High-grade gliomas are associated with poor prognosis. Tumour heterogeneity and invasiveness create challenges for effective treatment and use of systemically administrated drugs. Furthermore, lack of functional predictive response-assays based on drug efficacy complicates evaluation of early treatment responses.We used microdialysis to deliver cisplatin into the tumour and to monitor levels of metabolic compounds present in the tumour and non-malignant brain tissue adjacent to tumour, before and during treatment. In parallel, we collected serum samples and used multivariate statistics to analyse the metabolic effects.We found distinct metabolic patterns in the extracellular fluids from tumour compared to non-malignant brain tissue, including high concentrations of a wide range of amino acids, amino acid derivatives and reduced levels of monosaccharides and purine nucleosides. We found that locoregional cisplatin delivery had a strong metabolic effect at the tumour site, resulting in substantial release of glutamic acid, phosphate, and spermidine and a reduction of cysteine levels. In addition, patients with long-time survival displayed different treatment response patterns in both tumour and serum. Longer survival was associated with low tumour levels of lactic acid, glyceric acid, ketoses, creatinine and cysteine. Patients with longer survival displayed lower serum levels of ketohexoses, fatty acid methyl esters, glycerol-3-phosphate and alpha-tocopherol, while elevated phosphate levels were seen in both tumour and serum during treatment.We highlight distinct metabolic patterns associated with high-grade tumour metabolism, and responses to cytotoxic cisplatin treatment. Parts of the tumour, especially the invasive migrating tumour cells outside the solid tumour areas, are often behind the blood–brain barrier, which makes it difficult for many systemically administered cytotoxic drugs to penetrate the tissue.2 Inside the tumour the blood–tumour barrier is partly disrupted, but still penetration of systemically administered drugs can be inefficient. The blood–brain and the blood–tumour barriers contribute to the fact that very few new cytotoxic drugs or targeted therapies have been successful in clinical trials. Another issue is the lack of tools for early evaluation of the effect of any given treatment. Today, magnetic resonance imaging (MRI) is used to determine changes in tumour size, the most important criterion for response evaluation. However, to improve response monitoring new functional predictive response-assays based on cellular effects are needed. Functional MRI, including MR spectroscopy, could provide a feasible technique for assessing early treatment response.4 However, the number of detectable metabolites using MR spectroscopy is still low and limited by the sometimes small or even non-visible postoperative tissue volume. Another challenge is pseudo-progression, which makes imaging unreliable during the first 12 weeks after radiotherapy.7Despite trimodal treatment based on surgery, radio-chemotherapy, and adjuvant chemotherapy, patients with glioblastoma, the most common primary brain tumour, have a median survival of only 14 months.10 However, due to the limited penetration of the blood–brain barrier it is difficult to administer cisplatin systemically in relevant doses without dose-limiting neuro- or nephrotoxicity. Therefore, interstitial drug administration is preferable.11 In the present study, we have circumvented the blood–brain barrier by delivering cisplatin locally into the tumour tissue using retrograde microdialysis.13 In addition, microdialysis allowed us to measure changes in the metabolome in the dialysate from the extracellular space in tumour tissue and brain tissue adjacent to tumour (BAT). We used mass spectrometry methods combined with chemometric multivariate statistical analysis14 to detect, early metabolic marker or a metabolic pattern in tumour, BAT and serum, and to find possible biomarkers for predicting treatment responses.Cisplatin exerts a strong cytotoxic effect on glioma cells.15 At further progression of the disease, the patients were found not suitable for further resection or any other conventional chemotherapy. When entering the study, the patients had an estimated survival of ~3 months. After interstitial treatment using microdialysis, two patients with long survival did, after further tumour progression, receive irinotecan/bevacizumab  and reirradiation 3.4 Gy X 10 , respectively.Nine patients with histologically verified glioblastoma and one with anaplastic oligoastrocytoma (pat no. 10) were included in the phase 1 trial, after tumour recurrence following second line chemotherapy. There were five male and five female patients with a mean age of 54.3 (range 40–80) years. Two patients have had several surgeries and a secondary malignification of their tumour from grade II to IV (no. 5) and from grade II to III (no. 10). Nine patients had been operated on by total or sub-total resection and one with biopsy only (no. 2). Resected tumours from patient no. 3 and no. 10 were confirmed to carry a mutation in the gene encoding isocitrate dehydrogenase 1 . All patients were treated primarily with radiotherapy and concomitant temozolomide followed by up to six cycles of adjuvant temozolomide. At recurrence, all patients, except nr. 6 and 7, have also been given second line treatment with bevacizumab based chemotherapy.16 The number of catheters used were based on the volume of the tumour tissue and previous experience calculating that the drug could penetrate approximately 10 mm from the catheter.12 One catheter was placed in BAT ~10 mm outside of the contrast enhancing lesion. In the first three patients, the intracranial catheters used were Dyphylon Cardiac Catheter (100 kDa), in the fourth patient CMA 62 (100 kDa), and the remaining six patients CMA 71 (20 kDa). Another catheter, CMA 71 (100 kDa) in the first three patients and CMA 60 (20 kDa) in the following, was placed as systemic reference in the abdominal subcutaneous tissue. All catheters had similar semipermeable polyamide membrane from the same manufacturer.Stereotactic implantation of microdialysis catheters was performed under general anaesthesia and with the aid of a Leksell stereotactic frame . A stereotactic contrast-enhanced computerised tomography (CT) was performed for target calculations. The stereotactic method to introduce the microdialysis catheters has been previously described in detail.The first night after surgery the patients recovered at the neuro-intensive care unit and baseline microdialysis samples were collected. The following day, the patients were mobilised, and administration of cisplatin was started in one of the tumour catheters, i.e. the most centrally located. On day 2 after surgery administration was started in the remaining tumour catheters. This procedure allowed us to study the pharmacokinetics of cisplatin in the tumour tissue.The study was designed as a phase 1 study with toxicity as the primary objective. The study protocol was based on a stepwise dose-escalation giving the first four patients a dose of 1 mg/day for a period of 12 days. The following patients were planned to be given 3 or 5 mg/day. Cisplatin was used in a solution of 1 ml containing 1 mg cisplatin in 9 mg/ml NaCl (Cisplatin Meda® (pat 1–5), Cisplatin Ebewe® (pat 6), Cisplatin Accord® (pat 7–9), or Cisplatin Hospira® (pat 10)). The solution used for patient no. 10 contained 1 mg/ml mannitol, added by the manufacturer.The concentration of cisplatin in the dialytic fluid was based on in vitro experiments and the flow-rate in each catheter was decided depending on the aimed daily dose and the number of catheters used. The catheters were connected to a 2.5 mL syringe placed in a micro infusion pump with an adjustable flow rate of 0.1–5.0 μL/min . All catheters were perfused with cisplatin 1 mg/ml or, before start of treatment and in the reference catheter, with an isotone Ringer solution . The pumps were attached to the head dressing of the patients and allowed the patients to move freely at the ward.The patients were carefully monitored, and blood samples were obtained during the treatment to evaluate renal or haematological toxicity and to provide pharmacokinetic information. Routine blood samples, blood cell count, electrolytes, liver enzymes and c-reactive protein were analysed preoperatively, day 1, 3, 6 and 12 and also when clinically needed. The concentration of cisplatin in blood was also monitored to evaluate if any cisplatin entering the circulation. All patients performed a CT-scan and an MRI preoperatively Fig. . To confThe microdialysis samples were collected every second hour and stored in a refrigerator (temperature 8 °C) until transferred to a −80 °C freezer every 12 h. Five timepoints were selected for analysis of the microdialysis samples. These samples were fasting samples collected between 4 am and 6 am the day before treatment, 1, 3 and 6 days into treatment and after the treatment has ended. Fasting serum samples were collected in plain tubes in the morning before start of treatment, 3 and 6 days into the treatment, and after the end of treatment. Serum samples were immediately frozen at −80 °C following centrifugation.17The samples were randomly divided into analytical batches, preserving individual patient sample groups and sampling source . Frozen 50 µl aliquots of microdialysis liquid and serum were thawed on ice at room temperature. Metabolite extraction was performed using 450 µl methanol:water extraction mix , followed by rigorous agitation at 30 Hz for 2 min in a bead mill ). The samples were incubated on ice for 2 h and centrifuged at 18,600 × g for 10 min at 4 °C. After pre-clearing, 200 μl supernatant were transferred to GC vials and evaporated until dry in a speedvac. Dried samples were methoxyaminated by the addition of 15 μl methoxyamine in pyridine (15 μg/μl), shaken for 10 min at room temperature and heated to 70 °C for 60 min. The reaction was allowed to continue for 16 h at room temperature. Trimethylsilylation was performed by addition of 15 μl MSTFA + 1% TMCS and incubated for 1 h at room temperature. Finally, 15 μl heptane, including methyl stearate (15 ng/μl), was added as an injection standard.18 The run order of the longitudinal samples was randomised, but consequently run in the same batch and directly adjacent to each other in the analytical run, thereby minimising variability in platform performance for individual patients. The metabolites were analysed with a Leco Pegasus HT time-of-flight mass spectrometer equipped with an Agilent 7890 A gas chromatograph. Leco ChromaTOF software was used for instrument control and raw data acquisition. The column used for the chromatographic separation was a 30 m, 0.25 mm i.d. DB5-MS UI column with 0.25 µm thick stationary phase. Splitless injection of 1 μl sample was performed with a PAL auto sampler system at an injection temperature of 270 °C. The purge time was 75 sec with a rate of 20 ml/min. Helium was used as carrier gas with a flow rate of 1 ml/min. The primary oven temperature was held constant at 70 °C for 2 min and then ramped at 20 °C/min to 320 °C, where it was held constant for 8 min. The transfer line temperature between the gas chromatograph and mass spectrometer was set to 250 °C. Electron impact ionisation at 70 eV was employed with an ion source temperature of 200 °C. Mass spectra were collected in the mass range of m/z 50–800 at 20 Hz and 1670 V detector voltage. A series of n-alkanes (C8-C40) were used as external retention index standards. As an additional quality control measure of analytical performance across and within samples batches, we analysed a pooled microdialysis liquid and serum quality control reference sample (QC) at the beginning and end of each analytical batch, as well as between every patient sample group . Relative standard deviation percentages (RSD%) were calculated for each detected metabolite.The collected samples were subjected to constrained randomisation within the analytical batches.19 The median RSD% for all identified metabolites in serum and dialysis fluid was 7.2 and over 95% of the identified metabolites had an RSD% below 30  in NetCDF format and processed using the RDA curve resolution script, developed in house. The RDA procedure generates chromatographic profiles for each compound in each sample with a corresponding common spectral profile. We used the integrated area under the resolved chromatographic profile for quantification. The identities of the resolved peaks were determined by comparing mass spectra and retention indices with data in the Swedish Metabolomics Centre in-house spectral library. NIST MS search software was used for manual verification of spectral identification. Compounds with a NIST match score value below 700 and retention index (RI) deviation larger than 25 units from the reference value were excluded. For identification with high confidence, all major fragment ions in the library hit should be present in the resolved spectra with correct spectral intensity profile. Only identified peaks were included in the multivariate analysis. Monosaccharides were defined as a mix of stereoisomers based on their retention index. Some monosaccharide mixes are detected chromatographically as multiple peaks. For example, ketohexoses, including D-Fructose, are found as two peaks due to their rapid transformation into tautomeric pyranose and furanose forms. D-Fructose appears mainly as β-D-fructopyranose and β-D-fructofuranose, approximately in a three to one ratio (in aqueous solution at 20 °C).13C3, salicylic acid-d6 and sucrose-13C12 were purchased from Cambridge Isotope Laboratories , Icon  and Campro Scientific , respectively. The stock solutions for IS were prepared in 0.5 μg/μl concentrations in methanol or water prior to metabolite extraction. Silylation grade pyridine and N-Methyl-N-trimethylsilyltrifluoroacetamide with 1% trimethylchlorosilane were purchased from Restek .All chemicals were of analytical grade. The isotopically labelled internal standards (IS) myristic acid-1,2,3-20 and orthogonal projections to latent structures effect projections (OPLS-EP).18 We used OPLS-DA for independent statistical analysis, short-time survivors vs. long-time survivors and tumour vs. BAT. While OPLS-EP was used to analyse the effect of treatments in dependent samples (pre-treatment vs. day 3 or 6). For all OPLS-DA models, the variables were mean-centred and scaled by division by the pooled standard deviation prior to modelling. For all OPLS-EP models, the variables were only scaled by division by the standard deviation prior to modelling. The significance of the OPLS models was calculated using cross-validated analysis of variance (CV-ANOVA)21 and expressed as CV-ANOVA p-values.22 The number of cross-validation groups equalled the number of patients included in each comparison, i.e. samples originating from the same patient were kept together in the cross-validation.Multivariate statistical data analysis was applied to elucidate metabolites and metabolic pattern in the collected metabolite data relating to effect of treatment, differences between short- and long-time survivors as well as differences between microdialysis fluids from tumour and BAT. Two different but related methods were used; orthogonal projections to latent structures discriminant analysis (OPLS-DA)t-test; dependent for the OPLS-EP models and independent OPLS-DA models. Correction for multiple testing was done using Benjamini-Hochberg false discovery rate.23 A milder criterion for correction of multiple testing was also included to find metabolites of importance when the data were divided into long- and short-time survivors. Here we calculated the distribution of t-values in each comparison using bootstrapping. For each variable the average and standard deviation were calculated. Based on the average and the standard deviation, a distribution for each variables t-values was calculated. All variables t-value distributions were added together forming a probability density function for the t-values in the comparison. In order to get a reference, the same procedure was performed 1000 times using data tables of the same size as the metabolite data but with random numbers form a normal distribution . The average of the 1000 probability density functions was used as reference, and t-values with ten times higher probability in the comparison than in the references were considered significant.Statistical significance of metabolites was calculated using Student’s p = 2.98E-05)  Fig. . After c05) Fig. . In thes18 to compare the metabolite concentrations before treatment with the concentrations after 3 or 6 days of cisplatin treatment, we found significant changes in both tumour tissue and serum  and serum (p = 0.00458)  days following treatment. The patients with poor outcome after treatment were classified as short-time survivors with a median survival of 34 (10–72) days Fig. , Table 158) Fig. . Scatter58) Fig. . In tumo58) Fig. .Fig. 2Vip-values of 0.0276   Fig. . Since t28) Fig.  and 4a. 28) Fig. . We foun28) Fig. . Of thesWe observed a more one-sided distribution of metabolites in microdialysis fluids from tumour. Almost all altered metabolites were lower in long-time survivors Fig. . Levels 25 or by using targeted methods.27 In the present study, we used microdialysis which has the advantage of delivering the drug locally and simultaneously allow for the monitoring of treatment effects in the target tissue.13 The results from this early phase study indicates that cisplatin delivery using the microdialysis technique is safe and allows metabolic monitoring of tumour response.Although in vitro experiments have found cisplatin to be effective on glioma cells, cisplatin’s limited ability to penetrate the blood–brain and blood–tumour barriers have restricted its practical use for the treatment of glioblastoma. Therefore, recent focus has been to deliver the drug interstitiallyIn this study, interstitial administration of cisplatin in high-grade glioma was highly cytotoxic. Following cisplatin treatment, the metabolic events in the tumour tissue were extensive. On the other hand, multivariate statistical models of the data did not show any major changes in BAT following treatment, demonstrating that the cisplatin treatment has the desired localised effect. In tumour tissue, glutamic acid increased, as a sign of cytotoxicity and many amino acids increased due to protein catabolism, serving as substrates for cellular maintenance and energetic currency. We also found increased levels of spermidine, an indicator of disruption of cellular integrity.3)2(H2O)Cl]+ and cis-[Pt(NH3)2(H2O)2]2+, which are very labile, and react readily with DNA nucleobases.28 However, the activated cisplatin-aqua complex reacts non-discriminately with many biologically important N-donor ligands or carriers of nucleophilic groups, including proteins, amino acids and nucleic acids.30 Cisplatin is also known to inhibit mitochondrial respiratory chain complexes and deplete the mitochondrial antioxidant defence system resulting in formation of cellular oxidative stress, which explains its toxicity and complex mechanism of action. It is known that cisplatin also reacts with acetate, phosphate and pyrophosphate groups, which in these cases replaces the chloro ligand. The resulting cisplatin chelates have similar reaction rate towards targets as cis-[Pt(NH3)2(H2O)Cl]+, but lower rate than the diaqua form.29 In our study, we saw a robust increase in levels of phosphate in both microdialysis fluid and serum upon intratumoural cisplatin administration. Interestingly, the release of phosphate was stronger in patients with longer survival, which may be an indication of chemosensitivity with higher turnover and reactivity rate of the phosphate bound cisplatin intermediate in these patients. As discussed below, cisplatin also induced several other metabolic events, a finding also noted in previous reports on cisplatin mechanisms.The anticancer activity of cisplatin has been attributed mainly to the formation of cisplatin-DNA adducts, which cause various cellular responses, culminating in apoptosis. Cisplatin is activated through hydrolysis, mainly inside the cell because of the low concentration of chloride ions (3 mM) in the cytosol. Hydrolysis of cisplatin in aqueous solution replaces stepwise the chloro- ligands to form cis-[Pt thereby involved in maintaining membrane potential and controlling intracellular pH and volume. In addition, spermidine is involved in regulating biological processes, such as neuroprotection and Ca2+ influx by glutamatergic N-methyl-d-aspartate receptor, which has been associated with nitric oxide synthase and cGMP/PKG pathway activation and a decrease of the Na+/K+-ATPase activity in cerebral cortex synaptosomes. Therefore, we may suggest that a similar increase in polyamine catabolism might play a part in the cisplatin-induced cytotoxicity in glioma.Cisplatin has been demonstrated to enhance polyamine catabolism in mice kidneys, which is suggested to be a strong mediator of acute kidney injury.33 Cysteine is needed for the synthesis of the antioxidant glutathione, which provides protection from the cytotoxic effects of ROS. Cysteine binds metals and has been shown to interact with cisplatin.35 The reduced concentration of free cysteine in microdialysis fluids detected here is likely a consequence of cisplatin-cysteine binding and accumulation in the tumour area. This depletion of cysteine will probably result in accumulation of ROS, followed by enhanced cell death. Before treatment, reduced levels of compounds with antioxidant properties—, such as erythritol, N-acetyl-L-aspartic acid and ascorbic acid,—were also detected in the extracellular fluid from tumour tissue compared to BAT. In tumour microdialysate, the levels of cysteine, ascorbic acid and citric acid were further depleted upon cisplatin treatment. Although debatable, presence of compounds with antioxidant properties or those that can sequester cisplatin in the tumour microenvironment could have cisplatin-neutralising effects, resulting in a reduced reactivity of the drug.Reactive oxygen species (ROS) have been shown to exert cytotoxic effects on glioma cells especially when combined with depletion of cysteine.2+ release from intracellular stores and cause massive ATP depletion, membrane oxidation and necrosis.36 ROS has a complex function in that it may also activate caspases, which are known to induce apoptosis.37 In gastric tumour cells cisplatin has been demonstrated to induce caspase-1, which in turn predicts apoptosis.38Other related mechanisms of possible cisplatin-induced cytotoxicity could result from the increased level of glutamic acid. Activations of inotropic glutamate receptors has shown alterations in calcium homeostasis with eventual cell death from ROS damage. In addition, glutamic acid has been shown to inhibit cysteine synthesis that will impair glutathione production and results in accumulation of ROS that cannot be neutralised. The accumulation of ROS will increase Ca39 indicating that lactic acid is not a metabolic marker for acute cisplatin toxicity in high-grade glioma patients. However, the obtained metabolic patterns described here are not representative of a naïve tumour, since all tumours have recurred from previous treatments. One of the aims of the present study was to investigate putative candidate markers for therapeutic response. However, there is a risk that the differences in metabolite levels possibly only reflect prognostic differences irrespective of treatment. When analysing single metabolites, the finding of high lactic acid in short-time survivors may indicate that these patients may have a more aggressive tumour compared to the long-time survivors. However, multivariate analysis demonstrated a similarity in tissue metabolic spectra before treatment, and the significant difference in metabolic response following treatment strongly suggests that the performed classification was relevant. Subsequently, the results may be of interest in the current discussion of potential markers for treatment response.It is important to note that this study included a relatively small number of treated patients. Obviously, the small patient number restricts the statistical power and the potential to discover novel response markers. This problem is especially prominent in the case where the sample data are divided into two subgroups according to survival time. Although these results should be interpreted with caution, the findings are still of great value for further studies using similar treatment approaches. In the analysed material we found significantly higher levels of lactic acid in microdialysis fluids from tumour of patients with shorter survival, further highlighting lactic acid as a marker of tumour aggressiveness. In both long- and short-time survivors, the lactic acid levels stayed constant and were not reduced during cisplatin treatment, as reported in a model of head and neck squamous carcinoma,In conclusion, this study demonstrates that significant changes in both the tumour and serum metabolome results from intratumoural administration of cisplatin in malignant glioma. An induction of protein catabolism as well as decreased levels of carbohydrates were evident. The detailed metabolomic analysis reveals that the mechanism of cisplatin cytotoxicity is highly complex, a finding supported in many in vitro studies. Metabolic patterns as well as single metabolites in serum such as phosphate may serve as potential markers for response to therapy. These findings stimulate further studies including other treatment modalities.Supplementary material


Angiogenesis plays an important role in tumour progression. However, anti‐angiogenesis therapy of inhibiting pro‐angiogenic factors failed to meet expectations in certain types of tumour in clinical trials. Recent studies reveal that tumour‐derived extracellular vesicles (EVs) are essential in tumour angiogenesis and anti‐angiogenesis drug resistance. This function has most commonly been attributed to EV contents including proteins and non‐coding RNAs. Here, we summarize the recent findings of tumour‐derived EV contents associated with regulating angiogenesis and illustrate the underlying mechanisms. In addition, the roles of EVs in tumour microenvironmental cells are also illustrated with a focus on how EVs participate in cell‐cell communication, contributing to tumour‐mediated angiogenesis. It will help offer new perspectives on developing targets of anti‐angiogenesis drugs and improve the efficacy of anti‐angiogenesis therapies based on tumour‐derived EVs. In the process of capillary sprouting, tip cells, which are highly migratory but non‐proliferative ECs, guide the new sprout growing in the right direction. Stalk cells, with high proliferative capacity, elongate the new sprout.Up to date, for the vital importance of VEGF, researchers have focused on developing VEGF and VEGFR inhibitors as anti‐angiogenic drugs.According to the EV biogenesis mechanisms, EVs can be classified into two broad categories, referred to here as exosomes and shed microvesicles.Here, we review the currently available data to clarify the mechanisms of intercellular communication these EVs participate in and the consequences of molecular transfer in EVs for tumour angiogenesis and anti‐angiogenesis drug resistance. It may help provide the researchers with new perspectives on developing alternative anti‐angiogenesis drug and improve the efficacy of anti‐angiogenesis therapies.2Accumulating studies reveal that tumour‐derived EVs can transport components including non‐coding RNAs such as circular RNAs (circRNAs), long non‐coding RNAs (lncRNAs), microRNAs (miRNAs) and proteins, thereby offering novel biomarkers and therapeutic targets on tumour angiogenesis.2.1Recently, further studies have highlighted the role of non‐coding RNAs from tumour‐derived EVs in the regulation of tumour angiogenesis.2.1.1circRNAs, formed via back splicing, are single‐stranded RNAs with covalent bonding. In eukaryotes, most of the circRNAs are non‐coding RNAs acting as microRNA or protein inhibitors, but some encode peptides with tissue‐specific and cell‐type–specific expression patterns.VEGFA.Recently, some circRNAs from tumour‐derived EVs are found to be related to poor prognosis and advanced tumour node metastasis classification stage, making them potential RNA biomarkers. For example, circ‐SHKBP1 and circ‐RanGAP1 in gastric cancer (GC) are reported to be up‐regulated and effectively delivered from GC‐derived EVs into the circulation. circ‐SHKBP1 from GC EVs promotes cancer progression by regulating the miR‐582‐3p/HUR/VEGF axis and suppressing the degradation of HSP90.2.1.2lncRNAs, greater than 200 nucleotides in length, exert essential biological functions by interacting with many molecules in both cytoplasm and nuclei.+ liver cancer cells’ EVs containing lncRNA‐H19 induce an obvious increase in the ICAM1 and VEGF expression, leading to increasing ability of ECs to arrange in vitro tubular‐like structures and promote heterotypic adhesion between ECs and CSC‐like liver cells.Rab5b mRNA, thereby reducing its stability and ultimately leading to decreased EV production. This action also inhibits the overactivation of the MAPK pathway in ECs and the subsequent suppression of angiogenesis.VEGFR2 expression by acting as a ceRNA of miR‐2355‐5p.VEGFA expression.Emerging evidence suggests that EV lncRNAs promote the key pro‐angiogenesis gene and protein expression, cell proliferation and drug resistance by acting as ceRNA or binding mRNA.2.1.3miRNAs, containing about 23 nucleotides, play an important role in gene regulation by directing protein‐coding genes post‐transcriptional repression via pairing to the mRNAs.cdkn1b and pik3r1.Several miRNAs from tumour‐derived EVs have been reported to act as predictive non‐invasive biomarkers for anti‐angiogenic therapy.2.2Up to date, it has been reported that multiple molecules and mechanisms , are observed in MSCs and ECs.Tumour cell–derived EVs have been reported to induce CAF differentiation from stromal cells or normal fibroblasts via a complex mixture of small RNAs and proteins. For example, intercellular contact between MSCs and cancer cells results in MSCs expressing the CAF‐like phenotype character such as FSP, α‐SMA and CXCL12.Moreover, further studies have suggested tumour‐derived EVs transdifferentiate CAFs by EndMT, initiating angiogenic and metastatic evolution. However, although cancer‐derived EVs promoted EndMT, MSC‐derived EVs were reported to suppress EndMT, maintain vascular homeostasis and induce angiogenesis, finally resulting in CAFs back to ECs.Additionally, it is revealed that EVs derived from different tumour stages can activate fibroblasts displaying various expressions. For example, pro‐angiogenic and pro‐proliferative proteins are found to be increased in fibroblasts activated by early‐stage tumour‐derived EVs. In contrast, activated by late‐stage tumour‐derived EVs, fibroblasts have a greater ability to invade through the extracellular matrix4+ stem cells are related to VEGF production and angiogenic phenotype in ECs.+ cells could promote angiogenesis by EVs containing lncRNA‐H19, thus influencing the tumour microenvironment.+MVs from cancer stem cells contained mRNAs for growth factors such as MMP9, ephrin A3, FGF, MMP2, angiopoietin 1 and VEGF.+EVs. Therefore, it suggests the cancer stem cell–derived EVs, rather than the whole tumour cell–derived EVs, promote tumour growth and invasionCancer stem cells (CSCs), with the ability to self‐replicate, differentiation and exist for a long time, are thought to have the potential to develop tumours and in particular, as the tumour metastasis, become the source of new tumour.5As tumour growth depends on angiogenesis, tumour blood vessels have been recognized as an important target for tumour therapy, and many anti‐angiogenesis drugs have been found and tested with good results. Up to date, the US Food and Drug Administration approves the clinical use of the anti‐angiogenic drugs targeting VEGF and several VEGF (receptor)‐based inhibitors in many types of cancer.5.1The contents of tumour‐derived EVs can be blocked to suppress tumour angiogenesis and offer new insight into the anti‐tumour activity of anti‐angiogenesis drugs, thereby supporting its positive use in tumour therapy.5.2It is also observed that treatment with anti‐angiogenesis drugs can interact with EVs, which are associated with therapeutic resistance. The emerging studies in EVs are conducive to further understanding the mechanism of anti‐angiogenesis drug resistance and improve treatment efficacy in cancer therapy.Accumulating studies reveal that EVs play an important role in anti‐angiogenesis drug resistance. For instance, GBM cells can capture bevacizumab directly and eventually classify bevacizumab at the surface of the GBM cell–derived EVs.90K has a weakened affinity for bevacizumab, leading to the inability of bevacizumab to effectively inhibit EV‐dependent VEGF receptor activation. However, VEGF90K is released from EVs with the treatment of an Hsp90 inhibitor, thereby restoring sensitivity to bevacizumab.189 and the surface of EVs, and that associated EV‐VEGF is hard to be neutralized by bevacizumab. The VEGFR2‐neutralizing antibody or VEGFR tyrosine kinase inhibitors can suppress the EV‐VEGF action. Therefore, these inhibitors rather than bevacizumab might be more effective as anti‐tumour therapy for patients who have elevated EV‐VEGF levels.Besides, recent studies have tried to improve the efficacy of anti‐angiogenic therapies by blocking EV drug‐resistant molecules. A further study demonstrated that the affinity of EV‐associated VEGF6An increasing number of studies have reported the capacity of EVs to actively regulate the tumour‐associated angiogenic programmes. It is now well established that EVs can carry and transfer proteins and mRNAs/ncRNAs into target cells, which not only help to reveal the angiogenesis and anti‐angiogenesis mechanisms but also created new perspectives on drug targets and drug resistance in anti‐angiogenesis. However, the inclusion of different molecules from different tumour‐derived EVs may be because of bias in the selection of experimental targets.The authors declared no conflict of interest.Shuqiong Zhang: Writing–original draft ; Writing–review & editing . Junyao Yang: Conceptualization ; Funding acquisition ; Writing–original draft ; Writing‐review & editing . Lisong Shen: Conceptualization (lead); Resources (lead); Writing‐original draft (lead); Writing–review & editing (lead).


Invasion of the surrounding tissue is one of the recognised hallmarks of cancer 81683-9), which is accomplished through a complex heterotypic multiscale dynamics involving tissue-scale random and directed movement of the population of both cancer cells and other accompanying cells  as well as the emerging cell-scale activity of both the matrix-degrading enzymes and the rearrangement of the cell-scale constituents of the extracellular matrix (ECM) fibres. The involved processes include not only the presence of cell proliferation and cell adhesion (to other cells and to the extracellular matrix), but also the secretion of matrix-degrading enzymes. This is as a result of cancer cells as well as macrophages, which are one of the most abundant types of immune cells in the tumour micro-environment. In large tumours, these tumour-associated macrophages (TAMs) have a tumour-promoting phenotype, contributing to tumour proliferation and spread. In this paper, we extend a previous multiscale moving-boundary mathematical model for cancer invasion, by considering also the multiscale effects of TAMs, with special focus on the influence that their directional movement exerts on the overall tumour progression. Numerical investigation of this new model shows the importance of the interactions between pro-tumour TAMs and the fibrous ECM, highlighting the impact of the fibres on the spatial structure of solid tumour. One of the key factors that distinguish cancer cells from normal cells is the ability of cancer cells to alter their local and non-local interactions to neighbouring cells (that are either cancerous or of different types) and to the extracellular matrix (ECM), which leads eventually to invasion and metastasis  with the macrophages. This will not only highlight the impact that the directionality of the macrophages dynamics has over the cancer cell invasion at macro-scale, but also explore its additional contribution towards the rearrangements of the ECM fibres at micro-scale , the rest of the tumour micro-environment and surrounding tissue is represented here simply by a generic ECM. To that end, while we acknowledge that, besides the tumour cells, some of the tumour micro-environment components are not ECM constituents and are rather only supported by the usual ECM, in this framework we still regard all those constituents  is denoted here by F. On the other hand, besides the major fibres components, the ECM contains also an entire host of other soluble constituents, such as calcium ions Canon-fibre ECM phase. The spatio-temporal distribution of the non-fibre ECM phase at  is denoted by l. Furthermore, we also consider the presence of a population of M2-like TAMs, denoted here by M, which infiltrate the tumour as an immune response though the outer boundary that we denote by x, i.e.,As this work builds on the multiscale modelling developed in Shuttleworth and Trucu , Trucu eu et al. , Peng etu et al.  and Shutu et al. , 2020b wnd Trucu , 2020b, nd Trucu ), and itmentclass2pt{minimc exercises not only random movement (captured here through diffusion), but their spatial transport of the cell population is further amended and biased by a directional movement induced by the cell-adhesion processes  is described byM, and M.Based on biological evidence that the components of non-fibre ECM phase (e.g. the amyloid fibrils) are degraded by several classes of matrix metalloproteinases  are degraded both by the cancer cells and by the macrophages . Thus, M. To describe the macrophages dynamics, we note that the experimental study in Redente et al.  . Thus, the proliferation law of the macrophages can therefore be mathematically formulated asRegarding macrophages proliferation, on the one hand biological evidence  is given byTherefore, the dynamics of the M2-like macrophages Since various experimental studies discuss the distribution of M2 TAMs macrophages inside tumour tissue and its prognostic value , , , the couThe macro-scale cancer invasion process is accompanied by several closely linked micro-dynamic processes Weinberg . ParticuF distributed at f on a cell-scale micro-domain x, t)) can be captured simultaneously through a vector field representation of the ECM fibres, barycentral vector-valued functionAs discussed in detail in Shuttleworth and Trucu , it is ind Trucu , these te Yosida  of the bUnder the incidence of the macro-scale spatial flux generated by the tumour macro-dynamics, the rearrangement of the ECM fibres takes place on each micro-domain nd Trucu ), but alx, t), the combined spatial flux micro-fibres rearrangement vectorf on x, t), namely:relocation vectorTherefore, at any spatio-temporal node , such as matrix metalloproteinases (MMP) of both type 2 (MMP-2) and type 9 (MMP-9) molecules \documentFocusing now on the MDE micro-scale . This effect is complemented by the rearrangement of the micro-fibres that ultimately induces new a fibre structure. For illustrative purposes, in Fig. In Fig. (see Eq. ) and thaThe Impact of Macrophage Interactions on Tumour DynamicsIn Fig. In Fig. In Fig. In Fig. mentclasspt{minimaIn Fig. Finally, in Fig. The Impact of ECM Fibre Structure and Fibre-Adhesion Strengths on Tumour DynamicsNext, we explore how the fibrous composition of ECM and the corresponding fibre-adhesion strengths affect the evolution of the solid tumour. In Fig. In Fig. In Fig. In Fig. In Fig. Next, in contrast to Fig. In these new conditions, Fig. However, as shown in the ECM regime with Finally, by comparing Fig. The Effect of Random Micro-fibre DistributionIn all our previous numerical simulations, we used the micro-fibre structure described in Fig. In Fig. In Fig. In Fig. In Fig. Based on biological knowledge ,mentclasspt{minimaAll these numerical results indicate that the combined effects of macrophage–tumour–ECM interactions (via different cell–cell and cell–fibre adhesions), and the constant-vs.-density-dependent diffusion, are important for tumour development. Moreover, these results allow us to conclude that it is difficult to ascertain at a macroscopic level the specific molecular (i.e. adhesion) mechanisms that could be responsible for the observed macroscopic patterns of tumour spread and/or accumulation.The complexity of the interaction between tumour cell and macrophages exceeds the context captured in this work through the mutual adhesion terms involved in the macroscopic model equations. For example, this approach does not capture explicitly the complex cross talk between tumour cells and macrophages via chemokines. Such aspects will be investigated in future studies. Further, due to the complexity of the multiscale moving-boundary framework and the numerical simulations of these equations, in this work we focused only on the dynamics of the tumour and the immune cells infiltrating it. For this reason, we assumed that macrophages enter the tumour though tumour boundary. In the future, we will extend this modelling approach to account also for the dynamics of macrophages outside the tumour (which are attracted to the tumour site via chemokines secreted by the tumour cells (Green et al. Finally, another aspect that will be addressed in future work is the analysis of the complex multiscale numerical framework proposed in this study. As this computational framework is new, this is an open problem that requires a proper independent investigation.


In response to the mode of fracturing, fluid supply, and mineral growth dynamics, calcites developed distinct blocky (precipitation into fluid-filled fractures), syntaxial , and antitaxial (diffusion-fed displacive growth) vein microtextures with vein type-specific geochemical signatures. Blocky veins predominate in all study areas, whereas syntaxial veins represent subordinate structures. Antitaxial veins occur in all study areas but are particularly abundant in the Izu–Bonin rear arc where the local geological setting was conducive of antitaxial veining. The temporal framework of major calcite veining coincides with the onset of extensional faulting in the respective areas and points to a tectonic control on veining. Thus, major calcite veining in the Troodos SSZ began contemporaneously with volcanic activity and extensional faulting and completed within ~ 10–20 Myr. This enabled deep seawater downflow and hydrothermal fluid upflow. In the Izu–Bonin forearc, reliable ages of vein calcites point to vein formation > 15 Myr after subduction initiation. Therefore, high-T mineralization  up to 230 °C is restricted to the Troodos SSZ.Based on the published data of pillow lava-hosted mineralized veins, this study compares post-magmatic fracturing, fluid flow, and secondary mineralization processes in the Troodos and Izu–Bonin supra-subduction zone (SSZ) and discusses the crucial factors for the development of distinct vein types. Thin section and cathodoluminescence petrography, Raman spectroscopy, fluid inclusion microthermometry, and trace element and isotope ( These data are complemented by new electron microprobe element mappings.Rocks and geological structures exposed in ophiolites and drill cores recovered during the International Ocean Discovery Program (IODP) and its predecessors provide insights into the tectono-magmatic evolution and architecture of the oceanic crust  intercalated with and overlying the pillow lavas  and radiolarian mudstones of the Perapedhi Formation  to specific vein types .This study is primarily based on the data published in Quandt et al. , 2020a wThe Troodos geochemical data set presented here comprises samples from several pillow lava outcrops throughout the Troodos ophiolite  were calculated according to Eu/Eu*PAAS = 2 × EuPAAS/[SmPAAS + GdPAAS]  and trace elements  were acquired using a JEOL JXA 8200 electron microprobe equipped with five wavelength-dispersive spectrometers at the University of Leoben (Austria). Prior to analysis, polished vein thin sections were carbon coated. Measurements were conducted with an acceleration voltage of 15 kV and a beam current of 50–100 nA.Based on thin section and CL microscopy, mineralized veins were subdivided into blocky, antitaxial, transitional, completely and incompletely sealed syntaxial vein types, micrite-filled fractures, and mineralized vesicles  and were entrapped during mineral growth (primary) or healing of microfractures  and − 1.8‰ VPDB (Izu–Bonin). Both normal distributions show similar standard deviations resulting in similar curve shapes that are offset by ~ 1‰ VPDB. Corresponding calcite δ18O formation temperatures fall within the range of 0–30 °C (Izu–Bonin) and 10–50 °C (Troodos), respectively  than syntaxial veins (≥ + 1.9‰ VPDB) and lower average δ13C values (+ 0.6‰ VPDB) than blocky veins (+ 1.4‰ VPDB).In contrast to δPDB Fig. a. Troodo18O temperatures. Corresponding parental fluid δ18O compositions fall predominantly within or close to the conservatively estimated seawater range between − 2 and + 2‰ VSMOW  and Izu–Bonin forearc/rear arc areas (− 1.5‰ VSMOW) excluding δ18O values < − 10‰ VSMOW fall within the seawater range.Clumped isotope temperatures of most vein calcites from both study areas are < 30 °C Fig.  and onlyMOW Fig. . A few sVein calcites from the Troodos ophiolite and Izu–Bonin forearc/rear arc show heavy rare earth element-enriched PAAS-normalized McLennan  REE + Y PAAS (0.5–1.5). In contrast, antitaxial vein calcites from all study areas show reduced Y/Ho ratios (< 50) and increased Eu/Eu*PAAS (up to 3.4) relative to seawater. Blocky vein calcites show a high variation in Y/Ho ratios (18–91) and Eu/Eu*PAAS (0.6–3.2) and, thus, overlap with syntaxial and antitaxial vein calcite fields. This compositional overlap is reflected by the normal distributions of Y/Ho and Eu/Eu*PAAS of each study area  are seawater-like. However, Troodos pillow lava-hosted vein calcites show a relatively high proportion of Eu/Eu*PAAS exceeding the Eu/Eu*PAAS range of seawater (up to 1.5). Besides antitaxial vein calcites that acquired their high Eu/Eu*PAAS during fluid interaction with hydrothermal sediments , provided that the calcites precipitated from pristine seawater and preserved its original 87Sr/86Sr composition  and between ~ 35 and ~ 33 Ma (Izu–Bonin forearc) represent reliable precipitation ages  have significantly reduced 87Sr/86Sr ratios compared to pristine seawater . Along each bisector of an angle, the relative degree of fluid–rock interaction increases from the incenter (pristine seawater) to the angle . Among blocky and antitaxial vein calcites, compositional ranges from seawater to hydrothermal fluids are common. The resulting triangles are larger than the triangles for syntaxial vein calcites, which are consistently characterized by relatively pristine seawater signatures.The parental fluid signatures inferred from Element mappings confirm observations from CL microscopy Fig. , 2020a. Striking differences between the different locations involve the Mg distribution in blocky calcites Fig. . In the Antitaxial vein calcites Fig.  from the87Sr/86Sr ratios below respective seawater values.This comparative analysis shows that the veins pervading the volcanic units of the Troodos ophiolite and Izu–Bonin forearc/rear arc share structural, microtextural, mineralogical, and vein type-specific geochemical characteristics. In all study areas, blocky calcite veins are the major vein type. The dominance of comparatively thick blocky calcite veins, which are consistently interpreted as the result of precipitation into fluid-filled fractures, implies substantial fracture-controlled advective fluid flow accompanied by high water–rock ratios in all study areas. This explains the predominance of seawater-dominated signatures (pristine to slightly modified seawater) and precipitation temperatures < 50 °C of most vein calcites from all study areas. Where calcite precipitation into fractures was delayed, fluid residence times increased and intensified fluid–rock interaction. This is demonstrated by argillaceous vein selvedges, vein-parallel alteration halos, early-stage euhedral quartz or zeolite growth along vein margins, and blocky calcites with reduced Y/Ho and At temperatures > 100 °C, blocky calcites developed Mn-controlled oscillatory growth zonations with (intra-) sectoral zones. Growth zonations of low-T blocky calcites are by far less complex. Hence, high-T conditions might be an important requirement for the development of oscillatory growth zonations. These patterns point to disequilibrium precipitation in a closed system in which the zonation develops without external input  were observed in secondary calcites hosted in sheeted dykes and gabbroic cumulates from the Troodos ophiolite  and drill core data (Izu–Bonin forearc/rear arc) is sophisticated since both approaches cover different dimensions . The comparison of Sites U1439–U1441 with Site 786 that are separated by ~ 500 km allow a test of horizontal continuity, which shows that both localities are characterized by similar mineralogical and stable isotopic features. Moreover, resembling geochemical distribution patterns and the variety of vein microtextures equally observed among veins from outcrops and drill cores suggest representative drill core data. We are, therefore, confident that our comparative approach is applicable.All study areas are predominantly pervaded by veins associated with extension fracture-controlled advective fluid flow. Depending on the mineral growth dynamics, these veins developed primarily blocky (precipitation into fluid-filled fractures) and secondarily syntaxial  microtextures.Fluid diffusion-fed and crystallization pressure-driven antitaxial fiber veins occur in all study areas. They are particularly abundant relative to blocky veins in low-permeable crust and seem to become more important with crustal aging.Independently of the vein type, calcite is the major vein mineral. Non-carbonate minerals such as quartz and zeolites are particularly abundant in blocky and syntaxial veins from the Troodos ophiolite.In response to the different vein growth dynamics, calcites developed vein type-specific REE + Y characteristics. Increased fluid residence times and diffusive fluid supply intensified fluid-rock interaction. In contrast, crack and sealing prevented extensive fluid–rock interaction and resulted in pristine seawater signatures. Independent of vein type and study area, most secondary calcites precipitated from seawater to seawater-like fluids at temperatures < 50 °C emphasizing the importance of low-T seawater-mediated crustal alteration.Reliable ages of vein calcite precipitation in the respective areas coincide with Late Cretaceous (Troodos SSZ) and Early Eocene (Izu–Bonin forearc) extensional tectonic phases indicating a tectonic control on the time of calcite vein formation. In addition, microtextural analysis of calcite veins associated with rock brecciation indicate that fracturing was partly assisted by hydro-fracturing.13C values are restricted to the Troodos ophiolite.Early tectonic extension shortly after emplacement of the Troodos pillow lavas and off-axis hydrothermal activity facilitated deep fluid flow along faults and fluid heating at depth. Late fracturing within the cold Izu–Bonin forearc crust, however, did not result in significant heating of downflowing fluids. Therefore, high peak precipitation temperatures (up to ~ 230 °C), high-T (up to ~ 300 °C) non-carbonate mineral assemblages, and mantle-derived δHigher peak fluid temperatures favored the entrapment of two-phase fluid inclusions in calcite, quartz, and analcime, and resulted in positive Eu anomalies of high-T blocky calcites. These well-preserved high-T features lack in the Izu–Bonin forearc/rear arc and suggest that uplift and emplacement of the Troodos ophiolite did not significantly overprint or alter the geochemical compositions of vein calcites.Based on the comparison of published petrographic, mineralogical, and geochemical data of pillow lava-hosted mineralized veins from the Troodos and Izu–Bonin SSZ crust, this study yields the following findings.


Conversely, we investigate here a novel concept for longitudinally elucidating anatomical and pathophysiological changes of solid tumours after adoptive T cell transfer in a preclinical set up, using previously unexplored in-tandem macroscopic and mesoscopic optoacoustic (photoacoustic) imaging. We show non-invasive in vivo observations of vessel collapse during tumour rejection across entire tumours and observe for the first time longitudinal tumour rejection in a label-free manner based on optical absorption changes in the tumour mass due to cellular decline. We complement these observations with high resolution episcopic fluorescence imaging of T cell biodistribution using optimized T cell labeling based on two near-infrared dyes targeting the cell membrane and the cytoplasm. We discuss how optoacoustic macroscopy and mesoscopy offer unique contrast and immunotherapy insights, allowing label-free and longitudinal observations of tumour therapy. The results demonstrate optoacoustic imaging as an invaluable tool in understanding and optimizing T cell therapy.Most imaging studies of immunotherapy have focused on tracking labeled T cell biodistribution  Clinical studies using autologous, ex vivo stimulated cytotoxic T lymphocytes have demonstrated therapeutic efficacy4. To achieve therapeutic effects and increase patient survival, it is necessary that the transferred T cells survive, home to and enter the tumour mass, recognize antigen peptides expressed by tumour cells and induce tumour rejection. Nevertheless, endogenous cancer mechanisms often prevent tumour cell killing by T cells even though the cells had shown specificity and functionality in vitro. Cancer immunoediting5 and editing the tumour microenvironment6 are major drawbacks in establishing successful T cell therapies.Immunotherapies are emerging as a potent cancer treatment approach10, for obtaining a mechanistic understanding of tumour elimination due to the CTL activity and for assessing therapeutic efficacy. Radiological and optical imaging methods such as PET11, SPECT-CT12, MRI9, bioluminescence13 and fluorescence imaging14 have been employed for visualizing labeled T cell biodistribution at different stages of therapy; a goal that has been generally achieved revealing T cell traffic and homing dynamics. Nevertheless, T cell homing and accumulation within the tumour does not always correlate with therapeutic efficacy16. Therefore, imaging studies that solely focus on T cell biodistribution may be limited in predicting therapeutic efficiency or revealing the process and mechanisms of tumour rejection.Imaging has been employed in the context of cellular based therapies for better understanding T cell trafficking and biodistributionin vivo17. Two-photon microscopy has revealed the primary role of CTLs as cytotoxic effectors in the process of tumour rejection8 and the key role of antigen expression in T cell motility. Moreover, confocal microscopy has recently showcased the visualization of T cell biodistribution over-time in high-resolution and revealed the important role of vasculature and vessel regression during immunotherapy17. Vessel normalization has been shown to be a critical aspect in delivering therapeutics as well as therapeutic cells into the tumour mass to allow tumour destruction18. Despite its essential role in discovery, intra-vital microscopy has a limited penetration depth and field of view and therefore cannot observe events occurring macroscopically throughout the entire tumour mass. Moreover, the complex imaging protocols often requiring surgical implantation of transparent windows limit widespread dissemination.Mouse imaging has been considered to optimize cytotoxic therapy by understanding efficacy and cell dynamics. In addition to macroscopic observations, intra-vital microscopy has enabled critical insights into the mechanisms behind T cell therapies by visualizing in high-resolution single cell motility and cell-cell interactions 19, vasculature and other optical alterations indicative of morphological and physiological information revealed at resolutions of a few tens (mesoscopy) to hundreds (macroscopy) of microns. We explored this information to resolve activity within the tumour mass and therefore spatially assessing therapeutic heterogeneity20. This feature can be potentially employed for monitoring tumour pathophysiological parameters occurring during immunotherapy progression.In this work, we aimed to retrieve multi-scale information on therapeutic effects following T cell therapy, using optoacoustic imaging at the macro- and mesoscopic level. The study aimed to explore optoacoustic imaging as a novel method to understand immunotherapy, improving in the future the study and optimization of cancer therapy. We sought to identify a multifunctional imaging protocol for longitudinally studying the impact of T cell therapy and elucidating optoacoustic signatures that could reveal unknown aspects of immunotherapy through the entire tumour mass. Optoacoustic imaging resolves optical absorption contrast in high resolution, therefore allowing for a detailed visualization of tumour oxygenation21, appropriate for whole body animal imaging with 200–300 micron resolution and Raster Scan Optoacoustic Mesoscopy (RSOM), revealing optical features such as vasculature with resolutions better than 10 microns through 3–4 mm of tissue22. Based on the unique optoacoustic performance, we observed over time therapeutic effects manifested as optical changes across the tumour mass using MSOT and obtained a detailed view of vessel regression during tumour elimination by CTLs using RSOM. Multispectral excitation was further employed for resolving pathophysiological changes within the tumours through the differentiation of tumour intrinsic molecules. In particular, optoacoustic imaging showed capability of monitoring tumour pathophysiology by resolving in vivo the process of T cell induced tumour rejection through the detection of intrinsic tissue chromophores accumulated in necrotic tumour areas. In addition, we analysed the biodistribution of T cells by optimizing T cell labeling through the combination of two near-infrared fluorescent dyes with overlapping absorption spectra but different binding capacities and performed ex vivo episcopical fluorescence imaging after the last in vivo imaging time point. Our multi-spectral and multi-scale analysis revealed that non-invasive label-free imaging in vivo using MSOT and RSOM can offer high specificity in assessing therapeutic efficiency that can complement studies of T cell presence in the tumour and, importantly, directly observe the function of the cells, rather than solely their biodistribution. In this role, multi-scale optoacoustics are shown well suited for understanding and optimizing immunotherapy protocols. The addition of episcopic fluorescence imaging of the T cell biodistribution provides complementary information on T cell presence, in addition to therapeutic effects resolved by optoacoustics. We further introduce and explore a novel imaging workflow to analyse cellular immunotherapy of established solid tumours in vivo with optoacoustic and episcopic fluorescence imaging and demonstrate its benefit by retrieving information about tumour size, tumour vasculature and tumour pathophysiology in high resolution.For understanding immunotherapy at different scales, we combined the imaging characteristics of Multi-Spectral Optoacoustic Tomography (MSOT)To assess whether optoacoustic tomography could reveal anatomical information related to cancer immunotherapy, we imaged two groups of B6-Albino (B6-A) mice (n = 12) with s.c. tumours in the mammary gland overexpressing the chicken ovalbumin protein. One group (n = 6) was treated with tumour-antigen-specific T cells derived from OT-1 mice. This group, hereby referred to as the therapy group, was used for monitoring anti-tumour response. The second group (n = 6) received T cells from wildtype B6 mice, hereby referred to as the control group.in vivo MSOT and RSOM23 was administered at indicated time points. Immediately after the last imaging time-point, mice were sacrificed and further submitted to histological analysis.Longitudinal Examination of MSOT images revealed in all animals the morphology of the entire tumour mass Fig. . The difin vivo RSOM images highlighted the destruction of the tumour vasculature after the transfer of tumour-specific T cells  as RSOM offers about an order of magnitude better resolution than MSOT (30 vs 300 microns), but only reaches approximately 2 mm into the tumour and does not reveal the entire tumour mass as in the case of MSOT. Longitudinal in vivo optoacoustic data appeared in agreement with post mortem histological analysis of tumour vessels  at 21 different wavelengths ranging from 700 to 900 nm.Inspection of MSOT images from all animals revealed spectral differences depending on the therapeutic outcome. Figure Label-free MSOT imaging offers the ability to longitudinally monitor the process of tumour rejection at different stages of immunotherapy and assess the time point when tumours start to undergo physiological changes without the need of contrast agent. In all animals of the therapy group, the gradient signal starts to be weakly detectable at day 3 after T cell transfer  in the percentage of gradient signal per tumour volume at day 5, day 7 and day 9 . T cells were labeled using a combination of a cell membrane staining near-infrared (NIR) dye (DiR) and a preferrentially cytoplasm staining NIR dye (VivoTag-S 750). In this manner, we were able to optimize T cell signal intensity without compromising cell viability or specificity  and control group (n = 4) and MSOT images prepared. After MSOT imaging at predefined time-points, animals were sacrificed and analysed using episcopic fluorescence imaging. Aim of this multi-modal imaging approach was to analyse the spatial T cell distribution in the tumour and their biodistribution at different time points after transfer with high resolution. Figure Tumour-unspecific B6 T cells though also home to the tumour mass and were detectable even until day 7 after transfer Fig. . HoweverConcerning the biodistribution, T cell homing to the bone marrow was visible in several mice of both, the control and the therapy group Fig. . In addiIn vivo imaging is critical to understand the function and interaction of immune cells in their native environment, and helps to optimize cancer immunotherapy strategies. Despite progress with clinical trials26, successful monitoring of immunotherapies remains a major challenge and an important goal for better understanding and optimizing immunotherapies. Methods like biopsies or ex vivo analysis of cell functions give a time-restricted picture of cell trafficking, homing and distribution prerequisites for a successful therapy that can be studied by in vivo imaging. Optical imaging is well suited for studying the longitudinal course for cell distribution but is limited by the low penetration depth of microscopy or the two-dimensional, low resolution views of bioluminescence or fluorescence imaging.in vivo, and reveal underlying morphological and physiological tumour features. In our view, this is an imaging capacity allowing observations of cell biology much deeper  than what was allowed today by established optical imaging modalities (beneath 1 mm)27.In this work, we introduce statistical sup-pixel detection MSOT as a highly potent alternative optical modality with the ability to offer three-dimensional high-resolution views of tumours undergoing immunotherapy 28 for obtaining high resolution information on tumour vasculature and correlated the process of tumour regression with the process of vascular collapse due to the action of CTLs. This ability of optoacoustic imaging systems cannot be offered by intra-vital microscopy where the imaging window and the penetration depth limit the ability to obtain a holistic tumour view. In addition, RSOM is superior over fluorescent microscopy as it allows label-free imaging of vessels. Changes in the tumour vasculature are of special interest within the establishment of effective cellular immunotherapies as tumour vessel normalization leads to an increased tumour response to transferred T cells29 and CTLs have been reported to efficiently destroy tumour vessels in established solid tumours30.We employed a combination of small-animal MSOT and RSOM in an established mouse model for adoptive cell therapy32. Besides, IFN-γ can act directly on tumour cells, but also remodels the microenvironment33. Kammertoens et al. showed the effect of IFN-γ on endothelial cells and the necessity of this interaction on the eradication of large established solid tumours34. Townsend et al.35 demonstrated that T cell infiltration and tumour elimination are dependent upon the degree of tumour vasculature and the corresponding oxygenation35. In addition to many other criteria, this shows the attractivity and importance of imaging tumour vasculature in preclinical and clinical studies. Using RSOM as label-free, 3D imaging system for visualizing the tumour vasculature will allow fast and non-invasive longitudinal studies in vivo.The mechanisms by which T cells kill tumour cells or modify the tumour microenvironment is still not fully resolved. Amongst others, tumour necrosis factor-α (TNF-α) and IFN-γ are two major effector molecules in tumour destruction by CTL both targeting the tumour vasculature12. To our best knowledge, it is the first time that such an ability has been reported.A key advantageous MSOT feature, with significant future potential, is the ability to measure a number of underlying morphological, physiological or biochemical tumour features like the process of tumour regression in a label-free manner through the detection of an intrinsic absorption signal related to cell death. The application of multiple wavelengths for exciting tissues and molecules and analysing these different spectra one by one directed us to the intrinsic gradient signal occurring in therapeutic responding tumours only. The gradient signal we identified (rapid decrease from 700 nm to 900 nm) was found to correlate well with cellular decline within the tumour tissue as assessed through histological analysis. Our observations are in accordance with previous reports stating that molecular and physiological changes within tumours undergoing T cell-based immunotherapy typically starts around day 3 after T cell transferex vivo episcopic fluorescence imaging. To achieve the most optimal signal we improved the cell labeling technique through the incorporation of two near infrared fluorescent dyes with identical spectra. We took advantage of an amine-reactive, predominantely cytoplasmic staining dye (VivoTag-S 750) and a membrane-intercalating (DiR) dye and optimized cell labeling in a way that cell viability and functionality was not compromised. As T cells do not exhibit large cytoplasmic regions (compared to microphages) and have a diameter of ~10 µm in the activated state, labeling is challenging. Alternative labeling strategies, for example using gold nanorods, may impair important cellular functions by the uptake of sufficient amounts of gold. Alternative labeling strategies could consider reporter gene technologies, for example using the tyrosinase gene36, which could lead to improving the cell detection sensitivity. Biodistribution of transferred cells is of high interest as off-site targets were reported to cause severe adverse events37. Furthermore, studying the biodistribution of different T cell subsets  in vivo is aim of several studies to identify the most optimal subset for immunotherapies38.To complete multi-imaging analysis of adoptively transferred T cells, we analysed T cell biodistribution using in vivo tumour anatomical and pathophysiological features related to therapy both for assessing therapy outcome and for better understand the mechanisms of tumour cell killing. We found that multi-functional multi-scale and multi-spectral optoacoustic imaging can complement current imaging efforts with the ability to offer three-dimensional high-resolution views of tumour anatomy and pathophysiology.Most imaging efforts so far have focused on imaging the distribution of labeled T cells during the course of immunotherapy only. However, there is a need for imaging methods that can reveal 257–264/SIINFEKL) in association with H-2Kb derived from approved experimental animal breedings . For the experiments, 8–12-week-old female mice were used. All procedures involving animals were in conformity with national and institutional guidelines, approved from the animal ethics committee of the local authority  and supervised by the Institutional’s Animal Care and Use Committees . Animals were housed in standard animal laboratories  in individually ventilated cage systems (IVC Techniplast) under pathogen-free conditions with free access to water and standard laboratory chow ad libitum. In total, 24 B6-A mice were subcutaneously injected with 0.5 × 106 EG7 cells in according to standard protocols. Each control or experimental group was composed of 6 animals which were chosen randomly at the beginning of the experiment. Sample size was chosen based on power analysis. Tumour sizes were measured using a caliper (FST) and tumour volume calculated using the formula tumour vol [mm3] = 0.5 × (length × width2). At the time of T cell transfer (tumour size 10–40 mm3), 1 × 106 unlabeled or 1 × 107 double labeled OT-1 and B6 T cells were systemically injected. For in vivo studies, animals were deeply anesthetized using 1.8% Isoflurane with medical O2 (0.8 l/min) and imaged at indicated time points (n = 12) or longitudinally at intervals of 36 hours with a maximum of 5 scans per animal (n = 12). After the final time-point of imaging (end-point of the experiment) animals were sacrificed under anesthetic using standard procedures and finally submitted to episcopic imaging using a fluorescence cryoslicing set up39.C57Bl/6-Albino (B6-A), C57Bl/6 (B6) and C57BL/6-Tg(TcraTcrb)1100Mjb/J (OT-1) mice transgenic for a Vα2, Vβ5 T cell receptor reacting with an octamer peptide of ovalbumin (OVATM) and its parental cell line EL4 (ATCC TIB-39TM) were maintained in RPMI 1640 medium (Gibco) containing 10% FBS, 1% Na-Pyruvat, 2 mM Glutamine, 50 µM 2-ME, 100 IU/ml penicilin/streptamycin (T cell medium). In addition, EG7 culture medium contained 0.4 mg/ml g418 (Life Technologies).Chicken ovalbumin expressing EG7 cells  by activation of 2 × 106 cells/ml splenocytes in T cell medium for 48 hours. OT-1 T cell suspensions were supplemented with 2 µg/ml 50 U/ml rm IL-2 (R&D) and 0.1 µg/ml CD28 (R&D), B6 T cell were activated in CD3 coated cell culture flasks  supplemented with 50 U/ml rmIL-2 and 0.1 µg/ml CD28. Next, cells were expanded at a density of 1 × 106 cells/ml in T cell medium supplemented with 40 ng/ml rmIL-15 (R&D) for additional 3 days. On day 5 after isolation, cells were labeled with 10 µM Xenolight DiR (Perkin Elmer) in 1x PBS at a concentration of 1 × 106 cells/ml for 15 minutes at 37 °C, with 24 µM VivoTag-S 750 (Perkin Elmer) in T cell medium at a concentration of 4 × 106 cells/ml for 30 minutes at 37 °C or double labeled with both dyes. After washing twice with 1x PBS, labeled T cells were incubated for 2 hours in T cell medium with 40 ng/ml IL-15 to recover. Subsequently, labeled cells were transferred into animals or further analysed in vitro. Labeling conditions for VivoTag-750 were analysed using full medium and serum-free medium and the same conditions as described earlier.Single T cell suspensions derived from erythrocyte-depleted spleen cell suspensions of OT-1 and B6 mice  and XTT  assay (Abnova) according to standard procedures. XTT assay was performed in triplets with 20.000 cells per well.Effectivity of labeling was analysed by fluorescence microscopy  and flow cytometry  using appropriate software . Flow cytometry measurements were done with ~0.5 × 104 T cells/well were cocultured with 2 × 104 target cells  in 96-well round- bottom plates at 37 °C. After 24 hours, supernatants were analysed for IFN-γ content by enzyme-linked immunosorbent assay  according to manufacturer’s protocol.For the detection of the IFN-γ production by OT-1 T cells, 4 × 10in vitro by utilizing 2 cm –diameter cylindrical phantoms made of 1.3% Agarose (Sigma-Aldrich) and 1.2% intralipid emulsion (Sigma-Aldrich) leading to an optically diffusive medium. Intralipid was employed to impart scattering properties in the phantom, similar to those of tissue, and achieve a diffusive illumination, homogeneously illuminating object placed in the middle of the phantom. By not adding an absorber, we opted for a low absorption background to explicitly study the optoacoustic signal produced by the labeled cells decomposed from attenuation and phantom geometry effects. The labeled T cells in a mixture of 1x PBS and low melting point agarose (Sigma-Aldrich) at a concentration of 106 cells/300 μl were enclosed within a 3 mm diameter plastic cylindrical tube implanted into the agar cylinder. The effective volume contributing to the optoacoustic signal was computed to be 5.6 μl, based on the diameter of the cylindrical tube and the elevation resolution of the MSOT scanner (~800 μm). The number of cells contributing to the optoacoustic signal was calculated from the effective volume and the cell concentration as 18.700 cells. Imaging was performed at the wavelength of 760 nm where the labeled cells exhibit and absorption peak. Next to the cells, we imaged a solution of water and black india ink (Higgins) with measured absorbance of 0.5 cm−1 at 760 nm. By comparing the signal contribution of the labeled cells to the India ink solution we were able to assess the absorbance of the cells.To assess the optoacoustic signal produced by NIR-labeled T cells, cells were first imaged 40.We used a CD-1 nude mouse for post mortem s.c. injection of 100.000, 75.000, 50.000, 30.000, 15.000 and 5.000 labeled T cells in 50 µl phenol-red free, growth factor reduced matrigel (Corning). In the following, the mouse was imaged using episcopic imaging and MSOT at wavelengths 700 nm, 730 nm, 760 nm, 790 nm, 805 nm, 820 nm, 835 nm, 850 nm and 865 nm. The data were spectrally analysed to identify the lowest detectable amount (concentration) of cells using MSOT. Spectral analysis was performed by means of AMF (Adoptive Matched Filters)41. Optical excitation is achieved by using a fast ns-laser operating at 2 kHz and 532 nm . For the acoustic detection, we used a spherically focused high frequency detector at 50 MHz with an f-number of 1.3 (Precision Acoustics), the signal is further amplified by a low noise 63 dB amplifier , and digitized at 500 MSps (Gage-Applied). The acoustic bandwidth is approximately 80%. For generating a three-dimensional image, we scan the detector in the xy-plane, and subsequently applying three-dimensional beamforming. Finally, before reconstruction, we filter the signals into two sub-bands, low frequencies and high frequencies, which we reconstruct separately, and then overlay using different colours for better visualization of the microvasculature.RSOM imaging was performed using an in-house-built raster-scan optoacoustic mesoscopeOptoacoustic imaging was performed using a commercially available “In Vision” MSOT scanner . Optical excitation is achieved by using an OPO tunable laser. Ultrasound detection is achieved using a cylindrically focused 256-element transducer array with 5 MHz central frequency (−6 dB of >50%), covering an angle of 270 degrees around the sample. The radius of the cylindrical array is 4.14 cm. The system acquires cross-sectional (transverse) images through the animal with an effective field of view of 2.5 cm × 2.5 cm, adequate for imaging through the whole body. By translating the animal through the focal plane of the ultrasound array and acquiring several cross-sectional slices allows for reconstructing three dimensional images after 3D rendering. Image acquisition speed for a single slice and wavelength is defined by the repetition rate of the laser (10 Hz). Multispectral imaging was performed by utilizing 9 excitation wavelengths, namely 700 nm, 730 nm, 760 nm, 790 nm, 805 nm, 820 nm, 835 nm, 850 nm and 865 nm.For the quantitative assessment of tumour growth and gradient signal distribution, the tumours were segmented manually from the optoacoustic images. The voxel size was estimated from the image resolution and scanning step in z direction. Tumour volume was estimated by multiplying the amounts of voxels in the segmentation masks by the voxel size. The presence of the gradient signal was quantitatively assessed by dividing the volume within the given tumour where the gradient signal was detected by the total volume of the tumour. Mean values and standard deviation were calculated for all individual parameters.t-test. For intra-group calculations, data from each day was compared to the data from day 3 as baseline. For inter-group calculations, data was normalized by deviding the data by the mean of day 3 (per group) to get changes relative to the baseline and comparing the results to the normalized baseline measurements (day 3) of the other group. p values ≤ 0.05 were considered to be significant. Rejection of the null hypothesis at the 5% significance level was indicated above the corresponding data point.Data was analysed using MATLAB . Differences in tumour size and MSOT signal intensity of the gradient signal per tumour volume within the control and therapy group and between the groups, were calculated by the Student’s unpaired two-sample 42. For episcopic fluorescence imaging, a custom made system consisting of a 750 nm-300 mW laser (BWTEK Inc.) and cold white light source , 2-filter-wheels for excitation  and emission  hosting reflection (RGB) and fluorescence (exc: 740/40/emm.: 780LP) filter sets (Chroma), a photographic lens with varying zooming and focus, a near-infrared electron multiplying charged coupled device  and a Labview  based automated acquisition software was used. The exposure time was automatically selected to ensure optimal use of the dynamic range of the EMCCD sensor and the images were normalized in the post-processing to ensure comparable results. Representative thin-slices of 10 μm thickness were extracted on histology glasses for further histopathological analysis and stored at −20 °C. After pre-fixation in 4% paraformaldehyde (PFA) (Santa Cruz Biotechnology), hematoxylin and eosin staining was performed according to the manufacturer protocol (Morphisto). Sections were immunohistochemically stained for CD31 (Histonova), Caspase 3 (Merck) and Cytochrome C  and visualized using Alexa Flour 594 (Life Technologies). Prolong Gold (LifeTechnologies) containing DAPI was used for tissue embedding and visualization of the cell nuclei. All slices were imaged using an Axio Imager M2 microscope (Carl Zeiss) equipped with the Zeiss Zen software (Carl Zeiss).After the last MSOT imaging time point, animals were euthanized and frozen at −80 °C. Before slicing, the body region of interest was selected and embedded in black optimal cutting temperature compound . The slicing was performed using a cryotome  as described inSupplementary Information.


A 47-year-old man with malignant lymphoma and secondary membranous nephropathy was admitted to our hospital due to fever and dyspnea. He was diagnosed as having Pneumocystis pneumonia, and treatment with sulfamethoxazole and trimethoprim was begun. His renal function gradually deteriorated during the course of the treatment, and hemodialysis was thus initiated using the cellulose triacetate membrane dialyzer. Hemodialysis was stably performed at first two sessions, but pneumonia worsened. In spite of no changes in the hemodialysis settings and conditions, milky fluid appeared on the venous side of the hemodialysis circuit, when the blood was returned with 0.9% saline at the end of the third session of hemodialysis . The fluThe results of lipoprotein electrophoresis of the fluid revealed that it was rich in chylomicron and very low-density lipoprotein . LaboratMedical Engineering Center, Keio University Hospital, Tokyo, JapanTadashi Yoshida Apheresis and Dialysis Center, Keio University School of Medicine, Tokyo, Japantayoshida-npr@umin.ac.jpShingo Watanabe, Masanori Morita, and Noriyuki Hirabayashi


Therefore, the aim of this study was to find the optimal tracer kinetic model for quantification of [18F]afatinib uptake in NSCLC tumours.Only a subgroup of non-small cell lung cancer (NSCLC) patients benefit from treatment using epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) such as afatinib. Tumour uptake of [18F]Afatinib PET scans were performed in 10 NSCLC patients. The first patient was scanned for the purpose of dosimetry. Subsequent patients underwent a 20-min dynamic [15O]H2O PET scan (370 MBq) followed by a dynamic [18F]afatinib PET scan (342 ± 24 MBq) of 60 or 90 min. Using the Akaike information criterion (AIC), three pharmacokinetic plasma input models were evaluated with both metabolite-corrected sampler-based input and image-derived (IDIF) input functions in combination with discrete blood samples. Correlation analysis of arterial on-line sampling versus IDIF was performed. In addition, perfusion dependency and simplified measures were assessed.[18F]afatinib was 341 ± 37 MBq. Fifteen tumours could be identified in the field of view of the scanner. Based on AIC, tumour kinetics were best described using an irreversible two-tissue compartment model and a metabolite-corrected sampler-based input function (Akaike 50%). Correlation of plasma-based input functions with metabolite-corrected IDIF was very strong (r2 = 0.93). The preferred simplified uptake parameter was the tumour-to-blood ratio over the 60- to 90-min time interval (TBR60–90). Tumour uptake of [18F]afatinib was independent of perfusion.Ten patients were included. The injected activity of [18F]afatinib uptake in NSCLC tumours was the 2T3K_vb model. TBR60–90 showed excellent correlation with this model and is the best candidate simplified method.The preferred pharmacokinetic model for quantifying [https://eudract.ema.europa.eu/ nr 2012-002849-38 Non-small cell lung cancer (NSCLC) is one of the most frequent cancer types and accounts for the highest cancer-related mortality worldwide –3. Over 11C] erlotinib, a radiolabelled first generation EGFR TKI [18F] afatinib, a radiolabelled second generation, irreversible EGFR TKI, was used as PET tracer. In mouse models with NSCLC cell lines (EGFR wild-type (A549) and EGFR mutated (HCC827) xenografts), Slobbe et al. showed accumulation of [18F]afatinib in NSCLC tumours [18F]afatinib studies have been performed in patients.Positron emission tomography (PET) may provide such an alternative. Indeed, Bahce et al. demonstrated that EGFR mutation positive tumours could be identified using PET and C erlotinEGFR TKI . In this tumours , 14. To 18F]afatinib in NSCLC patients and to derive the optimal tracer kinetic model for quantifying [18F]afatinib uptake in tumours. A secondary aim was to determine the simplified uptake parameter that best correlated with the preferred quantitative uptake parameter.The aim of the present study was to assess kinetics of afatinib was performed as described by Slobbe et al. [18F]afatinib was purified by semipreparative HPLC chromatography .Synthesis of [e et al. . Briefly18F]afatinib. Each whole body scan  consisted of 11 bed positions with 3 min per bed position. Scanning protocols 2 and 3 started with a low-dose CT scan, which was used for attenuation correction and image segmentation, followed by a 10-min [15O]H2O PET scan for assessment of perfusion. Four patients were scanned according to protocol 2, and 5 patients were scanned according to protocol 3. In the second sub-study (protocol 2), patients underwent a 90-min dynamic [18F]afatinib PET scan. In protocol 3, a [15O]H2O PET scan was followed by a low-dose CT and finally, a 60-min dynamic [18F]afatinib PET scan. All [18F]afatinib PET scans in protocols 2 and 3 were obtained after an intravenous bolus injection of 370 MBq (± 10%) [18F]afatinib. All [15O]H2O PET scans were obtained after an intravenous bolus injection of 370 MBq [15O]H2O.15O]H2O, 26 frames were used . For the 90-min [18F]afatinib scan (protocol 2), 22 frames were used . For the 60-min [18F]afatinib scan (protocol 3), 19 frames were used .All PET emission scans were acquired in list mode and reconstructed retrospectively using a 3-dimensional row-action maximum-likelihood algorithm into time frames with progressive increase in frame duration . ReconstThe study comprised 3 scanning protocols. Each patient was scanned on an Ingenuity TF PET/CT . The first patient, included for dosimetry purposes, underwent 4 whole body scans following injection of 74 MBq [18F]afatinib PET scan; in protocol 2, sampling was only performed during the first dynamic scan. Manual samples were used to calibrate the on-line curve and to determine plasma to whole blood ratios together with fractions of labelled metabolites in plasma. In protocol 2, on-line sampling was performed for 40 min post injection (p.i.), at a rate of 5 mL/min for the first 5 min and 2.5 mL/min thereafter. In protocol 3, on-line sampling was performed for 20 min post injection (p.i.), again at a rate of 5 mL/min for the first 5 min and 2.5 mL/min thereafter. In all patients, manual arterial samples of 7 mL were taken at 5, 10, 15, 30, 40 and 60 min p.i. For protocol 2, a seventh sample was drawn at 75 min p.i.In patients of protocols 2 and 3, both on-line (continuous) and manual (discrete) arterial sampling was performed during the dynamic [18F]afatinib and its labelled metabolites.Metabolites were analysed as described by Slobbe et al. . Briefly3 for all patients. VOIs were projected onto the dynamic PET images to extract time activity curves (TACs) for all regions.Each image is checked using the VINCI software (version 2.56.0) for patient movement or image artefacts. Motion correction is necessary if PET images do not fully overlap the CT scan that is done prior to scan acquisition. All volumes of interest (VOIs) were defined manually for each individual tumour using the software developed in-house. VOI definitions of tumours were based primarily on acquired CT images with images projected in parallel, avoiding necrosis and blood vessels as much as possible. If the shape of the tumour was erratic, a summed image of the last 4 frames was used to ensure correct definition of the VOI. If this PET image appeared to have relatively high uptake  near the boundaries of a tumour, a 0.5-cm margin was added to the VOI. Definition of the image derived input function (IDIF) VOI within the descending aorta was performed in a standardized manner by drawing fixed VOIs in 10 slices of the descending thoracic aorta on a summed PET image of the first 6 frames, resulting in an overall VOI of 7.8 cmConventional pharmacokinetic model analyses generally assume that only the unaltered parent tracer in plasma is capable of binding specifically to its target. Therefore, input functions were corrected for non-parent fractions, i.e. polar and non-polar metabolites, as it was not known whether polar fractions were metabolites or covalently bound/unavailable parent compounds.y = A*e−Bt + C). Next, plasma to whole blood ratios and metabolite fractions obtained from the blood samples were fitted using a linear and a Hill-type function, respectively. Finally, a metabolite-corrected plasma curve was derived from the whole blood curve using the fitted plasma to whole blood ratios and metabolite fractions.The on-line arterial curve was calibrated using the manual arterial samples and extrapolated to the late manual samples (> 20 min p.i.) using an exponential fit .18F]afatinib tumour TACs was performed using 3 conventional pharmacokinetic plasma input models, all including an additional parameter for blood volume fraction (Vb). A single-tissue compartment model (1T2k_Vb), an irreversible two-tissue compartment model (2T3k_Vb) and a reversible two-tissue compartment model (2T4k_Vb) were assessed afatinib tumour uptake.Each model provides its own outcome measure. The main outcome measure for both single-tissue and reversible two-tissue compartment models is the volume of distribution (on (AIC) . Results15O]H2O scans using the standard single-tissue compartment (1T2k_Vb) model combined with an IDIF [18F]afatinib PET scans, dependency of tumour tracer uptake on tumour perfusion was evaluated.Tumour perfusion was derived from the [ an IDIF . By usin18F]afatinib tumour uptake would be possible, several simplified measures were assessed, including standard uptake value (SUV) and tumour-to-blood ratio (TBR) afatinib uptake. A list of simplified measures is given in In order to assess whether simplification of scanning protocol and quantification of [io (TBR) . SUV is 18F]afatinib. Correlation analysis using the Pearson correlation coefficient was also performed for the preferred parameter versus K1 of [15O]H2O as well as for K1 of the preferred pharmacokinetic model versus K1 of [15O]H2O to asses perfusion dependency. Correlation analysis for each simplified measure versus the preferred pharmacokinetic outcome measure was also performed using the Pearson correlation coefficient. Correlations were considered strong if the Pearson correlation coefficient was > 0.7 and very strong if it was > 0.9. Analyses were performed using IBM SPSS (version 22).All kinetic analyses were performed using the software developed in-house. Descriptive statistical analyses were performed to assess and report endpoints. The Pearson correlation coefficient was used to analyse the correlation between on-line plasma input and IDIF input using the preferred parameter of interest obtained through pharmacokinetic modelling of [15O]H2O was 370 MBq for all patients. The injected dose of [18F]afatinib was 341 ± 37 MBq with a specific activity of > 18 MBq/μmol (i.e. 97 ± 55 MBq/μmol). There were no adverse or clinically detectable effects observed in any of the subjects.Ten patients were included in this study. In 7 patients, an activating EGFR mutation was found in tumour DNA prior to inclusion. In the other patients, no mutation was found (EGFR wild type). No resistance mutations were found. Within the field of view of the scanner, a total of 16 tumours were identified. In patient 8, the start of arterial sampling was not captured by the on-line sampler, so the tumour TAC could only be analysed using IDIF. Therefore, 15 tumour VOIs were included in the analysis. Patient and PET scanning characteristics are given in Table Radioactivity concentrations of arterial whole blood and plasma samples are shown in Fig. Metabolite-corrected input functions using both sampler-based curves and IDIFs were created and used to fit the tumour TACs to the 3 conventional pharmacokinetic models. All fits were assessed for quality, both visually and using the AIC. Both indicated a preference for the 2T3k_Vb model (AIC 50/36% plasma/IDIF), followed by 1T2k_Vb (AIC 43/29%) and 2T4k_Vb (AIC 7/36%) models. In 2 patients, AIC indicated a preference for the 1T2k_Vb model, but subsequent visual assessment due to small (< 10%) AIC differences showed a similar or more probable fit of the 2T3k_Vb model. In Fig. Ki obtained using measured sampler-based input functions and those obtained using IDIFs (r2 = 0.93), as shown in Fig. There was a very strong correlation between tumour  r2 = 0.9, as show r2 = 0.9, as show r2 = 0.9, as showIn protocol 2, tumour TACs were also fitted to the 3 models using input functions not corrected for metabolites to assess the necessity of metabolite correction. In 7 patients, visual assessment showed poorer quality of fits compared with fits obtained using metabolite-corrected plasma input functions (data not shown). The other 2 patients showed similar quality of fits. Corrections for labelled metabolites were therefore considered necessary and subsequent analyses were performed using metabolite-corrected input functions.15O]H2O scans. Clearly, the [15O]H2O PET scan was not part of the dosimetry protocol. In one patient, the [15O]H2O PET scan was cancelled due to time constraints. Blood flow in the tumour of patient 4 appeared to be unrealistically high (K1~1.9 mL/(min g)), presumably due to technical issues and patient motion. Therefore, all H2O data of this patient were excluded. No correlation between [15O]H2O K1 and [18F]afatinib Ki (r2 = 0.0003) was found, indicating that [18F]afatinib tumour uptake is not perfusion dependent, as shown in Fig. All except 2 patients underwent [60–90 (tumour-to-blood ratio from 60 to 90 min) using plasma-based input-correlated best with [18F]afatinib Ki obtained using kinetic analysis , as shown in Fig. 18F]afatinib can reliably be quantified using TBR60–90 as uptake measure.Analysis of several simplified measures showed that generally TBR based on metabolite-corrected plasma input generally performed better than TBR based on whole-blood input. TBRKi are given in All simplified measures, i.e. various SUV and TBR over other time intervals, and their correlations with 18F]afatinib tumour uptake in NSCLC patients. Dynamic scanning in combination with a metabolite-corrected sampler-based function was used to identify the tracer kinetic model that best describes [18F]afatinib tumour uptake. The irreversible two-tissue model showed the best fits, yielding Ki as parameter of interest. This result is in accordance with in vitro data, showing irreversible binding of afatinib to the EGF receptor Afatinib Ki values obtained using metabolite-corrected IDIF correlated strongly with Ki values obtained using arterial input functions based on the on-line sampler. These results validate the use of an IDIF instead of an invasively measured input functions . Since the on-line sampler was not used during the total duration of the scan, extrapolation was needed to create the sampler-based input functions, possibly leading to overestimation of the Ki. In contrast, IDIF-based input functions are based on the full scan, which negates the need for extrapolation, producing more reliable input functions. Using IDIF instead of the on-line sampler allows for simplifying the scanning protocol in future studies. However, IDIF still needs to be corrected for metabolites. The parent fraction was around 70% after 5 min with a gradual decrease to approximately 30% after 75 min, indicating a rapid metabolism. In future studies, metabolite correction based on venous blood should be compared to metabolite correction using arterial blood to further simplify the scanning protocol .[18F]afatinib is perfusion independent, the scanning protocol can be simplified by omitting the [15O]H2O perfusion scans. This allows for quantification of tumour [18F]afatinib uptake using a simplified measure.Considering that tumour uptake of [60–90. This time interval of 60–90 min needs to be validated in larger studies.The present results suggest that static whole body scanning is feasible using TBR11C] erlotinib, a first-generation reversible EGFR TKI showed that uptake of [11C] erlotinib was significantly higher in tumours with an EGFR mutation [11C restricts the application of [11C] erlotinib in widespread clinical practice. By using a fluorine-18 tracer instead of a carbon-11 tracer, the problem of a short half-life can be addressed, thereby enabling its potential use in clinical practice.Afatinib binds covalently to the kinase domain of the epidermal growth factor receptor. Affinity for this bond increases in the presence of an activating EGFR mutation . PreviouThe next important step for this tracer is to assess its capacity to predict the tumour EGFR mutational status, as this is a strong predictor for response to EGFR-directed TKI therapy, such as afatinib , 20, 21.18F]afatinib in NSCLC tumours is best described by an irreversible two-tissue compartment model, where IDIF can be used rather than on-line arterial sampling. Tumour tracer uptake was shown to be perfusion independent. In this small series of patients, an excellent correlation was observed between TBR60–90 and Ki suggesting that [18F]afatinib can potentially be used in combination with (static) whole body scans.Kinetics of [