Predicting regrowth of low-grade gliomas after radiotherapy.

Diffuse low grade gliomas are invasive and incurable brain tumors that inevitably transform into higher grade ones. A classical treatment to delay this transition is radiotherapy (RT). Following RT, the tumor gradually shrinks during a period of typically 6 months to 4 years before regrowing. To improve the patient's health-related quality of life and help clinicians build personalized follow-ups, one would benefit from predictions of the time during which the tumor is expected to decrease. The challenge is to provide a reliable estimate of this regrowth time shortly after RT (i.e. with few data), although patients react differently to the treatment. To this end, we analyze the tumor size dynamics from a batch of 20 high-quality longitudinal data, and propose a simple and robust analytical model, with just 4 parameters. From the study of their correlations, we build a statistical constraint that helps determine the regrowth time even for patients for which we have only a few measurements of the tumor size. We validate the procedure on the data and predict the regrowth time at the moment of the first MRI after RT, with precision of, typically, 6 months. Using virtual patients, we study whether some forecast is still possible just three months after RT. We obtain some reliable estimates of the regrowth time in 75% of the cases, in particular for all "fast-responders". The remaining 25% represent cases where the actual regrowth time is large and can be safely estimated with another measurement a year later. These results show the feasibility of making personalized predictions of the tumor regrowth time shortly after RT.

The Effect of Radiotherapy on Diffuse Low-Grade Gliomas Evolution: Confronting Theory with Clinical Data.

Diffuse low-grade gliomas are slowly growing tumors that always recur after treatment. In this paper, we revisit the modeling of the evolution of the tumor radius before and after the radiotherapy process and propose a novel model that is simple yet biologically motivated and that remedies some shortcomings of previously proposed ones. We confront this with clinical data consisting of time series of tumor radii from 43 patient records by using a stochastic optimization technique and obtain very good fits in all cases. Since our model describes the evolution of a tumor from the very first glioma cell, it gives access to the possible age of the tumor. Using the technique of profile likelihood to extract all of the information from the data, we build confidence intervals for the tumor birth age and confirm the fact that low-grade gliomas seem to appear in the late teenage years. Moreover, an approximate analytical expression of the temporal evolution of the tumor radius allows us to explain the correlations observed in the data.

Experimental and modeling study of the formation of cell aggregates with differential substrate adhesion.

The study of cell aggregation in vitro has a tremendous importance these days. In cancer biology, aggregates and spheroids serve as model systems and are considered as pseudo-tumors that are more realistic than 2D cell cultures. Recently, in the context of brain tumors (gliomas), we developed a new poly(ethylene glycol) (PEG)-based hydrogel, with adhesive properties that can be controlled by the addition of poly(L-lysine) (PLL), and a stiffness close to the brain's. This substrate allows the motion of individual cells and the formation of cell aggregates (within one day), and we showed that on a non-adhesive substrate (PEG without PLL is inert for cells), the aggregates are bigger and less numerous than on an adhesive substrate (with PLL). In this article, we present new experimental results on the follow-up of the formation of aggregates on our hydrogels, from the early stages (individual cells) to the late stages (aggregate compaction), in order to compare, for two cell lines (F98 and U87), the aggregation process on the adhesive and non-adhesive substrates. We first show that a spaceless model of perikinetic aggregation can reproduce the experimental evolution of the number of aggregates, but not of the mean area of the aggregates. We thus develop a minimal off-lattice agent-based model, with a few simple rules reproducing the main processes that are at stack during aggregation. Our spatial model can reproduce very well the experimental temporal evolution of both the number of aggregates and their mean area, on adhesive and non-adhesive soft gels and for the two different cell lines. From the fit of the experimental data, we were able to infer the quantitative values of the speed of motion of each cell line, its rate of proliferation in aggregates and its ability to organize in 3D. We also found qualitative differences between the two cell lines regarding the ability of aggregates to compact. These parameters could be inferred for any cell line, and correlated with clinical properties such as aggressiveness and invasiveness.

Modeling the dynamics of oligodendrocyte precursor cells and the genesis of gliomas.

Oligodendrocyte precursor cells (OPCs) have remarkable properties: they represent the most abundant cycling cell population in the adult normal brain and they manage to achieve a uniform and constant density throughout the adult brain. This equilibrium is obtained by the interplay of four processes: division, differentiation or death, migration and active self-repulsion. They are also strongly suspected to be at the origin of gliomas, when their equilibrium is disrupted. In this article, we present a model of the dynamics of OPCs, first in a normal tissue. This model is based on a cellular automaton and its rules are mimicking the ones that regulate the dynamics of real OPCs. The model is able to reproduce the homeostasis of the cell population, with the maintenance of a constant and uniform cell density and the healing of a lesion. We show that there exists a fair quantitative agreement between the simulated and experimental parameters, such as the cell velocity, the time taken to close a lesion, and the duration of the cell cycle. We present three possible scenarios of disruption of the equilibrium: the appearance of an over-proliferating cell, of a deadless/non-differentiating cell, or of a cell that lost any contact-inhibition. We show that the appearance of an over-proliferating cell is sufficient to trigger the growth of a tumor that has low-grade glioma features: an invasive behaviour, a linear radial growth of the tumor with a corresponding growth velocity of less than 2 mm per year, as well a cell density at the center which exceeds the one in normal tissue by a factor of less than two. The loss of contact inhibition leads to a more high-grade-like glioma. The results of our model contribute to the body of evidence that identify OPCs as possible cells of origin of gliomas.

Surgical Decision Making From Image-Based Biophysical Modeling of Glioblastoma: Not Ready for Primetime.

BACKGROUND: Biophysical modeling of glioma is gaining more interest for clinical practice. The most popular model describes aggressivity of tumor cells by two parameters: net proliferation rate (ρ) and propensity to migrate (D). The ratio ρ/D, which can be estimated from a single preoperative magnetic resonance imaging (MRI), characterizes tumor invasiveness profile (high ρ/D: nodular; low ρ/D: diffuse). A recent study reported, from a large series of glioblastoma multiforme (GBM) patients, that gross total resection (GTR) would improve survival only in patients with nodular tumors. OBJECTIVE: To replicate these results, that is to verify that benefit of GTR would be only observed for nodular tumors. METHODS: Between 2005 and 2012, we considered 234 GBM patients with pre- and postoperative MRI. Stereotactic biopsy (BST) was performed in 109 patients. Extent of resection was assessed on postoperative MRI and classified as GTR or partial resection (PR). Invasiveness ρ/D was estimated from the preoperative tumor volumes on T1-Gadolinium-enhanced and fluid-attenuated inversion recovery sequences. RESULTS: We demonstrate that patients with diffuse GBM (low ρ/D), as well as more nodular (mid and high ρ/D) GBM, presented significant survival benefit from GTR over PR/BST ( P < .001). CONCLUSION: Whatever the degree of tumor invasiveness, as estimated from MRI-driven biophysical modeling, GTR improves survival of GBM patients, compared to PR or BST. This conflicting result should motivate further studies.

Arterial Spin Labeling to Predict Brain Tumor Grading in Children: Correlations between Histopathologic Vascular Density and Perfusion MR Imaging.

Purpose To compare arterial spin labeling (ASL) data between low- and high-grade brain tumors in children to establish a cutoff to distinguish low- from high-grade neoplasms and to assess potential correlations between cerebral blood flow (CBF) and quantitative histologic microvascular data. Materials and Methods Approval was obtained from the regional review board. ASL data obtained in 129 children between 2011 and 2015 were retrospectively analyzed. CBF and relative CBF in the most perfused area of each neoplasm and contrast enhancement were quantified with a semiquantitative ratio. The correlation between CBF and microvascular density was analyzed in specimens stained with anti-CD34. Results were controlled in two validation cohorts with 1.5- and 3.0-T magnetic resonance (MR) imaging. Results Mean CBF was significantly higher for high-grade than for low-grade hemispheric (116 mL/min/100 g [interquartile range {IQR}, 73-131 mL/min/100 g] vs 29 mL/min/100 g [IQR, 23-35 29 mL/min/100 g], P < .001), thalamic (87 mL/min/100 g [IQR, 73-100 mL/min/100 g] vs 36 mL/min/100 g [IQR, 30-40 mL/min/100 g], P = .016), and posterior fossa (59 mL/min/100 g [IQR, 45-91 mL/min/100 g] vs 33 mL/min/100 g [IQR, 25-40 mL/min/100 g], P < .001) tumors. With a cutoff of 50 mL/min/100 g, sensitivity and specificity were 90% (95% confidence interval [CI]: 68, 100) and 93% (95% CI: 66, 100), respectively, for hemispheric tumors; 100% (95% CI: 48, 100) and 80% (95% CI: 28, 100), respectively, for thalamic tumors; and 65% (95% CI: 51, 78) and 94% (95% CI: 80, 99), respectively, for posterior fossa tumors. In posterior fossa tumors, additional use of the CBF-to-contrast enhancement ratio yielded sensitivity and specificity of 96% (95% CI: 87, 100) and 97% (95% CI: 84, 100), respectively. Use of a simple algorithm based on these values yielded an accuracy of 93% (95% CI: 87, 97). Validation sets yielded similar results, with grading accuracy of 88% (95% CI: 62, 98) with 1.5-T MR imaging and 77% (95% CI: 46, 95) with 3.0-T MR imaging. CBF was strongly correlated with microvascular density (R = 0.66, P < .001). Conclusion High-grade pediatric brain tumors display higher CBF than do low-grade tumors, and they may be accurately graded by using these values. CBF is correlated with tumor microvascular density. © RSNA, 2016 Online supplemental material is available for this article.

ROCK1 and 2 differentially regulate actomyosin organization to drive cell and synaptic polarity.

RhoGTPases organize the actin cytoskeleton to generate diverse polarities, from front-back polarity in migrating cells to dendritic spine morphology in neurons. For example, RhoA through its effector kinase, RhoA kinase (ROCK), activates myosin II to form actomyosin filament bundles and large adhesions that locally inhibit and thereby polarize Rac1-driven actin polymerization to the protrusions of migratory fibroblasts and the head of dendritic spines. We have found that the two ROCK isoforms, ROCK1 and ROCK2, differentially regulate distinct molecular pathways downstream of RhoA, and their coordinated activities drive polarity in both cell migration and synapse formation. In particular, ROCK1 forms the stable actomyosin filament bundles that initiate front-back and dendritic spine polarity. In contrast, ROCK2 regulates contractile force and Rac1 activity at the leading edge of migratory cells and the spine head of neurons; it also specifically regulates cofilin-mediated actin remodeling that underlies the maturation of adhesions and the postsynaptic density of dendritic spines.

The silent phase of diffuse low-grade gliomas. Is it when we missed the action?

BACKGROUND: It is commonly believed that, before being diagnosed after onset of symptoms, diffuse low-grade glioma evolve silently for a long time. The present study aimed to estimate for the first time the exact duration of this silent phase, during which the glioma is radiologically visible but undiscovered. METHODS: We retrospectively reviewed our French national database of diffuse low-grade glioma, searching for patients with an MRI-based assessment of their velocity of diameter growth at diagnosis and before any treatment (at least three MRIs over more than 6 months). For each patient, the duration of the silent phase was estimated by the formula: duration = initial diameter / initial velocity of growth. RESULTS: A total of 148 patients were included in the study. The mean lead-time duration (i.e., duration of the silent phase) was 14.0 ± 7.8 years (median, 11.6 ; range, 1.6-39.4). The lead-time is statistically not correlated to the tumor volume. It is markedly decreasing with the velocity of diameter expansion. CONCLUSIONS: Diffuse low-grade glioma are radiologically detectable but clinically silent for more than a decade. Such a long period of silent evolution could explain our current failure to cure these tumors. It can also be viewed as a window of opportunity to detect these tumors earlier, suggesting the need to set up a screening program.

Analyzing huge pathology images with open source software.

BACKGROUND: Digital pathology images are increasingly used both for diagnosis and research, because slide scanners are nowadays broadly available and because the quantitative study of these images yields new insights in systems biology. However, such virtual slides build up a technical challenge since the images occupy often several gigabytes and cannot be fully opened in a computer's memory. Moreover, there is no standard format. Therefore, most common open source tools such as ImageJ fail at treating them, and the others require expensive hardware while still being prohibitively slow. RESULTS: We have developed several cross-platform open source software tools to overcome these limitations. The NDPITools provide a way to transform microscopy images initially in the loosely supported NDPI format into one or several standard TIFF files, and to create mosaics (division of huge images into small ones, with or without overlap) in various TIFF and JPEG formats. They can be driven through ImageJ plugins. The LargeTIFFTools achieve similar functionality for huge TIFF images which do not fit into RAM. We test the performance of these tools on several digital slides and compare them, when applicable, to standard software. A statistical study of the cells in a tissue sample from an oligodendroglioma was performed on an average laptop computer to demonstrate the efficiency of the tools. CONCLUSIONS: Our open source software enables dealing with huge images with standard software on average computers. They are cross-platform, independent of proprietary libraries and very modular, allowing them to be used in other open source projects. They have excellent performance in terms of execution speed and RAM requirements. They open promising perspectives both to the clinician who wants to study a single slide and to the research team or data centre who do image analysis of many slides on a computer cluster. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here:http://www.diagnosticpathology.diagnomx.eu/vs/5955513929846272.

Quantitative characterization of the imaging limits of diffuse low-grade oligodendrogliomas.

BACKGROUND: Supratentorial diffuse low-grade gliomas in adults extend beyond maximal visible MRI-defined abnormalities, and a gap exists between the imaging signal changes and the actual tumor margins. Direct quantitative comparisons between imaging and histological analyses are lacking to date. However, they are of the utmost importance if one wishes to develop realistic models for diffuse glioma growth. METHODS: In this study, we quantitatively compared the cell concentration and the edema fraction from human histological biopsy samples (BSs) performed inside and outside imaging abnormalities during serial imaging-based stereotactic biopsy of diffuse low-grade gliomas. RESULTS: The cell concentration was significantly higher in BSs located inside (1189 ± 378 cell/mm(2)) than outside (740 ± 124 cell/mm(2)) MRI-defined abnormalities (P = .0003). The edema fraction was significantly higher in BSs located inside (mean, 45% ± 23%) than outside (mean, 5 %± 9%) MRI-defined abnormalities (P < .0001). At borders of the MRI-defined abnormalities, 20% of the tissue surface area was occupied by edema and only 3% by tumor cells. The cycling cell concentration was significantly higher in BSs located inside (10 ± 12 cell/mm(2)), compared with outside (0.5 ± 0.9 cell/mm(2)), MRI-defined abnormalities (P = .0001). CONCLUSIONS: We showed that the margins of T2-weighted signal changes are mainly correlated with the edema fraction. In 62.5% of patients, the cycling tumor cell fraction (defined as the ratio of the cycling tumor cell concentration to the total number of tumor cells) was higher at the limits of the MRI-defined abnormalities than closer to the center of the tumor. In the remaining patients, the cycling tumor cell fraction increased towards the center of the tumor.

Exclusion processes: short-range correlations induced by adhesion and contact interactions.

We analyze the out-of-equilibrium behavior of exclusion processes where agents interact with their nearest neighbors, and we study the short-range correlations which develop because of the exclusion and other contact interactions. The form of interactions we focus on, including adhesion and contact-preserving interactions, is especially relevant for migration processes of living cells. We show the local agent density and nearest-neighbor two-point correlations resulting from simulations on two-dimensional lattices in the transient regime where agents invade an initially empty space from a source and in the stationary regime between a source and a sink. We compare the results of simulations with the corresponding quantities derived from the master equation of the exclusion processes, and in both cases, we show that, during the invasion of space by agents, a wave of correlations travels with velocity v(t)~t(-1/2). The relative placement of this wave to the agent density front and the time dependence of its height may be used to discriminate between different forms of contact interactions or to quantitatively estimate the intensity of interactions. We discuss, in the stationary density profile between a full and an empty reservoir of agents, the presence of a discontinuity close to the empty reservoir. Then we develop a method for deriving approximate hydrodynamic limits of the processes. From the resulting systems of partial differential equations, we recover the self-similar behavior of the agent density and correlations during space invasion.

Pregnancy increases the growth rates of World Health Organization grade II gliomas.

Twelve pregnancies in 11 adult women harboring World Health Organization (WHO) grade II gliomas (GIIGs) prior to pregnancy were reviewed to address whether pregnancy affects tumor growth using a quantitative approach of the radiological velocity of diametric expansion (VDE) on successive magnetic resonance images. VDE was significantly increased during pregnancy as compared to prepregnancy (p < 0.001) and to postdelivery (p = 0.012) periods. Pregnancy increases the radiological growth rates of GIIGs. An increase in seizure frequency was observed concomitantly in 40% of cases and further oncological treatment was started after delivery in 25% of cases.

Modeling tumor cell migration: From microscopic to macroscopic models.

It has been shown experimentally that contact interactions may influence the migration of cancer cells. Previous works have modelized this thanks to stochastic, discrete models (cellular automata) at the cell level. However, for the study of the growth of real-size tumors with several million cells, it is best to use a macroscopic model having the form of a partial differential equation (PDE) for the density of cells. The difficulty is to predict the effect, at the macroscopic scale, of contact interactions that take place at the microscopic scale. To address this, we use a multiscale approach: starting from a very simple, yet experimentally validated, microscopic model of migration with contact interactions, we derive a macroscopic model. We show that a diffusion equation arises, as is often postulated in the field of glioma modeling, but it is nonlinear because of the interactions. We give the explicit dependence of diffusivity on the cell density and on a parameter governing cell-cell interactions. We discuss in detail the conditions of validity of the approximations used in the derivation, and we compare analytic results from our PDE to numerical simulations and to some in vitro experiments. We notice that the family of microscopic models we started from includes as special cases some kinetically constrained models that were introduced for the study of the physics of glasses, supercooled liquids, and jamming systems.

Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity.

Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends on the respective timing of pre- and post-synaptic spikes. The biophysical mechanisms underlying this form of plasticity are currently not known. We present here a biophysical model which captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. We also make links with other well-known plasticity rules. A simplified phenomenological model is also derived, which should be useful for fast numerical simulation and analytical investigation of the impact of STDP at the network level.

A method to estimate synaptic conductances from membrane potential fluctuations.

In neocortical neurons, network activity can activate a large number of synaptic inputs, resulting in highly irregular subthreshold membrane potential (V(m)) fluctuations, commonly called "synaptic noise." This activity contains information about the underlying network dynamics, but it is not easy to extract network properties from such complex and irregular activity. Here, we propose a method to estimate properties of network activity from intracellular recordings and test this method using theoretical and experimental approaches. The method is based on the analytic expression of the subthreshold V(m) distribution at steady state in conductance-based models. Fitting this analytic expression to V(m) distributions obtained from intracellular recordings provides estimates of the mean and variance of excitatory and inhibitory conductances. We test the accuracy of these estimates against computational models of increasing complexity. We also test the method using dynamic-clamp recordings of neocortical neurons in vitro. By using an on-line analysis procedure, we show that the measured conductances from spontaneous network activity can be used to re-create artificial states equivalent to real network activity. This approach should be applicable to intracellular recordings during different network states in vivo, providing a characterization of the global properties of synaptic conductances and possible insight into the underlying network mechanisms.

Barrages of synaptic activity control the gain and sensitivity of cortical neurons.

Ongoing synaptic activity, ever present in cortical neurons, may vary widely in its amplitude and characteristics, potentially having a strong influence on neuronal processing. Intracellular recordings in layer 5 pyramidal cells in prefrontal and visual cortical slices maintained in vitro revealed spontaneous periods of synaptic bombardment. Testing the responsiveness of these cortical cells to synaptic inputs or the injection of artificial excitatory postsynaptic conductances of various amplitudes revealed that background synaptic activity dramatically increased the probability of response to small inputs, decreased the slope of the input-output curve, and decreased both the latency and jitter of action potential activation. Examining the effects of different components of synaptic barrages (namely, depolarization, increase in membrane conductance, and increase in membrane potential variance) revealed that the effects observed were dominated by the membrane depolarization and increase in variance. Depolarization increased the peak cross-correlation between injected complex in vivo-like waveforms through enhancement of responsiveness to small inputs, whereas increases in variance did so through a shift in firing mode from one of threshold detection to probabilistic discharge. These results indicate that rapid increases in neuronal responsiveness, as well as increases in spike timing precision, can be achieved through balanced barrages of excitatory and inhibitory synaptic activity.

Kinesin motion in the absence of external forces characterized by interference total internal reflection microscopy.

We study the motion of the kinesin molecular motor along microtubules using interference total internal reflection microscopy. This technique achieves nanometer scale resolution together with a fast time response. We describe the first in vitro observation of kinesin stepping at high ATP concentration in the absence of an external load, where the 8-nm step can be clearly distinguished. The short-time resolution allows us to measure the time constant related to the relative motion of the bead-motor connection; we deduce the associated bead-motor elastic modulus.

Persistent cortical activity: mechanisms of generation and effects on neuronal excitability.

Local cortical networks in the prefrontal cortex and visual cortex are capable of spontaneously generating sustained activity for periods of seconds or longer. This sustained activity is generated through recurrent excitation between pyramidal cells that is controlled by feedback inhibition and can have both a rapid onset and a rapid offset. The period of activity is associated with a marked increase in neuronal responsiveness to the intracellular injection of current pulses, especially those of smaller amplitude. Independently mimicking the depolarization, increase in membrane conductance and increase in noise associated with sustained activity revealed that the depolarization is largely responsible for the increase in neuronal responsiveness, although an increase in membrane noise also facilitates responses to small inputs. These results indicate that the persistent activity associated with the performance of working memory tasks may be generated largely through recurrent networks. They also suggest that feedback pathways, such as those involved in selective attention, may exert a powerful influence on neuronal responsiveness through synaptic bombardment.