Malignant Pleural Mesothelioma (MPM), a rare thoracic tumor strongly linked to asbestos exposure, is one of the most aggressive cancer with a very poor prognosis. Clinical trials have highlighted MPM diversity in terms of prognosis and patients’ response to anti-cancer agents, suggesting an underlying tumor heterogeneity. As current treatment options are rarely curative, a better characterization of inter and intra-tumor heterogeneity is essential for the identification of new therapeutic strategies, and for the implementation of precision medicine with the aim to improve the cure to this dreadful cancer. The first level of inter-tumor heterogeneity in MPM is histologic with three main histologic types i.e. epithelioid, sarcomatoid and biphasic. The latter is also an evidence of intra-tumor heterogeneity as the biphasic histologic type is a mix of variable proportion of epithelioid and sarcomatoid tumor cells. The histologic heterogeneity is even more complex with the characterization of several histologic subtypes (1). Large-scale omics and NGS (Next Generation Sequencing) studies also highlighted MPM heterogeneity at the molecular level. MPM show a complex pattern of chromosomal abnormalities and mutations, so it is difficult to take into account this molecular heterogeneity of MPM solely on the basis of chromosomal or genetic alterations. We and others, using unsupervised hierarchical clustering based on transcriptomic or integrated multi-omics data, defined molecular classification in 2 and 4 tumor subtypes (2-4). These molecular subtypes are related to histology and associated to prognosis, to specific mutations in genes such as BAP1 and to the deregulation of specific signal pathways such as epithelial-mesenchymal transition (EMT). Smaller and highly homogeneous subtypes were also defined by taking into account molecular subtypes and mutation profiles such as the one characterized by a double inactivation in the two tumor suppressor genes related to Hippo signal pathway, NF2 and LATS2 (5). Interestingly, based on preclinical studies, a potential target therapy has been proposed for this subtype illustrating the interest to define homogenous tumor subtypes in order to develop new therapeutic approaches. However, these molecular classifications in subtypes have some limitations. First, they take into account only inter-tumor heterogeneity but not intra-tumor heterogeneity, which is poorly described at the molecular level in MPM (6). Second, a meta-analysis comparing all molecular subtypes obtained by unsupervised hierarchical clustering of several different transcriptomic dataset highlighted only two main subtypes, which are highly correlated in all datasets. Apart from these two opposite subtypes corresponding to pure epithelioid and sarcomatoid phenotypes, intermediate subtypes could simply reflect various cut-offs of a continuum combining epithelioid and sarcomatoid entities, which could be better defined using molecular gradients (7). For these reasons with the aim to better characterize MPM molecular heterogeneity, we used a deconvolution method that decomposes the MPM transcriptomic profile of each tumor as a combination of epithelioid and sarcomatoid components. We determined the proportion of these epithelioid and sarcomatoid components (E.score and S.score, respectively) in large series of tumors. These two opposite histo-molecular gradients were related to histology types and to subtypes of MPM molecular classification (7). The underlying oncogenic pathways driving the establishment of the epithelioid and sarcomatoid related cell entities were specified. Integration of transcriptome, methylome and miRNome data showed the strong contribution of epigenetic regulation. We also highlighted the link between the histo-molecular gradients and the tumor microenvironment and the immune contexts. A strong positive correlation was observed between the S.score and the infiltration of T lymphocytes, monocytes, fibroblasts and endothelial cells, while the E.score was linked to natural killer cells infiltration and complement pathway. These results suggested the presence of an adaptive immune response in tumors with a high S-score and of an innate immune response in tumors with a high E-score. The S.score was also strongly correlated with high expression of most immune checkpoint inhibitors, including CD274 (PDL1) and CTLA4 (7). More importantly, we highlighted the potent clinical impact of histo-molecular gradients on prognosis and on personalized therapeutic strategies in MPM. First, we showed that the S.score has a strong prognostic value, higher than histologic and molecular classifications. Second, our data supported that these histo-molecular gradients might be used to guide therapeutic strategies such as targeted therapies by performing preclinical studies. Third, the strong correlation of the S.score with T lymphocytes infiltration and immune checkpoint inhibitors expression supports that a high S.score could be predictive of immunotherapy based on anti-PDL1 and anti-CTLA4 inhibitors (7). Prediction of patients responding to these inhibitors is particularly important given the recent promising results of this immunotherapy for some MPM patients (8). More recently, we have performed a genetic profiling, focusing on the main key genes altered in mesothelial carcinogenesis, of a large collection of MPM with complete clinical annotations and well-characterized for heterogeneity using current available tumor classifications. The unpublished results provided a comprehensive overview of the genetic landscape of MPM taking into account the histologic and molecular heterogeneities. 1. Husain A. N. et al. Guidelines for pathologic diagnosis of malignant mesothelioma: 2012 update of the consensus statement from the International Mesothelioma Interest Group. Arch Pathol Lab Med. 2013, 137: 647-667. 2. Bueno R. et al. Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations. Nat Genet. 2016, 48: 407-416. 3. de Reynies A. et al. Molecular classification of malignant pleural mesothelioma: identification of a poor prognosis subgroup linked to the epithelial-to-mesenchymal transition. Clin Cancer Res. 2014, 20: 1323-1334. 4. Hmeljak J. et al. Integrative Molecular Characterization of Malignant Pleural Mesothelioma. Cancer Discov. 2018, 8: 1548-1565. 5. Tranchant R. et al. Co-occurring mutations of tumor suppressor genes, LATS2 and NF2, in malignant pleural mesothelioma. Clin Cancer Res. 2017, 23: 3191-3202. 6. Oehl K. et al. Heterogeneity in Malignant Pleural Mesothelioma. Int J Mol Sci. 2018, 19. 7. Blum Y. et al. Dissecting heterogeneity in malignant pleural mesothelioma through histo-molecular gradients for clinical applications. Nat Commun. 2019, 10: 1333. 8. Scherpereel A. et al. Nivolumab or nivolumab plus ipilimumab in patients with relapsed malignant pleural mesothelioma (IFCT-1501 MAPS2): a multicentre, open-label, randomised, non-comparative, phase 2 trial. Lancet Oncol. 2019, 20: 239-253. Molecular classification, Histo-molecular gradient, tumor heterogeneity