An updated State-of-the-Art Overview of transcriptomic Deconvolution Methods

Although bulk transcriptomic analyses have significantly contributed to an enhanced comprehension of multifaceted diseases, their exploration capacity is impeded by the heterogeneous compositions of biological samples. Indeed, by averaging expression of multiple cell types, RNA-Seq analysis is oblivious to variations in cellular changes, hindering the identification of the internal constituents of tissues, involved in disease progression. On the other hand, single-cell techniques are still time, manpower and resource-consuming analyses.To address the intrinsic limitations of both bulk and single-cell methodologies, computational deconvolution techniques have been developed to estimate the frequencies of cell subtypes within complex tissues. These methods are especially valuable for dissecting intricate tissue niches, with a particular focus on tumour microenvironments (TME).In this paper, we offer a comprehensive overview of deconvolution techniques, classifying them based on their methodological characteristics, the type of prior knowledge required for the algorithm, and the statistical constraints they address. Within each category identified, we delve into the theoretical aspects for implementing the underlying method, while providing an in-depth discussion of their main advantages and disadvantages in supplementary materials.Notably, we emphasise the advantages of cutting-edge deconvolution tools based on probabilistic models, as they offer robust statistical frameworks that closely align with biological realities. We anticipate that this review will provide valuable guidelines for computational bioinformaticians in order to select the appropriate method in alignment with their statistical and biological objectives.We ultimately end this review by discussing open challenges that must be addressed to accurately quantify closely related cell types from RNA sequencing data, and the complementary role of single-cell RNA-Seq to that purpose

Hepatitis B virus-specific T cells associate with viral control upon nucleos(t)ide-analogue therapy discontinuation.

BACKGROUND: The clinical management of chronic hepatitis B virus (HBV) patients is based exclusively on virological parameters that cannot independently determine in which patients nucleos(t)ide-analogue (NUC) therapy can be safely discontinued. NUCs efficiently suppress viral replication, but do not eliminate HBV. Thus, therapy discontinuation can be associated with virological and biochemical relapse and, consequently, therapy in the majority is life-long. METHODS: Since antiviral immunity is pivotal for HBV control, we investigated potential biomarkers for the safe discontinuation of NUCs within immune profiles of chronic HBV patients by utilizing traditional immunological assays (ELISPOT, flow cytometry) in conjunction with analyses of global non-antigen-specific immune populations (NanoString and CyTOF). Two distinct cohorts of 19 and 27 chronic HBV patients, respectively, were analyzed longitudinally prior to and after discontinuation of 2 different NUC therapy strategies. RESULTS: Absence of hepatic flares following discontinuation of NUC treatment correlated with the presence, during NUC viral suppression, of HBV core and polymerase-specific T cells that were contained within the ex vivo PD-1+ population. CONCLUSIONS: This study identifies the presence of functional HBV-specific T cells as a candidate immunological biomarker for safe therapy discontinuation in chronic HBV patients. Furthermore, the persistent and functional antiviral activity of PD-1+ HBV-specific T cells highlights the potential beneficial role of the expression of T cell exhaustion markers during human chronic viral infection. FUNDING: This work was funded by a Singapore Translational Research Investigator Award (NMRC/STaR/013/2012), the Eradication of HBV TCR Program (NMRC/TCR/014-NUHS/2015), the Singapore Immunology Network, the Wellcome Trust (107389/Z/15/Z), and a Barts and The London Charity (723/1795) grant.This work was funded by a Singapore Translational Research Investigator Award (NMRC/STaR/013/2012), the Eradication of HBV TCR Program (NMRC/TCR/014-NUHS/2015), the Singapore Immunology Network, the Wellcome Trust (107389/Z/15/Z), and a Barts and The London Charity (723/1795) grant

Analyse transcriptomique du microenvironnement immunitaire des tumeurs humaines non-hématopoïetiques

Tumors grow within a complex microenvironment composed of immune cells, fibroblasts, endothelial cells and other non-malignant cells. The study of the composition of tumor microenvironments has led to classifications with prognostic and theranostic values, as well as the discovery of treatments modulating the composition and the functional orientation of the microenvironment. Concurrently, molecular classifications of tumors have proposed taxonomies within cancers that define groups of patients with different prognoses and are associated with response to treatments. Recent evidence suggest that the phenotype of the malignant cell is a critical determinant in the shaping of its microenvironment, suggesting potential correlations between immune and molecular classifications. The goal of this PhD project was therefore to analyze the microenvironment of molecularly-classified human tumors. Colorectal cancer represents a paradigm for tumor immunology, as it is the humancancer in which it was exemplified that an adaptive immune response can control tumor Growth and metastasis. Conversely, clear-cell renal cell carcinoma represents an exception in tumor immunology, as an extensive adaptive immune response is associated with more aggressive diseases. Molecular transcriptomic classifications were recently proposed for both of these apparently immunologically contrasted cancers. In this work, I propose a methodology that enables the characterization of the tumor microenvironment using transcriptomic data, and apply it to describe the immune contexture of molecular subgroups of colorectal and clear-cell renal cell carcinomas. These analyses argue in favor of the unification of molecular and immune classifications of human cancers, challenge our current views of the relationship between the composition of the tumor microenvironment and patient’s prognosis, and suggest immunotherapeutic approaches that could benefit subgroups of patients in these two cancers.Le microenvironnement des tumeurs est composé de cellules immunitaires, de fibroblastes et de cellules endothéliales, ainsi que d’autres cellules non-malignes. Son étude a permis d’établir des classifications qui ont une valeur pronostique et théranostique, ainsi que de développer des traitements modulant la composition et l’orientation fonctionnelle du microenvironnement. En parallèle, des classifications moléculaires des tumeurs ont proposé des taxonomies stratifiant les cancers humains en sous-groupes associés à des différences de survie des patients et leur réponse aux traitements. Des études récentes suggèrent que le phénotype de la cellule cancéreuse est un facteur critique dans le façonnement du microenvironnement tumoral, suggérant un possible consensus entre les classifications immunitaires et moléculaires. Le but de cette thèse était donc de caractériser le microenvironnement des sous-groupes moléculaires de tumeurs humaines. Le cancer colorectal a été le premier cancer humain dans lequel il a été mis en évidence qu’une réponse immunitaire adaptative était associée à un contrôle de la croissance tumorale, et représente ainsi un exemple type pour l’immunologie des tumeurs. A l’inverse, le carcinome du rein à cellules claires est une exception vis-à-vis de l’immunologie des tumeurs, puisqu’une forte réponse immunitaire adaptative y est associée à des tumeurs plus agressives. Des classifications transcriptomiques ont été récemment établies pour ces deux cancers, qu’à première vue tout oppose sur le plan immunitaire. Dans ce travail, je propose une méthode permettant l’étude du microenvironnement tumoral à partir de données transcriptomiques, et décris son application à l’étude du contexte immunitaire des cancers colorectaux et du rein à cellules claires. Ces analyses suggèrent qu’une unification des classifications moléculaires et immunitaires des tumeurs humaines est possible, remettent en cause notre conceptualisation des liens entre la composition du microenvironnement tumoral et le pronostic du patient, et évoque des pistes immunothérapeutiques potentiellement adaptées à certains sous-groupes de patients dans ces cancers

R package MCPcounter v1.1

MCPcounter is an R package which implements the MCP-counter method.It predicts the abundance of 10 cell populations (8 immune populations, endothelial cells and fibroblasts) from transcriptomic profiles of human tissues

Cancer immune contexture and immunotherapy

International audienceThe immune contexture that characterizes the density, the location, the organization and the functional orientation of tumor-infiltrating immune cells in cancers has a clinical impact on patient's outcome. It is, in great part, shaped by the malignant cells, as in a given cancer type, tumors presenting different oncogenic processes have different immune contextures. Moreover, the immune contexture in metastatic sites reflects that of the corresponding primary tumors. Finally, the components forming the immune contexture represent targets and markers of efficient anti-cancer immunotherapies

Transcriptomic analysis of the tumor microenvironment to guide prognosis and immunotherapies

International audienceTumors are highly heterogeneous tissues where malignant cells are surrounded by and interact with a complex tumor microenvironment (TME), notably composed of a wide variety of immune cells, as well as vessels and fibroblasts. As the dialectical influence between tumor cells and their TME is known to be clinically crucial, we need tools that allow us to study the cellular composition of the microenvironment. In this focused research review, we report MCP-counter, a methodology based on transcriptomic markers that assesses the proportion of several immune and stromal cell populations in the TME from transcriptomic data, and we highlight how it can provide a way to decipher the complex mechanisms at play in tumors. In several malignancies, MCP-counter scores have been used to show various prognostic impacts of the TME, which we also show to be linked with the mutational burden of tumors. We also compared established molecular classifications of colorectal cancer and clear-cell renal cell carcinoma with the output of MCP-counter, and show that molecular subgroups have different TME profiles, and that these profiles are consistent within a given subgroup. Finally, we provide insights as to how knowing the TME composition may shape patient care in the near future