Compartmentalized spatial profiling of the tumor microenvironment in head and neck squamous cell carcinoma identifies immune checkpoint molecules and tumor necrosis factor receptor superfamily members as biomarkers of response to immunotherapy

Mucosal head and neck squamous cell carcinoma (HNSCC) are the seventh most common cancer, with approximately 50% of patients living beyond 5 years. Immune checkpoint inhibitors (ICIs) have shown promising results in patients with recurrent or metastatic (R/M) disease, however, only a subset of patients benefit from immunotherapy. Studies have implicated the tumor microenvironment (TME) of HNSCC as a major factor in therapy response, highlighting the need to better understand the TME, particularly by spatially resolved means to determine cellular and molecular components. Here, we employed targeted spatial profiling of proteins on a cohort of pre-treatment tissues from patients with R/M disease to identify novel biomarkers of response within the tumor and stromal margins. By grouping patient outcome categories into response or non-response, we show that immune checkpoint molecules, including PD-L1, B7-H3, and VISTA, were differentially expressed. Patient responders possessed significantly higher tumor expression of PD-L1 and B7-H3, but lower expression of VISTA. Analysis of response subgroups by Response Evaluation Criteria in Solid Tumors (RECIST) criteria indicated that tumor necrosis factor receptor (TNFR) superfamily members including OX40L, CD27, 4-1BB, CD40, and CD95/Fas, were associated with immunotherapy outcome. OX40L expression in tumor regions was higher in patient-responders than those with progressive disease (PD), while other TNFR members, CD27 and CD95/Fas were lower expressed in patients with a partial response (PR) compared to those with PD. Furthermore, we found that high 4-1BB expression in the tumor compartment, but not in the stroma, was associated with better overall survival (OS) (HR= 0.28, p-adjusted= 0.040). Moreover, high CD40 expression in tumor regions (HR= 0.27, p-adjusted= 0.035), and high CD27 expression in the stroma (HR= 0.2, p-adjusted=0.032) were associated with better survival outcomes. Taken together, this study supports the role of immune checkpoint molecules and implicates the TNFR superfamily as key players in immunotherapy response in our cohort of HNSCC. Validation of these findings in a prospective study is required to determine the robustness of these tissue signatures

Graph-based Mathematical Morphology for the Characterization of the Spatial Organization of Histological Structures in High-Content Images : Application to Tumor Microenvironement in Breast Cancer

L'un des problèmes les plus complexes dans l'analyse des images histologiques est l'évaluation de l¿organisation spatiale des structures histologiques dans le tissu. En fait, les sections histologiques peuvent contenir un très grand nombre de cellules de différents types et irrégulièrement réparties dans le tissu, ce qui rend leur contenu spatial indescriptible d'une manière simple. Les méthodes fondées sur la théorie des graphes ont été largement explorées dans cette direction, car elles sont des outils de représentation efficaces ayant la capacité expressive de décrire les caractéristiques spatiales et les relations de voisinage qui sont interprétées visuellement par le pathologiste. On peut distinguer trois familles principales de méthodes des graphes utilisées à cette fin: analyse de structure syntaxique, analyse de réseau et analyse spectrale. Cependant, un autre ensemble distinctif de méthodes basées sur la morphologie mathématique sur les graphes peut être développé et adapté pour ce problème. L'objectif principal de cette thèse est le développement d'un outil capable de fournir une évaluation quantitative des arrangements spatiaux des structures histologiques en utilisant la morphologie mathématique basée sur les graphes.One of the most challenging problems in histological image analysis is the evaluation of the spatial organizations of histological structures in the tissue. In fact, histological sections may contain a very large number of cells of different types and irregularly distributed, which makes their spatial content indescribable in a simple manner. Graph-based methods have been widely explored in this direction, as they are effective representation tools having the expressive ability to describe spatial characteristics and neighborhood relationships that are visually interpreted by the pathologist. We can distinguish three main families of graph-based methods used for this purpose: syntactic structure analysis, network analysis and spectral analysis. However, another distinctive set of methods based on mathematical morphology on graphs can be additionally developed for this issue. The main goal of this dissertation is the development of a framework able to provide quantitative evaluation of the spatial arrangements of histological structures using graph-based mathematical morphology

Morphologie mathématique sur les graphes pour la caractérisation de l’organisation spatiale des structures histologiques dans les images haut-contenu : application au microenvironnement tumoral dans le cancer du sein

One of the most challenging problems in histological image analysis is the evaluation of the spatial organizations of histological structures in the tissue. In fact, histological sections may contain a very large number of cells of different types and irregularly distributed, which makes their spatial content indescribable in a simple manner. Graph-based methods have been widely explored in this direction, as they are effective representation tools having the expressive ability to describe spatial characteristics and neighborhood relationships that are visually interpreted by the pathologist. We can distinguish three main families of graph-based methods used for this purpose: syntactic structure analysis, network analysis and spectral analysis. However, another distinctive set of methods based on mathematical morphology on graphs can be additionally developed for this issue. The main goal of this dissertation is the development of a framework able to provide quantitative evaluation of the spatial arrangements of histological structures using graph-based mathematical morphology.L'un des problèmes les plus complexes dans l'analyse des images histologiques est l'évaluation de l¿organisation spatiale des structures histologiques dans le tissu. En fait, les sections histologiques peuvent contenir un très grand nombre de cellules de différents types et irrégulièrement réparties dans le tissu, ce qui rend leur contenu spatial indescriptible d'une manière simple. Les méthodes fondées sur la théorie des graphes ont été largement explorées dans cette direction, car elles sont des outils de représentation efficaces ayant la capacité expressive de décrire les caractéristiques spatiales et les relations de voisinage qui sont interprétées visuellement par le pathologiste. On peut distinguer trois familles principales de méthodes des graphes utilisées à cette fin: analyse de structure syntaxique, analyse de réseau et analyse spectrale. Cependant, un autre ensemble distinctif de méthodes basées sur la morphologie mathématique sur les graphes peut être développé et adapté pour ce problème. L'objectif principal de cette thèse est le développement d'un outil capable de fournir une évaluation quantitative des arrangements spatiaux des structures histologiques en utilisant la morphologie mathématique basée sur les graphes

Preliminary approach for crypt detection in Inflammatory Bowel Disease

Congrès sous l’égide de la Société Française de Génie Biologique et Médical (SFGBM).National audienceCrypt architecture is one of the most significant histological features used for the examination of colorectal biopsy specimens enabling clinical decisions in the investigation of Inflammatory Bowel Diseases. However , the architecture modelling remains a challenging problem leading to variability in reporting and subjectiv-ity in pathological examination. In this context, intestinal gland detection represent a necessary step before a clinical study of their architecture. This work presents a graph-based technique describing spatial relationships over sparse structures for crypt detection using morphological mesh filtering operators