A new algorithm for a better characterization and timing of the anti-VEGF vascular effect named "normalization".

Antiangiogenics are widely used in cancer treatment in combination with chemotherapy and radiotherapy for their vascular effects. Antiangiogenics are supposed to induce morphological and functional changes in the chaotic tumor vasculature that would help enhance the therapeutic efficacy of chemotherapy and radiotherapy through the amelioration of the drug delivery or the oxygenation in the tumor, respectively. However, finding the best treatment sequence is not an easy task to achieve and no consensus has yet been established because of the lack of knowledge regarding when and for how long the vascular network is ameliorated. The aim of this work was to develop a dedicated image processing algorithm able to analyze the vascular structures on optical microscopy images of the vascular network and to follow its fine modifications in vivo, over time. We applied this algorithm to follow the evolution of the vascular parameters (vascularized tissue surface, branches, sprouts and length), in response or not to anti-VEGF therapy (10 mg/kg/day) and determine precisely whether there is really a vascular "normalization" with anti-VEGF therapy in comparison with the parameters extracted from healthy vascular networks. We found that for this determination, the choice of region of interest to analyze is critical as it is important to compare only microcirculation areas and avoid areas with arteriole-venule-capillary hierarchy. The algorithm analysis allowed us to define a vascular "normalization" in treated tumors, between 8 and 12 days of bevacizumab treatment that was confirmed by standard immunohistochemical analysis, microvascular permeability assessment and immunohistological blood perfusion assessment.

Anti-Tumoral and Anti-Angiogenic Effects of Low-Diluted Phenacetinum on Melanoma.

Melanoma is the most aggressive form of skin cancer and the most rapidly expanding cancer in terms of worldwide incidence. If primary cutaneous melanoma is mostly treated with a curative wide local excision, malignant melanoma has a poor prognosis and needs other therapeutic approaches. Angiogenesis is a normal physiological process essential in growth and development, but it also plays a crucial role in crossing from benign to advanced state in cancer. In melanoma progression, angiogenesis is widely involved during the vertical growth phase. Currently, no anti-angiogenic agents are efficient on their own, and combination of treatments will probably be the key to success. In the past, phenacetin was used as an analgesic to relieve pain, causing side effects at large dose and tumor-inducing in humans and animals. By contrast, Phenacetinum low-dilution is often used in skin febrile exanthema, patches profusely scattered on limbs, headache, or flushed face without side effects. Herein are described the in vitro, in vivo, and ex vivo anti-angiogenic and anti-tumoral potentials of Phenacetinum low-dilution in a B16F1 tumor model and endothelial cells. We demonstrate that low-diluted Phenacetinum inhibits in vivo tumor growth and tumor vascularization and thus increases the survival time of B16F1 melanoma induced-C57BL/6 mice. Moreover, Phenacetinum modulates the lung metastasis in a B16F10 induced model. Ex vivo and in vitro, we evidence that low-diluted Phenacetinum inhibits the migration and the recruitment of endothelial cells and leads to an imbalance in the pro-tumoral macrophages and to a structural malformation of the vascular network. All together these results demonstrate highly hopeful anti-tumoral, anti-metastatic, and anti-angiogenic effects of Phenacetinum low-dilution on melanoma. Continued studies are needed to preclinically validate Phenacetinum low-dilution as a complementary or therapeutic strategy for melanoma treatment.

LRP-1 Matricellular Receptor Involvement in Triple Negative Breast Cancer Tumor Angiogenesis.

BACKGROUND: LRP-1 is a multifunctional scavenger receptor belonging to the LDLR family. Due to its capacity to control pericellular levels of various growth factors and proteases, LRP-1 plays a crucial role in membrane proteome dynamics, which appears decisive for tumor progression. METHODS: LRP-1 involvement in a TNBC model was assessed using an RNA interference strategy in MDA-MB-231 cells. In vivo, tumorigenic and angiogenic effects of LRP-1-repressed cells were evaluated using an orthotopic xenograft model and two angiogenic assays (Matrigel® plugs, CAM). DCE-MRI, FMT, and IHC were used to complete a tumor longitudinal follow-up and obtain morphological and functional vascular information. In vitro, HUVECs' angiogenic potential was evaluated using a tumor secretome, subjected to a proteomic analysis to highlight LRP-1-dependant signaling pathways. RESULTS: LRP-1 repression in MDA-MB-231 tumors led to a 60% growth delay because of, inter alia, morphological and functional vascular differences, confirmed by angiogenic models. In vitro, the LRP-1-repressed cells secretome restrained HUVECs' angiogenic capabilities. A proteomics analysis revealed that LRP-1 supports tumor growth and angiogenesis by regulating TGF-ß signaling and plasminogen/plasmin system. CONCLUSIONS: LRP-1, by its wide spectrum of interactions, emerges as an important matricellular player in the control of cancer-signaling events such as angiogenesis, by supporting tumor vascular morphology and functionality.

A signal demodulation-based method for the early detection of Cheyne-Stokes respiration.

Cheyne-Stokes respiration (CSR) is a sleep-disordered breathing characterized by recurrent central apneas alternating with hyperventilation exhibiting a crescendo-decrescendo pattern of tidal volume. This respiration is reported in patients with heart failure, stroke or damage in respiratory centers. It increases mortality for patients with severe heart failure as it has adverse impacts on the cardiac function. Early stage of CSR, also called periodic breathing, is often undiagnosed as it only provokes hypopneas instead of apneas, which are much more difficult to detect. This paper demonstrates the proof of concept of a new method devoted to the early detection of CSR. The proposed approach relies on a signal demodulation technique applied to ventilation signals measured on 15 patients with chronic heart failure whose respiration goes from normal to severe CSR. Based on a modulation index and its instantaneous frequency, oscillation zones are detected and classified into three categories: CSR, periodic breathing and no abnormal pattern. The modulation index is used as an efficient indicator to quantify the degree of certainty of the pathology for each patient. Results show high correlation with experts' annotations with sensitivity and specificity values of 87.1% and 89.8% respectively. A final decision leads to a classification which is confirmed by the experts' conclusions.

Mammary epithelial cell phenotype disruption in vitro and in vivo through ERalpha36 overexpression.

Estrogen receptor alpha 36 (ERα36) is a variant of the canonical estrogen receptor alpha (ERα66), widely expressed in hormone sensitive cancer cells and whose high expression level correlates with a poor survival prognosis for breast cancer patients. While ERα36 activity have been related to breast cancer progression or acquired resistance to treatment, expression level and location of ERα36 are poorly documented in the normal mammary gland. Therefore, we explored the consequences of a ERα36 overexpression in vitro in MCF-10A normal mammary epithelial cells and in vivo in a unique model of MMTV-ERα36 transgenic mouse strain wherein ERα36 mRNA was specifically expressed in the mammary gland. By a combination of bioinformatics and computational analyses of microarray data, we identified hierarchical gene networks, downstream of ERα36 and modulated by the JAK2/STAT3 signaling pathway. Concomitantly, ERα36 overexpression lowered proliferation rate but enhanced migration potential and resistance to staurosporin-induced apoptosis of the MCF-10A cell line. In vivo, ERα36 expression led to duct epithelium thinning and disruption in adult but not in prepubescent mouse mammary gland. These phenotypes correlated with a loss of E-cadherin expression. Here, we show that an enhanced expression of ERα36 is sufficient, by itself, to disrupt normal breast epithelial phenotype in vivo and in vitro through a dominant-positive effect on nongenomic estrogen signaling pathways. These results also suggest that, in the presence of adult endogenous steroid levels, ERα36 overexpression in vivo contributes to alter mammary gland architecture which may support pre-neoplastic lesion and augment breast cancer risk.

Regularization Parameter Estimation for Non-Negative Hyperspectral Image Deconvolution.

This paper aims at studying a method to automatically estimate the regularization parameters of non-negative hyperspectral image deconvolution methods. The deconvolution problem is formulated as a multi-objective optimization problem and the properties of the corresponding response surface are studied. Based on these properties, the minimum distance criterion (MDC) and the maximum curvature criterion (MCC) are proposed to estimate regularization parameters especially for the non-negativity constrained deconvolution problem. MDC has good theoretical properties (convexity and uniqueness) but requires to choose a reference point. On the contrary, MCC does not need to choose any reference point but does not have interesting theoretical properties. A grid-search-based approach to minimize the computational cost of MDC and MCC is proposed. It results in fast approaches to estimate the regularization parameters. Based on simulated 2D images, the proposed approaches are compared with the state-of-the-art methods, confirming the effectiveness of the MDC and MCC for the non-negativity constrained image deconvolution problem. In the case of non-negative hyperpsectral image deconvolution, the fast MDC yields better performances than the fast MCC. An application to real-world hyperspectral fluorescence microscopy images is also provided; it confirms the superiority of MDC.

Identification of dynamical biological systems based on random effects models.

System identification is a data-driven modeling approach more and more used in biology and biomedicine. In this application context, each assay is always repeated to estimate the response variability. The inference of the modeling conclusions to the whole population requires to account for the inter-individual variability within the modeling procedure. One solution consists in using random effects models but up to now no similar approach exists in the field of dynamical system identification. In this article, we propose a new solution based on an ARX (Auto Regressive model with eXternal inputs) structure using the EM (Expectation-Maximisation) algorithm for the estimation of the model parameters. Simulations show the relevance of this solution compared with a classical procedure of system identification repeated for each subject.

A new adaptive subband decomposition approach for automatic analysis of NMR data.

This paper presents a non-iterative, fast, and almost automated time-data analysis method for NMR spectroscopy, based on a new adaptive implementation of high resolution methods used in spectral subbands. It is intended to avoid the choice of the decimation factor (or the width of the spectral windows) which, in the case of a uniform decomposition, strongly conditions the estimation results, and to diminish the computational burden. It is achieved through successive decimation/estimation stages each followed by a test procedure in order to decide whether or not the process should continue. The proposed test is based on a local spectral flatness measure of the estimation residuals. This stop-criterion involves an a posteriori validation of the estimation, thus the method proposed allows one to obtain a better detection rate at a lower complexity comparatively to other stopping rules, while preserving a reasonable estimation variance. Moreover, the reliability of the fitting algorithms considered is improved, by decreasing the influence of the model order and the number of false detections. Finally, the method is more efficient than Fourier transform (FT) at low signal-to-noise ratio (SNR). The effectiveness of the method is demonstrated by analyzing a simulation signal and raw carbon-13 experimental data.