < 0.024) for HBCx34, but not for HBCx39. No effects of GW3965 were observed at any time (p > 0.1).

Conclusions

Twenty-four h after onset of treatment was too early to evaluate treatment effects by DCE-MRI. Early enhancement and K^trans were approximately equally sensitive metrics to capture treatment effects six days pt. Pharmacokinetic modelling however allowed us to attribute the observed effect to changes in tumour perfusion rather than increased retention.

Postcontrast T1 Mapping for Differential Diagnosis of Recurrence and Radionecrosis after Gamma Knife Radiosurgery for Brain Metastasis

BACKGROUND AND PURPOSE

The differential diagnosis of radionecrosis and tumor recurrence in brain metastases is challenging. We investigated the diagnostic efficiency of postcontrast T1 mapping in solving this problem.

MATERIALS AND METHODS

Between March 2016 and June 2017, fifty-six patients with brain metastases who underwent contrast-enhanced cerebral T1 mapping were recruited for this prospective study. The findings revealed new enhancement after gamma knife radiosurgery. The subjects were assigned to radionecrosis and recurrence groups based on follow-up (median, 11.5 months) and histopathologic results. T1 values of lesions 5 (T1_5min) and 60 (T1_60min) minutes after administration of contrast agent and their difference (T1_differ) were compared between the 2 groups with the 2-tailed Mann-Whitney U test. Receiver operating characteristic curves were used to determine the optimum cutoff values for differential diagnosis.

RESULTS

There were significant differences between the 2 groups in T1_5min, T1_60min, and T1_differ values (P = .012, P = .004, and P < .001, respectively). Relative to T1_5min and T1_60min, T1_differ exhibited greater sensitivity and specificity (P < .001, respectively) in identifying radionecrosis. The optimum T1_differ value for differential diagnosis was 71.1 ms (area under the curve = 0.97; 95% CI, 0.93-1.00), with sensitivity and specificity of 81.5% and 96.5%, respectively.

CONCLUSIONS

Postcontrast T1 mapping is optimal for the differential diagnosis of radionecrosis and tumor recurrence. Among T1 parameters, T1_differ is the most powerful parameter for differential diagnosis. Advantages in terms of quantitative analysis and high resolution portend the wide use of postcontrast T1 mapping in the future.

Contrast agents in dynamic contrast-enhanced magnetic resonance imaging

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive method to assess angiogenesis, which is widely used in clinical applications including diagnosis, monitoring therapy response and prognosis estimation in cancer patients. Contrast agents play a crucial role in DCE-MRI and should be carefully selected in order to improve accuracy in DCE-MRI examination. Over the past decades, there was much progress in the development of optimal contrast agents in DCE-MRI. In this review, we describe the recent research advances in this field and discuss properties of contrast agents, as well as their advantages and disadvantages. Finally, we discuss the research perspectives for improving this promising imaging method.

Characterisation of Lesions after Stereotactic Radiosurgery for Brain Metastases: Impact of Delayed Contrast Magnetic Resonance Imaging

AIMS

To investigate if brain metastases and radiation injuries after stereotactic radiosurgery (SRS) have different signal intensity (SI) time courses up to 55 min after contrast agent application and if delayed contrast magnetic resonance imaging (MRI) contributes to improve diagnostic accuracy.

MATERIALS AND METHODS

Thirty-four consecutive patients treated with SRS for cerebral metastases were prospectively enrolled in the study. T1-weighted images were acquired on a 3-Tesla MR unit at three time points, at 2 (TP1), 15 (TP2) and 55 (TP3) min after administering contrast agent. A simultaneous, matched-pairs approach was used for region of interest analysis of the entire contrast-enhancing lesion (SI-e), the centre (SI-c), the border of the lesion (SI-b) and the adjacent non-contrast-enhancing tissue (SI-p). SIs of brain metastases and radiation injuries after SRS were compared using a two-level, linear, mixed-effects regression model.

RESULTS

In total, 41 lesions were analysed: 16 metastases and 25 radiation injuries. The SI time course of SI-e, SI-c and SI-b proved to be significantly different for both entities (P < 0.001) from TP2 to TP3. The SI of 39/41 lesions increased from TP1 to TP2 for the three parameters. Radiation injuries showed a further signal increase at least for SI-c from TP2 to TP3, whereas for all the three parameters SI decreased in all metastases.

CONCLUSION

Brain metastases and radiation injuries after SRS have a characteristic and statistically significantly different SI time course on sequential gadolinium enhancement MRI when late MR studies are included.

Development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging

Background

Tumor hypoxia is involved in every stage of solid tumor development: formation, progression, metastasis, and apoptosis. Two types of hypoxia exist in tumors—chronic hypoxia and acute hypoxia. Recent studies indicate that the regional hypoxia kinetics is closely linked to metastasis and therapeutic responses, but regional hypoxia kinetics is hard to measure. We propose a novel approach to determine the local pO₂ by fusing the parameters obtained from in vivo functional imaging through the use of a modified multivariate Krogh model.

Methods

To test our idea and its potential to translate into an in vivo setting through the use of existing imaging techniques, simulation studies were performed comparing the local partial oxygen pressure (pO₂) from the proposed multivariate image fusion model to the referenced pO₂ derived by Green’s function, which considers the contribution from every vessel segment of an entire three-dimensional tumor vasculature to profile tumor oxygen with high spatial resolution.

Results

pO₂ derived from our fusion approach were close to the referenced pO₂ with regression slope near 1.0 and an r² higher than 0.8 if the voxel size (or the spatial resolution set by functional imaging modality) was less than 200 μm. The simulation also showed that the metabolic rate, blood perfusion, and hemoglobin concentration were dominant factors in tissue oxygenation. The impact of the measurement error of functional imaging to the pO₂ precision and accuracy was simulated. A Gaussian error function with FWHM equal to 20 % of blood perfusion or fractional vascular volume measurement contributed to average 7 % statistical error in pO₂.

Conclusion

The simulation results indicate that the fusion of multiple parametric maps through the biophysically derived mathematical models can monitor the intra-tumor spatial variations of hypoxia in tumors with existing imaging methods, and the potential to further investigate different forms of hypoxia, such as chronic and acute hypoxia, in response to cancer therapies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12938-016-0235-5) contains supplementary material, which is available to authorized users.

Decline of Tumor Vascular Function as Assessed by Dynamic Contrast-Enhanced Magnetic Resonance Imaging Is Associated With Poor Responses to Radiation Therapy and Chemotherapy

PURPOSE

To investigate whether changes in the volume transfer coefficient (K(trans)) in a growing tumor could be used as a surrogate marker for predicting tumor responses to radiation therapy (RT) and chemotherapy (CT).

METHODS AND MATERIALS

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was consecutively performed on tumor-bearing mice, and temporal and spatial changes of K(trans) values were measured along with tumor growth. Tumor responses to RT and CT were studied before and after observed changes in K(trans) values with time.

RESULTS

Dynamic changes with an initial increase and subsequent decline in K(trans) values were found to be associated with tumor growth. When each tumor was divided into core and peripheral regions, the K(trans) decline was greater in core, although neither vascular structure or necrosis could be linked to this spatial difference. Tumor responses to RT were worse if applied after the decline of K(trans), and there was less drug distribution and cell death in the tumor core after CT.

CONCLUSION

The K(trans) value in growing tumors, reflecting the changes of tumor microenvironment and vascular function, is strongly associated with tumor responses to RT and CT and could be a potential surrogate marker for predicting the tumor response to these treatments.

Effect of fluid friction on interstitial fluid flow coupled with blood flow through solid tumor microvascular network

A solid tumor is investigated as porous media for fluid flow simulation. Most of the studies use Darcy model for porous media. In Darcy model, the fluid friction is neglected and a few simplified assumptions are implemented. In this study, the effect of these assumptions is studied by considering Brinkman model. A multiscale mathematical method which calculates fluid flow to a solid tumor is used in this study to investigate how neglecting fluid friction affects the solid tumor simulation. The mathematical method involves processes such as blood flow through vessels and solute and fluid diffusion, convective transport in extracellular matrix, and extravasation from blood vessels. The sprouting angiogenesis model is used for generating capillary network and then fluid flow governing equations are implemented to calculate blood flow through the tumor-induced capillary network. Finally, the two models of porous media are used for modeling fluid flow in normal and tumor tissues in three different shapes of tumors. Simulations of interstitial fluid transport in a solid tumor demonstrate that the simplifications used in Darcy model affect the interstitial velocity and Brinkman model predicts a lower value for interstitial velocity than the values that Darcy model predicts.

A novel approach to tracer-kinetic modeling for (macromolecular) dynamic contrast-enhanced MRI

PURPOSE

To develop a novel tracer-kinetic modeling approach for multi-agent dynamic contrast-enhanced MRI (DCE-MRI) that facilitates separate estimation of parameters characterizing blood flow and microvascular permeability within one individual.

METHODS

Monte Carlo simulations were performed to investigate the performance of the constrained multi-agent model. Subsequently, multi-agent DCE-MRI was performed on tumor-bearing mice (n = 5) on a 7T Bruker scanner on three measurement days, in which two dendrimer-based contrast agents having high and intermediate molecular weight, respectively, along with gadoterate meglumine, were sequentially injected within one imaging session. Multi-agent data were simultaneously fit with the gamma capillary transit time model. Blood flow, mean capillary transit time, and bolus arrival time were constrained to be identical between the boluses, while extraction fractions and washout rate constants were separately determined for each agent.

RESULTS

Simulations showed that constrained multi-agent model regressions led to less uncertainty and bias in estimated tracer-kinetic parameters compared with single-bolus modeling. The approach was successfully applied in vivo, and significant differences in the extraction fraction and washout rate constant between the agents, dependent on their molecular weight, were consistently observed.

CONCLUSION

A novel multi-agent tracer-kinetic modeling approach that enforces self-consistency of model parameters and can robustly characterize tumor vascular status was demonstrated.

Tracer kinetic model selection for dynamic contrast-enhanced magnetic resonance imaging of locally advanced cervical cancer

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) offers a unique capability to probe tumour microvasculature. Different analysis of the acquired data will possibly lead to different conclusions. Therefore, the objective of this study was to investigate under which conditions the Tofts (TM), extended Tofts (ETM), compartmental tissue uptake model (C-TU) and 2-compartment exchange model (2CXM) were the optimal tracer kinetic models (TKMs) for the analysis of DCE-MRI in patients with cervical cancer.

MATERIAL AND METHODS

Ten patients with locally advanced cervical cancer (FIGO: IIA/IIB/IIIB/IVA - 1/5/3/1) underwent DCE-MRI prior to radiotherapy. From the two-parameter TM it was possible to extract the forward volume transfer constant (K(trans)) and the extracellular-extravascular volume fraction (ve). From the three-parameter ETM, additionally the plasma volume fraction (vp) could be extracted. From the three-parameter C-TU it was possible to extract information about the blood flow (Fp), permeability-surface area product (PS) and vp. Finally, the four-parameter 2CXM extended the C-TU to include ve. For each voxel, corrected Akaike information criterion (AICc) values were calculated, taking into account both the goodness-of-fit and the number of model parameters. The optimal model was defined as the model with the lowest AICc.

RESULTS

All four TKMs were the optimal model in different contiguous regions of the cervical tumours. For the 24 999 analysed voxels, the TM was optimal in 17.0%, the ETM was optimal in 2.2%, the C-TU in 23.4% and the 2CXM was optimal in 57.3%. Throughout the tumour, a high correlation was found between K(trans)(TM) and Fp(2CXM), ρ = 0.91.

CONCLUSION

The 2CXM was most often optimal in describing the contrast agent enhancement of pre-treatment cervical cancers, although this model broke down in a subset of the tumour voxels where overfitting resulted in non-physiological parameter estimates. Due to the possible overfitting of the 2CXM, the C-TU was found more robust and when 2CXM was excluded from comparison the C-TU was the preferred model.

Combining antiangiogenic therapy with adoptive cell immunotherapy exerts better antitumor effects in non-small cell lung cancer models

Introduction

Cytokine-induced killer cells (CIK cells) are a heterogeneous subset of ex-vivo expanded T lymphocytes which are characterized with a MHC-unrestricted tumor-killing activity and a mixed T-NK phenotype. Adoptive CIK cells transfer, one of the adoptive immunotherapy represents a promising nontoxic anticancer therapy. However, in clinical studies, the therapeutic activity of adoptive CIK cells transfer is not as efficient as anticipated. Possible explanations are that abnormal tumor vasculature and hypoxic tumor microenvironment could impede the infiltration and efficacy of lymphocytes. We hypothesized that antiangiogenesis therapy could improve the antitumor activity of CIK cells by normalizing tumor vasculature and modulating hypoxic tumor microenvironment.

Methods

We combined recombinant human endostatin (rh-endostatin) and CIK cells in the treatment of lung carcinoma murine models. Intravital microscopy, dynamic contrast enhanced magnetic resonance imaging, immunohistochemistry, and flow cytometry were used to investigate the tumor vasculature and hypoxic microenvironment as well as the infiltration of immune cells.

Results

Our results indicated that rh-endostatin synergized with adoptive CIK cells transfer to inhibit the growth of lung carcinoma. We found that rh-endostatin normalized tumor vasculature and reduced hypoxic area in the tumor microenvironment. Hypoxia significantly inhibited the proliferation, cytotoxicity and migration of CIK cells in vitro and impeded the homing of CIK cells into tumor parenchyma ex vivo. Furthermore, we found that treatment with rh-endostatin significantly increased the homing of CIK cells and decreased the accumulation of suppressive immune cells in the tumor tissue. In addition, combination therapy produced higher level of tumor-infiltration lymphocytes compared with other treatments.

Conclusions

Our results demonstrate that rh-endostatin improves the therapeutic effect of adoptive CIK cells therapy against lung carcinomas and unmask the mechanisms of the synergistic antitumor efficacy, providing a new rationale for combining antiangiogenesis therapy with immunotherapy in the treatment of lung cancer.