Is the Vascular Network Discriminant Enough to Classify Renal Cell Carcinoma?

International audienceThe renal cell carcinoma (RCC) is the most frequent type of kidney cancer (between 90% and 95%). Twelve subtypes of RCC can be distinguished, among which the clear cell carcinoma (ccRCC) and the papillary carcinoma (pRCC) are the two most common ones (75% and 10% of the cases, respectively). After resection (i.e., surgical removal), the tumor is prepared for histological examination (fixation, slicing, staining, observation with a microscope). Along with protein expression and genetic tests, the histological study allows to classify the tumor and define its grade in order to make a prognosis and to take decisions for a potential additional chemotherapy treatment. Digital histology is a recent domain, since routinely, histological slices are studied directly under the microscope. The pioneer works deal with the automatic analysis of cells. However, a crucial factor for RCC classification is the tumoral architecture relying on the structure of the vascular network. For example, coarsely speaking, ccRCC is characterized by a ``fishnet'' structure while the pRCC has a tree-like structure. To our knowledge, no computerized analysis of the vascular network has been proposed yet. In this context, we developed a complete pipeline to extract the vascular network of a given histological slice and compute features of the underlying graph structure. Then, we studied the potential of such a feature-based approach in classifying a tumor into ccRCC or pRCC. Preliminary results on patient data are encouraging

DIFFERENTIAL EFFECTS OF PROTONTHERAPY AND PHOTONTHERAPY ON HEAD AND NECK SQUAMOUS CELL CARCINOMA (HNSCC) POST-TREATMENT AGGRESSIVENESS

International audienceHead and neck cancers, the 7 th cause of death worldwide, are currently treated with a combination of surgical resection of the primary tumor, chemotherapy and radiotherapy, depending on the disease stage. Conventional photontherapy nevertheless remains difficult to apply to tumors such as head and neck squamous cell carcinomas (HNSCC), due to the proximity of numerous organs at risk (i.e. salivary glands, esophagus, larynx). Protontherapy has been proposed to treat such sensitive tumors, due to its high precision in tumor targeting. Despite the current therapeutic strategies, the five-year overall survival rate of HNSCC patients is only 53%, with a high percentage of poor response to therapy and a high recurrence rate. Lymph node metastasis, the first sign of tumor progression, has been directly correlated to Vascular Endothelial Growth Factor-C (VEGF-C) expression levels in HNSCC and to VEGF-C-dependent tumoral lymphatic vessel development. In the present study, we investigated the hypothesis that, beside the advantage in dose deposition, protontherapy may show distinct biological properties than photontherapy (at similar doses). We thus examined several in vitro biological behaviors of HNSCC-derived cells when exposed to photons or protons, focusing on molecules with key roles in the progression and prognosis of HNSCC, such as genes/proteins involved in (lymph)angiogenesis/metastasis, inflammation, tumor cell proliferation and anti-tumor immunity, tumorigenic potential. We showed that cell proliferation decreased with the irradiation dose, both in proton and photon irradiated cells. Proton and photon irradiations increased VEGF-C and PD-L1 expression in HNSCC cells. In cells surviving multiple irradiation, key (lymph)angiogenesis and inflammation genes were down-regulated (except for VEGF-C) after protontherapy and up-regulated after photontherapy. Both irradiation types stimulated VEGF-C promoter activity via NF-kB-dependent transcriptional regulation. We conclude that cell resistance, tumor progression and lymphangiogenesis induction is less pronounced after proton irradiation than after photon irradiation. We validated these results by in vivo experiments: Photon-or proton-irradiated HNSCC-derived cells were xenografted subcutaneously into immunodeficient mice. Cells surviving to multiple irradiations by protons or photons generated tumors with higher volume, anarchic architecture and increased density of blood vessels than non-irradiated cells. Increased lymphangiogenesis and a transcriptomic analysis in favor of a more aggressive phenotype were observed in tumors generated with X irradiated cells. Detection of a denser lymphatic vessel network in relapsed tumors from patients receiving conventional X radiotherapy is consistent with these results

Renal cell carcinoma : molecular characterization and metabolic pathways dependent on hypoxic mechanisms

Les carcinomes rénaux (RCC) sont subdivisés en plusieurs sous-types, définis selon des critères histologiques, phénotypiques et génétiques. Le diagnostic différentiel de ces tumeurs est primordial avec des conséquences pronostiques et thérapeutiques. Génétique et diagnostic : Nous avons étudié les données cliniques, histologiques, immunohistochimiques et génétiques d'une série de RCC papillaires (PRCC) de type 1 et 2. Une caractérisation génomique exhaustive complétée par NGS nous a permis de classer les PRCC de type 2 dans plusieurs groupes d'évolution variable. Nos résultats fournissent des informations inédites sur la pathogenèse des PRCC qui donnent des pistes pour un traitement personnalisé. Métabolisme, grade tumoral et phénotype : Dans une série de RCC à cellules claires (ccRCC), nous avons analysé les caractéristiques de ces tumeurs et l'expression des protéines impliquées dans le métabolisme et les isoformes de HIF. Cette étude nous a permis de mettre en évidence quantitativement une corrélation entre l'expression de MCT1, GLUT1 et CAXII et le grade de Fuhrman, et qualitativement une localisation périphérique de HIF2alpha et la co-localisation des protéines HIF2alpha et HAF. Stratégies théranostiques : Dans l’optique de définir les traitements les plus appropriés pour les patients atteints de RCC, nous avons fait un parallèle entre la sensibilité aux thérapies ciblées des patients (in vivo) et de cellules dérivées de la tumeur initiale (in vitro). Nous avons démontré que la réponse chez les patients et dans les cellules était équivalente et donc que des tests in vitro sont une piste pour définir des traitements personnalisés des patients atteints de ccRCC.Renal carcinomas (RCC) are divided into several subtypes, defined by histological, genetic and phenotypic criteria. The differential diagnosis of these tumors is important with prognostic and therapeutic implications. Genetics and diagnosis: We studied the clinical, histological, immunohistochemical and genetic of papillary RCC (PRCC) type 1 and 2 cohort. An extensive genomic characterization completed by NGS has allowed us to classify type 2 PRCC in several groups of variable clinical evolution. Our results provide new information on the pathogenesis of PRCC that provide perspectives for personalized treatment. Metabolism, tumor grade and phenotype: In a series of clear cell RCC (ccRCC), we analyzed the characteristics of these tumors and the expression of proteins involved in the metabolism and isoforms of HIF. This study allowed us to demonstrate quantitative correlation between the expression of MCT1, GLUT1 and CA XII and Fuhrman grade, and qualitatively peripheral HIF2alpha localization and co-localization of proteins HIF2alpha and HAF. Theranostic strategies: In order to define the most appropriate treatment for patients with RCC, we made a parallel between sensitivity to targeted therapies of patients (in vivo), and cells derived from the original tumor (in vitro). We have demonstrated that the response in patients and in cells and was similar, thus in vitro assays are a way to define personalized treatment for ccRCC

Carcinomes rénaux : caractérisation moléculaire et des voies métaboliques dépendant des mécanismes hypoxiques

Renal carcinomas (RCC) are divided into several subtypes, defined by histological, genetic and phenotypic criteria. The differential diagnosis of these tumors is important with prognostic and therapeutic implications. Genetics and diagnosis: We studied the clinical, histological, immunohistochemical and genetic of papillary RCC (PRCC) type 1 and 2 cohort. An extensive genomic characterization completed by NGS has allowed us to classify type 2 PRCC in several groups of variable clinical evolution. Our results provide new information on the pathogenesis of PRCC that provide perspectives for personalized treatment. Metabolism, tumor grade and phenotype: In a series of clear cell RCC (ccRCC), we analyzed the characteristics of these tumors and the expression of proteins involved in the metabolism and isoforms of HIF. This study allowed us to demonstrate quantitative correlation between the expression of MCT1, GLUT1 and CA XII and Fuhrman grade, and qualitatively peripheral HIF2alpha localization and co-localization of proteins HIF2alpha and HAF. Theranostic strategies: In order to define the most appropriate treatment for patients with RCC, we made a parallel between sensitivity to targeted therapies of patients (in vivo), and cells derived from the original tumor (in vitro). We have demonstrated that the response in patients and in cells and was similar, thus in vitro assays are a way to define personalized treatment for ccRCC.Les carcinomes rénaux (RCC) sont subdivisés en plusieurs sous-types, définis selon des critères histologiques, phénotypiques et génétiques. Le diagnostic différentiel de ces tumeurs est primordial avec des conséquences pronostiques et thérapeutiques. Génétique et diagnostic : Nous avons étudié les données cliniques, histologiques, immunohistochimiques et génétiques d'une série de RCC papillaires (PRCC) de type 1 et 2. Une caractérisation génomique exhaustive complétée par NGS nous a permis de classer les PRCC de type 2 dans plusieurs groupes d'évolution variable. Nos résultats fournissent des informations inédites sur la pathogenèse des PRCC qui donnent des pistes pour un traitement personnalisé. Métabolisme, grade tumoral et phénotype : Dans une série de RCC à cellules claires (ccRCC), nous avons analysé les caractéristiques de ces tumeurs et l'expression des protéines impliquées dans le métabolisme et les isoformes de HIF. Cette étude nous a permis de mettre en évidence quantitativement une corrélation entre l'expression de MCT1, GLUT1 et CAXII et le grade de Fuhrman, et qualitativement une localisation périphérique de HIF2alpha et la co-localisation des protéines HIF2alpha et HAF. Stratégies théranostiques : Dans l’optique de définir les traitements les plus appropriés pour les patients atteints de RCC, nous avons fait un parallèle entre la sensibilité aux thérapies ciblées des patients (in vivo) et de cellules dérivées de la tumeur initiale (in vitro). Nous avons démontré que la réponse chez les patients et dans les cellules était équivalente et donc que des tests in vitro sont une piste pour définir des traitements personnalisés des patients atteints de ccRCC

Métabolisme énergétique et régulation du PH dans les carcinomes rénaux à cellules claires (à la recherche de biomarqueurs)

NICE-BU Médecine Odontologie (060882102) / SudocSudocFranceF

Multi-Task Semi-Supervised Learning for Vascular Network Segmentation and Renal Cell Carcinoma Classification

International audienceVascular network analysis is crucial to define the tumoral architecture and then diagnose the cancer subtype. However, automatic vascular network segmentation from Hematoxylin and Eosin (H&E) staining histopathological images is still a challenge due to the background complexity. Moreover, there is a lack of large manually annotated vascular network databases. In this paper, we propose a method that reduces reliance on labeled data through semi-supervised learning (SSL). Additionally, considering the correlation between tumor classification and vascular segmentation, we propose a multi-task learning (MTL) model that can simultaneously segment the vascular network using SSL and predict the tumor class in a supervised context. This multi-task learning procedure offers an end-to-end machine learning solution to joint vascular network segmentation and tumor classification. Experiments were carried out on a database of histopathological images of renal cell carcinoma (RCC) and then tested on both own RCC and open-source TCGA datasets. The results show that the proposed MTL-SSL model outperforms the conventional supervised-learning segmentation approach

Improving CNNs classification with pathologist-based expertise: the renal cell carcinoma case study

International audienceAbstract The prognosis of renal cell carcinoma (RCC) malignant neoplasms deeply relies on an accurate determination of the histological subtype, which currently involves the light microscopy visual analysis of histological slides, considering notably tumor architecture and cytology. RCC subtyping is therefore a time-consuming and tedious process, sometimes requiring expert review, with great impact on diagnosis, prognosis and treatment of RCC neoplasms. In this study, we investigate the automatic RCC subtyping classification of 91 patients, diagnosed with clear cell RCC, papillary RCC, chromophobe RCC, or renal oncocytoma, through deep learning based methodologies. We show how the classification performance of several state-of-the-art Convolutional Neural Networks (CNNs) are perfectible among the different RCC subtypes. Thus, we introduce a new classification model leveraging a combination of supervised deep learning models (specifically CNNs) and pathologist’s expertise, giving birth to a hybrid approach that we termed ExpertDeepTree (ExpertDT). Our findings prove ExpertDT’s superior capability in the RCC subtyping task, with respect to traditional CNNs, and suggest that introducing some expert-based knowledge into deep learning models may be a valuable solution for complex classification cases

Renal Cell Carcinoma Classification from Vascular Morphology

International audienceRenal Cell Carcinoma (RCC) is one of the most common malignancies, and pathological diagnosis is the most reliable RCC diagnostic method. Recognizing the type of RCC tumor and the possibility of cell migration highly depends on the geometric and topological properties of the vascular network. Motivated by the diagnosis pipeline, we explore the use of the vascular network from the RCC histopathological image to boost the RCC classification result. To realize this, we firstly build a new vascular network-based RCC histopathological image dataset, namely VRCC200, with 200 well-labeled vascular network annotations. Based on these vascular networks of RCC histopathological images, we propose new hand-craft features, namely skeleton feature and lattice feature. These features well represent the geometric and topological properties of the vascular networks of RCC histopathological images. Then we build strong benchmark results with various algorithms (both traditional and deep learning models) on the VRCC200 dataset. The result of lattice features can beat the popular deep learning models with other features. Finally, we proved the robustness and advantage of our proposed features on a more patients' dataset VRCC60. All of the results of our experiments prove that the vascular network structure of RCC is one of the most important biomarkers for RCC diagnosis