Haematologica

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Molecular Markers of Prognostic and Therapeutic Interest in Mantle Cell Lymphoma Patients

Le lymphome à cellules du manteau (LCM) est une pathologie rare qui présente une grande diversité de profil clinique, biologique et de réponse aux thérapeutiques. Actuellement l’immunochimiothérapie et la consolidation par autogreffe de cellules souches hématopoïétiques est le traitement standard proposé à tous les patients jeunes mais environ 10% d’entre eux sont d’emblée réfractaires alors que d’autres sont en rémission très prolongée. C’est pourquoi le développement de nouveaux marqueurs pronostiques permettrait d’adapter de façon personnalisée la stratégie thérapeutique. Une première partie de ce document rappelle les aspects fondamentaux et le contexte scientifique de cette pathologie. Une seconde partie rapporte les résultats des travaux menés dans le cadre de cette thèse. Nous avons exploré le potentiel pronostique des anomalies du nombre de copies des chromosomes intrinsèques aux cellules de la tumeur chez 100 patients traités en première ligne par une immunochimiothérapie dans l’essai clinique LyMa. Cette évaluation pan-génomique, réalisée par SNP-array sur de l’ADN hautement dégradé de la tumeur, nous a permis d’identifier que des gains de 7p22 sont associés à une bonne réponse en dépit de la présence d’autres anomalies de haut risque (délétion TP53 et CDKN2A). Nous avons identifié que des gains de CCND1 correspondant à des copies surnuméraires des réarrangements CCND1/IGH sont associés à une mauvaise réponse clinique. Ces derniers résultats ont été confirmés dans une seconde cohorte indépendante de patients. Ce travail est publié..Des travaux en cours de finalisation sont également présentés. Nous sommes ainsi en train d’évaluer, en corrélation avec l’immunohistochimie, dans la même cohorte protocolaire, le rôle des anomalies de CDKN2A et p16INK4A. La surexpression de p16INK4A ou la délétion homozygote de CDKN2A qui ont été mises en évidence pourraient être utilisées comme biomarqueurs pronostiques au moment du diagnostic pour prédire la réponse au traitement de première ligne. Enfin, nous avons utilisé une approche innovante d’analyse transcriptomique par Reverse Transcriptase Multiplex Ligation-dependent Probe Amplification (RT-MLPA) adaptée aux acides nulcéiques dégradés des échantillons conservés en paraffine. Des variations intrinsèques ou extrinsèques à la cellule tumorale ont été explorées. Des variations quantitatives des monocytes/macrophages, des lymphocytes T et NK ont été observées en comparaison avec des tissus non tumoraux. D’autres variations ont été observées dans le contexte de lymphome agressif, en cas d’anomalies de p53 et à la rechute. Une troisième partie aborde les travaux envisagés en perspective. Une étude de la clonalité des lymphocytes infiltrant la tumeur devrait permettre d’approfondir le contexte immunitaire dans le LCM. Enfin, une analyse combinée des anomalies intrinsèques et extrinsèques à l’échelle de la cellule unique pourrait permettre d’identifier de nouvelles cibles thérapeutiques et de nouveaux biomarqueurs permettant de mieux positionner les différentes stratégies thérapeutiques disponibles.Mantle cell lymphoma (MCL) is a rare disease displaying a great diversity in terms of clinic, biology and response to therapies. Currently, immunochemotherapy and consolidation by autologous hematopoietic stem cell transplantation is the standard treatment proposed to all younger patients, yet about 10% are refractory while others enjoy long remissions. The development of new prognostic markers could help to adapt a personalized approach of therapeutic strategies. In the first part of this document, fundamental and scientific aspects of this disease are described The second part reports the results obtained in the context of this thesis. The prognostic value of chromosomal copy number anomalies intrinsic to the tumor cells was investigated in 100 MCL patients enrolled for first-line therapy in the LyMa clinical trial. This pan-genomic approach, performed on highly degraded tumoral DNA by SNP-array, allowed to identify that 7p22 gains are associated to a good response is spite of the presence of high-risk anomalies (TP53 or CDKN2A deletions). We also identified that gains of CCND1, corresponding to additional copies of CCND1/IGH rearrangements, are associated to poor response to therapy. These data were confirmed in a second independent cohort of MCL patients. This work has been published..Investigations in progress are also presented. In the same LyMa cohort, the role of CDKN2A and p16INK4 anomalies is being evaluated, in correlation with immunohistochemistry. Overexpression of the p16INK4A protein or homozygous deletion of CDKN2A have been characterized, that could be used as prognostic biomarkers at diagnosis, to predict the patient’s response to first line therapy. Finally, we used a novel approach of transcriptomic analysis by Reverse Transcriptase Multiplex Ligation-dependent Probe Amplification (RT-MLPA), adapted to the degraded nucleic acids of our formalin-fixed paraffin-embedded samples. Variations intrinsic or extrinsic to the tumor cell have been explored. Quantitative anomalies of monocytes/macrophages, as well as T and NK cells have been observed, in comparison to non-tumoral samples. Other variations have been identified to be associated to aggressive forms of the MCL, in case of p53 anomalies or at relapse. A third part announces projects envisioned in the future. An analysis of the clonality of tumor-infiltrating T-cells should allow to better understand the immune context of MCL. Finally, a combined analysis of intrinsic and extrinsic anomalies at the single cell level could allow to identify new therapeutic targets and new biomarkers for a better-adapted management of patients

Impact pronostique des nouveaux marqueurs cytogénétiques et moléculaires dans une cohorte toulousaine de 207 patients atteints de leucémie lymphoïde chronique

TOULOUSE3-BU Santé-Centrale (315552105) / SudocSudocFranceF

Detection and area estimation for photovoltaic panels in urban hyperspectral remote sensing data by an original nmf-based unmixing method

International audienceHyperspectral remote sensing data offer unique opportunities for the characterization of land surface in urban areas. However, no hyperspectral-unmixing based studies have been conducted to automatically detect photovoltaic panels, which represent one of the important components of energy systems in such areas. In this paper, a hyperspectral-unmixing based method is proposed to detect photovoltaic panels and to estimate their areas. This approach is based on an original multiplicative nonnegative matrix factorization (NMF) algorithm with some known photovoltaic panel spectra. The proposed method can be considered as a partial/informed NMF approach. Experiments are conducted on realistic synthetic and real data to evaluate the performance of the proposed approach. In both cases, obtained results show that the proposed method yields much better overall performance than a method from the literature

AN NMF-BASED UNMIXING METHOD WITH KNOWN SPECTRA OF PHOTOVOLTAIC PANELS FOR THEIR DETECTION AND AREA ESTIMATION FROM URBAN HYPERSPECTRAL REMOTE SENSING DATA

International audienceCurrently, photovoltaic panels constitute an important part of renewable energy systems in urban areas of developed countries. They are expected to generate greener electrical energy from non-polluting solar resources. Therefore, government agencies, electricity grid operators and decision makers encourage their setting up by funding and tax reduction. To avoid frauds with these substitute energies, several organizations are interested in detailed information, including localization and energy production, about these solar systems. Field surveys constitute one of the methods to obtain the above information on photovoltaic panels. However, this approach consumes a lot of time and can be very expensive, which leads to the use of other less expensive and faster approaches. The integration of remote sensing data is an interesting alternative for automatic detection of photovoltaic installations and their localization. Some remote sensing-based methods use high spatial resolution airborne/spaceborne images with a limited number of spectral bands. Such data do not allow an effective detection of photovoltaic panels principally due to their material properties. Indeed, when the visual properties of these panels are altered by specular reflections, their detection becomes difficult. High spectral resolution hyperspectral data may be considered to overcome the above limitation. These data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum, which offer unique opportunities for precise material recognition. In the investigation reported here, a hyperspectral-unmixing based method is proposed to detect photovoltaic panels and to estimate their areas. This approach is based on a new multiplicative nonnegative matrix factorization (NMF) algorithm which exploits known panel spectra. The designed approach, which can be considered as a partial NMF method, is applied to real airborne hyperspectral data acquired over the urban region of Toulouse, France. The obtained results (detection and area estimation) are confirmed by using a very high spatial resolution ortho-image of the same region. Also, these results are compared with those obtained by the standard multiplicative NMF algorithm introduced by Lee and Seung. This comparison shows that the proposed method yields much better overall performance than the considered method from the literature

Aggressive, early resistant and relapsed mantle cell lymphoma distinct extrinsic microenvironment highlighted by transcriptome analysis

Abstract Immunotherapy strategies relying on innate or adaptive immune components are increasingly used in onco‐haematology. However, little is known about the infiltrated lymph nodes (LN) or bone marrow (BM) landscape of mantle cell lymphoma (MCL). The original transcriptomic approach of reverse transcriptase multiplex ligation‐dependent probe amplification (RT‐MLPA) was applied here to explore the expression of 24 genes of interest in MCL at diagnosis (21 LN and 15 BM) or relapse (18 LN). This allowed us to identify that at baseline, samples from MCL patients with an aggressive morphology (i.e. blastoid or pleomorphic) or a high proliferative profile, displayed significantly higher monocyte/macrophage‐associated transcripts (CD14 and CD163) in LN and BM. Regarding T‐cells, aggressive MCL forms had significantly lower amounts of LN CD3E transcripts, yet an increased expression of cytotoxic markers in LN (CD8) and BM (CD94). A very high‐risk group with early treatment resistance displayed, at diagnosis, high proliferation (KI67) and high macrophages and cytotoxic transcript levels. Post‐immunochemotherapy relapsed samples revealed lower levels of T‐ and natural killer‐cells markers, while monocyte/macrophage markers remained similar to diagnosis. This study suggests that rapid analysis of MCL microenvironment transcriptome signatures by RT‐MLPA could allow for an early distinction of patient subgroups candidates for adapted treatment strategies

Partial Linear NMF-Based Unmixing Methods for Detection and Area Estimation of Photovoltaic Panels in Urban Hyperspectral Remote Sensing Data

International audienceHigh-spectral-resolution hyperspectral data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer unique opportunities for characterization and precise land surface recognition in urban areas. So far, few studies have been conducted with these data to automatically detect and estimate areas of photovoltaic panels, which currently constitute an important part of renewable energy systems in urban areas of developed countries. In this paper, two hyperspectral-unmixing-based methods are proposed to detect and to estimate surfaces of photovoltaic panels. These approaches, related to linear spectral unmixing (LSU) techniques, are based on new nonnegative matrix factorization (NMF) algorithms that exploit known panel spectra, which makes them partial NMF methods. The first approach, called Grd-Part-NMF, is a gradient-based method, whereas the second one, called Multi-Part-NMF, uses multiplicative update rules. To evaluate the performance of these approaches, experiments are conducted on realistic synthetic and real airborne hyperspectral data acquired over an urban region. For the synthetic data, obtained results show that the proposed methods yield much better overall performance than NMF-unmixing-based methods from the literature. For the real data, the obtained detection and area estimation results are first confirmed by using very high-spatial-resolution ortho-images of the same regions. These results are also compared with those obtained by standard NMF-unmixing-based methods and by a one-class-classification-based approach. This comparison shows that the proposed approaches are superior to those considered from the literature

Vexas Syndrome: A Complex Necessarily Integrated Diagnosis That Can Benefit from the Help of Deep Learning

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CSF1R and BTK inhibitions as novel strategies to disrupt the dialogue between mantle cell lymphoma and macrophages: MCL/macrophage protumoral interplay

International audienceThe microenvironment strongly influences mantle cell lymphoma (MCL) survival, proliferation and chemoresistance. However, little is known regarding the molecular characterization of lymphoma niches. Here, we focused on the interplay between MCL cells and associated monocytes/macrophages. Using circulating MCL cells (n=58), we showed that, through the secretion of CSF1 and, to a lesser extent, IL-10, MCL polarized monocytes into specific CD163+ M2-like macrophages (MϕMCL). In turn, MϕMCL favored lymphoma survival and proliferation ex vivo. We next demonstrated that BTK inhibition abrogated CSF1 and IL-10 production in MCL cells leading to the inhibition of macrophage polarization and consequently resulting in the suppression of microenvironment-dependent MCL expansion. In vivo, we showed that CSF1 and IL-10 plasma concentrations were higher in MCL patients than in healthy donors, and that monocytes from MCL patients overexpressed CD163. Further analyses of serial samples from ibrutinib-treated patients (n=8) highlighted a rapid decrease of CSF1, IL-10 and CD163 in responsive patients. Finally, we showed that targeting the CSF1R abrogated MϕMCL-dependent MCL survival, irrespective of their sensitivity to ibrutinib. These data reinforced the role of the microenvironment in lymphoma and suggested that macrophages are a potential target for developing novel therapeutic strategies in MCL