Clostridial spores (CG) cause significant oncolysis in hypoxic tumour microenvironment and result in tumour regression in both animal models and clinical trials. The immune mediated response plays a critical role in the antitumour effect by the anaerobic spore treatment. Anaerobic8 CFU/kg) or high dosage (3 × 108 CFU/kg) of Derivative Clostridial spore (DCG). Human papillomavirus 16 E6/E7 transformed TC-1 tumour bearing mice were intravenously administered with low  spores leads to both tumour and systemic inflammatory responses characterized by increased IFNγ/IL-9 secreting T cells in the spleen and the tumour. Low numbers of antigen specific T cells (<20/106 spleen cells) in the spleen of the tumour bearing mice are also detected after intravenous DCG delivery. Interestingly, our results showed that a mixed IL-9/IFNγ secreting T cell response was induced when the tumour bearing mice received a low dose of DCG spore (1 × 108 CFU/kg), while a strong IFNγ response was elicited with a high dosage of DCG spore (3 × 108 CFU/kg). Intravenous administration of the derivative of The dosage of DCG spore will determine the types of the DCG induced immune responses. Cancer immunotherapy is the most promising new cancer treatment of the past years and has achieved clinical responses by enhancing the immune system to fight cancer , 2. For Cervical cancer is the 3rd most diagnosed cancer in women worldwide . It resuSolid tumours at advanced stages show angiogenesis of abnormal blood vessels restricting blood supply and resulting in reduced oxygen levels in the tumour microenvironment . As a coi.v.) injected spores from an anaerobic bacterium germinated and proliferated inside the hypoxic environment of the solid tumour [ Clostridium, have been developed for bacterial-assisted oncolysis [ Clostridial spores (DCG) have shown distinct advantages which are selectively germinated and multiplied as rods inside solid tumours [ novyi NT to a patient with advanced leiomyosarcoma was effective at inducing tumour regression [ Clostridial spore treatment of solid tumour mass will not eradiate tumour completely, because of its anaerobic character where the peripheral rim of the tumour is often not anaerobic.Patients with large inoperable tumours have been observed to become tumour-free after fever. Intravenous  mice were purchased specific pathogen free (SPF) from the Animal Resource Centre, Shan Dong University, and kept at the Animal Resource Centre, Shandong Xing Wei Biopharm Company, Jinan, Shandong Province, China. All experiments were approved by and performed in compliance with the guidelines of Shandong Xing Wei Biopharm Company Animal Experimentation Ethics Committee .6-8-week-old adult female C57BL/6 (H-2γ PerCP-Cyanine5.5 (XMG1.2), Anti-IL5-PE (TRFK5), Anti-IL9-APC (RM9A4), and Anti-IL-10-PerCP-Cyanine5.5 (JES5-16E3) were purchased from eBioscience .Anti-CD45.2-FITC (104), Anti-CD8a-PerCP-Cyanine5.5 (53-6.7), Anti-CD3-FITC (17A2), Anti-CD3-APC (17A2), Anti-CD4-PE (RM4-5), Anti-NK1.1-APC (PK136), Anti-B220-PE (RA3-6B2), Anti-F4/80-PerCP-Cyanine5.5 (BM8), Anti-IFNμg of streptomycin/ml (GIBCO), 0.2 mM non-essential amino acid solution, 1.0 mM sodium pyruvate, 2 mM L-glutamine, and 0.4 mg/ml G418 and were cultured at 37°C with 5% CO2. The TC-1 cells were routinely tested for mycoplasma as described elsewhere [Murine TC-1 cell line was purchased from Shanghai institutes for cell resource centre, Chinese Academy of Sciences, and cultured following the protocols in the product sheets. TC-1 tumour cell line is derived from primary lung epithelial cells of C57BL/6 mice and transformed with HPV16 E6/E7, which is often used in mice model of cervical cancer . Briefly6) were injected subcutaneously into the hind flank area in 0.2 ml of PBS using a 25G needle. TC-1 tumour bearing mice were divided into 4 different groups: (1) Untreated; (2) PBS treated; (3) Low Dose DCG treated (1x108 CFU/kg); and (4) High dose DCG treated (3x108 CFU/kg) group. Each group has six mice. Between 4 and 7 days later and every 3 days thereafter, the area was observed and palpated for the presence of a tumour nodule. Tumour sizes were assessed every 3 days using callipers to determine the average diameter of each tumour. Tumour volumes were calculated as width×width×length. Mice were sacrificed when the tumour diameter reached 20 mm.TC-1 tumour challenge has been described elsewhere . Briefly C. ghonii spores for in vivo anti-tumour study, C. ghonii spores were first retrieved from −80°C and streaked on HI medium agar plates, which were incubated at 37°C under anaerobic conditions for 2 days. Bacterial colonies were inoculated into 100 ml HI media and grown under anaerobic conditions at 37°C overnight. The bacterial culture was then added to 1 L of sporulation medium , and pH 7.4) and incubated under anaerobic conditions at 37°C for 2 weeks for sporulation. After 2 weeks, the sporulation medium was gently mixed and centrifuged at 13000 rpm for 15 min at 4°C. The cell pellet was then washed with cold PBS twice followed by resuspending in cold PBS and sonicated for 5 min to release endospores and eradicate vegetative forms of clostridia. Spores were then washed 6-10 times with cold PBS to remove vegetative cell debris. The purity of the spore suspension was finally confirmed by microscope inspection. The spore number was determined by serial dilution and plating. To determine colony forming unit (CFU), C. ghonii spores were plated on HI medium agar plates and incubated anaerobically at 37°C for 2 days. CFU of on HI agar plates were counted and calculated.To prepareA multicytokine ELISA kit for cytokines was purchased from Qiagen , and experiments were performed following the instructions provided within the kit. ELISA results were read by using an ELISA plate reader  at 450 nm.Single spleen cells or single cells isolated from tumour were washed with wash buffer extensively with PBS containing 2% FCS, before being surface stained with relevant antibodies. After extensive wash, cells were acquired on BD FACS Calibur (Becton Dickinson). Flow cytometry data was analysed by NovoExpress™ . The method has been described elsewhere , 18.μg of collagenase D/ml, 20 μg of DNase I/ml, and 10% fetal calf serum in RPMI-1640 at 37°C. The digested tissue was passed through a 70 μm nylon mesh, and the resultant cells were washed twice in PBS. Mononuclear cells were obtained with Lymphocyte Separation Medium (Axis shield) following a centrifugation at 2000 rpm for 25 minutes. Cells were stained for surface molecules and then fixed and permeabilized using a permeabilization kit (Biolegend) before being intracellularly stained with anti-mouse IFN-γ, IL-5, or IL-9, respectively, or isotype-matched control mAb for 20 minutes in the dark at room temperature. Samples were analysed by flow cytometry using a FACS Calibur analyser (Becton Dickinson).The method has been described elsewhere , 18. Briγ . HPV16 E7 MHC class I restricted peptide RAHYNIVTF was added at various concentrations and cells were held at/incubated at 37°C with peptide for 18 h. Antigen specific IFN-γ secreting cells were detected by sequential exposure of the plate to biotinylated anti-IFN-γ before s.c. vaccination (BD Harlingen), avidin-horseradish peroxidase (Sigma-Aldrich), and DAB (Sigma-Aldrich). The results were measured by ELISPOT reader system ELR02 . Antigen specific CD8+ T cells were shown as the numbers of spots/106 per splenic cells.ELISPOT was performed as described . BrieflyStatistical analysis was performed by using the two tailed Student's test, or Mann-Whitney test using Prism 6.0 . P<0.05 is considered statistically significant.i.v.) administration of DCG spore inhibits TC-1 growth compared with nontreated group. In the current study, the same experiment was performed and similar results were obtained  were similar among DCG treated groups and the control groups. The numbers of T cells including both CD4+ and CD8+ T cells are similar among the four experimental groups. The percentages of F4/80+, NK1.1+, and B cells of the spleen are slightly reduced in DCG spore treated groups compared with the control groups  in DCG spore treated mice; the percentages of T cells, infiltrating to tumour, are slightly increased in DCG spore treated mice, while B220+, NK1.1+, and F4/80+ cells are similar among the DCG treated and nontreated groups .γ secreting T cells in the 3 × 108 DCG spore treated group were significantly increased compared with nontreated and PBS treated groups, while the 1 × 108 DCG spore treated group is only statistically significant compared with nontreated group. Interestingly, IL-9 secreting T cells are significantly increased in the 1 × 108 CFU/kg treated group compared with control groups and the 3 × 108 DCG spore treated group, while IL-9 secreting T cells are similar between the 3 × 108 DCG spore treated group and control groups  in mice treated with the 3 × 108 CFU/kg DCG spore treated group, further supporting the above intracellular staining result  of tumour antigen E7 specific CD8+ T cell responses are detected in the spleen of the tumour bearing mice; moreover, whether a strong INFγ response or a mixed IFNγ/IL-9 response is determined by the dosage of DCG spore administered intravenously to the TC-1 tumour bearing mice.In the current paper, we demonstrated that intravenous administration of DCG spores to TC-1 tumour bearing mice elicited strong splenic as well as tumour local immune responses, characterised by increased IFN C. novyi-NT spores were administered together with conventional chemotherapeutic drugs, extensive hemorrhagic necrosis of tumour often developed within 24 hours, resulting in significant and prolonged antitumour effects [α, and IFN-γ in such T cells that might be involved in the antitumour activity [ C. novyi-NT, could eradicate large established tumours [It has been shown previously that facultative anaerobic bacteria colonizing solid tumors often result in tumor growth retardation or even clearance. This phenomenon has been observed in tumour models of mouse, rat, and dog and in clinics , 20–23.  effects . Innate  effects . Depletiactivity . In anot tumours .γ/IL-10 in the spleen and increased IFNγ/IL-9 secreting T cells infiltrating to the tumour site. High dose (3 × 108 CFU/kg) of DCG spore treatment leads to increased IFNγ secreting T cells in tumour, and low dose (1 × 108 CFU/kg) of DCG spore treatment leads to both IL-9 secreting T cells infiltrating to the tumour site, while the numbers of splenic IL-10 secreting T cells are increased in the low dose DCG spore treated group. IL-9 secreting T cells have been shown [γ has been shown to be critical for rejecting the tumour. It seems that the mechanism underlying the tumour growth inhibition in low and high dose DCG treated mice is different, as more IFNγ secreting T cells were infiltrated to the tumour site in high dose DCG treated mice, while more IL-9 secreting T cells were observed in low dose DCG treated mice.We further demonstrated in the current study that the DCG administration results in increased levels of IFNen shown  to mediaγ secreting CD8+ T cells from the spleens of DCG treated mice were also detected, although at low numbers. E7 specific IFNγ secreting CD8+ T cells cannot be detected from the tumour by ELISPOT assay. Antigen specific CD8+ T cells that infiltrate to the tumour site are critical for killing the tumour cells; therefore, attempts should be made to increase the antitumour T cell responses mediated by anaerobic bacteria treatment. Genetically modified anaerobic bacteria that express HPV16 E7 may be useful to increase the E7 specific CD8+ T cell responses.E7 specific IFNγ/IL-9 secreting T cells to the tumour. In future, DCG spore treatment should induce and attract more tumour antigen specific T cells to the tumour site; the new strategy warrants a further investigation.Taken together, the DCG spore treatment increases the levels of proinflammatory cytokines at the tumour site and attracts IFN


One of many important features of the tumour microenvironment is that it is a place of active Darwinian selection where different tumour clones become adapted to the variety of ecological niches that make up the microenvironment. These evolutionary processes turn the microenvironment into a powerful source of tumour heterogeneity and contribute to the development of drug resistance in cancer. Here, we describe a computational tool to study the ecology of the microenvironment and report results about the ecology of the tumour microenvironment and its evolutionary dynamics. The presence of high gradients favours the formation of different ecological niches which can become an important source of tumour heterogeneityμm, and the regions in between them can become highly hypoxic4.The actual size of the niches is quite small, as it is mostly determined by the structure of the scaffold of capillary vessels that envelope and feed the tumour mass. This structure is highly irregular, with a chaotic blood vessel network, a missing lymphatic network, elevated acidity, poor oxygenation, and high interstitial fluid pressure. The distance between blood vessels may be as large as a few hundred 13, and the reviews15.The fine-graininess and the large spatial variability are essential features in the formation of the ecological niches and they must belong to any mathematical model that aims to explain tumour heterogeneity as the result of Darwinian selection driven by the local environment. The importance of the microenvironment is widely recognized, and several researchers have tackled the problem of its description and understanding from the computational point of view, e.g.17, the adaptive evolution of tumour clones  is driven by the formation of new environmental niches. Many practical difficulties limit the experimental study of the adaptation process, while computer simulations can shed light — albeit in a limited way — on the dynamics of many steps like the convergent evolution of different genotypes to the same phenotype, and the selective loss of specific cell functions.The studies, however, mainly focus on the biochemical, cellular and biophysical properties of the tumour microenvironment and not on its active role in providing varied evolutionary paths to genetic variants of the tumour cells. As it has already been pointed out18. After the angiogenic transition, however, the microenvironment differentiates in unpredictable ways. To identify underlying processes and provide reasoning, we have developed a computational model that is a tool to understand the fine-grained features of a simplified tumour microenvironment, i.e. of a microenvironment that contains only tumour cells and blood vessels. This is a rather strong simplifying assumption, since experimental observations show that in addition to tumour cells the microenvironment contains several other important cell types such as stromal and immune cells. We wish to stress that this assumption is a necessary compromise, because no computer model can presently describe the huge biological complexity of the tumour microenvironment and capture its growth at appropriate spatial and temporal resolutions.Simulations of avascular solid tumours show that the microenvironment of these small cell aggregates is formed by rather homogeneous niches with smooth gradients of oxygen, of other nutrients, waste molecules and cell viability19. We follow the complex dynamics of this process in detail and in real time and show that, already at these early stages of tumour development, the diversity of environmental niches is high enough to promote adaptive evolution of tumour variants.Our computational model combines two different simulation programs, namely, a lattice-free simulation of small avascular solid tumours and a lattice-based simulation of the blood vessels dynamics in solid tumours. As we discuss below, both models have been individually validated with experimental data, and here we provide a first validation of the combined model by correctly reproducing the relevant observed gradients of tumour oxygenation and other biochemical and biological features. Next, we use the combined model to explore how the tumour microenvironment takes shape in small solid tumours at the transition from the avascular to the vascular growth phases, the so-called angiogenic switch23, for Tumorcode, see26).In this section we give a short description of the two programs that have been merged. The present account is very brief as both programs have been described in detail in past papers  including the intercellular space.The surrounding environment is an integral part of the simulation.VBL is a lattice-free, cell-based program that can simulate both: the growth and proliferation of disperse cells and of more complex, avascular cell clusters (tumour spheroids). It contains a detailed simulation of the metabolic activity of each cell including discrete events at the individual cell level , and has the potential to activate phenotypic differentiation. More specifically, the model has the following features:27, here we only mention that although the simulation is lattice-free, continuum processes are actually discretized on the continually-variable irregular lattice defined by the Delaunay triangulation28 based on the cells’ centres which also defines the proximity relations among cells that are necessary to compute the cell-cell forces. We find that the computational geometry library CGAL 29 is an easy tool that offers necessary triangulation features amended by the possibility to calculate alpha shapes. This is necessary to define the surface or contact zone of the spheroid with the environment. It is important to note that the simulation spans many orders of magnitude in time which makes the large system of differential equations describing the deterministic part of the program extremely stiff. Therefore we solve the equations by means of implicit integration methods, that allow for comparatively large time steps (of the order of 1–10 s in terms of simulated time)30.The numerical methods used in VBL have been described elsewhere27.Figure 31.While cell-cell interactions like in VBL naturally produce nearly spherical structures, modelling the growth of blood vessels moves the complexity of the simulation one step higher to the level of tissue morphogenesis. The list of models is long. For an appropriate discussion of the existing models for tumor vasculature see32), here we use the simulation framework Tumorcode described in25, which does not only create artificial arterio-venous blood vessel networks representing the healthy initial vasculature, but includes vascular remodeling during solid tumour growth by angiogenesis, vessel dilation, regression and collapse. The created networks were tested against experimental data and successfully reproduced: 1) morphology and flow characteristics34, 2) interstitial fluid flow and pressure35, and 3) tissue oxygenation26 for both, healthy and tumour tissue.Solid tumours grow in originally healthy tissue featuring a normal vasculature build up by a hierarchically organized arterio-venous blood vessel network that is then dynamically modified by the growing tumor. An integrative modeling approach thus has to address two issues: first, it has to find an appropriate representation of the original vasculature of the host tissue; and second, the dynamics of the given blood vessel network has to be defined, which includes the insertion of new vessels via angiogenesis as well as the removal of existing vessels (old and new) via vessel regression, and the modification of existing vessels via dilation, constriction or occlusion. Moreover, the network carries a blood flow and transports and emits oxygen, nutrients and drugs, which has to be represented, too. Various modeling approaches have been presented in the past (for a review see e.g.Hemodynamics includes the phase separation effect (Fåhræus-Lindqvist effect) and different rheologies.https://trilinos.org)36.Concentrations in the surrounding environment are computed according to a continuum model, i.e., according to a set of partial differential equations discretized by finite elements method solved with the Trilinos C++ library  in Tumorcode. In contrast to VBL, it cannot describe the evolutionary features of the microenvironment at the single cell level.Figure To merge the two programs into one, we had to match the spatial structures of the two programs. The cells are not confined to a lattice however the diffusion processes in VBL are calculated by means of a disordered Delaunay triangulation which is not permanent and has a variable size, as cells position change at each time step while the cells grow and proliferate. The situation is almost the reverse in Tumorcode where blood vessel segments belong to an FCC lattice and the continous space is discretized by a cubic lattice to solve the partial differential equations. We interpolated cell properties from the cells position to the surrounding nodes of the cubic lattice  where cells not receive a sufficient input of oxygen and nutrients.The computational approach provides an easy access to information that is hard to obtain with experiments and allows multiple ways of visualization  and pH (fredrich:pH:2018) during the complete of the growth processes.Intuitively the increase in the mean of cell-blood vessel distances leads to a decrease of available oxygen and nutrients, and to an increase of lactate ions and to a lower pH. Since the cell-blood vessel distance is known, we can display space-time data on the distributions of cell radii, pO pH Fig.  - the daFinally, the changes in the microenvironment stretch the cell proliferation period and therefore modifies the cell cycle distribution as seen in Fig. Actual tumours have irregular shapes, while the cells in VBL are not polarized, and when grown in uniform environments produce nearly spherical shapes. In the presented case the blood vessel network drives the growth in specific directions and we expect a marked deviation from sphericity already with small simulated tumours. Ideally, in a spherical tumour all cells on the surface have the same distance to the centroid. We find that the distribution of distances from the spheroid’s surface to the centroid a) broadens and b) deviates from symmetric form as the tumour grows Fig.  which iset al.38 measured the partial oxygen pressure and pH within different tumours. To compare our simulations with their experimental results, we seeded the initial tumour cell close to bifurcations, both arterial and venous, and sampled the cells along different lines  source filesTumorcode source files


Single fibre electromyography is the most sensitive neurophysiological test for the diagnosis of neuromuscular junction disorders, particularly myasthenia gravis. The study aimed at establishing concentric needle (CN) normal jitter values for voluntarily activated orbicularis-oculi (V-OOc) & Frontalis (V-FRO) muscles in Sudanese population. 57 healthy volunteers  were included in the study . V-OOc and V-FRO were tested in the same individual using CN. Jitter values were expressed as the mean consecutive difference (MCD) of 30 potential pairs in microseconds. The mean jitter, mean individual fibre pairs jitter & mean outliers jitter values with (upper 95% Confidence Limit-CL) for [OOc] were  & for [FRO] were  respectively. The suggested practical upper limits for mean jitter & for outliers were  for OOc &  for FRO. Our CN-jitter values were within the range of the few published studies. The study was unique in that it established and compared between CN reference jitter values of two voluntarily activated facial muscles (V-OOc & V-FRO) in the same individual in large number of healthy subjects. It is a valuable test used for the diagnosis of neuromuscular junction disorders, e.g. myasthenia gravis (MG)6. It is carried out using a specially designed single fibre needle (SF) electrode which can identify and record potentials from single individual muscle fibres. Expert neurophysiologists & equipped neurophysiology clinics are required to perform this safe but time-consuming test. Jitter is defined as the variability in the interpotential interval between two consecutive muscle fibre potentials of the same motor unit. Fluctuations in the time for endplate potentials to reach the action potential threshold at the neuromuscular junction produce most of this variation. Compared to Tensilon (edrophonium), repetitive nerve stimulation, and acetylcholine receptor antibody tests; jitter was proven to be the most sensitive test in the diagnosis of neuromuscular junction (NMJ) abnormalities11, yet not specific test as it was found to be increased in diseases of nerve (neuropathies) and muscle (myopathies)18.Single-fibre Electromyography (SFEMG) is a special electromyographic test developed early in the nineteen sixties by the Swedish Scientists Stalberg and Ekstedt11. Conventional sterilization methods cannot easily remove these proteins as it strongly adherent to metal surfaces, therefore reusable needle electrodes carry a risk of transmitting spongiform encephalopathies between humans19. Therefore, there is an increasing awareness about the use of the disposable, cheaper, less uncomfortable, readily available and easier to operate concentric needle (CN) and monopolar (MNP) electrodes worldwide23 instead of the standard SF electrode in jitter measurement for which normal jitter values had been established27.Although SF electrode is the most accurate and highly selective in detecting potentials produced by individual muscle fibres, there are some disadvantages associated with the use of this needle electrode. These include the cost of the needle, electrodes sterilization, the time-consuming and technically difficult nature of the study and the risk of transmission of blood borne infectious agents, such as prion protein which is present in skeletal muscleet al.28, who tested the two muscles together in the same individual using SF electrode on the electrically stimulated OOc and FRO muscles in 46 healthy subjects as a control for 20 patients with (OMG). They reported higher sensitivity of the stimulated SFEMG of the orbicularis oculi than the frontalis muscle in the diagnosis of OMG. Consequently, they recommended doing jitter first in the OOc muscle in patients with possible OMG as it may show negative results if only the frontalis muscle is examined. In contradistinction, the Japanese study carried out by Hiroko et al.29, who used the CN needle applying the voluntary activation method in 16 OMG patients, showed a slightly higher sensitivity of the FRO than the OOc.With regard to ocular myasthenia gravis (OMG), jitter test (SFEMG) had been mostly carried out in either orbicularis oculi or frontalis muscles. Reviewing Literature, revealed one previous study carried out by Valls-Canals 18. SFEMG was not only necessary for early diagnosis of patients with MG and other NMJ disorders, but also extremely important for carrying out an objective evaluation of their clinical progression and response to treatment5. In addition, it was extremely important to set normal baseline data for our Sudanese population taking into consideration that studies on jitter measurements had never been done in Sudan. Apart from two multicenter studies30 performed to establish concentric needle reference values for jitter measurements, a limited number of research articles addressed this issue, some of which targeted a small number of healthy subjects to serve as a control for myasthenic patients. Most of these studies tested only one facial muscle (either OOc or FRO). Axonal stimulation SFEMG method was performed testing the orbicularis oculi muscle32, while frontalis muscle was tested using the stimulation technique34. Regarding voluntary activation technique, it was performed on OOc36 as well as on FRO muscle37. Establishing upper limits for normal jitter values is essential for interpretation of patients’ test results.It is necessary to establish baseline reference SFEMG jitter values of the OOc & FRO because they are more easy to study, more sensitive in detecting abnormal jitter, and the earlier to be affected during the course of ocular myasthenia than limb musclesThe aim of this study was to establish and to compare normal (reference) concentric jitter values obtained performing voluntary activation technique in OOc & FRO muscles tested in the same control subject. Finally to find out the effect of age, gender, height and weight on CN-jitter values of voluntarily activated OOc & FRO in healthy subjects.2 and needle length of 25 mm .The machine used to conduct SFEMG was Digital Medelec Synergy EMG (Electromyography) machine with a built-in SFEMG software used for recording and analysis. The analysis was done using extra Medelec Synergy Reader software. The recording was performed using CN with a red hub, diameter of 0.3 mm (30 G), recording area of 0.03 mmThis prospective cross-sectional analytic study was carried out in a period of one year, at the department of physiology, in the Faculty of Medicine of Khartoum University on healthy human volunteers who were selected randomly from the members and students of the Faculty in addition to healthy patients’ relatives accompanying patients referred to the Faculty neurophysiology clinic. The study recruited 57 normal subjects [37 of whom were females and 20 were males]. Their mean age (43.2 ± 14.0years) and their ages range (18–70 years). Only healthy Subjects were included in this study . Subjects excluded from the study included those whose age was below 15 or above 70 years, diabetics, have thyroid or other autoimmune diseases, known to have a neuromuscular disease , and on regular medication that is known to cause peripheral neuropathy or to affect NMJ transmission. Performing voluntary activation method, the OOc was investigated first using CN followed by the FRO muscle on the same subject at the same session. The OOc was tested in 55 out of 57 and the FRO in 52 out of 57 and both muscles were tested in 50 out of 57 control subjects.38.Jitter measurement was performed using CN instead of the standard SF electrode, the active electrode (G1) of which is an elliptical detection surface of the core located in the tip of the CN electrode. The outer diameter of the cannula represents the reference electrode (G2)41. In this study, we used 1 KHz as a practical limit rather than 2 KHz as the signal amplitude would be higher resulting in an easier way to detect visually the summation of the single fibre action potentials (SFAPs) as recommended by Stalberg40. The low pass filter was set at 10 KHz, as for the SF40.A high pass filter setting of 1–2 KHz for CN-jitter instead of the standard (500 Hz) used in SF recordings was recommended in many studies8. Criteria recommended for CN recorded potentials to be accepted as SFAP is the same as for SF recordings except for the amplitude. Potential’s shape should be smooth, biphasic or triphasic , stable and identical across a set of MU discharges with a sharp rising potential (rise time <300 µsec) and adequate amplitude (>100 µV) which is lower than the standard amplitude recommended for SF recordings (>200 µV)36.The amplitudes of the APs were optimized by slightly adjusting the electrode position in the best recording position for jitter measurements. Often, the spikes obtained with CN and MNP are not obtained from single muscle fibres so they are called apparent single fibre potentials, “aSFAP”42.Voluntary-activation method was performed by inserting CN into the muscle, usually in the middle third of its length, away from the end-plate zone (which lies somewhere near the midpoint of the muscle fibre) to record two or more time-locked muscle fibre potential (MFP) from the same motor unit during minimal voluntary contraction of the muscleConcerning OOc, it was most easily studied laterally at its periphery . OOc activation was controlled by asking the subject to close the eyes gently so as to avoid excessive blinking. Regarding FRO, it was best examined when the patient was lying. The needle was inserted to the forehead from a lateral position and activation was controlled by asking the subject to raise the eyebrows.Neuromuscular jitter represents the variation in the interpotential interval (IPI) between pairs of aSFAPs of the same motor unit in a voluntarily activated muscle.43.Jitter was recorded from at least 20 different MFP pairs [the software in Medelec Synergy machine has the capability to record up to 30 MFP pairs] from different portions of the muscle, using up to three skin insertions at various angles. In addition, the software has the ability to create more than one pair from the single muscle fibre pair giving a total of more than 30 MFP pairs in one muscle. A constant detection position was maintained and the subject was asked to maintain a steady contraction while at least 50 discharges [up to 100] for each of the 20 single MFP pairs were collected. For jitter analysis, the examiners selected and accepted only individual potentials (spikes) with a minimal summation of many components (without notches or shoulders). The spike components used for analysis had a clear separation (150 µs) with no or minimal merging of one signal onto the other. Recordings were rejected by manual editing if overlapping potentials were present, less than 50 consecutive discharges were recorded or the interpotential interval was greater than 4 msAutomatic measurements of the IPI were made between the green and yellow cursors adjusted manually by the examiner on a point relatively close to the baseline in the steep positive negative rising phase of the triggering potential and the corresponding point on succeeding or preceding jittering potential this is known as Amplitude level method of jitter measurement.To minimize the influence of the slow variations in the IPIs, the jitter was expressed as the mean value of consecutive differences (MCD) of successive IPIs instead of using the standard deviation (SD). The MCD equation was then calculated automatically using the SFEMG software for each of the 30 different MFP pairs. Finally, the results of jitter measurements were presented for each muscle in individual subject as the average (mean) of the jitter values for the 30 pairs [all these calculation done automatically using the SFEMG software].(MSD) instead of MCD. This again was done automatically using the SFEMG software, which calculates MCD and MSD values for each single MFP pair and chooses the smaller value as a measure of jitter for that pair.The effect of variable firing rates occur during voluntary activation was minimized by using the mean sorted-data difference-All recordings were made and analyzed by the first examiner (Afraa.M.M.Musa) and revised by the second examiner (Prof. Ammar.E.M. Ahmed). ± SD]. The mean jitter + 2 SD [the 95% upper confidence limit] was considered as the upper practical limits of jitter for that muscle. In addition we calculated the mean individual fibre pair jitter value + 2 SD [the 95% upper confidence limit] as it was set by the  as the upper practical limits of normal for individual jitter values24. Finally, a new practical definition of abnormality includes the determination outlier values of individual fibre pairs which was introduced by Stalberg35. Ten percent of the recordings were allowed to be outside of the calculated limits. Therefore, the 90th centile value was calculated for each set of 20 or more recordings. The 95% upper confidence limit (95% CL) of the 90th centile value was calculated to define the outlier limit from which the upper practical limits for the individual fibre pair jitter values was estimated.Statistical analysis was performed using the Statistical Package for Social Science program (SPSS version 17). The mean jitter value for each individual muscle was calculated as the mean of individual jitter values of at least 20 a-SFAP pairs automatically using the machine. Then, the mean jitter of this muscle for the entire subject population was calculated  and males [35%] of the studied population.There was no observed significant correlation (P > 0.05) between jitter values with age, weight, height or gender as shown in Table .Table 1TComparison between the jitter values of V-OOc and V-FRO using CN revealed neither a significant statistical difference on paired t-test (P = 0.789) nor a significant correlation on a paired samples correlations (P = 0.169).Summary of jitter values of this study compared to previous published data in normal subjects was illustrated in Table .Table 2SOur estimated values for upper 95% confidence limit (95% CL) of mean jitter, mean individual fibre pair jitter and mean outliers jitter [i.e. values above which the study should be considered abnormal] were summarized in Table .Table 3S37 and were summarized in , were shown to be higher than those obtained by Stalberg and colleagues from 92 OOc studies {41 µs35 and 45 µs18} and 72 FRO studies {36 µs37, 38 µs18}. On the other hand, our values were lower than FRO values [56.8 μs] obtained in a Japanese multicenter30 which included a total of 69 normal subjects below the age of 60 years at six Japanese institutes. Their cut-off values for individual MCD were calculated using +2.5 SD or 95% CL of the upper 10th percentile MCD value30 which was different from the statics applied by Stalberg18.The cut-off values for individual MCD in the current study were calculated in the same way as recommended by Stalberg and colleagues in their multicenter study41. As well as this study revealed no significant effect of weight, height, and gender on jitter values of OOc and FRO muscles [Table Looking at the effect of age on OOc and FRO jitter values in Table 18.Comparison between CN-jitter values of V-OOc and V-FRO showed no significant statistical difference and no significant correlation, as well as the cut-off values for mean MCD (32 vs. 31.3 µs) and individual MCD (49 vs. 49.2 µs) which were almost the same  in addition to different criteria for defining and selecting a fibre pair to be analyzed and included in the jitter measurement.CN reference jitter values for voluntarily activated OOc and FRO, labeling the patient as having a positive jitter test, have been established in our Sudanese population. In fact, this study had provided practical upper limits of mean jitter and outliers CN-jitter values for V-OOc  and  for V-FRO. Age, gender, height, and weight did not show any effect on jitter values.


Given that up to one third of the population will clinically reactivate VZV to develop herpes zoster (shingles) and suffer from its common long term problematic sequelae, there is still a need for both in vivo and in vitro model systems. This review will summarize the evolution of models of VZV persistence and address insights that have arisen from the establishment of new in vitro human neuron culture systems that not only harbor a latent state, but permit experimental reactivation and renewed virus production. These models will be discussed in light of the recent data gleaned from the study of VZV latency in human cadaver ganglia.The latent state of the human herpesvirus varicella zoster virus (VZV) has remained enigmatic and controversial. While it is well substantiated that VZV persistence is established in neurons after the primary infection (varicella or chickenpox), we know little of the types of neurons harboring latent virus genomes, if all can potentially reactivate, what exactly drives the reactivation process, and the role of immunity in the control of latency. Viral gene expression during latency has been particularly difficult to resolve, although very recent advances indicate that it is more restrictive than was once thought. We do not yet understand how genes expressed in latency function in the maintenance and reactivation processes. Model systems of latency are needed to pursue these questions. This has been especially challenging for VZV because the development of  Herpesviridae family that persists for life in the host after a primary infection (varicella or chickenpox). The site of latency is within neurons in ganglia of the peripheral somatic, autonomic, and enteric (Varicella zoster virus (VZV) is a common and ubiquitous human-restricted neurotropic alphaherpesvirus of the  enteric  nervous  enteric , vascula enteric , and gas enteric  that mayin vivo model of reactivated disease. A possible exception for the growth restriction of VZV in small animals is the guinea pig, discussed below. Many insights have come from study of the VZV-related Simian Varicella Virus (SVV), which replicates in several macaque species and African green monkeys, but SVV is a distinct virus that may not completely model all aspects of VZV. There are also significant financial and ethical issues associated with primate research. The relevance of the SVV model to VZV was recently reviewed  . Early rA third block to study of VZV latency is that, despite the efforts of many virologists, VZV remains a difficult virus to work with. Virus replicates inefficiently in a limited number of human cell lines, produces virus with high particle to infectivity ratios, and releases little to no infectious virus from cultured cells. Titers of cell-free virus released from infected cell sonicates are on the order of five magnitudes lower than that which can be obtained for HSV-1. As such, most experimental infections are initiated with infected cells. As detailed below, this is an important issue in the new stem cell-derived neuronal models of reactivatable latency, because persistence has not yet been obtained with cell-associated infection, but rather requires cell-free virus. Intriguingly, recent work suggests that cultured human neurons do release some infectious virus .in vitro models (This review complements other recent reviews of the current state of understanding of VZV latency from studies of cadaver ganglia  and in vo models . We rest+ T cells are considered to be a predominant vehicle for systemic spread (Primary varicella (chicken pox) is easily spread in unvaccinated Westernized populations, usually during childhood, by the inhalation of aerosolized droplets from individuals with varicella or active zoster. This exposure establishes primary replication sites in the upper respiratory mucosa and respiratory lymphoid tissues such as the tonsils, where lymphocytes become infected . VZV infc spread , but othc spread . Immune c spread . Virus ac spread , allowinc spread . In the c spread . LesionsReactivation leading to HZ occurs in about one third of those harboring latent VZV in their lifetimes. While the molecular triggers of VZV reactivation are not well understood, there are clear contributing factors that predispose to HZ development, including host genetics . The rolZoster is frequently complicated by scarring and bacterial superinfection, but the most common complication is pain. Most individuals with zoster seek medication for pain, but a significant fraction progress to debilitating chronic pain states termed post herpetic neuralgia (PHN). PHN may last months to years after the skin lesions have resolved and is often refractory to therapy. This is consistent with PHN reflecting neuronal events downstream of VZV replication and clearance after reactivation . ReactivThe observation that adaptive immunity protects individuals from further varicella stimulated the development of successful vaccines. The FDA-approved live attenuated varicella vaccine (Varivax in the United States) has been in widespread use in the United States since the mid-1990s as a two-dose regimen. It is based on a VZV strain (vOka) that was attenuated by sequential passage in embryonic guinea pig fibroblasts. The vaccine strain is genetically heterogeneous, containing some fixed mutations and variable levels of other mutations and combinations thereof . VarivaxTwo FDA-approved HZ vaccines are available to boost VZV-specific adaptive immunity in those at risk for HZ. Zostavax is a 14 times larger infectious dose of the attenuated varicella vaccine that was licensed in 2006, and reduces HZ incidence by about half, and the burden of illness by two thirds . The morreversible persistence of non-productive viral genomes, since reactivation demonstrates maintained genome functionality.We consider it important to define “latency.” Herpesvirus lytic infections involve the organized and regulated expression of most viral genes and proteins, the replication of the DNA genome, and the assembly and egress of progeny virus for spread and transmission. VZV latency is often defined as the persistence of viral genomes without virus production, in an endless or episomal form  as seen + T cells. In the well characterized HSV-1 murine model, accumulated work suggests that ganglionic T cells may control reactivation events after viral antigen recognition, acting through non-cytolytic mechanisms  without need for complete replication  was attenuated by serial passaging in guinea pig embryonic fibroblasts. The genetic changes in vOka that are associated with adaption are many, including several in the gene encoding IE62, the key pression . Early wpression , resultipression . Viral tpression , 1985. Tpression  and the pression . After iexanthem . The guiexanthem . Others exanthem .in vitro neuron cultures could produce infectious virus when infected with replication defective adenoviruses expressing VZV ORF61 or the HSV ortholog ICP0. This establishes functionality of the persisting genome, albeit using a non-physiological stimulus. The model has developed in parallel with data indicating VZV persistence in the human enteric nervous system  and have proven to be a powerful tool for location . The applocation . Immune location . Since win situ, despite axotomy during acquisition. Neurons are surrounded by ganglionic glia and satellite cells, in a physiological state that permits glial cell–neuron interactions in VZV pathogenesis to be followed  and induced pluripotent human stem cells (iPSC) have been developed into neuron cultures for VZV study, and differentiation protocols continuously evolve . At the Several groups have reported the use of iPSC to generate neuron culture models for VZV infection. Baird and colleagues report use of a commercially available iPSC-derived neuronal precursor that is immediately plated in defined proprietary media to generate “iCell” neurons  . The supUsing hESCs as the starting material permits some level of reproducibility that can be followed by other groups. At the time of this review, three reports detail their use for study of VZV replication, latency and experimental reactivation. While each differs in the process by which neurons are differentiated, the similarities between the models are more important, as they clearly point to the first bone-fide models of VZV reactivation. The first, detailed in several publications from the laboratories of the Goldstein and Kinchington groups, used the WiCell hESC line WA09 . FISH showed the viral genome to be localized to single or dual nuclear puncta, similar to that recently reported for HSV latent infections The differentiation of hESC into specific subtypes of neurons and lineages is a field of intense interest and continuous development that will impact not only the study of the VZV latent state, but also that of HSV-1 and HSV-2. Evidence is accumulating that suggests some neuronal subtypes are more restrictive for virus replication than others. Work from the human fetal ganglionic implant-SCID-hu model has suggested that peripherin-positive nociceptors are more susceptible to VZV infection as compared to mechanoreceptor neurons marked by the RT97 antibody, which exhibit some restriction to VZV infection . Advance(2)in vivo or in vitro in vivo animal models does imply that LAT roles are not essential. We will likely soon see whether VLT is required for establishing latency and reactivation in the human neuron systems that are now available. It is likely that HSV-1 LAT function will be re-assessed in these human neuron systems.VZV genetic model systems can now be used to probe possible functions of VLT and other genes in the onset of human neuronal infection, latency and reactivation. VZV is readily genetically manipulated through the use of BAC and cosmid systems  and viru(4)in vitro human neuron platforms in use do have some limitations that may require a further study of the in vivo model systems of persistence. The in vitro systems are not yet sufficiently developed to permit studies of how latent infections interact with the adaptive immune system. This is a particularly important issue, given that declining adaptive cellular immunity is a major predictor of zoster incidence, and that vaccination-mediated boosting of immunity reduces rates of zoster. The contribution of adaptive immunity will probably need an in vivo system, and the guinea pig model appears to be the most likely permissive in vivo system. However, while the guinea pig model has been reported to enter a latent state that is experimentally reactivatable  and Schwann cells in peripheral ganglia may play a role in establishment and/or maintenance of latent VZV infections. Evidence from the SCID-hu mouse xenografted with fetal human DRG suggests that SGC are infected by VZV and fuse with neighboring neurons . Schwannin vitro human neuron systems for modeling VZV latency and reactivation and the recent discovery of the VZV latency-associated transcript have set the stage for a new era in resolving this perplexing persistent state.Advances in the development of As this manuscript is a review, all authors were involved in writing and editing the manuscript. There were no new experiments included in this work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The low response rate of immune checkpoint blockade in breast cancer has highlighted the need for predictive biomarkers to identify responders. While a number of clinical trials are ongoing, testing all possible combinations is not feasible. In this study, a quantitative systems pharmacology model is built to integrate immune–cancer cell interactions in patients with breast cancer, including central, peripheral, tumour-draining lymph node (TDLN) and tumour compartments. The model can describe the immune suppression and evasion in both TDLN and the tumour microenvironment due to checkpoint expression, and mimic the tumour response to checkpoint blockade therapy. We investigate the relationship between the tumour response to checkpoint blockade therapy and composite tumour burden, PD-L1 expression and antigen intensity, including their individual and combined effects on the immune system, using model-based simulations. The proposed model demonstrates the potential to make predictions of tumour response of individual patients given sufficient clinical measurements, and provides a platform that can be further adapted to other types of immunotherapy and their combination with molecular-targeted therapies. The patient predictions demonstrate how this systems pharmacology model can be used to individualize immunotherapy treatments. When appropriately validated, these approaches may contribute to optimization of breast cancer treatment. NumerBreast cancer cells have several mechanisms of immune evasion, which help them survive the attack by cytotoxic T lymphocytes (CTLs). Among these mechanisms, the checkpoint interactions are identified to be one of the key mechanisms, and thus the expression of PD-L1 on tumour cells is considered to be a predictive biomarker . AlthougAnother possible factor that leads to the limited efficacy of immune checkpoint blockade therapy is the low mutational load of breast cancer ,15. To eAlthough the correlation between individual factors above and tumour response to immunotherapy is well characterized in some cancer types, their combined effects remain unclear. PD-L1 expression has not consistently correlated with response to immunotherapy; likely because immune–cancer cell interactions are exceedingly complex and not captured by a single biomarker . Computaet al. [The proposed model aims to identify potential predictive biomarkers, hypothesize the possible immune evasion mechanism, and eventually predict the response of patient cohorts or even individual patients to immunotherapy. In this study, we will focus on the combination therapy using durvalumab and tremelimumab using the dosing regimen in a clinical trial of metastatic breast cancer by Santa-Maria et al. . Tremeliet al. ,22. In t2.2.1.a illustrates the dynamics of major species in the model. To ensure reproducibility of the model, the complete set of governing ODEs, model parameters and SBML code are presented in the electronic supplementary material.The quantitative systems pharmacology (QSP) model consists of four compartments: central, peripheral, tumour and tumour-draining lymph node (TDLN). Central and peripheral compartments represent the total volume of blood and peripheral tissues, respectively. Tumour compartment represents the total tumour volume, which is assumed to be constant for the purpose of antibody pharmacokinetics and effector T-cell transport. TDLN compartment represents a lumped lymph node assuming that the antibody will be evenly distributed among multiple TDLNs that have the same antibody and T-cell dynamics. The model comprises 275 ordinary differential equations (ODEs) and 206 algebraic equations and is implemented using SimBiology toolbox in MATLAB . Figure 12.2.The immune response is initiated from the tumour where neoantigens are produced. Neoantigens can be either engulfed by antigen-presenting cells (APCs) via phagocytosis in the tumour or transported into the lymph node via lymphatic vessels, where they can be taken up by resident APCs in TDLNs . Mature 2.3.b. While all ligands and receptors are expressed on cell surfaces, whose interactions occur within the immunological synapses, CTLA-4 can also be secreted and released from Treg cell surfaces into the TME, where it can bind CD80/86 receptors on mAPCs [Regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs), which contribute to the immunosuppressive tumour microenvironment, are included in the tumour compartment . The numfigure 1on mAPCs –31. Thes2.4.All the ligand–receptor interactions are implemented using two steps. First, the engagement between two cell types forms a cell–cell complex. Then, the cell–cell complex can either dissociate without any effect or results in the activation/inhibition of either cell. This decision is dependent on the checkpoint signals, calculated by receptor occupancies or antigen intensity, which refers to the binding affinity between the T cell receptor (TCR) and neoantigen-specific MHC. For example, the T-cell priming occurs in three distinct stages in TDLNs . Naive T2.5.et al. [Once T cells are produced from TDLNs, they are transported into the tumour compartment through the circulatory system. The T-cell transport is implemented as a three-step mechanism: reversible attachment of free CTLs onto the vascular wall, irreversible adhesion of attached CTLs and transmigration into the extracellular space, adopted from the physiologically based kinetic model by Zhu et al. . The T-cfk and rk, attachment and detachment rate, Lk, lymphatic transport rate, AR, adhesion rate, J, transmigration rate, B, ad hoc adhesion site density, Vv, vascular space volume, Q, blood flow rate, and EffTTurnover, cell death rate, are estimated based on the literature data [Here EffectorTB is the number of T cells in the central (blood) compartment, EffectorTf is the number of free T cells in the vascular space, EffectorTb is the number of captured T cells in the vascular space, and EffectorTa is the number of arrested T cells in the vascular space. Transport parameters, ure data ,33.2.6.There are published population pharmacokinetic (PK) models of tremelimumab and durvalumab built using pharmacokinetic data from clinical trials ,35. The mabBCTLA4, mabPCTLA4, mabTCTLA4 and mabCTLA4 refer to the concentration of tremelimumab in central, peripheral, tumour and TDLN compartment, respectively. VBlood, total blood volume, VPeripheral, total peripheral volume, VTumour, constant tumour volume for antibody PK only, tdlnV, volume of each TDLN, BPSA, surface area to volume ratio between the central and the peripheral and TDLN compartments, BTSA, surface area to volume ratio between blood and tumour compartments, TK, available volume fraction in the tumour, LNK, available volume fraction in each TDLN, CTLA4,BPk, permeability to antibody between blood and peripheral compartments, Ab,BTk, permeability to antibody between blood and tumour compartments, Ab,BLNk, permeability to antibody between blood and TDLN compartments, and Ltk, lymph flow rate, are estimated based on literature data assuming that large antibody proteins of similar sizes have the same permeability between compartments and that surface area between TDLN and other compartments equals to BPSA [Cl, clearance rate, BK, available volume fraction in the blood, and PK, available volume fraction in the peripheral compartment, are optimized based on the published population PK models [k,CTLA4mAb-CTLA4on and k,CTLA4mAb-CTLA4off, association and dissociation rate of tremelimumab with CTLA-4 are estimated based on experimental data [CTLA4TTreg, CTLA-4 expressed on Treg surface in the tumour, CTLA4TSTreg, soluble CTLA-4 secreted by Treg in the tumour, CTLA4PNT, CTLA-4 expressed on primed T-cell surface in TDLNs, CTLA4Tr:mAPC, CTLA-4 expressed on Treg interacting with mAPCs in TDLNs, CTLA4TrLN, CTLA-4 expressed on non-interacting Treg surface in TDLNs, CTLA4STrLN, soluble CTLA-4 secreted by Treg in TDLNs, and the corresponded complexes when bound with tremelimumab, TrTCTLA4:CTLA4, TrTSCTLA4:aCTLA4, mAb:CTLA4CTLA4, TrALN:CT:aCT, TrLN:CT:aCT and TrLN:CTLA4S:aCTLA4S are further specified, along with the values of other parameters for both antibodies, in electronic supplementary material, table S2. to SABP ,37. Cl, K models ,35. The tal data . CTLA4Tr2.7.The model is used to simulate PK/PD for anti-CTLA-4, anti-PD-1 and anti-PD-L1 antibodies in monotherapy and combination therapy. Since this study focuses on a specific clinical trial, namely on combination therapy for 18 breast cancer patients using tremelimumab and durvalumab, the parameters, including the number of tumour-draining lymph nodes, tumour growth rate, checkpoint expression and cancer cell diameter, are estimated to be metastatic breast cancer-specific . The val−14 (day) and 10−13, respectively. Within a time period typically on the order of two months, the number of effector T cells reaches a quasi-steady state, because the T-cell activation is balanced by the inhibition of effector T cells in the TME. Once the steady state is reached, the antibody is administered into the central compartment through intravenous infusion, and the simulation is continued. Tumour growth is simulated for 15 months after therapy begins.Baseline starting tumour size, antigen intensity and PD-L1 expression are assumed to be 30 mm, 0.7 and 25%, respectively, which are selected to best illustrate the heterogeneity of tumour response and to investigate the sensitivity of each parameter of interest. The steady-state and dynamic solutions are calculated using the Sundials solver. The absolute tolerance and relative tolerance are set to be 103.3.1.−1 in anti-CTLA-4 monotherapy (a). While the monotherapy causes a significant increase of effector T cells and mature antigen-presenting cells (mAPCs), based on the tumour size, patients show progressive disease, per RECIST criteria, for doses of 0, 1, 3 mg kg−1, and for higher doses of 10, 15, 20 mg kg−1 stable disease for about five months followed by progressive disease. For anti-PD-L1 monotherapy, durvalumab is administrated monthly four times at doses of 0, 1, 3, 10, 15, 20 mg kg−1, where 3, 10, 15, 20 mg kg−1 doses are followed by 1, 3, 5, 10 mg kg−1 every two weeks up to 15 months, respectively. Although anti-PD-L1 monotherapy did not increase T-cell production and APC maturation as much as anti-CTLA-4 monotherapy, patients show responses with four doses of 1 mg kg−1 and higher (b). When durvalumab is combined with a fixed dose of 1 mg kg−1 tremelimumab, T-cell production is increased by three- to fourfold compared to the beginning of the therapy, which is higher than that in anti-PD-L1 monotherapy (c). The results suggest that the PD-1/PD-L1 pathway plays a vital role in the TME as a resistance mechanism to protect tumour cells from tumour-infiltrating lymphocytes. In combination therapy, the upregulation of T-cell production by CTLA-4 blockade in TDLNs is observed, and the effect on tumour size is moderately enhanced, compared to the anti-PD-L1 monotherapy.Since the model aims at predicting patients' responses to immunotherapies, we define the main outputs to be: analysis of time-dependent tumour size change, effector T-cell production and antigen-presenting cell maturation in TDLNs. To illustrate the predictions, we first consider a baseline case and show the dose response to each monotherapy, anti-CTLA-4 and anti-PD-L1, and to their combination, following the regimen of the clinical trial . In charotherapy a. While d higher b. When dotherapy c. The re3.2.−1 tremelimumab are administrated monthly in anti-CTLA-4 therapy, four doses of 20 mg kg−1 durvalumab are administrated monthly followed by 10 mg kg−1 every two weeks up to 15 months in anti-PD-L1 therapy. As shown in To investigate the effects of starting tumour size, PD-L1 expression and antigen intensity on tumour response, we plot the time-dependent tumour size, percentage change of tumour size with respect to the starting tumour size, average number of effector T cells and mAPCs in TDLN over the 15 months by changing one parameter at a time in figures 3In 3.3.a,b show the relationship between the antigen intensity and PD-L1 expression and the tumour response to combination therapy, demonstrated by the percentage change of tumour size at the end of simulations, at 15 months. Both high PD-L1 expression and strong antigen intensity are required to induce a partial response in both subtypes. In figure 6c,d, the relationship between the antigen intensity and starting tumour size and the tumour response illustrates that antigen intensity is more critical to tumour response than the tumour size at the beginning of the therapy.Now that we have investigated the correlations between the tumour response and each parameter of interest individually, we would like to simulate the combined effect of multiple parameters on each patient with ER+ and TNBC subtype in the clinical trial . For eacfigure 6a. Not surprisingly, a higher antigen intensity is required for TNBC to have a partial response to combination therapy since TNBC has a higher tumour growth rate than ER+ breast cancer [figure 7b). Interestingly, given the difference of tumour growth rate and PD-L1 expression between the two subtypes, the antigen intensity required for ER+ breast cancer is higher than that for TNBC and becomes similar to the starting tumour size increases. This result suggests that its high antigen intensity requirement may account for the fact that ER+ breast cancer has a lower response rate to immunotherapy due to its low immunogenicity [To further investigate the effect of antigen intensity, its threshold for partial response is plotted against PD-L1 expression in figure 7t cancer . Similargenicity .Figure The clinical measurements of tumour size change  allow usWe further perform a Latin hypercube sampling and partial rank correlation coefficient (LHS\PRCC) analysis using a sample size of 1000 to examine the uncertainty and sensitivity of predicted tumour response against potential biomarkers that have not been confirmed by clinical studies . As show4.The heterogeneity of response to checkpoint blockade therapy among the subtypes of breast cancer has highlighted the need for predictive biomarkers to identify responders. As single-agent monotherapies result in limited efficacy among most of the subtypes, combination strategies will be required . While aStarting from factors including the composite tumour burden, PD-L1 expression and antigen intensity, we investigate correlations between each factor and the tumour response to monotherapy and combination therapy. By changing one factor at a time, we confirm that each factor is critical to inducing tumour response to both anti-PD-L1 monotherapy and combination therapy using characteristics of an average breast cancer patient. However, it is difficult to conclude whether the starting tumour size has a strong correlation with tumour response to immunotherapy as small tumour size is unlikely to trigger enough T-cell production for tumour eradication; this question requires further investigation, especially since there is evidence that pretreatment tumour size correlates poorly with treatment outcome in the metastatic setting ,56. FurtAccording to model-based analysis, the tumour response to combination therapy is highly sensitive to both PD-L1 expression on tumour cells and antigen intensity. A higher PD-L1 expression and antigen intensity are required by TNBC patients than ER+ breast cancer patients due to the higher tumour growth rate of TNBC; the relatively high response rate of TNBC to immunotherapy may be related to its relatively higher PD-L1 expression on average . NotablyIn addition, we present personalized simulations of tumour response to combination therapy using a range of parameters, within the limitations of the available data from the pilot clinical trial . By contIn the current model, we focus on the immunosuppression in TDLNs and TME due to the inhibition of host immune response by Tregs and MDSCs, and their inhibitory effect on both T cells and APCs are heavily dependent on checkpoint expression. As a result, simulated checkpoint blockade therapy can result in a robust immune response that leads to a complete response in patients with high PD-L1 expression and antigen intensity. Although this result aligns with the strong correlation between the tumour response and the factors above reported by clinical studies, the additional resistance mechanisms that are independent of checkpoint expression should be incorporated into the model. An important factor that should be considered is the effects of heterogeneous expression of cytokines among patients. As suggested by the global sensitivity analysis , effectoAlthough the model aims to focus on a metastatic setting, the difference in metastatic site, agents used in previous chemotherapy, and their unknown effects on immune system among patients make it difficult to estimate their effects on the efficacy of adjuvant checkpoint blockade therapy. The pharmacokinetics of the antibody may also vary due to the difference in permeability and surface area between blood capillaries and metastatic lesions. In addition, the model has only one type of neoantigen-specific effector T cell produced from TDLNs that has a constant binding affinity with neoantigens, defined by a normalized antigen intensity, due to the lack of clinical measurements of tumour-specific effector T-cell level and the binding affinity of TCR with neoantigen peptides in breast cancer. As more data become available, a detailed antigen presentation mechanism can be incorporated into the model. Importantly, the model provides a platform that can be adapted to other therapies to investigate synergies with the checkpoint blockade therapy . In receIn addition to our proposed ODE-based model, there are other ODE-based and agent-based models that aim to predict the efficacy of cancer vaccines and small-molecule modulators of cancer immunity –69. Base5.The proposed quantitative systems pharmacology model integrates relevant immune and cancer-related compartments and processes of individual breast cancer patients and specifies a number of immune suppression and evasion mechanisms in both TDLN and TME. It aims to identify potential biomarkers, additional resistance mechanisms, and provide a basis for future predictions of tumour response to immunotherapy for cohorts of patients and eventually for personalized therapy.


Glioblastoma is the most frequent malignant intra-cranial tumour. Magnetic resonance imaging is the modality of choice in diagnosis, aggressiveness assessment, and follow-up. However, there are examples where it lacks diagnostic accuracy. Magnetic resonance spectroscopy enables the identification of molecules present in the tissue, providing a precise metabolomic signature. Previous research shows that combining imaging and spectroscopy information results in more accurate outcomes and superior diagnostic value. This study proposes a method to combine them, which builds upon a previous methodology whose main objective is to guide the extraction of sources. To this aim, prior knowledge about class-specific information is integrated into the methodology by setting the metric of a latent variable space where Non-negative Matrix Factorisation is performed. The former methodology, which only used spectroscopy and involved combining spectra from different subjects, was adapted to use selected areas of interest that arise from segmenting the T2-weighted image. Results showed that embedding imaging information into the source extraction  improved the quality of the tumour delineation, as compared to those obtained without this information . Both approaches were applied to pre-clinical data, involving thirteen brain tumour-bearing mice, and tested against histopathological data. On results of twenty-eight images, the proposed Semi-Supervised Source Extraction (SSSE) method greatly outperformed the unsupervised one, as well as an alternative semi-supervised approach from the literature, with differences being statistically significant. SSSE has proven successful in the delineation of the tumour, while bringing benefits such as 1) not constricting the metabolomic-based prediction to the image-segmented area, 2) ability to deal with signal-to-noise issues, 3) opportunity to answer specific questions by allowing researchers/radiologists define areas of interest that guide the source extraction, 4) creation of an intra-subject model and avoiding contamination from inter-subject overlaps, and 5) extraction of meaningful, good-quality sources that adds interpretability, conferring validation and better understanding of each case. It is generally applied as MR imaging (MRI), see Magnetic Resonance (MR) is widely used for non-invasive investigations of brain tumours, in particular Glioblastoma (GB) is the most frequent malignant intra-cranial tumour, having a very poor prognosis. Currently there is no cure for the disease. The standard treatment is surgery, with the aim of maximal safe tumour removal, followed by radio- and chemotherapy . DespitePattern recognition and machine learning methods have been extensively applied to MR data of brain tumours, to assist with different clinical issues, from diagnosis and prognosis of several pathologies, to delineation of tumour masses –11. MostIn biomedical research, animal  models are useful for testing new drugs or treatments. One of the main advantages is the possibility of performing post-mortem studies–for example in the case of brain studies, to excise the whole brain and perform a detailed histopathological analysis. This allows the validation of imaging techniques and mathematical analyses or transformations of those, in contrast to patient studies, in which such validation approaches are not feasible due to obvious ethical restrictions.ad-hoc manner by concatenating or mixing selected characteristics from each approach (that can be MR images and spectra), such as in , according to the regional and state legislation (protocol DARP-3255/CEEAH-530). Mice were periodically subjected to welfare inspections to check for any early symptoms of suffering and an objective scale for signs and symptoms was established. Mice were obtained from Charles River Laboratories (France) and housed at the animal facility of the Universitat Autònoma de Barcelona (Servei d’Estabulari).No ethics approval was required for the current retrospective study. All studies with mice were approved previously by the local ethics committee [www.nanbiosis.es), with a 7 Tesla horizontal magnet . Details of the acquisition parameters can be found in the Supporting Information file.MR studies were carried out at the joint nuclear MR facility of UAB and CIBER-BBN, Unit 25 of NANBIOSIS  from these data had been used in previous pattern recognition studies –28, perfIn this section, we describe the control (untreated) mice data that was used in this study to evaluate the ability of SSSE to delineate the tumour mass. This data is summarised in AMRI and MRSI  data of six mice from . The MRSBMRI and MRSI  data of five mice from ,28. The In this section, we describe the follow-up, longitudinal data that was used in this study. This is data obtained from mice under temozolomide (TMZ) treatment with the administration schedule described in  and in tThe aim was to assess SSSE’s ability to produce an accurate delineation of the tumour mass, as well as recognise the volume changes at different time points, and tumour response to therapy. This data is also summarised in CMRI and MRSI  data of two treated mice from . Again, After the MRSI study (or at after MRSI endpoint in the Group C cases), the animals were euthanised according to the ethics protocol by experienced personnel and the brains were collected and analysed by histopathology as described in . CaspaseThe evaluation of necrosis was performed by the histopathologist on the haematoxylin and eosin stained slides. Different features were considered: isolated necrotic cells, moderate amounts of eosinophilic debris and large empty spaces. For each tumour, the percentage of the tissue section affected was semi-quantified. When a tumour showed less than 20% of the mentioned necrotic features, a low grade of necrosis was assigned, while high grade of necrosis was assigned to those having more than 20% of the features. More details are included in .a posteriori, or ex-vivo), the preclinical bioimaging expert produced a set of images with the aim of using them as the gold standard for this study. These images are compiled in Using all the available histopathological information such as Ki67 (which is information obtained In Non-negative Matrix Factorisation (NMF) methods ,33 the nConvex Non-negative Matrix Factorisation (Convex-NMF)  is a varThe semi-supervised methodology proposed in  involvesDefinition of a Fisher Information (FI) metric to model pairwise similarities and dissimilarities between data points, using a Multi-Layer Perceptron (MLP) classifier to estimate the conditional probabilities of class membership.Approximation of the empirical data distribution in a Euclidean projective space in which we can apply NMF-based techniques. Multi-dimensional Scaling (MDS) is one of the algorithms proposed to do this mapping while retaining the distance structure generated by the FI matrix.Application of Convex-NMF for the source decomposition of the data, which includes the identification of the underlying sources and the calculation of the corresponding mixing matrix.single-voxel data in [multi-voxel data in [This semi-supervised methodology was previously applied to  data in , and mul data in , althougThe proposed methodology, SSSE, builds upon the semi-supervised method proposed in  for the Importantly, the semi-supervised nature of the proposed methodology has the benefit that it allows using only partial regions, so that areas of uncertainty can be left outside the initial segmentation, while allowing for concentration on the areas of maximum interest.Following the initial selection of the areas of interest (or segmentation), we compute the posterior probability of each class (or segmented region) for each pixel. As mentioned previously, our choice of estimator of these probabilities, p(c|x), is a Multi-Layer Perceptron (MLP), which is a feed-forward artificial neural network. This is a semi-parametric non-linear probabilistic model of class membership, for which a FI metric can be derived . The parAfter calculating the posterior probability of each class for each pixel, as the resolution of the T2w image is higher than the one of the MRSI, we implement a voting system to bring the resolution of the T2w image down to the spectroscopic imaging. Then, we train the neural network model using the spectra from the MRSI and the labels provided by the segmentation, targeting the computed posterior probability of each class. From this point onwards, the rest of SSSE is similar to the semi-supervised methodology proposed in , but appi, was studied independently from the others (even excluding information from the same case acquired on a different day). From this data of case Ci, a proportion (detailed previously) was used for the initial training step as required by the methodology, and from there the rest of the proposed pipeline is unsupervised.It is important to stress that SSSE was designed to create a model of an individual case, as the purpose was to better understand and reflect the characteristics of each single patient. Hence, in order to avoid contamination from inter-subject overlaps, SSSE does not involve combining spectra from different subjects, thus focusing on intra-subject variation. What this means in practice is that every single case, CTo evaluate whether embedding information from the MRI into the data source extraction improves the quality of the tumour delineation, we compare SSSE segmentations with the ones obtained without prior knowledge (Convex-NMF). We also compared SSSE with the approach proposed by Sauwen et al. in , as theyAs measures used for comparison, we start by calculating the sensitivity and specificity of detecting the tumour regions. The sensitivity, or true positive rate, is calculated as TP/(TP+FN), where true positive (TP) are tumour pixels correctly labelled as tumours; and false negative (FN) are the tumour pixels labelled as non-tumours. The specificity, or true negative rate, is calculated as TN/(TN+FP), where true negative (TN) are the non-tumour pixels correctly identified as non-tumours, and false positive (FP) are the non-tumour pixels labelled as tumours. A related measure frequently used in this area  for compAs an overlap-based metric such as DSC can be dependent on the segmentation size, we also calculate two distance-based metrics: the Euclidean distance, and the Hausdorff distance. These distances were calculated between each of the resulting images and the corresponding one from the preclinical bioimaging expert, considered the gold standard in this study. The purpose was to determine how far (based on these distances) from the gold standard was the estimation of the tumour area when using each of the benchmarked approaches .E and fG, respectively, have the following distance:The Euclidean distance was calculated as in . Hence, Where n is the number of voxels in the images. The distance between two identical images is zero, i.e. Euc_dist = 0; and the larger the value (distance), the bigger the difference between them.Turning the distance measure into a shape similarity score that is easier to interpret, we calculated then the number of pixels that match values in the two images with reference to the total number of pixels, giving us a percentage of success.E and BG the boundaries of the tumour areas of the estimated and the gold standard images, respectively, of which we want to calculate the Hausdorff distance. It was then calculated as follows:sup represents the supremum and inf the infimum. This distance will be zero if and only if BE and BG have the same closure, i.e. both tumour areas are exactly the same.The Hausdorff distance, in turn, measures how far two sets of points are from each other and was used here to measure the most mismatched tumour delineation between the three segmentations  with respect to the gold standard used. Let us consider BFinally, the Kruskal-Wallis test was used to determine whether the results between the three approaches, i.e. Convex-NMF, Sauwen’s and SSSE, were statistically significant.The presentation of results is divided into two sections. Firstly, we show the results of applying SSSE to the mice in Groups A and B, which include untreated and treated cases, respectively; and to the mice in Group C, belonging to a longitudinal study with treated mice. , 5% < PI ≤ 30 (secondary mass) and PI > 30% (core mass)–see These colour-coded maps show how the volume of the tumour mass in each mouse changes as a result of their response to the three cycles of therapy. The RECIST criteria , which pThe adapted RECIST criteria were applied for mouse C819  and in rThe adapted RECIST criteria for mouse C821  indicateThe results presented in the previous section for the three approaches were quantitatively evaluated using two main criteria: i) their ability to delineate the tumour regions (therefore discriminating the tumour from the non-tumour regions) with high sensitivity and specificity; and ii) their ability to produce colour-coded maps that are as close as possible to the gold standard see , which iDetails of the sensitivity and specificity values corresponding to the ability of each approach to detect the tumour masses can be found in Tables A in Next, we present the results of the Euclidean distance between each of the three methods to the gold standard, followed by a shape similarity score , and the Hausdorff distance between the same set of images. The results for the individual cases in Groups A and B are shown in Table D in The results presented in Tables D and E in This section mirrors the structure of the Results section, to facilitate the discussion.Starting with a discussion of the tumour delineation results for mice in Groups A and B, we can see that the high resemblance between the sources representing the same tissue type for each approach Figs  to 6, isOverall, for mice in Group A, SSSE and Sauwen’s approach provided a more meaningful representation than Convex-NMF. This is especially relevant for mouse C179. Metabolically, regarding the spectral pattern represented, the yellow source obtained with Convex-NMF included metabolic features that would be expected both in tumour and non-tumour areas, such as high mobile lipids and high NAA, respectively. See relevant metabolites highlighted in In contrast, for mouse C179, SSSE yielded a source (coloured in yellow) that was very similar to the red (tumour) source . The maiMost importantly, in the case of Convex-NMF, when looking at the area in the colour-coded map that this yellow source is representing , it is cThe results for Group B show that, in general, the spectral pattern exhibited a great variation among cases: for example, case C520, C529 and C583 show prominent peaks of lipids/lactate even in the non-tumour region, whereas case C255 and C288 did not. This is not totally unexpected as the first three cases had larger tumour volumes than the other ones, with most voxels within the MRSI grid being represented either by the tumour itself or by the peritumoral infiltrating zone. However, even for the cases in which the similarities between the sources are higher, again the resulting maps were quite different, as seen in the results reported for the Group A. Regardless of this apparent high similarity, the colour maps show a different representation of the tumour areas for each approach, which are visually more coincident with the initial manual segmentation, but with the benefit that they also include the researcher-dubious (grey/uncertain) areas.In addition to all the aspects mentioned before, both semi-supervised approaches have shown the ability to learn more meaningful, better-quality sources, as a way to overcome the susceptibility to the presence of artefacts and the lower signal-to-noise spectra issues reported in , with SSThe delineation of the tumour area in the maps for the Group C show that, as observed for Groups A and B, SSSE results in more coincidental areas with the abnormal regions. Additionally, they are in accordance with the RECIST criteria for each of these images. Indeed, for both mice, there was a much higher correspondence between the RECIST criteria, and the maps obtained when using SSSE.In most cases of Groups A and B, the sensitivity and specificity when using SSSE was better than the other two approaches , and in When looking at the results of the analysis of the two abnormal masses from mouse C179 see , we can The sensitivity and specificity results for Group C are also consistent with the ones of Groups A and B. A detailed discussion for this group can be found in the Supporting Information file (Discussion of the tumour delineation sensitivity and specificity for Group C).The Dice scores also show agreement with what was discussed previously. The semi-supervised approaches greatly outperform the unsupervised one, with SSSE exhibiting better performance. As recommended by Zijdenbos et al  in the la posteriori. The exceptions in Group C were case C819 at day 41 and case C821 at day 21, in which the differences were marginal, and again the Hausdorff distances contrarily show a better performance by SSSE. It is worth mentioning that the distance between two images will indicate how much they differ, meaning that the shorter the distance between them the higher their similarity.The Euclidean distance between the SSSE resulting maps and the gold standard were smaller in most of the cases than the distances between the maps obtained with the other two approaches and the gold standard  most of the delineation maps produced by SSSE showed a notorious improvement over the unsupervised approach, and b) the maps produced by the counterpart, semi-supervised approach proposed by Sauwen et al. did not generally outperform SSSE, we can confidently say that overall, the distance of the maps produced by SSSE to the gold standard were considerably shorter, meaning that SSSE was better suited for the problem and data at hand.Previous results for the Euclidean distances between the three approaches to the gold standard harmonise with those obtained for the shape similarity score, as these two evaluation methods are highly related, except that the latter provides a better understanding and interpretability of what these distances mean. Overall, for Groups A and B, the proposed methodology exhibited an improvement over the unsupervised approach in the shape similarity score of nearly 10%; and Group C showed an improvement of more than 15% .The results presented in Tables D and E in One final note goes to the fact that the proposed methodology does not combine MRI and MRSI information by concatenation, nor it constrains the MRSI to fit the MRI segmentation, as opposed to several previous works, such as ,20–22. IOverall, the quality of the resulting colour-coded maps indicating the tumour delineation with the proposed methodology was considerably better than when using only Convex-NMF in a completely unsupervised approach, as in , or evenThese results also come with additional advantages, namely: a) the proposed methodology is able to effectively deal with signal-to-noise issues, which is not the case of the unsupervised approach in ; b) it aTherefore, given the quality of the obtained results, and the advantages identified in the use of the proposed methodology, we consider that the extra pre-processing steps related to the embedding of the MRI information into the MRSI data source extraction are worthwhile, as they have shown to improve the tumour delineation in the preclinical GB model. Once again, the multiparametric character of the proposed approach (fusing MRI and MRSI) has shown to provide better results compared with using a single approach (MRSI) .S1 FileThis document includes additional information regarding the Magnetic Resonance studies, TMZ administration and preparation, how Sauwen et al. method was used, how the initial segmentation was performed, and further details on the evaluation and discussion of the results.(DOCX)Click here for additional data file.


Somatostatin‐secreting neuroendocrine tumours may present with diabetes, cholelithiasis and steatorrhoea. In addition, hypoglycaemia has been associated with somatostatinomas. However, the mechanism of hypoglycaemia in patients with somatostatinomas has not been well characterized.We describe two patients with recurrent neuroglycopenic episodes caused by somatostatin‐secreting neuroendocrine tumours in the liver, detected by abdominal CTs and whole‐body octreotide scintigraphy scans and confirmed by biopsy.Pancreatic islet hyperplasia and co‐secretion of insulin (in addition to somatostatin) from tumour cells, respectively, have been characterized as completely distinct mechanisms of hypoglycaemia at both the functional and morphological levels in these two patients.Hypoglycaemia may be caused by different mechanisms in patients with somatostatinomas. Somatostatinomas are rare neuroendocrine tumours usually located in the pancreas or in the periampullary region of the duodenum, rarely in the jejunum, the ovaries or elsewhere. Patients with somatostatinomas most often present with nonspecific symptoms such as abdominal pain or weight loss. A more specific clinical manifestation is the somatostatinoma syndrome characterized by diabetes, cholelithiasis and steatorrhoea.Patient 1, a 76‐year‐old man, presented at his local hospital with hypoglycaemic episodes occurring exclusively in the late postprandial period, often preceded by a short episode of diarrhoea following carbohydrate‐rich meals. On abdominal CT, he was found to have multiple liver tumour masses, and he was then referred to our clinic for further investigations. Fasting plasma glucose was 7.7 mmol/L. No hypoglycaemic symptoms occurred during a 72‐hour fast (plasma glucose concentration 4.8 mmol/L at the end of the fast). In contrast, venous plasma glucose concentration dropped to 1.5 mmol/L and the patient developed severe neuroglycopenic symptoms 240 minutes after intake of 75 g glucose during an oral glucose tolerance test (oGTT). Insulin concentrations during the oGTT showed an excessive increase from 44 pmol/L at baseline up to 6998 pmol/L within 120 minutes following the oral glucose challenge. An intravenous GTT, however, did not provoke hypoglycaemia. A tumour‐produced incretin could not be identified , but somatostatin levels were markedly increased in the basal state  and decreased during the glucose challenge. Selective arterial calcium stimulation with hepatic venous sampling (ASVS) was performed, and insulin concentrations were measured as previously described.Because the patient declined resection of the right hepatic lobe, transarterial tumour embolization was performed. Postprandial hypoglycaemia improved following the procedure. However, within three years, the neuroendocrine tumour progressed and the patient died at the age of 79 years. Post‐mortem analysis revealed a malignant somatostatinoma with disseminated tumour infiltrates forming nodules up to 10 mm in the pancreas and prominent liver metastasis. The primary localization of the somatostatinoma in the pancreas was not detected until autopsy. In addition to the somatostatinoma, histological examination of the pancreas revealed islet hyperplasia throughout the pancreas. Most strikingly, the number of islets of Langerhans was increased, and their size and shape were very variable with individual hypertrophic islets. Islet hyperplasia as cause of hypoglycaemia in this patient was well characterized at the functional (ASVS test) and morphological level.Patient 2, a 56‐year‐old woman, presented with recurrent hypoglycaemic episodes. Neuroglycopenic symptoms developed in an erratic manner but preferentially while fasting. In a local hospital, an HbA1c of 4.0%, random low blood glucose readings and a liver tumour by an abdominal CT scan were found. She was sent to us for further investigations and treatment. During a supervised fast, plasma glucose concentration dropped to 1.1 mmol/L after 21 hours. Insulin concentration at the time of hypoglycaemia (458 pmol/L) confirmed hyperinsulinaemic hypoglycaemia. The abdominal CT scan showed a large liver tumour (10 cm in diameter) in the left hepatic lobe. An octreotide scan of the liver tumour was positive Figure . FollowiIn conclusion, pancreatic islet cell hyperplasia and co‐secretion of insulin (in addition to somatostatin) from tumour cells, respectively, have been characterized as completely distinct mechanisms of hypoglycaemia at the functional and morphological level in these two patients with malignant somatostatinomas.The authors declare no conflict of interest in relation to this work.PW, VP, MB and CS were directly involved in the management of the patients and contributed to the final manuscript. TP performed the ASVS tests, and AP performed the pathological examinations.Both patients gave informed consent to publish their data.